Darko Anicic

EP-SPARQL: a unified language for event processing and stream reasoning

Streams of events appear increasingly today in various Web applications such as blogs, feeds, sensor data streams, geospatial information, on-line financial data, etc. Event Processing (EP) is concerned with timely detection of compound events within streams of simple events. State-of-the-art EP provides on-the-fly analysis of event streams, but cannot combine streams with background knowledge and cannot perform reasoning tasks. On the other hand, semantic tools can effectively handle background knowledge and perform reasoning thereon, but cannot deal with rapidly changing data provided by event streams. To bridge the gap, we propose Event Processing SPARQL (EP-SPARQL) as a new language for complex events and Stream Reasoning. We provide syntax and formal semantics of the language and devise an effective execution model for the proposed formalism. The execution model is grounded on logic programming, and features effective event processing and inferencing capabilities over temporal and static knowledge. We provide an open-source prototype implementation and present a set of tests to show the usefulness and effectiveness of our approach.

Stream reasoning and complex event processing in ETALIS

Addressing dynamics and notifications in the Semantic Web realm has recently become an important area of research. Run time data is continuously generated by multiple social networks, sensor networks, various on-line services and so forth. How to get advantage of this continuously arriving data events remains a challenge --that is, how to integrate heterogeneous event streams, combine them with background knowledge e.g., an ontology, and perform event processing and stream reasoning. In this paper we describe ETALIS --a system which enables specification and monitoring of changes in near real time. Changes can be specified as complex event patterns, and ETALIS can detect them in real time. Moreover the system can perform reasoning over streaming events with respect to background knowledge. ETALIS implements two languages for specification of event patterns: ETALIS Language for Events, and Event Processing SPARQL. ETALIS has various applicabilities in capturing changes in semantic networks, broadcasting notifications to interested parties, and creating further changes based on processing of the temporal, static, or slowly evolving knowledge.

A rule-based language for complex event processing and reasoning

Complex Event Processing (CEP) is concerned with timely detection of complex events within multiple streams of atomic occurrences. It has useful applications in areas including financial services, mobile and sensor devices, click stream analysis etc. Numerous approaches in CEP have already been proposed in the literature. Event processing systems with a logic-based representation have attracted considerable attention as (among others reasons) they feature formal semantics and offer reasoning service. However logic-based approaches are not optimized for run-time event recognition (as they are mainly query-driven systems). In this paper, we present an expressive logic-based language for specifying and combining complex events. For this language we provide both a syntax as well as a formal declarative semantics. The language enables efficient run time event recognition and supports deductive reasoning. Execution model of the language is based on a compilation strategy into Prolog. We provide an implementation of the language, and present the performance results showing the competitiveness of our approach.

ETALIS: Rule-Based Reasoning in Event Processing

Complex Event Processing (CEP) is concerned with timely detection of complex events within multiple streams of atomic occurrences, and has useful applications in areas including financial services, mobile and sensor devices, click stream analysis and so forth. In this chapter, we present ETALIS Language for Events. It is an expressive language for specifying and combining complex events. For this language we provide both a syntax as well as a clear declarative formal semantics. The execution model of the language is based on a compilation strategy into Prolog. We provide an implementation of the language, and present experimental results of our running prototype. Further on, we show how our logic rule-based approach compares with a non-logic approach in respect of performance.

REAL-TIME COMPLEX EVENT RECOGNITION AND REASONING–A LOGIC PROGRAMMING APPROACH

Complex Event Processing (CEP) deals with the analysis of streams of continuously arriving events, with the goal of identifying instances of predefined meaningful patterns (complex events). Complex events are detected in order to trigger time-critical actions in many areas, including sensors networks, financial services, transaction management, business intelligence, etc. In existing approaches to CEP, a complex event is represented as a composition of more simple events satisfying certain temporal relationships. In this article, we advocate a knowledge-rich CEP, which, apart from events, also processes additional (contextual) knowledge (e.g., in order to prove semantic relations among matched events or to define more complex situations). In particular, we present a novel approach for realizing knowledge-rich CEP, including detection of semantic relations among events and reasoning. We present a rule-based language for pattern matching over event streams, with a precise syntax and the declarative semantics. We devise an execution model for the proposed formalism, and provide a prototype implementation. Extensive experiments have been conducted to demonstrate the efficiency and effectiveness of our approach.

Event-Driven Approach for Logic-Based Complex Event Processing

In this paper, we present a powerful logical encoding of complex event patterns into Transaction Logic programs. Complex Event Processing (CEP) deals with finding composed events and has useful applications in areas ranging from agile business and enterprise processes management, financial market applications to active Web and service oriented computation. Many systems for event processing have ad-hoc semantics with unexpected behaviors. Hence formal logical semantics is an important requirement for event-driven reactive systems. On the other hand, many logic-based approaches for CEP (based on formal semantics) fail, due to their inability to compute complex events in the data-driven fashion. Our approach enables both logic-based and data-driven complex event detection. Moreover, the proposed backward chaining approach allows for very efficient reasoning of complex events and actions triggered by these events.

Lifting Events in RDF from Interactions with Annotated Web Pages

In this paper we present a method and an implementation for creating and processing semantic events from interaction with Web pages which opens possibilities to build event-driven applications for the (Semantic) Web. Events, simple or complex, are models for things that happen e.g., when a user interacts with a Web page. Events are consumed in some meaningful way e.g., for monitoring reasons or to trigger actions such as responses. In order for receiving parties to understand events e.g., comprehend what has led to an event, we propose a general event schema using RDFS. In this schema we cover the composition of complex events and event-to-event relationships. These events can then be used to route semantic information about an occurrence to different recipients helping in making the Semantic Web active. Additionally, we present an architecture for detecting and composing events in Web clients. For the contents of events we show a way of how they are enriched with semantic information about the context in which they occurred. The paper is presented in conjunction with the use case of Semantic Advertising, which extends traditional clickstream analysis by introducing semantic short-term profiling, enabling discovery of the current interest of a Web user and therefore supporting advertisement providers in responding with more relevant advertisements.

Retractable complex event processing and stream reasoning

Complex Event Processing (CEP) deals with processing of continuously arriving events with the goal of identifying meaningful patterns (complex events). In existing stream database approaches, CEP is manly concerned by temporal relations between events. This paper advocates for a knowledge-rich CEP with Stream Reasoning capabilities. Secondly, we address the problem of revision in event processing. Events are often assumed to be immutable and therefore always correct. Revision in event processing deals with the circumstance that certain events may be revoked. This necessitates to reconsider complex events which might have been computed based on the original, flawy history as soon as part of that history is corrected. In this paper, we present a novel approach for knowledge-based CEP and Stream Reasoning, including revisions of events too. We present a rule-based language for pattern matching over event streams with a precise syntax and the declarative semantics. We devise an execution model for the proposed formalism, and provide a prototype implementation. Extensive experiments have been conducted to demonstrate the efficiency and effectiveness of our approach.

An approach for more efficient energy consumption based on real-time situational awareness

In this paper we present a novel approach for achieving energy efficiency in public buildings (especially sensor-enabled offices) based on the application of intelligent complex event processing and semantic technologies. In the nutshell of the approach is an efficient method for realizing the real-time situational awareness that helps in recognizing the situations where a more efficient energy consumption is possible and reaction on those opportunities promptly. Semantics allows a proper contextualization of the sensor data (i.e. its abstract interpretation), whereas complex event processing enables the efficient real-time processing of sensor data and its logic-based nature supports a declarative definition of the situations of interests. The approach has been implemented in the iCEP framework for intelligent Complex Event Reasoning. The results from a preliminary evaluation study are very promising: the approach enables a very precise real-time detection of the office occupancy situations that limit the operation of the lighting system based on the actual use of the space.

Semantic Complex Event Reasoning—Beyond Complex Event Processing

Complex event processing is about processing huge amounts of information in real time, in a rather complex way. The degree of complexity is determined by the level of the interdependencies between information to be processed. There are several more or less traditional operators for defining these interdependencies, which are supported by existing approaches and the main competition is around the speed (throughput) of processing. However, novel application domains like Future Internet are challenging complex event processing for a more comprehensive approach: from how to create complex event patterns over the heterogeneous event sources (including textual data), to how to efficiently detect them in a distributed setting, including the usage of background knowledge. In this chapter we present an approach for intelligent CEP (iCEP) based on the usage of semantic technologies. It represents an end-to-end solution for iCEP starting from the definition of complex event patterns, through intelligent detection, to advanced 3-D visualization of complex events. At the center of the approach is the semantic model of complex events that alleviates the process of creating and maintaining complex event patterns. The approach utilizes logic-based processing for including domain knowledge in the complex event detection process, leading to complex event reasoning. This approach has been implemented in the web-based framework called iCEP Studio.