The process of knowledge discovery in databases
Advances in knowledge discovery and data mining
A framework for measuring changes in data characteristics
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
NiagaraCQ: a scalable continuous query system for Internet databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Maintaining stream statistics over sliding windows: (extended abstract)
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Data streams: algorithms and applications
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Maintaining variance and k-medians over data stream windows
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
What's hot and what's not: tracking most frequent items dynamically
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Aurora: a data stream management system
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
An Intuitive Framework for Understanding Changes in Evolving Data Streams
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Finding recent frequent itemsets adaptively over online data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
estWin: adaptively monitoring the recent change of frequent itemsets over online data streams
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Finding frequent items in data streams
Theoretical Computer Science - Special issue on automata, languages and programming
Architecture for knowledge discovery and knowledge management
Knowledge and Information Systems
Approximate counts and quantiles over sliding windows
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
What's new: finding significant differences in network data streams
IEEE/ACM Transactions on Networking (TON)
Monitoring streams: a new class of data management applications
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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Online mining of changes from data streams is an important problem in view of growing number of applications such as network flow analysis, e-business, stock market analysis etc. Monitoring of these changes is a challenging task because of the high speed, high volume, only-one-look characteristics of the data streams. User subjectivity in monitoring and modeling of the changes adds to the complexity of the problem. This paper addresses the problem of i) capturing user subjectivity and ii) change modeling, in applications that monitor frequency behavior of item-sets. We propose a three stage strategy for focusing on item-sets, which are of current interest to the user and introduce metrics that model changes in their frequency (support) behavior.