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Exploring Data Streams: Algorithms, Patterns, and Applications

Delve into data streams, their characteristics, mining methods, and application areas like finance, surveillance, and telecommunications.

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Exploring Data Streams: Algorithms, Patterns, and Applications

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    1. Xiao Gu Yan Wang Sujing Wang Sathish Kumar Bharath Kumar Srividhya Chandrasekaran  

    2. Outline Introduction & motivation Mining data streams Querying data streams Discussion about the sub-field algorithm Conclusions and applications

    3. What Is a Data Stream? Data Streams Ordered sequence of points, x1,…, xi,…, xn, that can be read only once or a small number of times in a fixed order Characteristics Huge volumes of data, possibly infinite Fast changing and requires fast response Single linear scan algorithm: can only have one look random access is expensive Store only the summary of the data seen thus far Most stream data are at pretty low-level and multi-dimensional in nature, needs multi-level and multi-dimensional processing

    5. Motivation Data streams are everywhere Business: credit card transactions Telecommunication: call detail records Financial market: stock exchange, ATM operations Engineering & industrial processes: power supply Sensor, Monitoring & Surveillance video streams Security monitoring Web logs and page click streams More suited to our data processing needs of today

    7. Previous Research Processing data streams: Goals Mine patterns, process queries and compute statistics on data streams in real-time Stream data mining Clustering & summarization Correlation of data streams Classification of stream data Stream query model Continuous Queries Sliding windows

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