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Knowledge-infused and consistent Complex Event Processing over real-time and persistent streams
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DOI：10.1016/j.future.2016.10.030.png)
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Abstract
En 中文
Emerging applications in Internet of Things (IoT) and Cyber-Physical Systems (CPS) present novel challenges to Big Data platforms for performing online analytics. Ubiquitous sensors from IoT deployments are able to generate data streams at high velocity, that include information from a variety of domains, and accumulate to large volumes on disk. Complex Event Processing (CEP) is recognized as an important real-time computing paradigm for analyzing continuous data streams. However, existing work on CEP is largely limited to relational query processing, exposing two distinctive gaps for query specification and execution: (1) infusing the relational query model with higher level knowledge semantics, and (2) seamless query evaluation across temporal spaces that span past, present and future events. These allow accessible analytics over data streams having properties from different disciplines, and help span the velocity (real-time) and volume (persistent) dimensions. In this article, we introduce a Knowledge infused CEP (chi-CEP) framework that provides domain-aware knowledge query constructs along with temporal operators that allow end-to-end queries to span across real-time and persistent streams. We translate this query model to efficient query execution over online and offline data streams, proposing several optimizations to mitigate the overheads introduced by evaluating semantic predicates and in accessing high-volume historic data streams. In particular, we also address temporal consistency issues that arise during fault recovery of query plans that span the boundary between real-time and persistent streams. The proposed chi-CEP query model and execution approaches are implemented in our prototype semantic CEP engine, SCEPter. We validate our query model using domain-aware CEP queries from a real-world Smart Power Grid application, and experimentally analyze the benefits of our optimizations for executing these queries, using event streams from a campus-microgrid loT deployment. Our results show that we are able to sustain a processing throughput of 3, 000 events/secs for chi-CEP queries, a 30 x improvement over the baseline and sufficient to support a Smart Township, and can resume consistent processing within 20 secs after stream outages as long as 2 hours. (C) 2016 Elsevier B.V. All rights reserved.
Keywords:
Complex Event Processing
Query optimization
Consistency
Internet of Things (IoT)
Big data
Semantic web
Knowledge-base
Real-time processing
Reliability
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