Roland Stühmer

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.

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.

PLAY: Semantics-Based Event Marketplace

In this paper we present PLAY Platform, a Web-oriented distributed semantic middleware that serves as an Event Marketplace: the place where heterogeneous events can be integrated and combined. The purpose of the platform is to derive useful information from diverse real-time sources such as collaborative processes. The platform provides technology where instant results are needed or where heterogeneous data must be integrated on the fly or where the data arrive fast enough to require the stream processing nature of our approach. The main advantages of the platforms are its scalability (cloud-based nature) and the expressivity of the event combinations that can be defined (using both real-time and historical data). The platform has been applied in a use case about Personal data management. In this paper we present some results from the validation, focusing on smartphone and social media integration.

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.

An approach for data-driven and logic-based complex Event Processing

In this paper, we present a powerful logic-based approach for Complex Event Processing (CEP). The approach is founded on a logical transformation of event processing into logic programs. Many systems for event processing have ad-hoc semantics with unexpected behaviors. Further on, many formal approaches (with well defined semantics) cannot effectively be used in CEP, due to their inability to compute events in the data-driven fashion. Our approach enables both logic-based and data-driven complex event detection. Moreover, the backward chaining approach allows for very efficient reasoning of complex events and actions triggered by these events.

Large-scale, situation-driven and quality-aware event marketplace: the concept, challenges and opportunities

This poster presents a novel approach for large scale, context-driven and quality-aware distributed event processing. We present the conceptual architecture of the system and mapping in an ongoing use-case deployment. In particular we give examples of the usage of the platform in this use-case. Additionally, we present the challenges and opportunities of such an architecture, namely logic-based CEP, combined with historic events and background knowledge, elastic run-time configuration in the cloud for CEP as a Service, maintenance of QoS for events, a unified language for reasoning and querying current and historic events. The approach leads to an Event Marketplace, a platform for mediating between event providers and (complex) event consumers in very large and heterogeneous environments.

Where events meet events: PLAY event marketplace

In this demo we present a high-scalable, distributed and semantic-enabled platform for the dynamic complex event-driven interaction in large highly distributed and heterogeneous federated service systems. In the nutshell, the PLAY platform enables (automatic) collection of relevant events generated by different systems, their complex processing in order to detect situations that will be disseminated (automatically) to interested parties (systems, devices, people). We demonstrate the current state of the development that integrates the information from several public available data streams, like Twitter, Facebook, Pachube, as well as the information from business applications involved in the PLAY project.

Computing complex events in an event-driven and logic-based approach

In this paper we propose to demonstrate a logic-based and data-driven complex event processor. The event processor is called ETALIS (Event-driven Transaction Logic Inference System). ETALIS is based on decomposition of complex event patterns into patterns goals. Goals are asserted by declarative rules, which are executed in the backward chaining mode. A specific property of these rules is that they are event-driven, and allow reasoning over events relationships.