SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Matching events in a content-based subscription system
Proceedings of the eighteenth annual ACM symposium on Principles of distributed computing
Filtering algorithms and implementation for very fast publish/subscribe systems
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Composite Event Specification in Active Databases: Model & Implementation
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
The UK e-science core programme and the grid
Future Generation Computer Systems - Grid computing: Towards a new computing infrastructure
Aurora: a data stream management system
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
TelegraphCQ: continuous dataflow processing
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
High-performance complex event processing over streams
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Monitoring streams: a new class of data management applications
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
S4: Distributed Stream Computing Platform
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Processing flows of information: From data stream to complex event processing
ACM Computing Surveys (CSUR)
Toward data-driven demand-response optimization in a campus microgrid
Proceedings of the Third ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Incorporating semantic knowledge into dynamic data processing for smart power grids
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part II
Monitoring and Autoscaling IaaS Clouds: A Case for Complex Event Processing on Data Streams
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
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Today there are so much data being available from sources like sensors (RFIDs, Near Field Communication), web activities, transactions, social networks, etc. Making sense of this avalanche of data requires efficient and fast processing. Processing of high volume of events to derive higher-level information is a vital part of taking critical decisions, and Complex Event Processing (CEP) has become one of the most rapidly emerging fields in data processing. e-Science use-cases, business applications, financial trading applications, operational analytics applications and business activity monitoring applications are some use-cases that directly use CEP. This paper discusses different design decisions associated with CEP Engines, and proposes some approaches to improve CEP performance by using more stream processing style pipelines. Furthermore, the paper will discuss Siddhi, a CEP Engine that implements those suggestions. We present a performance study that exhibits that the resulting CEP Engine--Siddhi--has significantly improved performance. Primary contributions of this paper are performing a critical analysis of the CEP Engine design and identifying suggestions for improvements, implementing those improvements through Siddhi, and demonstrating the soundness of those suggestions through empirical evidence.