On The Optimality Of All To All Broadcast In k ary n dimensional Tori

Abstract: All to all broadcast is a collective communication in a network with the constraint that every node must send to each other certain piece of its data. This paper addresses the problem of optimal all port all to all broadcast in multidimensional tori. The optimality criteria considered are the minimum exchange steps, no duplicated data in the sense that only new data are conveyed to receivers and the balance of the communication links load. It is proved that under these constraints, an optimal broadcast is not feasible in any multidimensional torus. Then, the tori which are capable of optimal broadcasts are characterized.


On Necessary and Sufficient Number of Cops in the Game of Cops and Robber in Multidimensional Grids

Abstract: We theoretically analyze the Cops and Robber Game for the first time in a multidimensional grid. It is shown that for an $n$-dimensional grid, at least $n$ cops are necessary to ensure capture of the robber. We also present a set of cop strategies for which $n$ cops are provably sufficient to catch the robber. Further, for two-dimensional grid, we provide an efficient cop strategy for which the robber is caught even by a single cop under certain conditions.


On the Relations Between Diffie-Hellman and ID-Based Key Agreement from Pairings

Abstract: This paper studies the relationships between the traditional Diffie-Hellman key agreement protocol and the identity-based (ID-based) key agreement protocol from pairings. For the Sakai-Ohgishi-Kasahara (SOK) ID-based key construction, we show that identical to the Diffie-Hellman protocol, the SOK key agreement protocol also has three variants, namely \emph{ephemeral}, \emph{semi-static} and \emph{static} versions. Upon this, we build solid relations between authenticated Diffie-Hellman (Auth-DH) protocols and ID-based authenticated key agreement (IB-AK) protocols, whereby we present two \emph{substitution rules} for this two types of protocols. The rules enable a conversion between the two types of protocols. In particular, we obtain the \emph{real} ID-based version of the well-known MQV (and HMQV) protocol. Similarly, for the Sakai-Kasahara (SK) key construction, we show that the key transport protocol underlining the SK ID-based encryption scheme (which we call the "SK protocol") has its non-ID counterpart, namely the Hughes protocol. Based on this observation, we establish relations between corresponding ID-based and non-ID-based protocols. In particular, we propose a highly enhanced version of the McCullagh-Barreto protocol.


Resource Matchmaking Algorithm using Dynamic Rough Set in Grid Environment

Abstract: Grid environment is a service oriented infrastructure in which many heterogeneous resources participate to provide the high performance computation. One of the bug issues in the grid environment is the vagueness and uncertainty between advertised resources and requested resources. Furthermore, in an environment such as grid dynamicity is considered as a crucial issue which must be dealt with. Classical rough set have been used to deal with the uncertainty and vagueness. But it can just be used on the static systems and can not support dynamicity in a system. In this work we propose a solution, called Dynamic Rough Set Resource Discovery (DRSRD), for dealing with cases of vagueness and uncertainty problems based on Dynamic rough set theory which considers dynamic features in this environment. In this way, requested resource properties have a weight as priority according to which resource matchmaking and ranking process is done. We also report the result of the solution obtained from the simulation in GridSim simulator. The comparison has been made between DRSRD, classical rough set theory based algorithm, and UDDI and OWL S combined algorithm. DRSRD shows much better precision for the cases with vagueness and uncertainty in a dynamic system such as the grid rather than the classical rough set theory based algorithm, and UDDI and OWL S combined algorithm.


Impact of Rushing attack on Multicast in Mobile Ad Hoc Network

Abstract: A mobile ad hoc network (MANETs) is a self-organizing system of mobile nodes that communicate with each other via wireless links with no fixed infrastructure or centralized administration such as base station or access points. Nodes in a MANETs operate both as host as well as routers to forward packets for each other in a multihop fashion. For many applications in wireless networks, multicasting is an important and frequent communication service. By multicasting, since a single message can be delivered to multiple receivers simultaneously. It greatly reduces the transmission cost when sending the same packet to multiple recipients. The security issue of MANETs in group communications is even more challenging because of involvement of multiple senders and multiple receivers. At that time of multicasting, mobile ad hoc network are unprotected by the attacks of malicious nodes because of vulnerabilities of routing protocols. Some of the attacks are Rushing attack, Blackhole attack, Sybil attack, Neighbor attack and Jellyfish attack. This paper is based on Rushing attack. In Rushing attack, the attacker exploits the duplicate suppression mechanism by quickly forwarding route discovery packets in order to gain access to the forwarding group and this will affect the Average Attack Success Rate. In this paper, the goal is to measure the impact of Rushing attack and their node positions which affect the performance metrics of Average Attack Success Rate with respect to three scenarios: near sender, near receiver and anywhere within the network. The performance of the Attack Success Rate with respect to above three scenarios is also compared.


A Hybrid Multi Objective Particle Swarm Optimization Method to Discover Biclusters in Microarray Data

Abstract: In recent years, with the development of microarray technique, discovery of useful knowledge from microarray data has become very important. Biclustering is a very useful data mining technique for discovering genes which have similar behavior. In microarray data, several objectives have to be optimized simultaneously and often these objectives are in conflict with each other. A Multi Objective model is capable of solving such problems. Our method proposes a Hybrid algorithm which is based on the Multi Objective Particle Swarm Optimization for discovering biclusters in gene expression data. In our method, we will consider a low level of overlapping amongst the biclusters and try to cover all elements of the gene expression matrix. Experimental results in the bench mark database show a significant improvement in both overlap among biclusters and coverage of elements in the gene expression matrix.


An Efficient and Publicly Verifiable Id-Based Multi-Signcryption Scheme

Abstract: Multi-signcryption is used when different senders wants to authenticate a single message without revealing it. This paper proposes a multi signcryption scheme in which no pairing is computed on the signcryption stage and the signatures can be verified publicly.


Accuracy Improvement for Stiffness Modeling of Parallel Manipulators

Abstract: The paper focuses on the accuracy improvement of stiffness models for parallel manipulators, which are employed in high-speed precision machining. It is based on the integrated methodology that combines analytical and numerical techniques and deals with multidimensional lumped-parameter models of the links. The latter replace the link flexibility by localized 6-dof virtual springs describing both translational/rotational compliance and the coupling between them. There is presented detailed accuracy analysis of the stiffness identification procedures employed in the commercial CAD systems (including statistical analysis of round-off errors, evaluating the confidence intervals for stiffness matrices). The efficiency of the developed technique is confirmed by application examples, which deal with stiffness analysis of translational parallel manipulators.


Design optimization of parallel manipulators for high-speed precision machining applications

Abstract: The paper proposes an integrated approach to the design optimization of parallel manipulators, which is based on the concept of the workspace grid and utilizes the goal-attainment formulation for the global optimization. To combine the non-homogenous design specification, the developed optimization technique transforms all constraints and objectives into similar performance indices related to the maximum size of the prescribed shape workspace. This transformation is based on the dedicated dynamic programming procedures that satisfy computational requirements of modern CAD. Efficiency of the developed technique is demonstrated via two case studies that deal with optimization of the kinematical and stiffness performances for parallel manipulators of the Orthoglide family.


Autonomic management of multiple non-functional concerns in behavioural skeletons

Abstract: We introduce and address the problem of concurrent autonomic management of different non-functional concerns in parallel applications build as a hierarchical composition of behavioural skeletons. We first define the problems arising when multiple concerns are dealt with by independent managers, then we propose a methodology supporting coordinated management, and finally we discuss how autonomic management of multiple concerns may be implemented in a typical use case. The paper concludes with an outline of the challenges involved in realizing the proposed methodology on distributed target architectures such as clusters and grids. Being based on the behavioural skeleton concept proposed in the CoreGRID GCM, it is anticipated that the methodology will be readily integrated into the current reference implementation of GCM based on Java ProActive and running on top of major grid middleware systems.


Minimum clique partition in unit disk graphs

Abstract: The minimum clique partition (MCP) problem is that of partitioning the vertex set of a given graph into a minimum number of cliques. Given $n$ points in the plane, the corresponding unit disk graph (UDG) has these points as vertices, and edges connecting points at distance at most~1. MCP in unit disk graphs is known to be NP-hard and several constant factor approximations are known, including a recent PTAS. We present two improved approximation algorithms for minimum clique partition in unit disk graphs: (I) A polynomial time approximation scheme (PTAS) running in time $n^{O(1/\eps^2)}$. This improves on a previous PTAS with $n^{O(1/\eps^4)}$ running time \cite{PS09}. (II) A randomized quadratic-time algorithm with approximation ratio 2.16. This improves on a ratio 3 algorithm with $O(n^2)$ running time \cite{CFFP04}.


Efficient and Spontaneous Privacy-Preserving Protocol for Secure Vehicular Communications

Abstract: This paper introduces an efficient and spontaneous privacy-preserving protocol for vehicular ad-hoc networks based on revocable ring signature. The proposed protocol has three appealing characteristics: First, it offers conditional privacy-preservation: while a receiver can verify that a message issuer is an authorized participant in the system only a trusted authority can reveal the true identity of a message sender. Second, it is spontaneous: safety messages can be authenticated locally, without support from the roadside units or contacting other vehicles. Third, it is efficient by offering fast message authentication and verification, cost-effective identity tracking in case of a dispute, and low storage requirements. We use extensive analysis to demonstrate the merits of the proposed protocol and to contrast it with previously proposed solutions.


Kernel Spectral Curvature Clustering (KSCC)

Abstract: Multi-manifold modeling is increasingly used in segmentation and data representation tasks in computer vision and related fields. While the general problem, modeling data by mixtures of manifolds, is very challenging, several approaches exist for modeling data by mixtures of affine subspaces (which is often referred to as hybrid linear modeling). We translate some important instances of multi-manifold modeling to hybrid linear modeling in embedded spaces, without explicitly performing the embedding but applying the kernel trick. The resulting algorithm, Kernel Spectral Curvature Clustering, uses kernels at two levels - both as an implicit embedding method to linearize nonflat manifolds and as a principled method to convert a multiway affinity problem into a spectral clustering one. We demonstrate the effectiveness of the method by comparing it with other state-of-the-art methods on both synthetic data and a real-world problem of segmenting multiple motions from two perspective camera views.


Motion Segmentation by SCC on the Hopkins 155 Database

Abstract: We apply the Spectral Curvature Clustering (SCC) algorithm to a benchmark database of 155 motion sequences, and show that it outperforms all other state-of-the-art methods. The average misclassification rate by SCC is 1.41% for sequences having two motions and 4.85% for three motions.


Extending WS-Security to Implement Security Protocols for Web Services

Abstract: Web services use tokens provided by the WS-Security standard to implement security protocols. We propose several extensions to the WS-Security standard, including name types, key and random number extensions. The extensions are used to implement existing protocols such as ISO9798, Kerberos or BAN-Lowe. The advantages of using these implementations rather than the existing, binary ones, are inherited from the advantages of using Web service technologies, such as extensibility and end-to-end security across multiple environments that do not support a connection-based communication.


Certificate-based Single Sign-On Mechanism for Multi-Platform Distributed Systems

Abstract: We propose a certificate-based single sign-on mechanism in distributed systems. The proposed security protocols and authentication mechanisms are integrated in a middleware. The novelty of our middleware lies on the use of XPCOM components, this way we provide a different services that can be used on every platform where Mozilla is available. The componen based architecture of the implemented services allows using the authentication components separately.


Qualitative Analysis of Partially-observable Markov Decision Processes

Abstract: We study observation-based strategies for partially-observable Markov decision processes (POMDPs) with omega-regular objectives. An observation-based strategy relies on partial information about the history of a play, namely, on the past sequence of observations. We consider the qualitative analysis problem: given a POMDP with an omega-regular objective, whether there is an observation-based strategy to achieve the objective with probability~1 (almost-sure winning), or with positive probability (positive winning). Our main results are twofold. First, we present a complete picture of the computational complexity of the qualitative analysis of POMDP s with parity objectives (a canonical form to express omega-regular objectives) and its subclasses. Our contribution consists in establishing several upper and lower bounds that were not known in literature. Second, we present optimal bounds (matching upper and lower bounds) on the memory required by pure and randomized observation-based strategies for the qualitative analysis of POMDP s with parity objectives and its subclasses.


Probabilistic Weighted Automata

Abstract: Nondeterministic weighted automata are finite automata with numerical weights on transitions. They define quantitative languages L that assign to each word w a real number L(w). The value of an infinite word w is computed as the maximal value of all runs over w, and the value of a run as the maximum, limsup, liminf, limit average, or discounted sum of the transition weights. We introduce probabilistic weighted automata, in which the transitions are chosen in a randomized (rather than nondeterministic) fashion. Under almost-sure semantics (resp. positive semantics), the value of a word w is the largest real v such that the runs over w have value at least v with probability 1 (resp. positive probability). We study the classical questions of automata theory for probabilistic weighted automata: emptiness and universality, expressiveness, and closure under various operations on languages. For quantitative languages, emptiness and universality are defined as whether the value of some (resp. every) word exceeds a given threshold. We prove some of these questions to be decidable, and others undecidable. Regarding expressive power, we show that probabilities allow us to define a wide variety of new classes of quantitative languages, except for discounted-sum automata, where probabilistic choice is no more expressive than nondeterminism. Finally, we give an almost complete picture of the closure of various classes of probabilistic weighted automata for the following pointwise operations on quantitative languages: max, min, sum, and numerical complement.


How does certainty enter into the mind?

Abstract: Any problem is concerned with the mind, but what do minds make a decision on? Here we show that there are three conditions for the mind to make a certain answer. We found that some difficulties in physics and mathematics are in fact introduced by infinity, which can not be rightly expressed by minds. Based on this point, we suggest a general observation system, where we use region (a type of infinity) to substitute for infinitesimal (another type of infinity) and thus get a consistent image with the mind. Furthermore, we declare that without world pictures we can never have ideas to any expressive events, which is the primary condition for a wave function like mind to collapse to a series of numbers. A following observation by expanding algorithm brings the final collapse: classifying the numbers and coming up with a certain yes or no answer.


Size Does Matter (in P2P Live Streaming)

Abstract: Optimal dissemination schemes have previously been studied for peer-to-peer live streaming applications. Live streaming being a delay-sensitive application, fine tuning of dissemination parameters is crucial. In this report, we investigate optimal sizing of chunks, the units of data exchange, and probe sets, the number peers a given node probes before transmitting chunks. Chunk size can have significant impact on diffusion rate (chunk miss ratio), diffusion delay, and overhead. The size of the probe set can also affect these metrics, primarily through the choices available for chunk dissemination. We perform extensive simulations on the so-called random-peer, latest-useful dissemination scheme. Our results show that size does matter, with the optimal size being not too small in both cases.


Teaching an Old Elephant New Tricks

Abstract: In recent years, column stores (or C-stores for short) have emerged as a novel approach to deal with read-mostly data warehousing applications. Experimental evidence suggests that, for certain types of queries, the new features of C-stores result in orders of magnitude improvement over traditional relational engines. At the same time, some C-store proponents argue that C-stores are fundamentally different from traditional engines, and therefore their benefits cannot be incorporated into a relational engine short of a complete rewrite. In this paper we challenge this claim and show that many of the benefits of C-stores can indeed be simulated in traditional engines with no changes whatsoever. We then identify some limitations of our ?pure-simulation? approach for the case of more complex queries. Finally, we predict that traditional relational engines will eventually leverage most of the benefits of C-stores natively, as is currently happening in other domains such as XML data.


Declarative Reconfigurable Trust Management

Abstract: In recent years, there has been a proliferation of declarative logic-based trust management languages and systems proposed to ease the description, configuration, and enforcement of security policies. These systems have different tradeoffs in expressiveness and complexity, depending on the security constructs (e.g. authentication, delegation, secrecy, etc.) that are supported, and the assumed trust level and scale of the execution environment. In this paper, we present LBTrust, a unified declarative system for reconfigurable trust management, where various security constructs can be customized and composed in a declarative fashion. We present an initial proof-of-concept implementation of LBTrust using LogicBlox, an emerging commercial Datalog-based platform for enterprise software systems. The LogicBlox language enhances Datalog in a variety of ways, including constraints and meta-programming, as well as support for programmer defined constraints which on the meta-model itself ? meta-constraints ? which act to restrict the set of allowable programs. LBTrust utilizes LogicBlox?s meta-programming and meta-constraints to enable customizable cryptographic, partitioning and distribution strategies based on the execution environment. We present uses cases of LBTrust based on three trust management systems (Binder, D1LP, and Secure Network Datalog), and provide a preliminary evaluation of a Binder-based trust management system.


LifeRaft: Data-Driven, Batch Processing for the Exploration of Scientific Databases

Abstract: Workloads that comb through vast amounts of data are gaining importance in the sciences. These workloads consist of "needle in a haystack" queries that are long running and data intensive so that query throughput limits performance. To maximize throughput for data-intensive queries, we put forth LifeRaft: a query processing system that batches queries with overlapping data requirements. Rather than scheduling queries in arrival order, LifeRaft executes queries concurrently against an ordering of the data that maximizes data sharing among queries. This decreases I/O and increases cache utility. However, such batch processing can increase query response time by starving interactive workloads. LifeRaft addresses starvation using techniques inspired by head scheduling in disk drives. Depending upon the workload saturation and queuing times, the system adaptively and incrementally trades-off processing queries in arrival order and data-driven batch processing. Evaluating LifeRaft in the SkyQuery federation of astronomy databases reveals a two-fold improvement in query throughput.


Remembrance: The Unbearable Sentience of Being Digital

Abstract: We introduce a world vision in which data is endowed with memory. In this data-centric systems paradigm, data items can be enabled to retain all or some of their previous values. We call this ability "remembrance" and posit that it empowers significant leaps in the security, availability, and general operational dimensions of systems. With the explosion in cheap, fast memories and storage, large-scale remembrance will soon become practical. Here, we introduce and explore the advantages of such a paradigm and the challenges in making it a reality.


Qunits: queried units in database search

Abstract: Keyword search against structured databases has become a popular topic of investigation, since many users find structured queries too hard to express, and enjoy the freedom of a ``Google-like'' query box into which search terms can be entered. Attempts to address this problem face a fundamental dilemma. Database querying is based on the logic of predicate evaluation, with a precisely defined answer set for a given query. On the other hand, in an information retrieval approach, ranked query results have long been accepted as far superior to results based on boolean query evaluation. As a consequence, when keyword queries are attempted against databases, relatively ad-hoc ranking mechanisms are invented (if ranking is used at all), and there is little leverage from the large body of IR literature regarding how to rank query results. Our proposal is to create a clear separation between ranking and database querying. This divides the problem into two parts, and allows us to address these separately. The first task is to represent the database, conceptually, as a collection of independent ``queried units'', or ``qunits'', each of which represents the desired result for some query against the database. The second task is to evaluate keyword queries against a collection of qunits, which can be treated as independent documents for query purposes, thereby permitting the use of standard IR techniques. We provide insights that encourage the use of this query paradigm, and discuss preliminary investigations into the efficacy of a qunits-based framework based on a prototype implementation.


RIOT: I/O-Efficient Numerical Computing without SQL

Abstract: R is a numerical computing environment that is widely popular for statistical data analysis. Like many such environments, R performs poorly for large datasets whose sizes exceed that of physical memory. We present our vision of RIOT (R with I/O Transparency), a system that makes R programs I/O-efficient in a way transparent to the users. We describe our experience with RIOT-DB, an initial prototype that uses a relational database system as a backend. Despite the overhead and inadequacy of generic database systems in handling array data and numerical computation, RIOT-DB significantly outperforms R in many large-data scenarios, thanks to a suite of high-level, inter-operation optimizations that integrate seamlessly into R. While many techniques in RIOT are inspired by databases (and, for RIOT-DB, realized by a database system), RIOT users are insulated from anything database related. Compared with previous approaches that require users to learn new languages and rewrite their programs to interface with a database, RIOT will, we believe, be easier to adopt by the majority of the R users.


Towards Eco-friendly Database Management Systems

Abstract: Database management systems (DBMSs) have largely ignored the task of managing the energy consumed during query processing. Both economical and environmental factors now require that DBMSs pay close attention to energy consumption. In this paper we approach this issue by considering energy consumption as a first-class performance goal for query processing in a DBMS. We present two concrete techniques that can be used by a DBMS to directly manage the energy consumption. Both techniques trade energy consumption for performance. The first technique, called PVC, leverages the ability of modern processors to execute at lower processor voltage and frequency. The second technique, called QED, uses query aggregation to leverage common components of queries in a workload. Using experiments run on a commercial DBMS and MySQL, we show that PVC can reduce the processor energy consumption by 49% of the original consumption while increasing the response time by only 3%. On MySQL, PVC can reduce energy consumption by 20% with a response time penalty of only 6%. For simple selection queries with no predicate overlap, we show that QED can be used to gracefully trade response time for energy, reducing energy consumption by 54% for a 43% increase in average response time. In this paper we also highlight some research issues in the emerging area of energy-efficient data processing.


Unbundling Transaction Services in the Cloud

Abstract: The traditional architecture for a DBMS engine has the recovery, concurrency control and access method code tightly bound together in a storage engine for records. We propose a different approach, where the storage engine is factored into two layers (each of which might have multiple heterogeneous instances). A Transactional Component (TC) works at a logical level only: it knows about transactions and their "logical" concurrency control and undo/redo recovery, but it does not know about page layout, B-trees etc. A Data Component (DC) knows about the physical storage structure. It supports a record oriented interface that provides atomic operations, but it does not know about transactions. Providing atomic record operations may itself involve DC-local concurrency control and recovery, which can be implemented using system transactions. The interaction of the mechanisms in TC and DC leads to multi-level redo (unlike the repeat history paradigm for redo in integrated engines). This refactoring of the system architecture could allow easier deployment of application-specific physical structures and may also be helpful to exploit multi-core hardware. Particularly promising is its potential to enable flexible transactions in cloud database deployments. We describe the necessary principles for unbundled recovery, and discuss implementation issues.


Interactive Data Integration through Smart Copy & Paste

Abstract: In many scenarios, such as emergency response or ad hoc collaboration, it is critical to reduce the overhead in integrating data. Ideally, one could perform the entire process interactively under one unified interface: defining extractors and wrappers for sources, creating a mediated schema, and adding schema mappings ? while seeing how these impact the integrated view of the data, and refining the design accordingly. We propose a novel smart copy and paste (SCP) model and architecture for seamlessly combining the design-time and run-time aspects of data integration, and we describe an initial prototype, the CopyCat system. In CopyCat, the user does not need special tools for the different stages of integration: instead, the system watches as the user copies data from applications (including the Web browser) and pastes them into CopyCat?s spreadsheet-like workspace. CopyCat generalizes these actions and presents proposed auto-completions, each with an explanation in the form of provenance. The user provides feedback on these suggestions ? through either direct interactions or further copy-and-paste operations ? and the system learns from this feedback. This paper provides an overview of our prototype system, and identifies key research challenges in achieving SCP in its full generality.


From Declarative Languages to Declarative Processing in Computer Games

Abstract: Recent work has shown that we can dramatically improve the performance of computer games and simulations through declarative processing: Character AI can be written in an imperative scripting language which is then compiled to relational algebra and executed by a special games engine with features similar to a main memory database system. In this paper we lay out a challenging research agenda built on these ideas. We discuss several research ideas for novel language features to support atomic actions and reactive programming. We also explore challenges for main-memory query processing in games and simulations including adaptive query plan selection, support for parallel architectures, debugging simulation scripts, and extensions for multi-player games and virtual worlds. We believe that these research challenges will result in a dramatic change in the design of game engines over the next decade.


The Role of Schema Matching in Large Enterprises

Abstract: To date, the principal use case for schema matching research has been as a precursor for code generation, i.e., constructing mappings between schema elements with the end goal of data transfer. In this paper, we argue that schema matching plays valuable roles independent of mapping construction, especially as schemata grow to industrial scales. Specifically, in large enterprises human decision makers and planners are often the immediate consumer of information derived from schema matchers, instead of schema mapping tools. We list a set of real application areas illustrating this role for schema matching, and then present our experiences tackling a customer problem in one of these areas. We describe the matcher used, where the tool was effective, where it fell short, and our lessons learned about how well current schema matching technology is suited for use in large enterprises. Finally, we suggest a new agenda for schema matching research based on these experiences.


Visualizing the robustness of query execution

Abstract: In database query processing, actual run-time conditions (e.g., actual selectivities and actual available memory) very often differ from compile-time expectations of run-time conditions (e.g., estimated predicate selectivities and anticipated memory availability). Robustness of query processing can be defined as the ability to handle unexpected conditions. Robustness of query execution, specifically, can be defined as the ability to process a specific plan efficiently in an unexpected condition. We focus on query execution (run-time), ignoring query optimization (compile-time), in order to complement existing research and to explore untapped potential for improved robustness in database query processing. One of our initial steps has been to devise diagrams or maps that show how well plans perform in the face of varying run-time conditions and how gracefully a system's query architecture, operators, and their implementation degrade in the face of adverse conditions. In this paper, we show several kinds of diagrams with data from three real systems and report on what we have learned both about these visualization techniques and about the three database systems


Search Driven Analysis of Heterogenous XML Data

Abstract: Analytical processing on XML repositories is usually enabled by designing complex data transformations that shred the documents into a common data warehousing schema. This can be very time-consuming and costly, especially if the underlying XML data has a lot of variety in structure, and only a subset of attributes constitutes meaningful dimensions and facts. Today, there is no tool to explore an XML data set, discover interesting attributes, dimensions and facts, and rapidly prototype an OLAP solution. In this paper, we propose a system, called SEDA that enables users to start with simple keyword-style querying, and interactively refine the query based on result summaries. SEDA then maps query results onto a set of known, or newly created, facts and dimensions, and derives a star schema and its instantiation to be fed into an off-the-shelf OLAP tool, for further analysis.


Social Systems: Can we Do More Than Just Poke Friends?

Abstract: Social sites have become extremely popular among users but have they attracted equal attention from the research community? Are they good only for simple tasks, such as tagging and poking friends? Do they present any new or interesting research challenges? In this paper, we describe the insights we have obtained implementing CourseRank, a course evaluation and planning social system. We argue that more attention should be given to social sites like ours and that there are many challenges (though not the traditional DBMS ones) that should be addressed by our community.


SCADS: Scale-Independent Storage for Social Computing Applications

Abstract: Collaborative web applications such as Facebook, Flickr and Yelp present new challenges for storing and querying large amounts of data. As users and developers are focused more on performance than single copy consistency or the ability to perform ad-hoc queries, there exists an opportunity for a highly-scalable system tailored specifically for relaxed consistency and pre-computed queries. The Web 2.0 development model demands the ability to both rapidly deploy new features and automatically scale with the number of users. There have been many successful distributed key-value stores, but so far none provide as rich a query language as SQL. We propose a new architecture, SCADS, that allows the developer to declaratively state application specific consistency requirements, takes advantage of utility computing to provide cost effective scale-up and scale-down, and will use machine learning models to introspectively anticipate performance problems and predict the resource requirements of new queries before execution.


Sailing the Information Ocean with Awareness of Currents: Discovery and Application of Source Dependence

Abstract: The Web has enabled the availability of a huge amount of useful information, but has also eased the ability to spread false information and rumors across multiple sources, making it hard to distinguish between what is true and what is not. Recent examples include the premature Steve Jobs obituary, the second bankruptcy of United airlines, the creation of Black Holes by the operation of the Large Hadron Collider, etc. Since it is important to permit the expression of dissenting and conflicting opinions, it would be a fallacy to try to ensure that the Web provides only consistent information. However, to help in separating the wheat from the chaff, it is essential to be able to determine dependence between sources. Given the huge number of data sources and the vast volume of conflicting data available on the Web, doing so in a scalable manner is extremely challenging and has not been addressed by existing work yet. In this paper, we present a set of research problems and propose some preliminary solutions on the issues involved in discovering dependence between sources. We also discuss how this knowledge can benefit a variety of technologies, such as data integration and Web 2.0, that help users manage and access the totality of the available information from various sources.


Capturing Data Uncertainty in High-Volume Stream Processing

Abstract: We present the design and development of a data stream system that captures data uncertainty from data collection to query processing to final result generation. Our system focuses on data that is naturally modeled as continuous random variables. For such data, our system employs an approach grounded in probability and statistical theory to capture data uncertainty and integrates this approach into high-volume stream processing. The first component of our system captures uncertainty of raw data streams from sensing devices. Since such raw streams can be highly noisy and may not carry sufficient information for query processing, our system employs probabilistic models of the data generation process and stream-speed inference to transform raw data into a desired format with an uncertainty metric. The second component captures uncertainty as data propagates through query operators. To efficiently quantify result uncertainty of a query operator, we explore a variety of techniques based on probability and statistical theory to compute the result distribution at stream speed. We are currently working with a group of scientists to evaluate our system using traces collected from the domains of (and eventually in the real systems for) hazardous weather monitoring and object tracking and monitoring.


A Case for A Collaborative Query Management System

Abstract: Over the past 40 years, database management systems (DBMSs) have evolved to provide a sophisticated variety of data management capabilities. At the same time, tools for managing queries over the data have remained relatively primitive. One reason for this is that queries are typically issued through applications. They are thus debugged once and re-used repeatedly. This mode of interaction, however, is changing. As scientists (and others) store and share increasingly large volumes of data in data centers, they need the ability to analyze the data by issuing exploratory queries. In this paper, we argue that, in these new settings, data management systems must provide powerful query management capabilities, from query browsing to automatic query recommendations. We first discuss the requirements for a collaborative query management system. We outline an early system architecture and discuss the many research challenges associated with building such an engine.


The Case for RodentStore, an Adaptive, Declarative Storage System

Abstract: Recent excitement in the database community surrounding new applications?analytic, scientific, graph, geospatial, etc.?has led to an explosion in research on database storage systems. New storage systems are vital to the database community, as they are at the heart of making database systems perform well in new application domains. Unfortunately, each such system also represents a substantial engineering effort including a great deal of duplication of mechanisms for features such as transactions and caching. In this paper, we make the case for RodentStore, an adaptive and declarative storage system providing a high-level interface for describing the physical representation of data. Specifically, RodentStore uses a declarative storage algebra whereby administrators (or database design tools) specify how a logical schema should be grouped into collections of rows, columns, and/or arrays, and the order in which those groups should be laid out on disk. We describe the key operators and types of our algebra, outline the general architecture of RodentStore, which interprets algebraic expressions to generate a physical representation of the data, and describe the interface between RodentStore and other parts of a database system, such as the query optimizer and executor. We provide a case study of the potential use of RodentStore in representing dense geospatial data collected from a mobile sensor network, showing the ease with which different storage layouts can be expressed using some of our algebraic constructs and the potential performance gains that a RodentStore-built storage system can offer.


uFLIP: Understanding Flash IO Patterns

Abstract: Does the advent of flash devices constitute a radical change for secondary storage? How should database systems adapt to this new form of secondary storage? Before we can answer these questions, we need to fully understand the performance characteristics of flash devices. More specifically, we want to establish what kind of IOs should be favored (or avoided) when designing algorithms and architectures for flash-based systems. In this paper, we focus on flash IO patterns, that capture relevant distribution of IOs in time and space, and our goal is to quantify their performance. We define uFLIP, a benchmark for measuring the response time of flash IO patterns. We also present a benchmarking methodology which takes into account the particular characteristics of flash devices. Finally, we present the results obtained by measuring eleven flash devices, and derive a set of design hints that should drive the development of flash-based systems on current devices.


Boosting XML Filtering with a Scalable FPGA-based Architecture

Abstract: The growing amount of XML encoded data exchanged over the Internet increases the importance of XML based publish-subscribe (pub-sub) and content based routing systems. The input in such systems typically consists of a stream of XML documents and a set of user subscriptions expressed as XML queries. The pub-sub system then filters the published documents and passes them to the subscribers. Pub-sub systems are characterized by very high input ratios, therefore the processing time is critical. In this paper we propose a "pure hardware" based solution, which utilizes XPath query blocks on FPGA to solve the filtering problem. By utilizing the high throughput that an FPGA provides for parallel processing, our approach achieves drastically better throughput than the existing software or mixed (hardware/software) architectures. The XPath queries (subscriptions) are translated to regular expressions which are then mapped to FPGA devices. By introducing stacks within the FPGA we are able to express and process a wide range of path queries very efficiently, on a scalable environment. Moreover, the fact that the parser and the filter processing are performed on the same FPGA chip, eliminates expensive communication costs (that a multi-core system would need) thus enabling very fast and efficient pipelining. Our experimental evaluation reveals more than one order of magnitude improvement compared to traditional pub/sub systems.


Principles for Inconsistency

Abstract: Data consistency is very desirable because strong semantic properties make it easier to write correct programs that perform as users expect. However, there are good reasons why consistency may have to be weakened to achieve other business goals. In this CIDR 2009 Perspectives paper, we present real-world reasons inconsistency may be necessary, offer principles for managing inconsistency coherently, and describe implementation approaches we are investigating for sustainably scalable systems that offer comprehensible user experiences despite inconsistency.


The Case for a Structured Approach to Managing Unstructured Data

Abstract: The challenge of managing unstructured data represents perhaps the largest data management opportunity for our community since managing relational data. And yet we are risking letting this opportunity go by, ceding the playing field to other players, ranging from communities such as AI, KDD, IR, Web, and Semantic Web, to industrial players such as Google, Yahoo, and Microsoft. In this essay we explore what we can do to improve upon this situation. Drawing on the lessons learned while managing relational data, we outline a structured approach to managing unstructured data. We conclude by discussing the potential implications of this approach to managing other kinds of non-relational data, and to the identify of our field.


Energy Efficiency: The New Holy Grail of Data Management Systems Research

Abstract: Energy costs are quickly rising in large-scale data centers and are soon projected to overtake the cost of hardware. As a result, data center operators have recently started turning into using more energy-friendly hardware. Despite the growing body of research in power management techniques, there has been little work to date on energy efficiency from a data management software perspective. In this paper, we argue that hardware-only approaches are only part of the solution, and that data management software will be key in optimizing for energy efficiency. We discuss the problems arising from growing energy use in data centers and the trends that point to an increasing set of opportunities for software-level optimizations. Using two simple experiments, we illustrate the potential of such optimizations, and, motivated by these examples, we discuss general approaches for reducing energy waste. Lastly, we point out existing places within database systems that are promising for energy-efficiency optimizations and urge the data management systems community to shift focus from performance-oriented research to energy-efficient computing.


Harnessing the Deep Web: Present and Future

Abstract: Over the past few years, we have built a system that has exposed large volumes of Deep-Web content to Google.com users. The content that our system exposes contributes to more than 1000 search queries per-second and spans over 50 languages and hundreds of domains. The Deep Web has long been acknowledged to be a major source of structured data on the web, and hence accessing Deep-Web content has long been a problem of interest in the data management community. In this paper, we report on where we believe the Deep Web provides value and where it does not. We contrast two very different approaches to exposing Deep-Web content -- the surfacing approach that we used, and the virtual integration approach that has often been pursued in the data management literature. We emphasize where the values of each of the two approaches lie and caution against potential pitfalls. We outline important areas of future research and, in particular, emphasize the value that can be derived from analyzing large collections of potentially disparate structured data on the web.


DBMSs Should Talk Back Too

Abstract: Natural language user interfaces to database systems have been studied for several decades now. They have mainly focused on parsing and interpreting natural language queries to generate them in a formal database language. We envision the reverse functionality, where the system would be able to take the internal result of that translation, say in SQL form, translate it back into natural language, and show it to the initiator of the query for verification. Likewise, information extraction has received considerable attention in the past ten years or so, identifying structured information in free text so that it may then be stored appropriately and queried. Validation of the records stored with a backward translation into text would again be very powerful. Verification and validation of query and data input of a database system correspond to just one example of the many important applications that would benefit greatly from having mature techniques for translating such database constructs into free-flowing text. The problem appears to be deceivingly simple, as there are no ambiguities or other complications in interpreting internal database elements, so initially a straightforward translation appears adequate. Reality teaches us quite the opposite, however, as the resulting text should be expressive, i.e., accurate in capturing the underlying queries or data, and effective, i.e., allowing fast and unique interpretation of them. Achieving both of these qualities is very difficult and raises several technical challenges that need to be addressed. In this paper, we first expose the reader to several situations and applications that need translation into natural language, thereby, motivating the problem. We then outline, by example, the research problems that need to be solved, separately for data translations and query translations.


Building on Quicksand

Abstract: Reliable systems have always been built out of unreliable components. Early on, the reliable components were small such as mirrored disks or ECC (Error Correcting Codes) in core memory. These systems were designed such that failures of these small components were transparent to the application. Later, the size of the unreliable components grew larger and semantic challenges crept into the application when failures occurred. As the granularity of the unreliable component grows, the latency to communicate with a backup becomes unpalatable. This leads to a more relaxed model for fault tolerance. The primary system will acknowledge the work request and its actions without waiting to ensure that the backup is notified of the work. This improves the responsiveness of the system. There are two implications of asynchronous state capture: 1) Everything promised by the primary is probabilistic. There is always a chance that an untimely failure shortly after the promise results in a backup proceeding without knowledge of the commitment. Hence, nothing is guaranteed! 2) Applications must ensure eventual consistency. Since work may be stuck in the primary after a failure and reappear later, the processing order for work cannot be guaranteed. Platform designers are struggling to make this easier for their applications. Emerging patterns of eventual consistency and probabilistic execution may soon yield a way for applications to express requirements for a "looser" form of consistency while providing availability in the face of ever larger failures. This paper recounts portions of the evolution of these trends, attempts to show the patterns that span these changes, and talks about future directions as we continue to "build on quicksand".


On Resource Aware Algorithms in Epidemic Live Streaming

Abstract: Epidemic-style diffusion schemes have been previously proposed for achieving peer-to-peer live streaming. Their performance trade-offs have been deeply analyzed for homogeneous systems, where all peers have the same upload capacity. However, epidemic schemes designed for heterogeneous systems have not been completely understood yet. In this report we focus on the peer selection process and propose a generic model that encompasses a large class of algorithms. The process is modeled as a combination of two functions, an aware one and an agnostic one. By means of simulations, we analyze the awareness-agnostism trade-offs on the peer selection process and the impact of the source distribution policy in non-homogeneous networks. We highlight that the early diffusion of a given chunk is crucial for its overall diffusion performance, and a fairness trade-off arises between the performance of heterogeneous peers, as a function of the level of awareness.


Heterogeneity in Distributed Live Streaming: Blessing or Curse?

Abstract: Distributed live streaming has brought a lot of interest in the past few years. In the homogeneous case (all nodes having the same capacity), many algorithms have been proposed, which have been proven almost optimal or optimal. On the other hand, the performance of heterogeneous systems is not completely understood yet. In this paper, we investigate the impact of heterogeneity on the achievable delay of chunk-based live streaming systems. We propose several models for taking the atomicity of a chunk into account. For all these models, when considering the transmission of a single chunk, heterogeneity is indeed a ``blessing'', in the sense that the achievable delay is always faster than an equivalent homogeneous system. But for a stream of chunks, we show that it can be a ``curse'': there is systems where the achievable delay can be arbitrary greater compared to equivalent homogeneous systems. However, if the system is slightly bandwidth-overprovisioned, optimal single chunk diffusion schemes can be adapted to a stream of chunks, leading to near-optimal, faster than homogeneous systems, heterogeneous live streaming systems.


Greedy Gossip with Eavesdropping

Abstract: This paper presents greedy gossip with eavesdropping (GGE), a novel randomized gossip algorithm for distributed computation of the average consensus problem. In gossip algorithms, nodes in the network randomly communicate with their neighbors and exchange information iteratively. The algorithms are simple and decentralized, making them attractive for wireless network applications. In general, gossip algorithms are robust to unreliable wireless conditions and time varying network topologies. In this paper we introduce GGE and demonstrate that greedy updates lead to rapid convergence. We do not require nodes to have any location information. Instead, greedy updates are made possible by exploiting the broadcast nature of wireless communications. During the operation of GGE, when a node decides to gossip, instead of choosing one of its neighbors at random, it makes a greedy selection, choosing the node which has the value most different from its own. In order to make this selection, nodes need to know their neighbors' values. Therefore, we assume that all transmissions are wireless broadcasts and nodes keep track of their neighbors' values by eavesdropping on their communications. We show that the convergence of GGE is guaranteed for connected network topologies. We also study the rates of convergence and illustrate, through theoretical bounds and numerical simulations, that GGE consistently outperforms randomized gossip and performs comparably to geographic gossip on moderate-sized random geometric graph topologies.


A Graph Theoretic Approach for Optimizing Key Pre-distribution in Wireless SensorNetworks

Abstract: Finding an optimal key assignment (subject to given constraints) for a key predistribution scheme in wireless sensor networks is a difficult task. Hence, most of the practical schemes are based on probabilistic key assignment, which leads to sub-optimal schemes requiring key storage linear in the total number of nodes. A graph theoretic framework is introduced to study the fundamental tradeoffs between key storage, average key path length (directly related to the battery consumption) and resilience (to compromised nodes) of key predistribution schemes for wireless sensor networks. Based on the proposed framework, a lower bound on key storage is derived for a given average key path length. An upper bound on the compromising probability is also given. This framework also leads to the design of key assignment schemes with a storage complexity of the same order as the lower bound.


Optimally fast incremental Manhattan plane embedding and planar tight span construction

Abstract: We describe a data structure, a rectangular complex, that can be used to represent hyperconvex metric spaces that have the same topology (although not necessarily the same distance function) as subsets of the plane. We show how to use this data structure to construct the tight span of a metric space given as an n x n distance matrix, when the tight span is homeomorphic to a subset of the plane, in time O(n^2), and to add a single point to a planar tight span in time O(n). As an application of this construction, we show how to test whether a given finite metric space embeds isometrically into the Manhattan plane in time O(n^2), and add a single point to the space and re-test whether it has such an embedding in time O(n).


Paired approximation problems and incompatible inapproximabilities

Abstract: This paper considers pairs of optimization problems that are defined from a single input and for which it is desired to find a good approximation to either one of the problems. In many instances, it is possible to efficiently find an approximation of this type that is better than known inapproximability lower bounds for either of the two individual optimization problems forming the pair. In particular, we find either a $(1+\epsilon)$-approximation to $(1,2)$-TSP or a $1/\epsilon$-approximation to maximum independent set, from a given graph, in linear time. We show a similar paired approximation result for finding either a coloring or a long path. However, no such tradeoff exists in some other cases: for set cover and hitting set problems defined from a single set family, and for clique and independent set problems on the same graph, it is not possible to find an approximation when both problems are combined that is better than the best approximation for either problem on its own.


Turbo NOC: a framework for the design of Network On Chip based turbo decoder architectures

Abstract: This work proposes a general framework for the design and simulation of network on chip based turbo decoder architectures. Several parameters in the design space are investigated, namely the network topology, the parallelism degree, the rate at which messages are sent by processing nodes over the network and the routing strategy. The main results of this analysis are: i) the most suited topologies to achieve high throughput with a limited complexity overhead are generalized de-Bruijn and generalized Kautz topologies; ii) depending on the throughput requirements different parallelism degrees, message injection rates and routing algorithms can be used to minimize the network area overhead.


Using Ellipsoidal Domains to Analyze Control Systems Software

Abstract: We propose a methodology for the automatic verification of safety properties of controllers based on dynamical systems, such as those typically used in avionics. In particular, our focus is on proving stability properties of software implementing linear and some non-linear controllers. We develop an abstract interpretation framework that follows closely the Lyapunov methods used in proofs at the model level and describe the corresponding abstract domains, which for linear systems consist of ellipsoidal constraints. These ellipsoidal domains provide abstractions for the values of state variables and must be combined with other domains that model the remaining variables in a program. Thus, the problem of automatically assigning the right type of abstract domain to each variable arises. We provide an algorithm that solves this classification problem in many practical cases and suggest how it could be generalized to more complicated cases. We then find a fixpoint by solving a matrix equation, which in the linear case is just the discrete Lyapunov equation. Contrary to most cases in software analysis, this fixpoint cannot be reached by the usual iterative method of propagating constraints until saturation and so numerical methods become essential. Finally, we illustrate our methodology with several examples.


Computing alignment plots efficiently

Abstract: Dot plots are a standard method for local comparison of biological sequences. In a dot plot, a substring to substring distance is computed for all pairs of fixed-size windows in the input strings. Commonly, the Hamming distance is used since it can be computed in linear time. However, the Hamming distance is a rather crude measure of string similarity, and using an alignment-based edit distance can greatly improve the sensitivity of the dot plot method. In this paper, we show how to compute alignment plots of the latter type efficiently. Given two strings of length m and n and a window size w, this problem consists in computing the edit distance between all pairs of substrings of length w, one from each input string. The problem can be solved by repeated application of the standard dynamic programming algorithm in time O(mnw^2). This paper gives an improved data-parallel algorithm, running in time $O(mnw/\gamma/p)$ using vector operations that work on $\gamma$ values in parallel and $p$ processors. We show experimental results from an implementation of this algorithm, which uses Intel's MMX/SSE instructions for vector parallelism and MPI for coarse-grained parallelism.


A Lightweight Distributed Solution to Content Replication in Mobile Networks

Abstract: Performance and reliability of content access in mobile networks is conditioned by the number and location of content replicas deployed at the network nodes. Facility location theory has been the traditional, centralized approach to study content replication: computing the number and placement of replicas in a network can be cast as an uncapacitated facility location problem. The endeavour of this work is to design a distributed, lightweight solution to the above joint optimization problem, while taking into account the network dynamics. In particular, we devise a mechanism that lets nodes share the burden of storing and providing content, so as to achieve load balancing, and decide whether to replicate or drop the information so as to adapt to a dynamic content demand and time-varying topology. We evaluate our mechanism through simulation, by exploring a wide range of settings and studying realistic content access mechanisms that go beyond the traditional assumptionmatching demand points to their closest content replica. Results show that our mechanism, which uses local measurements only, is: (i) extremely precise in approximating an optimal solution to content placement and replication; (ii) robust against network mobility; (iii) flexible in accommodating various content access patterns, including variation in time and space of the content demand.


Size Bounds for Conjunctive Queries with General Functional Dependencies

Abstract: This paper extends the work of Gottlob, Lee, and Valiant (PODS 2009)[GLV], and considers worst-case bounds for the size of the result Q(D) of a conjunctive query Q to a database D given an arbitrary set of functional dependencies. The bounds in [GLV] are based on a "coloring" of the query variables. In order to extend the previous bounds to the setting of arbitrary functional dependencies, we leverage tools from information theory to formalize the original intuition that each color used represents some possible entropy of that variable, and bound the maximum possible size increase via a linear program that seeks to maximize how much more entropy is in the result of the query than the input. This new view allows us to precisely characterize the entropy structure of worst-case instances for conjunctive queries with simple functional dependencies (keys), providing new insights into the results of [GLV]. We extend these results to the case of general functional dependencies, providing upper and lower bounds on the worst-case size increase. We identify the fundamental connection between the gap in these bounds and a central open question in information theory. Finally, we show that, while both the upper and lower bounds are given by exponentially large linear programs, one can distinguish in polynomial time whether the result of a query with an arbitrary set of functional dependencies can be any larger than the input database.


Business in the Grid

Abstract: From 2004 to 2007 the Business In the Grid (BIG) project took place and was driven by the following goals: Firstly, make business aware of Grid technology and, secondly, try to explore new business models. We disseminated Grid computing by mainly concentrating on the central European market and interviewed several companies in order to gain insights into the Grid acceptance in industrial environments. In this article we present the results of the project, elaborate on a critical discussion on business adaptations, and describe a novel dynamic authorization workflow for business processes in the Grid.


SocialScope: Enabling Information Discovery on Social Content Sites

Abstract: Recently, many content sites have started encouraging their users to engage in social activities such as adding buddies on Yahoo! Travel and sharing articles with their friends on New York Times. This has led to the emergence of {\em social content sites}, which is being facilitated by initiatives like OpenID (http://www.openid.net/) and OpenSocial (http://www.opensocial.org/). These community standards enable the open access to users' social profiles and connections by individual content sites and are bringing content-oriented sites and social networking sites ever closer. The integration of content and social information raises new challenges for {\em information management and discovery} over such sites. We propose a logical architecture, named \kw{SocialScope}, consisting of three layers, for tackling the challenges. The {\em content management} layer is responsible for integrating, maintaining and physically accessing the content and social data. The {\em information discovery} layer takes care of analyzing content to derive interesting new information, and interpreting and processing the user's information need to identify relevant information. Finally, the {\em information presentation} layer explores the discovered information and helps users better understand it in a principled way. We describe the challenges in each layer and propose solutions for some of those challenges. In particular, we propose a uniform algebraic framework, which can be leveraged to uniformly and flexibly specify many of the information discovery and analysis tasks and provide the foundation for the optimization of those tasks.


Inter-Operator Feedback in Data Stream Management Systems via Punctuation

Abstract: High-volume, high-speed data streams may overwhelm the capabilities of stream processing systems; techniques such as data prioritization, avoidance of unnecessary processing and on-demand result production may be necessary to reduce processing requirements. However, the dynamic nature of data streams, in terms of both rate and content, makes the application of such techniques challenging. Such techniques have been addressed in the context of static and centralized query optimization; however, they have not been fully addressed for data stream management systems. In this work, we present a comprehensive framework that supports prioritization, avoidance of unnecessary work, and on-demand result production over distributed, unreliable, bursty, disordered data sources, typical of many data streams. We propose a form of inter-operator feedback, which flows against the stream direction, to communicate the information needed to enable execution of these techniques. This feedback leverages punctuations to describe the subsets of interest. We identify potential sources of feedback information, characterize new types of punctuation to support feedback, and describe the roles of producers, exploiters, and relayers of feedback that query operators may implement. We present initial experimental observations using the NiagaraST data-stream system.


Indirect jumps improve instruction sequence performance

Abstract: Instruction sequences with direct and indirect jump instructions are as expressive as instruction sequences with direct jump instructions only. We show that, in the case where the number of instructions is not bounded, we are faced with increases of the maximal internal delays of instruction sequences on execution that are not bounded by a linear function if we strive for acceptable increases of the lengths of instruction sequences on elimination of indirect jump instructions.


Context Aware Adaptable Applications - A global approach

Abstract: Actual applications (mostly component based) requirements cannot be expressed without a ubiquitous and mobile part for end-users as well as for M2M applications (Machine to Machine). Such an evolution implies context management in order to evaluate the consequences of the mobility and corresponding mechanisms to adapt or to be adapted to the new environment. Applications are then qualified as context aware applications. This first part of this paper presents an overview of context and its management by application adaptation. This part starts by a definition and proposes a model for the context. It also presents various techniques to adapt applications to the context: from self-adaptation to supervised approached. The second part is an overview of architectures for adaptable applications. It focuses on platforms based solutions and shows information flows between application, platform and context. Finally it makes a synthesis proposition with a platform for adaptable context-aware applications called Kalimucho. Then we present implementations tools for software components and a dataflow models in order to implement the Kalimucho platform.


Paired Comparisons-based Interactive Differential Evolution

Abstract: We propose Interactive Differential Evolution (IDE) based on paired comparisons for reducing user fatigue and evaluate its convergence speed in comparison with Interactive Genetic Algorithms (IGA) and tournament IGA. User interface and convergence performance are two big keys for reducing Interactive Evolutionary Computation (IEC) user fatigue. Unlike IGA and conventional IDE, users of the proposed IDE and tournament IGA do not need to compare whole individuals each other but compare pairs of individuals, which largely decreases user fatigue. In this paper, we design a pseudo-IEC user and evaluate another factor, IEC convergence performance, using IEC simulators and show that our proposed IDE converges significantly faster than IGA and tournament IGA, i.e. our proposed one is superior to others from both user interface and convergence performance points of view.


MESURE Tool to benchmark Java Card platforms

Abstract: The advent of the Java Card standard has been a major turning point in smart card technology. With the growing acceptance of this standard, understanding the performance behavior of these platforms is becoming crucial. To meet this need, we present in this paper a novel benchmarking framework to test and evaluate the performance of Java Card platforms. MESURE tool is the first framework which accuracy and effectiveness are independent from the particular Java Card platform tested and CAD used.


Performance of Opportunistic Epidemic Routing on Edge-Markovian Dynamic Graphs

Abstract: Connectivity patterns in intermittently-connected mobile networks (ICMN) can be modeled as edge-Markovian dynamic graphs. We propose a new model for epidemic propagation on such graphs and calculate a closed-form expression that links the best achievable delivery ratio to common ICMN parameters such as message size, maximum tolerated delay, and link lifetime. These theoretical results are compared to those obtained by replaying a real-life contact trace.


A general XML-based distributed software architecture for accessing and sharing ressources

Abstract: This paper presents a general xml-based distributed software architecture in the aim of accessing and sharing resources in an opened client/server environment. The paper is organized as follows : First, we introduce the idea of a "General Distributed Software Architecture". Second, we describe the general framework in which this architecture is used. Third, we describe the process of information exchange and we introduce some technical issues involved in the implementation of the proposed architecture. Finally, we present some projects which are currently using, or which should use, the proposed architecture.


Navigation in tilings of the hyperbolic plane and possible applications

Abstract: This paper introduces a method of navigation in a large family of tilings of the hyperbolic plane and looks at the question of possible applications in the light of the few ones which were already obtained.


A Survey on Service Composition Middleware in Pervasive Environments

Abstract: The development of pervasive computing has put the light on a challenging problem: how to dynamically compose services in heterogeneous and highly changing environments? We propose a survey that defines the service composition as a sequence of four steps: the translation, the generation, the evaluation, and finally the execution. With this powerful and simple model we describe the major service composition middleware. Then, a classification of these service composition middleware according to pervasive requirements - interoperability, discoverability, adaptability, context awareness, QoS management, security, spontaneous management, and autonomous management - is given. The classification highlights what has been done and what remains to do to develop the service composition in pervasive environments.


SeeReader: An (Almost) Eyes-Free Mobile Rich Document Viewer

Abstract: Reading documents on mobile devices is challenging. Not only are screens small and difficult to read, but also navigating an environment using limited visual attention can be difficult and potentially dangerous. Reading content aloud using text-tospeech (TTS) processing can mitigate these problems, but only for content that does not include rich visual information. In this paper, we introduce a new technique, SeeReader, that combines TTS with automatic content recognition and document presentation control that allows users to listen to documents while also being notified of important visual content. Together, these services allow users to read rich documents on mobile devices while maintaining awareness of their visual environment.


Embedded Sensor System for Early Pathology Detection in Building Construction

Abstract: Structure pathology detection is an important security task in building construction, which is performed by an operator by looking manually for damages on the materials. This activity could be dangerous if the structure is hidden or difficult to reach. On the other hand, embedded devices and wireless sensor networks (WSN) are becoming popular and cheap, enabling the design of an alternative pathology detection system to monitor structures based on these technologies. This article introduces a ZigBee WSN system, intending to be autonomous, easy to use and with low power consumption. Its functional parts are fully discussed with diagrams, as well as the protocol used to collect samples from sensor nodes. Finally, several tests focused on range and power consumption of our prototype are shown, analysing whether the results obtained were as expected or not.


Approximate Nearest Neighbor Search through Comparisons

Abstract: This paper addresses the problem of finding the nearest neighbor (or one of the R-nearest neighbors) of a query object q in a database of n objects. In contrast with most existing approaches, we can only access the ``hidden'' space in which the objects live through a similarity oracle. The oracle, given two reference objects and a query object, returns the reference object closest to the query object. The oracle attempts to model the behavior of human users, capable of making statements about similarity, but not of assigning meaningful numerical values to distances between objects.


Slicing: A New Approach to Privacy Preserving Data Publishing

Abstract: Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving microdata publishing. Recent work has shown that generalization loses considerable amount of information, especially for high-dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi-identifying attributes and sensitive attributes. In this paper, we present a novel technique called slicing, which partitions the data both horizontally and vertically. We show that slicing preserves better data utility than generalization and can be used for membership disclosure protection. Another important advantage of slicing is that it can handle high-dimensional data. We show how slicing can be used for attribute disclosure protection and develop an efficient algorithm for computing the sliced data that obey the l-diversity requirement. Our workload experiments confirm that slicing preserves better utility than generalization and is more effective than bucketization in workloads involving the sensitive attribute. Our experiments also demonstrate that slicing can be used to prevent membership disclosure.


Simulation of Resource Usage in Parallel Evolutionary Peptide Optimization using JavaSpaces Technology

Abstract: Peptide Optimization is a highly complex problem and it takes very long time of computation. This optimization process uses many software applications in a cluster running GNU/Linux Operating System that perform special tasks. The application to organize the whole optimization process had been already developed, namely SEPP (System for Evolutionary Pareto Optimization of Peptides/Polymers). A single peptide optimization takes a lot of computation time to produce a certain number of individuals. However, it can be accelerated by increasing the degree of parallelism as well as the number of nodes (processors) in the cluster. In this master thesis, I build a model simulating the interplay of the programs so that the usage of each resource (processor) can be determined and also the approximated time needed for the overall optimization process. There are two Evolutionary Algorithms that could be used in the optimization, namely Generation-based and Steady-state Evolutionary Algorithm. The results of each Evolutionary Algorithm are shown based on the simulations. Moreover, the results are also compared by using different parameters (the degree of parallelism and the number of processors) in the simulation to give an overview of the advantages and the disadvantages of the algorithms in terms of computation time and resource usage. The model is built up using JavaSpaces Technology.


Logic with Verbs

Abstract: The aim of this paper is to introduce a logic in which nouns and verbs are handled together as a deductive reasoning, and also to observe the relationship between nouns and verbs as well as between logics and conversations.


Two-Phase Flow in Heterogeneous Media

Abstract: In this study, we investigate the appeared complexity of two-phase flow (air-water) in a heterogeneous soil where the supposed porous media is non-deformable media which is under the time-dependent gas pressure. After obtaining of governing equations and considering the capillary pressure-saturation and permeability functions, the evolution of the models unknown parameters were obtained. In this way, using COMSOL (FEMLAB) and fluid flow-script Module, the role of heterogeneity in intrinsic permeability was analysed. Also, the evolution of relative permeability of wetting and non-wetting fluid, capillary pressure and other parameters were elicited.


Back analysis based on SOM-RST system

Abstract: This paper describes application of information granulation theory, on the back analysis of Jeffrey mine southeast wall Quebec. In this manner, using a combining of Self Organizing Map (SOM) and rough set theory (RST), crisp and rough granules are obtained. Balancing of crisp granules and sub rough granules is rendered in close-open iteration. Combining of hard and soft computing, namely finite difference method (FDM) and computational intelligence and taking in to account missing information are two main benefits of the proposed method. As a practical example, reverse analysis on the failure of the southeast wall Jeffrey mine is accomplished.


Weblog Clustering in Multilinear Algebra Perspective

Abstract: This paper describes a clustering method to group the most similar and important weblogs with their descriptive shared words by using a technique from multilinear algebra known as PARAFAC tensor decomposition. The proposed method first creates labeled-link network representation of the weblog datasets, where the nodes are the blogs and the labels are the shared words. Then, 3-way adjacency tensor is extracted from the network and the PARAFAC decomposition is applied to the tensor to get pairs of node lists and label lists with scores attached to each list as the indication of the degree of importance. The clustering is done by sorting the lists in decreasing order and taking the pairs of top ranked blogs and words. Thus, unlike standard co-clustering methods, this method not only groups the similar blogs with their descriptive words but also tends to produce clusters of important blogs and descriptive words.


Improvement of Text Dependent Speaker Identification System Using Neuro-Genetic Hybrid Algorithm in Office Environmental Conditions

Abstract: In this paper, an improved strategy for automated text dependent speaker identification system has been proposed in noisy environment. The identification process incorporates the Neuro- Genetic hybrid algorithm with cepstral based features. To remove the background noise from the source utterance, wiener filter has been used. Different speech pre-processing techniques such as start-end point detection algorithm, pre-emphasis filtering, frame blocking and windowing have been used to process the speech utterances. RCC, MFCC, MFCC, MFCC, LPC and LPCC have been used to extract the features. After feature extraction of the speech, Neuro-Genetic hybrid algorithm has been used in the learning and identification purposes. Features are extracted by using different techniques to optimize the performance of the identification. According to the VALID speech database, the highest speaker identification rate of 100.000 percent for studio environment and 82.33 percent for office environmental conditions have been achieved in the close set text dependent speaker identification system.


Global Heuristic Search on Encrypted Data (GHSED)

Abstract: Important document are being kept encrypted in remote servers. In order to retrieve these encrypted data, efficient search methods needed to enable the retrieval of the document without knowing the content of the documents In this paper a technique called a global heuristic search on encrypted data (GHSED) technique will be described for search in an encrypted files using public key encryption stored on an untrusted server and retrieve the files that satisfy a certain search pattern without revealing any information about the original files. GHSED technique would satisfy the following: (1) Provably secure, the untrusted server cannot learn anything about the plaintext given only the cipher text. (2) Provide controlled searching, so that the untrusted server cannot search for a word without the user's authorization. (3) Support hidden queries, so that the user may ask the untrusted server to search for a secret word without revealing the word to the server. (4) Support query isolation, so the untrusted server learns nothing more than the search result about the plaintext.


Philosophical Survey of Passwords

Abstract: Over the years security experts in the field of Information Technology have had a tough time in making passwords secure. This paper studies and takes a careful look at this issue from the angle of philosophy and cognitive science. We have studied the process of passwords to rank its strengths and weaknesses in order to establish a quality metric for passwords. Finally we related the process to human senses which enables us to propose a constitutional scheme for the process of password. The basic proposition is to exploit relationship between human senses and password to ensure improvement in authentication while keeping it an enjoyable activity.


Web Single Sign-On Authentication using SAML

Abstract: Companies have increasingly turned to application service providers (ASPs) or Software as a Service (SaaS) vendors to offer specialized web-based services that will cut costs and provide specific and focused applications to users. The complexity of designing, installing, configuring, deploying, and supporting the system with internal resources can be eliminated with this type of methodology, providing great benefit to organizations. However, these models can present an authentication problem for corporations with a large number of external service providers. This paper describes the implementation of Security Assertion Markup Language (SAML) and its capabilities to provide secure single sign-on (SSO) solutions for externally hosted applications.


Self-Partial and Dynamic Reconfiguration Implementation for AES using FPGA

Abstract: This paper addresses efficient hardware/software implementation approaches for the AES (Advanced Encryption Standard) algorithm and describes the design and performance testing algorithm for embedded system. Also, with the spread of reconfigurable hardware such as FPGAs (Field Programmable Gate Array) embedded cryptographic hardware became cost-effective. Nevertheless, it is worthy to note that nowadays, even hardwired cryptographic algorithms are not so safe. From another side, the self-reconfiguring platform is reported that enables an FPGA to dynamically reconfigure itself under the control of an embedded microprocessor. Hardware acceleration significantly increases the performance of embedded systems built on programmable logic. Allowing a FPGA-based MicroBlaze processor to self-select the coprocessors uses can help reduce area requirements and increase a system's versatility. The architecture proposed in this paper is an optimal hardware implementation algorithm and takes dynamic partially reconfigurable of FPGA. This implementation is good solution to preserve confidentiality and accessibility to the information in the numeric communication.


Comprehensive Security Framework for Global Threads Analysis

Abstract: Cyber criminality activities are changing and becoming more and more professional. With the growth of financial flows through the Internet and the Information System (IS), new kinds of thread arise involving complex scenarios spread within multiple IS components. The IS information modeling and Behavioral Analysis are becoming new solutions to normalize the IS information and counter these new threads. This paper presents a framework which details the principal and necessary steps for monitoring an IS. We present the architecture of the framework, i.e. an ontology of activities carried out within an IS to model security information and User Behavioral analysis. The results of the performed experiments on real data show that the modeling is effective to reduce the amount of events by 91%. The User Behavioral Analysis on uniform modeled data is also effective, detecting more than 80% of legitimate actions of attack scenarios.


Dpraodv: A Dyanamic Learning System Against Blackhole Attack in Aodv Based Manet

Abstract: Security is an essential requirement in mobile ad hoc networks to provide protected communication between mobile nodes. Due to unique characteristics of MANETS, it creates a number of consequential challenges to its security design. To overcome the challenges, there is a need to build a multifence security solution that achieves both broad protection and desirable network performance. MANETs are vulnerable to various attacks, blackhole, is one of the possible attacks. Black hole is a type of routing attack where a malicious node advertise itself as having the shortest path to all nodes in the environment by sending fake route reply. By doing this, the malicious node can deprive the traffic from the source node. It can be used as a denial-of-service attack where it can drop the packets later. In this paper, we proposed a DPRAODV (Detection, Prevention and Reactive AODV) to prevent security threats of blackhole by notifying other nodes in the network of the incident. The simulation results in ns2 (ver- 2.33) demonstrate that our protocol not only prevents blackhole attack but consequently improves the overall performance of (normal) AODV in presence of black hole attack.


An Efficient Secure Multimodal Biometric Fusion Using Palmprint and Face Image

Abstract: Biometrics based personal identification is regarded as an effective method for automatically recognizing, with a high confidence a person's identity. A multimodal biometric systems consolidate the evidence presented by multiple biometric sources and typically better recognition performance compare to system based on a single biometric modality. This paper proposes an authentication method for a multimodal biometric system identification using two traits i.e. face and palmprint. The proposed system is designed for application where the training data contains a face and palmprint. Integrating the palmprint and face features increases robustness of the person authentication. The final decision is made by fusion at matching score level architecture in which features vectors are created independently for query measures and are then compared to the enrolment template, which are stored during database preparation. Multimodal biometric system is developed through fusion of face and palmprint recognition.


Similarity Matching Techniques for Fault Diagnosis in Automotive Infotainment Electronics

Abstract: Fault diagnosis has become a very important area of research during the last decade due to the advancement of mechanical and electrical systems in industries. The automobile is a crucial field where fault diagnosis is given a special attention. Due to the increasing complexity and newly added features in vehicles, a comprehensive study has to be performed in order to achieve an appropriate diagnosis model. A diagnosis system is capable of identifying the faults of a system by investigating the observable effects (or symptoms). The system categorizes the fault into a diagnosis class and identifies a probable cause based on the supplied fault symptoms. Fault categorization and identification are done using similarity matching techniques. The development of diagnosis classes is done by making use of previous experience, knowledge or information within an application area. The necessary information used may come from several sources of knowledge, such as from system analysis. In this paper similarity matching techniques for fault diagnosis in automotive infotainment applications are discussed.


Performing Hybrid Recommendation in Intermodal Transportation-the FTMarket System's Recommendation Module

Abstract: Diverse recommendation techniques have been already proposed and encapsulated into several e-business applications, aiming to perform a more accurate evaluation of the existing information and accordingly augment the assistance provided to the users involved. This paper reports on the development and integration of a recommendation module in an agent-based transportation transactions management system. The module is built according to a novel hybrid recommendation technique, which combines the advantages of collaborative filtering and knowledge-based approaches. The proposed technique and supporting module assist customers in considering in detail alternative transportation transactions that satisfy their requests, as well as in evaluating completed transactions. The related services are invoked through a software agent that constructs the appropriate knowledge rules and performs a synthesis of the recommendation policy.


Geometric and Signal Strength Dilution of Precision (DoP)Wi-Fi

Abstract: The democratization of wireless networks combined to the emergence of mobile devices increasingly autonomous and efficient lead to new services. Positioning services become overcrowded. Accuracy is the main quality criteria in positioning. But to better appreciate this one a coefficient is needed. In this paper we present Geometric and Signal Strength Dilution of Precision (DOP) for positioning systems based on Wi-Fi and Signal Strength measurements.


Implementation of Rule Based Algorithm for Sandhi-Vicheda Of Compound Hindi Words

Abstract: Sandhi means to join two or more words to coin new word. Sandhi literally means `putting together' or combining (of sounds), It denotes all combinatory sound-changes effected (spontaneously) for ease of pronunciation. Sandhi-vicheda describes [5] the process by which one letter (whether single or cojoined) is broken to form two words. Part of the broken letter remains as the last letter of the first word and part of the letter forms the first letter of the next letter. Sandhi- Vicheda is an easy and interesting way that can give entirely new dimension that add new way to traditional approach to Hindi Teaching. In this paper using the Rule based algorithm we have reported an accuracy of 60-80% depending upon the number of rules to be implemented.


Fast and flexible selection with a single switch

Abstract: Selection methods that require only a single-switch input, such as a button click or blink, are potentially useful for individuals with motor impairments, mobile technology users, and individuals wishing to transmit information securely. We present a single-switch selection method, "Nomon," that is general and efficient. Existing single-switch selection methods require selectable options to be arranged in ways that limit potential applications. By contrast, traditional operating systems, web browsers, and free-form applications (such as drawing) place options at arbitrary points on the screen. Nomon, however, has the flexibility to select any point on a screen. Nomon adapts automatically to an individual's clicking ability; it allows a person who clicks precisely to make a selection quickly and allows a person who clicks imprecisely more time to make a selection without error. Nomon reaps gains in information rate by allowing the specification of beliefs (priors) about option selection probabilities and by avoiding tree-based selection schemes in favor of direct (posterior) inference. We have developed both a Nomon-based writing application and a drawing application. To evaluate Nomon's performance, we compared the writing application with a popular existing method for single-switch writing (row-column scanning). Novice users wrote 35% faster with the Nomon interface than with the scanning interface. An experienced user (author TB, with > 10 hours practice) wrote at speeds of 9.3 words per minute with Nomon, using 1.2 clicks per character and making no errors in the final text.


Excel Modelling - Transparency, Auditing and Business Use

Abstract: Within Lloyds Banking Group the heritage HBOS Corporate division deals with Corporate loans, and is required to assess these loans for risk in accordance with the Basle Accord regulations. Statistical Risk Rating models are developed by the risk analysts to assess the obligors credit worthiness. It is necessary then to provide the bankers who originated the loan ('Relationship Managers' or RMs) with an assessment tool to generate the loan rating upon which they base their lending decisions. Heritage HBoS Corporate required a new model build system for holding its Risk Rating models in 2006 as a result of more complex models being created to comply with the Basle Accord. The use of Excel was promoted by the IT department for a number of reasons; the Excel solution now in use is reviewed in this paper.


End User Computing in AIB Capital Markets: A Management Summary

Abstract: This paper is a management summary of how the area of End User Computing (EUC) has been addressed by AIB Capital Markets. The development of an effective policy is described, as well as the process by which a register of critical EUC applications was assembled and how those applications were brought into a controlled environment. A number of findings are included as well as recommendations for others who would seek to run a similar project.


Design of an ultrasound-guided robotic brachytherapy needle insertion system

Abstract: In this paper we describe a new robotic brachytherapy needle-insertion system that is designed to replace the template used in the manual technique. After a brief review of existing robotic systems, we describe the requirements that we based our design upon. A detailed description of the proposed system follows. Our design is capable of positioning and inclining a needle within the same workspace as the manual template. To help improve accuracy, the needle can be rotated about its axis during insertion into the prostate. The system can be mounted on existing steppers and also easily accommodates existing seed dispensers, such as the Mick Applicator.


PrisCrawler: A Relevance Based Crawler for Automated Data Classification from Bulletin Board

Abstract: Nowadays people realize that it is difficult to find information simply and quickly on the bulletin boards. In order to solve this problem, people propose the concept of bulletin board search engine. This paper describes the priscrawler system, a subsystem of the bulletin board search engine, which can automatically crawl and add the relevance to the classified attachments of the bulletin board. Priscrawler utilizes Attachrank algorithm to generate the relevance between webpages and attachments and then turns bulletin board into clear classified and associated databases, making the search for attachments greatly simplified. Moreover, it can effectively reduce the complexity of pretreatment subsystem and retrieval subsystem and improve the search precision. We provide experimental results to demonstrate the efficacy of the priscrawler.