SSM : A Frequent Sequential Data Stream Patterns Miner

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Provided by: University of Westminster
Topic: Big Data
Format: PDF
Data stream applications like sensor network data, click stream data, have data arriving continuously at high speed rates and require online mining process capable of delivering current and near accurate results on demand without full access to all historical stored data. Frequent sequential mining is the process of discovering frequent sequential patterns in data sequences as found in applications like web log access sequences. Mining frequent sequential patterns on data stream applications contend with many challenges such as limited memory for unlimited data, inability of algorithms to scan infinitely flowing original dataset more than once and to deliver current and accurate result on demand.
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