Stream data management

Researchers in data management have recently recognized the importance of a new class of data-intensive applications that requires managing data streams, i.e., data composed of continuous, real-time sequence of items. Streaming applications pose new and interesting challenges for data management systems. Such application domains require queries to be evaluated continuously as opposed to the one time evaluation of a query for traditional applications. Streaming data sets grow continuously and queries must be evaluated on such unbounded data sets. These, as well as other challenges, require a major rethink of almost all aspects of traditional database management systems to support streaming applications.Stream Data Management comprises eight invited chapters by researchers active in stream data management. The collected chapters provide exposition of algorithms, languages, as well as systems proposed and implemented for managing streaming data.Stream Data Management is designed to appeal to researchers or practitioners already involved in stream data management, as well as to those starting out in this area. This book is also suitable for graduate students in computer science interested in learning about stream data management.

the Focus Of Mining Sequential Patterns From Large Data Sets Is On Sequential Pattern Mining. In Many Applications, Such As Bioinformatics, Web Access Traces, System Utilization Logs, Etc., The Data Is Naturally In The Form Of Sequences. This Information Has Been Of Great Interest For Analyzing The Sequential Data To Find Its Inherent Characteristics. Examples Of Sequential Patterns Include, But Are Not Limited To, Protein Sequence Motifs And Web Page Navigation Traces.

to Meet The Different Needs Of Various Applications, Several Models Of Sequential Patterns Have Been Proposed. This Volume Not Only Studies The Mathematical Definitions And Application Domains Of These Models, But Also The Algorithms On How To Effectively And Efficiently Find These Patterns.

mining Sequential Patterns From Large Data Sets Provides A Set Of Tools For Analyzing And Understanding The Nature Of Various Sequences By Identifying The Specific Model(s) Of Sequential Patterns That Are Most Suitable. This Book Provides An Efficient Algorithm For Mining These Patterns.

mining Sequential Patterns From Large Data Sets Is Designed For A Professional Audience Of Researchers And Practitioners In Industry, And Also Suitable For Graduate-level Students In Computer Science.

The focus of Mining Sequential Patterns from Large Data Sets is on sequential pattern mining. In many applications, such as bioinformatics, web access traces, system utilization logs, etc., the data is naturally in the form of sequences. This information has been of great interest for analyzing the sequential data to find its inherent characteristics. Examples of sequential patterns include, but are not limited to, protein sequence motifs and web page navigation traces. To meet the different needs of various applications, several models of sequential patterns have been proposed. This volume not only studies the mathematical definitions and application domains of these models, but also the algorithms on how to effectively and efficiently find these patterns. Mining Sequential Patterns from Large Data Sets provides a set of tools for analyzing and understanding the nature of various sequences by identifying the specific model(s) of sequential patterns that are most suitable. This book provides an efficient algorithm for mining these patterns. Mining Sequential Patterns from Large Data Sets is designed for a professional audience of researchers and practitioners in industry, and also suitable for graduate-level students in computer science. "Mining Sequential Patterns from Large Data Sets provides a set of tools for analyzing and understanding the nature of various sequences by identifying the specific model(s) of sequential patterns that are most suitable. This book provides an efficient algorithm for mining these patterns." "Mining Sequential Patterns from Large Data Sets is designed for a professional audience of researchers and practitioners in industry and is also suitable for graduate-level students in computer science."--Jacket The focus of this book is sequential pattern mining. In many applications, such as bioinformatics, web access traces, system utilization logs, etc., the data is naturally in the form of sequences. This information has been of great interest for analyzing the sequential data to find its inherent characteristics. Examples of sequential patterns include but are not limited to protein sequence motifs and web page navigation traces