Abstract
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.
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Notes
- 1.
iCEP Website http://icep.fzi.de/ for more details.
- 2.
Twitter is a micro-blogging service which enables users to send messages of up to 140 characters to a set of users, the so-called followers: http://twitter.com/.
- 3.
Google Finance: http://google.com/finance.
- 4.
Such information does not strictly need to be taken from Twitter. Other sources also provide real-time updates e.g., the Wall Street Journal blog: http://blogs.wsj.com/.
- 5.
Named entities extraction is out of scope of this chapter. For this task, in our example, we used the OpenCalais service http://opencalais.com. OpenCalais uses natural language processing, machine learning and other methods to analyze text (in our case, tweets) and finds named entities within it.
- 6.
There are several reasons for using ontologies for information integration. As the most advanced knowledge representation model available today, ontologies can include essentially all currently used data structures. They also can accommodate complexity because the inclusion of deductive logic extends existing mapping and business logic capabilities. Further, ontologies provide shared conceptualization and agreed upon understanding of a domain, both prerequisites for (semi-)automatic integration.
- 7.
A more descriptive definition of pattern is given in Sect. 5.
- 8.
Depending on the nature of an application some phases can be skipped.
- 9.
The CEP gears logo is ©IBM Haifa.
- 10.
We call the middle stage “detection” instead of “matching” in order to include a wider field of event operations such as reasoning which goes beyond pattern matching.
- 11.
Pattern and complex event pattern are used synonymously in this article.
- 12.
For the sake of convenience the pattern is represented in a pseudocode form.
- 13.
Acronym for Generation Refinement Usage eVolution of complex (e)vent patterns.
- 14.
RDF Schema (RDFS) is an extensible knowledge representation language providing basic elements for the description of ontologies.
- 15.
The phenomenon of out-of-order events meaning delayed notification about events that have happened earlier, is outside the focus of this chapter.
- 16.
Our prototype, ETALIS, is an open source project, available at: http://code.google.com/p/etalis.
- 17.
Note that also comparison operators like =, < and > can be seen as boolean-typed binary functions and, hence, fit well into the framework.
- 18.
More precisely, by “an event a” is meant an instance of the event a.
- 19.
That is, if no parentheses are given, we assume all operators to be left-associative. While in some cases, like seq sequences, this is irrelevant, other operators such as par are not associative, whence the precedence matters.
- 20.
This holds even if patterns with negated events are added.
- 21.
Later on, we will introduce the rules implementing the for each loop.
- 22.
Apart from the timestamp, an event may carry other data parameters. They are omitted here for the sake of readability.
- 23.
Removal of “consumed” goals is often needed for space reasons but might be omitted if events are required in a log for further processing or analyzing.
- 24.
If a request is represented as a query (what is a usual case).
- 25.
- 26.
TIBCO BusinessEvents: http://www.tibco.com/software/complex-event-processing/businessevents/businessevents.jsp.
- 27.
Jess: http://jessrules.com/.
- 28.
Drools: http://jboss.org/drools/.
- 29.
BizTalk Rules Engine: http://msdn.microsoft.com/en-us/library/dd879260%28BTS.10%29.aspx.
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Stojanovic, N., Stojanovic, L., Anicic, D., Ma, J., Sen, S., Stühmer, R. (2011). Semantic Complex Event Reasoning—Beyond Complex Event Processing. In: Fensel, D. (eds) Foundations for the Web of Information and Services. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19797-0_14
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