|Institution:||Technische Universitt Dortmund|
|Keywords:||ddc:004; Event Stream Processing Distributed Systems Adaptive Systems|
|Full text PDF:||https://dokumente.ub.tu-clausthal.de/receive/clausthal_mods_00000548;https://dokumente.ub.tu-clausthal.de/receive/clausthal_mods_00000548;https://dokumente.ub.tu-clausthal.de/servlets/MCRFileNodeServlet/clausthal_derivate_00000331/Db113468.pdf|
This work defines a situation aware adaptive event stream processing model and scenariospecification language. The processing model and language allow the specification of streamprocessing tasks, which support an automatic scenario specific adaptation of their processinglogic based on detected situations and interim processing results.The motivation for this work lies in the missing support of current state of the artEvent Stream Processing (ESP) systems for such a situation-aware adaptive Event StreamProcessing which leads to the problem that for each scenario that requires this kind ofprocessing, a new processing system needs to be designed, implemented and maintained. Itis therefore the aim of this work to ease the development of such situation aware adaptiveprocessing systems.An example for such a scenario is the detection and tracing of solar energy productiondrops caused by clouds shading solar panels as they pass. The scenario requires a processingsystem to handle large amounts of streaming data to detect a cloud (possible situation).The later verification of the cloud as well as its tracking however only requires a smallsituation specific subset of the overall streaming data, e.g. the measurements from solarpanels of the affected area and its surroundings. This subset changes its characteristics(location, shape, etc) dynamically as the cloud traverses the region. The scenario thusrequires a situation-aware adaptation of its processing setup in order to focus on a detectedcloud and to track it.This work approaches the problem by defining a situation-aware adaptive stream processingmodel and a matching scenario definition language to allow the definition of suchprocessing scenarios for a scenario independent processing system. The requirements forthe definition of the model and language are the result of an analysis of three distinctscenarios from two application domains. The designed model defines situation awareadaptive processing in three main phases:Phase 1: In the Possible Situation Indication phase, possible situations are detected in alarge set of streaming data.Phase 2: The Focused Situation Processing Initialization phase determines whether an indicatedpossible situation needs to be investigated or if it can be ignored, for examplebecause the situation was already under investigation. If a potential situation needsto be investigated, a new situation specific focused processing is started.Phase 3: In the Focused Situation Processing phase, possible situations are verified andiiian in depth investigation of the situation including the adaptation of the processingsetup based on interim results is possible.The evaluation demonstrates that the language and processing model fulfill the definedrequirements by providing an application domain and scenario independent mechanismto define and execute situation aware adaptive processing tasks. For the evaluation, aprocessing system prototype was created and two scenarios from twoAdvisors/Committee Members: Rausch, Andreas.