E-Cube: Multi-dimensional event sequence processing using concept and pattern hierarchies

Many modern applications including tag based mass transit systems, RFID-based supply chain management systems and online financial feeds require special purpose event stream processing technology to analyze vast amounts of sequential multi-dimensional data available in real-time data feeds. Traditional online analytical processing (OLAP) systems are not designed for real-time pattern-based operations, while Complex Event Processing (CEP) systems are designed for sequence detection and do not support OLAP operations. We will demonstrate a novel E-Cube model that combines CEP and OLAP techniques for multi-dimensional event pattern analysis at different abstraction levels. A London transit scenario will be given to demonstrate the utility and performance of this proposed technology.

[1]  Jun'ichi Tatemura,et al.  AFilter: adaptable XML filtering with prefix-caching suffix-clustering , 2006, VLDB.

[2]  Samuel Madden,et al.  Continuously adaptive continuous queries over streams , 2002, SIGMOD '02.

[3]  Quanzhong Li,et al.  Indexing and Querying XML Data for Regular Path Expressions , 2001, VLDB.

[4]  Jiawei Han,et al.  Knowledge Discovery in Databases: An Attribute-Oriented Approach , 1992, VLDB.

[5]  Sharma Chakravarthy,et al.  Composite Events for Active Databases: Semantics, Contexts and Detection , 1994, VLDB.

[6]  Surajit Chaudhuri,et al.  An overview of data warehousing and OLAP technology , 1997, SGMD.

[7]  Ashish Gupta,et al.  Aggregate-Query Processing in Data Warehousing Environments , 1995, VLDB.

[8]  Sang-Won Lee,et al.  A Relational Nested Interval Encoding Scheme for XML Data , 2006, DEXA.

[9]  Jeffrey D. Ullman,et al.  Implementing data cubes efficiently , 1996, SIGMOD '96.

[10]  Jose-Norberto Mazón,et al.  A survey on summarizability issues in multidimensional modeling , 2009, Data Knowl. Eng..

[11]  Yanlei Diao,et al.  High-performance complex event processing over streams , 2006, SIGMOD Conference.

[12]  M. Tamer Özsu,et al.  A comprehensive XQuery to SQL translation using dynamic interval encoding , 2003, SIGMOD '03.

[13]  Chetan Gupta,et al.  E-Cube: multi-dimensional event sequence analysis using hierarchical pattern query sharing , 2011, SIGMOD '11.

[14]  Neil Immerman,et al.  Efficient pattern matching over event streams , 2008, SIGMOD Conference.

[15]  David Wai-Lok Cheung,et al.  OLAP on sequence data , 2008, SIGMOD Conference.

[16]  Samuel Madden,et al.  ZStream: a cost-based query processor for adaptively detecting composite events , 2009, SIGMOD Conference.

[17]  Yixin Chen,et al.  Stream Cube: An Architecture for Multi-Dimensional Analysis of Data Streams , 2005, Distributed and Parallel Databases.

[18]  Johannes Gehrke,et al.  Cayuga: A General Purpose Event Monitoring System , 2007, CIDR.

[19]  Chetan Gupta,et al.  CHAOS: A Data Stream Analysis Architecture for Enterprise Applications , 2009, 2009 IEEE Conference on Commerce and Enterprise Computing.

[20]  Jiawei Han,et al.  Flowcube: constructing RFID flowcubes for multi-dimensional analysis of commodity flows , 2006, VLDB.

[21]  Elke A. Rundensteiner,et al.  Sequence Pattern Query Processing over Out-of-Order Event Streams , 2009, 2009 IEEE 25th International Conference on Data Engineering.