Ömer Faruk Çelebi

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Due to prevalent use of sensors and network monitoring tools, big volumes of data or “big data” today traverse the enterprise data processing pipelines in a streaming fashion. While some companies prefer to deploy their data processing infrastructures and services as private clouds, others completely outsource these services to public clouds.(More)
— Proliferation of data services has made it mandatory for operators to be able identify geographical regions with 3G connectivity discontinuity in a scalable and cost-efficient manner. The currently used methods for such analysis are either costly-such as in drive tests, partly unreliable-such as in network simulation approaches, or are not precise(More)
Today, the IT world is trying to cope with “big data” problems (data volume, velocity, variety, veracity) on the path to obtaining useful information. In this paper, we present implementation details and performance results of realizing “online” Association Rule Mining (ARM) over big data streams for the first time in the(More)
Event correlation engines help us find events of interest inside raw sensor data streams and help reduce the data volume, simultaneously. This paper discusses some of the challenges faced in finding event correlations over federated wireless sensor networks (WSNs) including high data volumes, uncertain or missing data, application-specific dependencies and(More)
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