Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Knowledge Discovery in Databases: An Attribute-Oriented Approach
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Composite Events for Active Databases: Semantics, Contexts and Detection
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Aggregate-Query Processing in Data Warehousing Environments
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Functional Programming, Concurrency, Simulation and Automated Reasoning: International Lecture Series 1991-1992, McMaster University, Hamilton, Ontario, Canada
Exploiting Punctuation Semantics in Continuous Data Streams
IEEE Transactions on Knowledge and Data Engineering
Stream Cube: An Architecture for Multi-Dimensional Analysis of Data Streams
Distributed and Parallel Databases
High-performance complex event processing over streams
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Flowcube: constructing RFID flowcubes for multi-dimensional analysis of commodity flows
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
ZStream: a cost-based query processor for adaptively detecting composite events
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
CHAOS: A Data Stream Analysis Architecture for Enterprise Applications
CEC '09 Proceedings of the 2009 IEEE Conference on Commerce and Enterprise Computing
The VLDB Journal — The International Journal on Very Large Data Bases
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Many modern applications, including online financial feeds, tag-based mass transit systems and RFID-based supply chain management systems transmit real-time data streams. There is a need for a special-purpose event stream processing technology to analyze this vast amount of sequential multi-dimensional data to enable online, operational decision making. Existing techniques such as traditional online analytical processing (OLAP) systems are not designed for real-time pattern-based operations, while state-of-the-art Complex Event Processing (CEP) systems designed for sequence detection do not support OLAP operations. Supporting complex pattern queries at different concept and pattern hierarchies must be devised by providing efficient computation and data sharing. In this dissertation, we propose a novel E-Cube model that combines CEP and OLAP techniques for multi-dimensional event pattern analysis at different abstraction levels. Further, we go beyond the linear sequence pattern queries targeted by ECube core system towards supporting an expressive composite pattern query language (composed of arbitrarily nested sequence, negation, recursion, AND and OR operators) to express powerful pattern matching requests.