Open AccessBook
Data Streams: Algorithms and Applications
TLDR
In this paper, the authors present a survey of basic mathematical foundations for data streaming systems, including basic mathematical ideas, basic algorithms, and basic algorithms and algorithms for data stream processing.Abstract:
1 Introduction 2 Map 3 The Data Stream Phenomenon 4 Data Streaming: Formal Aspects 5 Foundations: Basic Mathematical Ideas 6 Foundations: Basic Algorithmic Techniques 7 Foundations: Summary 8 Streaming Systems 9 New Directions 10 Historic Notes 11 Concluding Remarks Acknowledgements Referencesread more
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Journal ArticleDOI
Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions
TL;DR: This work surveys and extends recent research which demonstrates that randomization offers a powerful tool for performing low-rank matrix approximation, and presents a modular framework for constructing randomized algorithms that compute partial matrix decompositions.
Data Mining: Concepts and Techniques (2nd edition)
Jiawei Han,Micheline Kamber +1 more
TL;DR: There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99].
Journal ArticleDOI
An improved data stream summary: the count-min sketch and its applications
Graham Cormode,S. Muthukrishnan +1 more
TL;DR: In this paper, the authors introduce a sublinear space data structure called the countmin sketch for summarizing data streams, which allows fundamental queries in data stream summarization such as point, range, and inner product queries to be approximately answered very quickly; in addition it can be applied to solve several important problems in data streams such as finding quantiles, frequent items, etc.
BookDOI
Compressed sensing : theory and applications
Yonina C. Eldar,Gitta Kutyniok +1 more
TL;DR: In this paper, the authors introduce the concept of second generation sparse modeling and apply it to the problem of compressed sensing of analog signals, and propose a greedy algorithm for compressed sensing with high-dimensional geometry.
Proceedings Article
Learning from Time-Changing Data with Adaptive Windowing
Albert Bifet,Ricard Gavaldà +1 more
TL;DR: A new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time is presented, using sliding windows whose size is recomputed online according to the rate of change observed from the data in the window itself.
References
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Book
Computers and Intractability: A Guide to the Theory of NP-Completeness
TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
Book
Compressed sensing
TL;DR: It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients, and a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing.
Book
The Art of Computer Programming
TL;DR: The arrangement of this invention provides a strong vibration free hold-down mechanism while avoiding a large pressure drop to the flow of coolant fluid.
Proceedings ArticleDOI
Wireless sensor networks for habitat monitoring
TL;DR: An in-depth study of applying wireless sensor networks to real-world habitat monitoring and an instance of the architecture for monitoring seabird nesting environment and behavior is presented.
Book
Randomized Algorithms
TL;DR: This book introduces the basic concepts in the design and analysis of randomized algorithms and presents basic tools such as probability theory and probabilistic analysis that are frequently used in algorithmic applications.