- Full Description
Data Streams: Models and Algorithms primarily discusses issues related to the mining aspects of data streams. Recent progress in hardware technology makes it possible for organizations to store and record large streams of transactional data. For example, even simple daily transactions such as using the credit card or phone result in automated data storage, which brings us to a fairly new topic called data streams. This volume covers mining aspects of data streams comprehensively: each contributed chapter contains a survey on the topic, the key ideas in the field for that particular topic, and future research directions. Data Streams: Models and Algorithms is intended for a professional audience composed of researchers and practitioners in industry. This book is also appropriate for advanced-level students in computer science.
- Table of Contents
Table of Contents
- An Introduction to Data Streams.
- On Clustering Massive Data Streams: A Summarization Paradigm.
- A Survey of Classification Methods in Data Streams.
- Frequent Pattern Mining in Data Streams.
- A Survey of Change Diagnosis Algorithms in Evolving Data Streams.
- Dimensional Analysis of Data Streams Using Stream Cubes.
- Load Shedding in Data Stream Systems.
- The Sliding Window Computation Model and Results.
- A Survey of Synopsis Construction in Data Streams.
- A Survey of Join Processing in Data Streams.
- Indexing and Querying Data Streams.
- Dimensionality Reduction and Forecasting on Streams.
- A Survey of Distributed Mining of Data Streams.
- Algorithms for Distributed Data Stream Mining.
- A Survey of Stream Processing.
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