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

Control-based Scheduling in a Distributed Stream Processing System

Stream processing systems receive continuous streams of messages with raw information and produce streams of messages with processed information. The utility of a stream-processing system depends, in part, on the accuracy and timeliness of the output. Streams in complex event processing systems are processed on distributed systems; several steps are taken on different processors to process each incoming message, and messages may be enqueued between steps. This paper deals with the problems of distributed dynamic control of streams to optimize the total utility provided by the system. A challenge of distributed control is that timeliness of output depends only on the total end-toend time and is otherwise independent of the delays at each separate processor whereas the controller for each processor takes action to control only the steps on that processor and cannot directly control the entire network. This paper identifies key problems in distributed control and analyzes two scheduling algorithms that help in an initial analysis of a difficult problem

Knowledge-infused and Consistent Complex Event Processing over Real-time and Persistent Streams

Emerging applications in Internet of Things (IoT) and Cyber-Physical Systems (CPS) present novel challenges to Big Data platforms for performing online analytics. Ubiquitous sensors from IoT deployments are able to generate data streams at high velocity, that include information from a variety of domains, and accumulate to large volumes on disk. Complex Event Processing (CEP) is recognized as an important real-time computing paradigm for analyzing continuous data streams. However, existing work on CEP is largely limited to relational query processing, exposing two distinctive gaps for query specification and execution: (1) infusing the relational query model with higher level knowledge semantics, and (2) seamless query evaluation across temporal spaces that span past, present and future events. These allow accessible analytics over data streams having properties from different disciplines, and help span the velocity (real-time) and volume (persistent) dimensions. In this article, we introduce a Knowledge-infused CEP (X-CEP) framework that provides domain-aware knowledge query constructs along with temporal operators that allow end-to-end queries to span across real-time and persistent streams. We translate this query model to efficient query execution over online and offline data streams, proposing several optimizations to mitigate the overheads introduced by evaluating semantic predicates and in accessing high-volume historic data streams. The proposed X-CEP query model and execution approaches are implemented in our prototype semantic CEP engine, SCEPter. We validate our query model using domain-aware CEP queries from a real-world Smart Power Grid application, and experimentally analyze the benefits of our optimizations for executing these queries, using event streams from a campus-microgrid IoT deployment.Comment: 34 pages, 16 figures, accepted in Future Generation Computer Systems, October 27, 201

Expressions as Data in Relational Data Base Management Systems

Numerous applications, such as publish/subscribe, website personalization, applications involving continuous queries, etc., require that user.s interest be persistently maintained and matched with the expected data. Conditional Expressions can be used to maintain user interests. This thesis focuses on the support for expression data type in relational database system, allowing storing of conditional expressions as .data. in columns of database tables and evaluating those expressions using an EVALUATE operator. With this context, expressions can be interpreted as descriptions, queries, and filters, and this significantly broadens the use of a relational database system to support new types of applications. The thesis presents an overview of the expression data type, storing the expressions, evaluating the stored expressions and shows how these applications can be easily supported with improved functionality. A sample application is also explained in order to show the importance of expressions in application context, with a comparison of the application with and without expressions