A Survey of the State-of-the-art in Event Processing

Applications that require real-time or near-real-time processing of high-volume of data streams are pushing the limits of traditional data processing infrastructures. These event-based applications include market feed processing and electronic trading on financial markets, network and infrastructure monitoring, cloud computing applications, fraud detection, and command and control in military environments. Furthermore, great changes were caused by cheap micro-sensor technology. This ubiquity of sensors in the real world can lead to a big field of novel monitoring and control applications with high-volume and low-latency processing requirements. This survey aims to review the state-of-the-art in event processing systems. A set of the most significant weaknesses and limitations is discussed at a high level, and we also outline requirements that a system should meet to excel at a variety of event processing applications.

[1]  Ying Xing,et al.  The Design of the Borealis Stream Processing Engine , 2005, CIDR.

[2]  Michael Stonebraker,et al.  Aurora: a new model and architecture for data stream management , 2003, The VLDB Journal.

[3]  Alessandro Margara,et al.  TESLA: a formally defined event specification language , 2010, DEBS '10.

[4]  Frederick Reiss,et al.  TelegraphCQ: continuous dataflow processing , 2003, SIGMOD '03.

[5]  Alessandro Margara,et al.  Processing flows of information: From data stream to complex event processing , 2012, CSUR.

[6]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[7]  Qiang Chen,et al.  Aurora : a new model and architecture for data stream management ) , 2006 .

[8]  Scott Shenker,et al.  Spark: Cluster Computing with Working Sets , 2010, HotCloud.

[9]  Alessandro Margara,et al.  Complex event processing with T-REX , 2012, J. Syst. Softw..

[10]  Haifeng Jiang,et al.  Photon: fault-tolerant and scalable joining of continuous data streams , 2013, SIGMOD '13.

[11]  Wolfgang Lehner,et al.  SAP HANA database: data management for modern business applications , 2012, SGMD.

[12]  Mahadev Konar,et al.  ZooKeeper: Wait-free Coordination for Internet-scale Systems , 2010, USENIX ATC.

[13]  Lu Liu,et al.  Muppet: MapReduce-Style Processing of Fast Data , 2012, Proc. VLDB Endow..

[14]  Walter Mann,et al.  Correction to "Specification and Analysis of System Architecture Using Rapide" , 1995, IEEE Trans. Software Eng..

[15]  Jay Kreps,et al.  Kafka : a Distributed Messaging System for Log Processing , 2011 .

[16]  Michael Stonebraker,et al.  A comparison of approaches to large-scale data analysis , 2009, SIGMOD Conference.

[17]  Jonathan Leibiusky,et al.  Getting Started with Storm , 2012 .

[18]  Leonardo Neumeyer,et al.  S4: Distributed Stream Computing Platform , 2010, 2010 IEEE International Conference on Data Mining Workshops.

[19]  Randy H. Katz,et al.  Chukwa: A System for Reliable Large-Scale Log Collection , 2010, LISA.