Sequence Data Mining (Advances in Database Systems (33))

Understanding sequence data, and the ability to utilize this hidden knowledge, creates a significant impact on many aspects of our society. Examples of sequence data include DNA, protein, customer purchase history, web surfing history, and more. **Sequence Data Mining** provides balanced coverage of the existing results on sequence data mining, as well as pattern types and associated pattern mining methods. While there are several books on data mining and sequence data analysis, currently there are no books that balance both of these topics. This professional volume fills in the gap, allowing readers to access state-of-the-art results in one place. **Sequence Data Mining** is designed for professionals working in bioinformatics, genomics, web services, and financial data analysis. This book is also suitable for advanced-level students in computer science and bioengineering. Forward by Professor Jiawei Han, University of Illinois at Urbana-Champaign. In recent years, the progress in hardware technology has made it possible for organizations to store and record large streams of transactional data. Such data sets which continuously and rapidly grow over time are referred to as data streams. Data Streams : Models and Algorithms primarily discusses issues related to the mining aspects of data streams rather than the database management aspect of streams. This volume covers mining aspects of data streams in a comprehensive style. Each contributed chapter, from a variety of well known researchers in the data mining field, contains a survey on the topic, the key ideas in the field from 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 graduate-level students in computer science. Charu C. Aggarwal obtained his B. Tech in Computer Science from IIT Kanpur in 1993 and Ph. D. from MIT in 1996. He has been a Research Staff Member at IBM since then, and has published over 90 papers in major conferences and journals in the database and data mining field. He has applied for, or been granted, over 50 US and International patents, and has twice been designated Master Inventor at IBM for the commercial value of his patents. He has been granted 14 invention achievement awards by IBM for his patents. His work on real time bio-terrorist threat detection in data streams won the IBM Epispire award for environmental excellence in 2003. He has served on the program committee of most major database conferences, and was program chair for the Data Mining and Knowledge Discovery Workshop, 2003, and a program vice-chair for the SIAM Conference on Data Mining, 2007. He is an associate editor of the IEEE Transactions on Data Engineering and an action editor of the Data Mining and Knowledge Discovery Journal. He is a senior member of the IEEE Annotation Fuzzy Database Modeling with XML aims to provide a single record of current research and practical applications in the fuzzy databases. This volume is the outgrowth of research the author has conducted in recent years. Fuzzy Database Modeling with XML introduces state-of-the-art information to the database research, while at the same time serving the information technology professional faced with a non-traditional application that defeats conventional approaches. The research on fuzzy conceptual models and fuzzy object-oriented databases is receiving increasing attention, in addition to fuzzy relational database models. With rapid advances in network and internet techniques as well, the databases have been applied under the environment of distributed information systems. It is essential in this case to integrate multiple fuzzy database systems. Since databases are commonly employed to store and manipulate XML data, additional requirements are necessary to model fuzzy information with XML. Secondly, this book maps fuzzy XML model to the fuzzy databases. Very few efforts at investigating these issues have thus far occurred. Fuzzy Database Modeling with XML is designed for a professional audience of researchers and practitioners in industry. This book is also suitable for graduate-level students in computer science Fuzzy Database Modeling with XML aims to provide a single record of current research and practical applications in the fuzzy databases. This volume is the outgrowth of research the author has conducted in recent years. Fuzzy Database Modeling with XML introduces state of the art information to the database research, while at the same time serving the information technology professional faced with a non-traditional application that defeats conventional approaches. The research on fuzzy conceptual models and fuzzy object-oriented databases is receiving increasing attention, in addition to fuzzy relational database models. With rapid advances in network and internet techniques as well, the databases have been applied under the environment of distributed information systems. It is essential in this case to integrate multiple fuzzy database systems. Since databases are commonly employed to store and manipulate XML data, additional requirements are necessary to model fuzzy information with XML. Second, this book maps fuzzy XML model to the fuzzy databases. Very few efforts at investigating these issues have thus far occurred. Fuzzy Database Modeling with XML is designed for a professional audience of researchers and practitioners in industry. This book is also suitable for graduate-level students in computer science. In the Information Society, information holds the master key to economic influence. Similarity Search: The Metric Space Approach will focus on efficient ways to locate user-relevant information in collections of objects, the similarity of which is quantified using a pairwise distance measure. This book is a direct response to recent advances in computing, communications and storage which have led to the current flood of digital libraries, data warehouses and the limitless heterogeneity of internet resources. Similarity Search: The Metric Space Approach will introduce state-of-the-art in developing index structures for searching complex data modeled as instances of a metric space. This book consists of two parts. Part 1 presents the metric search approach in a nutshell by defining the problem, describes major theoretical principals, and provides an extensive survey of specific techniques for a large range of applications. Part 2 concentrates on approaches particularly designed for searching in very large collections of data. Similarity Search: The Metric Space Approach is designed for a professional audience, composed of academic researchers as well as practitioners in industry. This book is also suitable as introductory material 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.

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 area of similarity searching is a very hot topic for both research and c- mercial applications. Current data processing applications use data with c- siderably less structure and much less precise queries than traditional database systems. Examples are multimedia data like images or videos that offer query by example search, product catalogs that provide users with preference based search, scientific data records from observations or experimental analyses such as biochemical and medical data, or XML documents that come from hetero- neous data sources on the Web or in intranets and thus does not exhibit a global schema. Such data can neither be ordered in a canonical manner nor meani- fully searched by precise database queries that would return exact matches. This novel situation is what has given rise to similarity searching, also - ferred to as content based or similarity retrieval. The most general approach to similarity search, still allowing construction of index structures, is modeled in metric space. In this book. Prof. Zezula and his co authors provide the first monograph on this topic, describing its theoretical background as well as the practical search tools of this innovative technology.

the Proliferation Of Information Housed In Computerized Domains Makes It Vital To Find Tools To Search These Resources Efficiently And Effectively. Ordinary Retrieval Techniques Are Inadequate Because Sorting Is Simply Impossible. Consequently, Proximity Searching Has Become A Fundamental Computation Task In A Variety Of Application Areas.

similarity Search Focuses On The State Of The Art In Developing Index Structures For Searching The Metric Space. Part I Of The Text Describes Major Theoretical Principles, And Provides An Extensive Survey Of Specific Techniques For A Large Range Of Applications. Part Ii Concentrates On Approaches Particularly Designed For Searching In Large Collections Of Data. After Describing The Most Popular Centralized Disk-based Metric Indexes, Approximation Techniques Are Presented As A Way To Significantly Speed Up Search Time At The Cost Of Some Imprecision In Query Results. Finally, The Scalable And Distributed Metric Structures Are Discussed.

in Recent Years, The Progress In Hardware Technology Has Made It Possible For Organizations To Store And Record Large Streams Of Transactional Data. Such Data Sets Which Continuously And Rapidly Grow Over Time Are Referred To As Data Streams. Data Streams: Models And Algorithms Primarily Discusses Issues Related To The Mining Aspects Of Data Streams Rather Than The Database Management Aspect Of Streams. This Volume Covers Mining Aspects Of Data Streams In A Comprehensive Style. Each Contributed Chapter, From A Variety Of Well Known Researchers In The Data Mining Field, Contains A Survey On The Topic, The Key Ideas In The Field From 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 Graduate-level Students In Computer Science.

"Understanding sequence data, and the ability to utilize this hidden knowledge, creates a significant impact on many aspects of our society. Examples of sequence data include DNA, protein, customer purchase history, web surfing history, and more." "Sequence Data Mining provides balanced coverage of the existing results on sequence data mining, as well as pattern types and associated pattern mining methods. While there are several books on data mining and sequence data analysis, currently there are no books that balance both of these topics. This volume fills in the gap, allowing readers to access state-of-the-art results in one place." "Sequence Data Mining is designed for professionals working in bioinformatics, genomics, web services, and financial data analysis. This book is also suitable for advanced-level students in computer science and bioengineering."--Jacket 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. Understanding sequence data, and the ability to utilize this hidden knowledge, will create a significant impact on many aspects of our society. Examples of sequence data include DNA, protein, customer purchase history, web surfing history, and more. This book provides thorough coverage of the existing results on sequence data mining as well as pattern types and associated pattern mining methods. It offers balanced coverage on data mining and sequence data analysis, allowing readers to access the state-of-the-art results in one place. This book primarily discusses issues related to the mining aspects of data streams and it is unique in its primary focus on the subject. 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. The book 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. "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 Streaming applications pose interesting challenges for data management systems. Such application domains require queries to be evaluated continuously. This book comprises eight chapters providing exposition of algorithms, languages, as well as systems proposed and implemented for managing streaming data This book primarily discusses issues related to the mining aspects of data streams and it is unique in its primary focus on the subject. It is intended for a professional audience, but is also appropriate for advanced-level students in computer science.