Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Online Algorithms for Mining Semi-structured Data Stream
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Finding recent frequent itemsets adaptively over online data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding hot query patterns over an XQuery stream
The VLDB Journal — The International Journal on Very Large Data Bases
Online Mining (Recently) Maximal Frequent Itemsets over Data Streams
RIDE '05 Proceedings of the 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A regression-based temporal pattern mining scheme for data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Mining adaptively frequent closed unlabeled rooted trees in data streams
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge Discovery over the Deep Web, Semantic Web and XML
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
Proceedings of the 2010 conference on Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
Mining frequent closed trees in evolving data streams
Intelligent Data Analysis - Ubiquitous Knowledge Discovery
The hidden web, XML and the Semantic Web: scientific data management perspectives
Proceedings of the 14th International Conference on Extending Database Technology
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In this paper, we proposed an online algorithm, called FQT-Stream (Frequent Query Trees of Streams), to mine the set of all frequent tree patterns over a continuous XML data stream. A new numbering method is proposed to represent the tree structure of a XML query tree. An effective sub-tree numeration approach is developed to extract the essential information from the XML data stream. The extracted information is stored in an effective summary data structure. Frequent query trees are mined from the current summary data structure by a depth-first-search manner.