Kia Teymourian

Semantic Rule-Based Complex Event Processing

One of the critical success factors of event-driven systems is the capability of detecting complex events from simple and ordinary event notifications. Complex events which trigger or terminate actionable situations can be inferred from large event clouds or event streams based on their event instance sequence, their syntax and semantics. Using semantics of event algebra patterns defined on top of event instance sequences for event detection is one of the promising approaches for detection of complex events. The developments and successes in building standards and tools for semantic technologies such as declarative rules and ontologies are opening novel research and application areas in event processing. One of these promising application areas is semantic event processing. In this work we describe our research on semantic rule-based complex events processing.

Fusion of background knowledge and streams of events

Usage of background knowledge about events and their relations to other concepts in the application domain can improve the expressiveness and flexibility of complex event processing systems. Huge amounts of domain background knowledge stored in external knowledge bases can be used in combination with event processing in order to achieve more knowledgeable complex event processing. In this paper, we discuss the benefits of background knowledge for event processing and describe different categories of event query rules. We propose different event processing approaches used for the fusion of background knowledge with real-time event streams, like plan-based event processing or event query preprocessing. We have implemented some of the proposed event processing methods for the stock market monitoring domain by using available real-time stock events and background knowledge about joint-stock companies from the linked open data. Our experiments show that some of the approaches can improve the performance of event processing compared to more basic approaches.

Reaction RuleML 1.0: standardized semantic reaction rules

RuleML is a family of XML languages whose modular system of schemas permits high-precision (Web) rule interchange. The familys top-level distinction is deliberation rules vs. reaction rules. In this paper we address the Reaction RuleML subfamily of RuleML and survey related work. Reaction RuleML is a standardized rule markup/serialization language and semantic interchange format for reaction rules and rule-based event processing. Reaction rules include distributed Complex Event Processing (CEP), Knowledge Representation (KR) calculi, as well as Event-Condition-Action (ECA) rules, Production (CA) rules, and Trigger (EA) rules. Reaction RuleML 1.0 incorporates this reactive spectrum of rules into RuleML employing a system of step-wise extensions of the Deliberation RuleML 1.0 foundation.

Towards semantic event processing

The developments and successes of the Semantic Web research community in building standards and tools for semantic technologies such as formalized vocabularies/ontologies and declarative rules are opening novel research and application areas. One of these promising application areas is semantic event processing. Semantic models of events can improve the quality of event processing by using event meta-data in combination with ontologies and rules (knowledge bases).

Enabling knowledge-based complex event processing

Event-driven systems are highly depending on the quality of detection and processing of events. Many of complex real-world events cannot be processed by the existing event processing systems because they are too complex to be understood and processed by the systems. Complex events can be inferred from raw primitive events based on their incoming sequence, their syntax and semantics. Usage of ontological knowledge about events and their relationship to other non-event concepts in the application domain, can improve the quality of event processing. In this Ph.D. thesis, I am aiming to address the challenges of adding formalized vocabularies/ontologies and declarative rules to the area of event processing for enabling more intelligent event processors which can understand the senses and semantics of events.

A self-organized semantic storage service

Traditional approaches for data storage and analysis are facing their limits when handling the enormous data amounts of todays applications. We believe that a radical departure from contemporary architectures of stores is necessary to satisfy that central scalability requirement. One of the most promising new schools of thought in system design are swarm intelligent and swarm-based approaches for data distribution and organization. In this paper, we describe our current work on a swarm-based storage service for Semantic Web data. This storage service utilizes algorithms discovered in the behavior of ant colonies. We describe these algorithms and our enhancements to them as well as our evaluation of the implementation.

Knowledge-based processing of complex stock market events

Usage of background knowledge about events and their relations to other concepts in the application domain, can improve the quality of event processing. In this paper, we describe a system for knowledge-based event detection of complex stock market events based on available background knowledge about stock market companies. Our system profits from data fusion of live event stream and background knowledge about companies which is stored in a knowledge base. Users of our system can express their queries in a rule language which provides functionalities to specify semantic queries about companies in the SPARQL query language for querying the external knowledge base and combine it with event data stream. Background makes it possible to detect stock market events based on companies attributes and not only based on syntactic processing of stock price and volume.

Towards Semantic Event-Driven Systems

One of the critical success factors of event-driven systems is the capability of detecting complex events from simple and ordinary event notifications. Complex events which trigger or terminate actionable situations can be inferred from large event clouds or event streams based on their event instance sequence, their syntax and semantics. Using semantics of event algebra patterns defined on top of event instance sequences for event detection is one of the promising approaches for detection of complex events. The developments and successes in building standards and tools for semantic technologies such as declarative rules and ontologies are opening novel research and application areas in event processing. One of these promising application areas is semantic event processing. In this paper we contribute with a conceptual approach which supports the implementation of the vision of semantic event-driven systems; using Semantic Web technologies, benefiting from complex event processing, and ensuring quality through trust and reputation management. All of these novel technologies leads to more intelligent decision supporting systems.

Plan-Based Semantic Enrichment of Event Streams

Background knowledge about the application domain can be used in event processing in order to improve processing quality. The idea of semantic enrichment is to incorporate background knowledge into events, thereby generating enriched events which, in the next processing step, can be better understood by event processing engines. In this paper, we present an efficient technique for event stream enrichment by planning multi-step event enrichment and processing. Our optimization goal is to minimize event enrichment costs while meeting application-specific service expectations. The event enrichment is optimized to avoid unnecessary event stream enrichment without missing any complex events of interest. Our experimental results shows that by using this approach it is possible to reduce the knowledge acquisition costs.

Complex event extraction from real-time news streams

Information overload on news data is a known problem these days. People and organizations have an increasing demand for extraction of relevant information from massive amounts of news data arriving in real-time as news streams. In this paper, we present a novel approach for real-time extraction of news, based on user specifications and by using background knowledge from specific news domains. We create a powerful filtering service which limits the news data to the concrete and essential preferences of a user. In our approach, enrichment of real-time news with background knowledge is a preprocessing step. We use a Complex Event Processor to detect complex events from the enriched articles and match them to the user specified query. Each time a news article is matched, its result is notified to the user immediately. Our experimental evaluation shows that our approach is feasible for detecting news in real-time with high precision and recall.