Fast Search for Dynamic Multi-Relational Graphs

Acting on time-critical events by processing ever growing social media or news streams is a major technical challenge. Many of these data sources can be modeled as multi-relational graphs. Continuous queries or techniques to search for rare events that typically arise in monitoring applications have been studied extensively for relational databases. This work is dedicated to answer the question that emerges naturally: how can we efficiently execute a continuous query on a dynamic graph? This paper presents an exact subgraph search algorithm that exploits the temporal characteristics of representative queries for online news or social media monitoring. The algorithm is based on a novel data structure called the Subgraph Join Tree (SJ-Tree) that leverages the structural and semantic characteristics of the underlying multi-relational graph. The paper concludes with extensive experimentation on several real-world datasets that demonstrates the validity of this approach.Comment: SIGMOD Workshop on Dynamic Networks Management and Mining (DyNetMM), 201

COST Action IC 1402 ArVI: Runtime Verification Beyond Monitoring -- Activity Report of Working Group 1

This report presents the activities of the first working group of the COST Action ArVI, Runtime Verification beyond Monitoring. The report aims to provide an overview of some of the major core aspects involved in Runtime Verification. Runtime Verification is the field of research dedicated to the analysis of system executions. It is often seen as a discipline that studies how a system run satisfies or violates correctness properties. The report exposes a taxonomy of Runtime Verification (RV) presenting the terminology involved with the main concepts of the field. The report also develops the concept of instrumentation, the various ways to instrument systems, and the fundamental role of instrumentation in designing an RV framework. We also discuss how RV interplays with other verification techniques such as model-checking, deductive verification, model learning, testing, and runtime assertion checking. Finally, we propose challenges in monitoring quantitative and statistical data beyond detecting property violation

A Survey on IT-Techniques for a Dynamic Emergency Management in Large Infrastructures

This deliverable is a survey on the IT techniques that are relevant to the three use cases of the project EMILI. It describes the state-of-the-art in four complementary IT areas: Data cleansing, supervisory control and data acquisition, wireless sensor networks and complex event processing. Even though the deliverable’s authors have tried to avoid a too technical language and have tried to explain every concept referred to, the deliverable might seem rather technical to readers so far little familiar with the techniques it describes

Active Data: A Data-Centric Approach to Data Life-Cycle Management

International audienceData-intensive science offers new opportunities for innovation and discoveries, provided that large datasets can be handled efficiently. Data management for data-intensive science applications is challenging; requiring support for complex data life cycles, coordination across multiple sites, fault tolerance, and scalability to support tens of sites and petabytes of data. In this paper, we argue that data management for data-intensive science applications requires a fundamentally different management approach than the current ad-hoc task centric approach. We propose Active Data, a fundamentally novel paradigm for data life cycle management. Active Data follows two principles: data-centric and event-driven. We report on the Active Data programming model and its preliminary implementation, and discuss the benefits and limitations of the approach on recognized challenging data-intensive science use-cases.Les importants volumes de données produits par la science présentent de nouvelles opportunités d'innovation et de découvertes. Cependant ceci sera conditionné par notre capacité à gérer efficacement de très grands jeux de données. La gestion de données pour les applications scientifiques data-intensive présente un véritable défi~; elle requière le support de cycles de vie très complexes, la coordination de plusieurs sites, de la tolérance aux pannes et de passer à l'échelle sur des dizaines de sites avec plusieurs péta-octets de données. Dans cet article nous argumentons que la gestion des données pour les applications scientifiques data-intensive nécessite une approche fondamentalement différente de l'actuel paradigme centré sur les tâches. Nous proposons Active Data, un nouveau paradigme pour la gestion du cycle de vie des données. Active Data suit deux principes~: il est centré sur les données et à base d'événements. Nous présentons le modèle de programmation Active Data, un prototype d'implémentation et discutons des avantages et limites de notre approche à partir d'étude de cas d'applications scientifiques

A model-driven approach to broaden the detection of software performance antipatterns at runtime

Performance antipatterns document bad design patterns that have negative influence on system performance. In our previous work we formalized such antipatterns as logical predicates that predicate on four views: (i) the static view that captures the software elements (e.g. classes, components) and the static relationships among them; (ii) the dynamic view that represents the interaction (e.g. messages) that occurs between the software entities elements to provide the system functionalities; (iii) the deployment view that describes the hardware elements (e.g. processing nodes) and the mapping of the software entities onto the hardware platform; (iv) the performance view that collects specific performance indices. In this paper we present a lightweight infrastructure that is able to detect performance antipatterns at runtime through monitoring. The proposed approach precalculates such predicates and identifies antipatterns whose static, dynamic and deployment sub-predicates are validated by the current system configuration and brings at runtime the verification of performance sub-predicates. The proposed infrastructure leverages model-driven techniques to generate probes for monitoring the performance sub-predicates and detecting antipatterns at runtime.Comment: In Proceedings FESCA 2014, arXiv:1404.043

Interacting Components

SystemCSP is a graphical modeling language based on both CSP and concepts of component-based software development. The component framework of SystemCSP enables specification of both interaction scenarios and relative execution ordering among components. Specification and implementation of interaction among participating components is formalized via the notion of interaction contract. The used approach enables incremental design of execution diagrams by adding restrictions in different interaction diagrams throughout the process of system design. In this way all different diagrams are related into a single formally verifiable system. The concept of reusable formally verifiable interaction contracts is illustrated by designing set of design patterns for typical fault tolerance interaction scenarios

Temporal Stream Algebra

Data stream management systems (DSMS) so far focus on event queries and hardly consider combined queries to both data from event streams and from a database. However, applications like emergency management require combined data stream and database queries. Further requirements are the simultaneous use of multiple timestamps after different time lines and semantics, expressive temporal relations between multiple time-stamps and exible negation, grouping and aggregation which can be controlled, i. e. started and stopped, by events and are not limited to fixed-size time windows. Current DSMS hardly address these requirements. This article proposes Temporal Stream Algebra (TSA) so as to meet the afore mentioned requirements. Temporal streams are a common abstraction of data streams and data- base relations; the operators of TSA are generalizations of the usual operators of Relational Algebra. A in-depth 'analysis of temporal relations guarantees that valid TSA expressions are non-blocking, i. e. can be evaluated incrementally. In this respect TSA differs significantly from previous algebraic approaches which use specialized operators to prevent blocking expressions on a "syntactical" level

Analysing Temporal Relations – Beyond Windows, Frames and Predicates

This article proposes an approach to rely on the standard operators of relational algebra (including grouping and ag- gregation) for processing complex event without requiring window specifications. In this way the approach can pro- cess complex event queries of the kind encountered in appli- cations such as emergency management in metro networks. This article presents Temporal Stream Algebra (TSA) which combines the operators of relational algebra with an analy- sis of temporal relations at compile time. This analysis de- termines which relational algebra queries can be evaluated against data streams, i. e. the analysis is able to distinguish valid from invalid stream queries. Furthermore the analysis derives functions similar to the pass, propagation and keep invariants in Tucker's et al. \Exploiting Punctuation Seman- tics in Continuous Data Streams". These functions enable the incremental evaluation of TSA queries, the propagation of punctuations, and garbage collection. The evaluation of TSA queries combines bulk-wise and out-of-order processing which makes it tolerant to workload bursts as they typically occur in emergency management. The approach has been conceived for efficiently processing complex event queries on top of a relational database system. It has been deployed and tested on MonetDB

Innovation at the point of sale

On February 27, 2003, the Payment Cards Center of the Federal Reserve Bank of Philadelphia sponsored a conference to examine a variety of new payments options available to consumers paying for goods and services at the point of sale. The conference included diverse representation from the payments industry and Federal Reserve staff. ; Conference presentations and discussions focused on current and prospective payments innovations and their implications for payments providers, merchants, and consumers. The need for alignment among these three parties as an essential condition for the success of payments innovation was emphasized throughout the conference discussion. This event benefited from the inclusion of merchants’ perspectives, which are sometimes seen to be at odds with the goals of payment networks and institutional providers. Although the conference did not resolve the inherent conflict between organizations that drive payments innovation and merchants, it initiated a useful dialogue between industry sectors with divergent views and provided policymakers with a clearer understanding of the interrelationships inherent in retail payments systems.Electronic commerce