Current Research Project

The Event Stream Processing Project

We are investigating two major directions for current work with event stream processing:

Application of a Temporal Database Framework for Processing Event Queries

Dissertation research of Foruhar Shiva (Arizona State University with co-advisors S. Urban and Y. Chen (Arizona State University)

Adaptive Event Stream Processing

Dissertation research of Samujjwal Bhandari with co-advisors S. Urban and M. Sridharan

Abstracts and Publishcations

(1) Application of a Temporal Database Framework for Processing Event Queries

Foruhar Shiva, Susan D. Urban, and Yi Chen

Project Summary:

Event Stream Processing enables online querying of streams of events to extract relevant data in a timely manner. Due to semantic ambiguity and lack of expressiveness, event stream processing languages have been moving away from point-based semantics, where an event is associated with a single time of occurrence, towards increasing adoption of interval-based semantics, where an event occurs over a period of time with a starting and ending timestamp. Transforming point-based operators to interval-based counterparts is not sufficient to capture the complex relationships possible between interval-based events. Temporal databases and temporal query languages have been a subject of research for more than 30 years and are a natural fit for expressing queries that involve a temporal dimension. This proposalpresents a research plan for the investigation of an event query language that incorporates temporal relational operators to provide a higher degree of expressivity for event queries. The approach is based on extending a preexisting relational framework for event stream processing to support temporal queries. The language features and formal semantic extensions required to extend the relational framework are identified. The research includes the development of a prototype that supports the integrated event and temporal query processing framework, with support for incremental evaluation and materialization of intermediate results.


Foruhar Ali Shiva and Susan D. Urban, “On Applying Temporal Database Concepts to Event Queries,” Rule-Based Modeling and Computing on the Semantic Web, F. Olken, M. Palmirani, D. Sottara (Eds.): RuleML 2011 - America, LNCS 7018, pp. 171-178.

(2) Adaptive Event Stream Processing

Samujjwal Bhandari, Susan D. Urban, Mohan Sridharan

Project Summary:

Software systems for contemporary applications are becoming increasingly complex, demanding the use of active, reactive, and proactive behavior to support context-aware and adaptive execution environments. To support these new application requirements, this research is transforming event stream processing into a dynamic and adaptive process through the integration of event detection with machine learning. This new approach to event processing involves the development of an adaptive environment for event detection that combines event query processing and logical inference with probability measures that can be used to detect well-known event patterns, to evolve existing event patterns, and to learn new and meaningful event patterns. As an application for the proposed work, we are investigating intrusion detection in the context of the Smart Grid, using a Zigbee home area network simulation as well as a real-time digital simulator for large-scale power system networks. Statistical analysis and data mining of network log files is initially performed to extract meaningful events from raw data streams and to discover new relationships between events. Recommended patterns are represented as weighted rules within a Markov logic network. Using this knowledge base and incoming event streams, structural learning techniques are used to discover new event patterns, developing similarity measures and scoring functions to determine the potential relevance of new patterns. A unique aspect of the work is that it combines the temporal context of event patterns with structural learning algorithms based on inductive logic programming and Markov logic. This new, adaptive environment for event processing is capable of providing a real-time approach to event detection that canadapt dynamically in response to changing patterns of behavior within an application.