Finding recent frequent itemsets adaptively over online data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
MAIDS: mining alarming incidents from data streams
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
SPADE: the system s declarative stream processing engine
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
A coarse-grain grid-based subspace clustering method for online multi-dimensional data streams
Proceedings of the 17th ACM conference on Information and knowledge management
Multiple continuous queries evaluation over data streams
ACS'08 Proceedings of the 8th conference on Applied computer scince
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The primary objective of various data stream applications is to monitor the on-going variations of data elements newly generated in data streams, so that appropriate services can be delivered to users timely. Basically, such monitoring activities can be divided into three categories: instant event monitoring, frequent behavior monitoring and analytic OLAP measures monitoring. Several prototype systems for data streams have been introduced but they are designated to support only the operations of one or two categories. However, many real-world data stream applications often require mixed operations of the three categories. This demonstration introduces an Integrated Stream Execution Environment (i-SEE) that can support the mixed execution of the operations of the three categories seamlessly, so that an i-SEE system can provide the multifaceted analytic results of on-line data streams continuously for various emerging applications.