Advances in Database Systems – serie

Conceptually, a database consists of objects and relationships. Object Relationship Notation (ORN) is a simple notation that more precisely defines relationships by combining UML multiplicities with uniquely defined referential actions.This book shows how ORN can be used in UML class diagrams and database definition languages (DDLs) to better model and implement relationships and thus more productively develop database applications. For the database developer, it presents many examples of relationships modeled using ORN-extended class diagrams and shows how these relationships are easily mapped to an ORN-extended SQL or Object DDL. For the DBMS developer, it presents the specifications and algorithms needed to implement ORN in a relational and object DBMS.  This book also describes tools that can be downloaded or accessed via the Web.  These tools allow databases to be modeled using ORN and implemented using automatic code generation that adds ORN support to Microsoft SQL Server and Progress Object Store.Object Relationship Notation (ORN) for Database Applications:  Enhancing the Modeling and Implementation of Associations is written for research scientists, research libraries, professionals, and advanced-level students in computer science.

The database industry is a multi-billion, world-wide, all-encompassing part of the software world. Quantifiers in Action: Generalized Quantification in Query, Logical and Natural Languages introduces a query language called GQs—Generalized Quantification in Query. Most query languages are simply versions of First Order Logic (FOL). GQs are an extension of the idea of quantifier in FOL. GQs are a perfect example of a practical theory within databases.This book provides a brief background in logic and introduces the concept of GQs, and then develops a query language based on GQs. Using Query Language with Generalized Quantifiers, the reader explores the efficient implementation of the concept, always a primary consideration in databases. This professional book also includes several extensions for use with documents employing question and answer techniques.Designed for practitioners and researchers within the database management field; also suitable for advanced-level students in computer science.

Managing and Mining Uncertain Data, a survey with chapters by a variety of well known researchers in the data mining field, presents the most recent models, algorithms, and applications in the uncertain data mining field in a structured and concise way. This book is organized to make it more accessible to applications-driven practitioners for solving real problems. Also, given the lack of structurally organized information on this topic, Managing and Mining Uncertain Data provides insights which are not easily accessible elsewhere. Managing and Mining Uncertain Data is designed for a professional audience composed of researchers and practitioners in industry. This book is also suitable as a reference book for advanced-level students in computer science and engineering, as well as the ACM, IEEE, SIAM, INFORMS and AAAI Society groups.

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.

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.

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.

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.

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.

This book 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 the 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.

Advances in hardware technology have increased the capability to store and record personal data about consumers and individuals, causing concerns that personal data may be used for a variety of intrusive or malicious purposes.Privacy-Preserving Data Mining: Models and Algorithms proposes a number of techniques to perform the data mining tasks in a privacy-preserving way. These techniques generally fall into the following categories: data modification techniques, cryptographic methods and protocols for data sharing, statistical techniques for disclosure and inference control, query auditing methods, randomization and perturbation-based techniques.This edited volume contains surveys by distinguished researchers in the privacy field. Each survey includes the key research content as well as future research directions.Privacy-Preserving Data Mining: Models and Algorithms is designed for researchers, professors, and advanced-level students in computer science, and is also suitable for industry practitioners.

In recent years, a new class of applications has come to the forefront { p- marily due to the advancement in our ability to collect data from multitudes of devices, and process them e ciently. These include homeland security - plications, sensor/pervasive computing applications, various kinds of mo- toring applications, and even traditional applications belonging to nancial, computer network management, and telecommunication domains. These - plications need to process data continuously (and as long as data is available) from one or more sources. The sequence of data items continuously gen- ated by sources is termed a data stream. Because of the possible never-ending nature of a data stream, the amount of data to be processed is likely to be unbounded. In addition, timely detection of interesting changes or patterns or aggregations over incoming data is critical for many of these applications. Furthermore, the data arrival rates may uctuate over a period of time and may be bursty at times. For most of these applications, Quality of Service (or QoS) requirements, such as response time, memory usage, and throughput are extremely important. These application requirements make it infeasible to simply load the incoming data streams into a persistent store and process them e ectively using currently available database management techniques.

Access to Mobile Services focuses on methods for accessing broadcast based M-services from multiple wireless channels. This book presents a novel infrastructure that provides a multi-channel broadcast framework for mobile users to effectively discover and access composite M-services. Multi-channel algorithms are proposed for efficiently accessing composite services.Access to Mobile Services provides an in-depth survey of wireless data access and motivates the need to treat mobile services differently. A wireless adaptation of Service Oriented Architecture (SOA) is also covered.Designed for researchers and practitioners who work in the general area of mobile services, this book is also suitable for advanced-level students in computer science.Forewords by:   Michael P. Papazoglou, Tilburg University, The NetherlandsFabio Casati, University of Trento, Italy

Data Quality provides an exposé of research and practice in the data quality field for technically oriented readers. It is based on the research conducted at the MIT Total Data Quality Management (TDQM) program and work from other leading research institutions. This book is intended primarily for researchers, practitioners, educators and graduate students in the fields of Computer Science, Information Technology, and other interdisciplinary areas. It forms a theoretical foundation that is both rigorous and relevant for dealing with advanced issues related to data quality. Written with the goal to provide an overview of the cumulated research results from the MIT TDQM research perspective as it relates to database research, this book is an excellent introduction to Ph.D. who wish to further pursue their research in the data quality area. It is also an excellent theoretical introduction to IT professionals who wish to gain insight into theoretical results in the technically-oriented data quality area, and apply some of the key concepts to their practice.

Extensive research and development has produce mutation tools for languages such as Fortran, Ada, C, and IDL; empirical evaluations comparing mutation with other test adequacy criteria; empirical evidence and theoretical justification for the coupling effect; and techniques for speeding up mutation testing using various types of high performance architectures. Mutation has received the attention of software developers and testers in such diverse areas as network protocols and nuclear simulation. This volume brings together up-to-date research results in mutation testing from a wide range of researchers. It provides answers to key questions related to mutation and raises questions yet to be answered.

In a global and increasingly competitive market, where organizations are driven by information, the search for ways to transform data into true knowledge is critical to a business's success. Few companies, however, have effective methods of managing the quality of this information. Because quality is a multidimensional concept, its management must consider a wide variety of issues related to information and data quality. Information and Database Quality is a compilation of works from research and industry that examines these issues, covering both the organizational and technical aspects of information and data quality. Information and Database Quality is an excellent reference for both researchers and professionals involved in any aspect of information and database research.

Information security is receiving a great deal of attention as computers increasingly process more and more sensitive information. A multilevel secure database management system (MLS DBMS) is designed to store, retrieve and process information in compliance with certain mandatory security requirements, essential for protecting sensitive information from unauthorized access, modification and abuse. Such systems are characterized by data objects labelled at different security levels and accessed by users cleared to those levels. Unless transaction processing modules for these systems are designed carefully, they can be exploited to leak sensitive information to unauthorized users. This text covers research in developing secure transaction processing for popular MLS DBMS architectures, such as kernelized, replicated, and distributed architectures, and advanced transaction models such as workflows, long duration and nested models. Further, it explores the technical challenges that require future attention.

Advanced Database Indexing begins by introducing basic material on storage media, including magnetic disks, RAID systems and tertiary storage such as optical disk and tapes. Typical access methods (e.g. B+ trees, dynamic hash files and secondary key retrieval) are also introduced. The remainder of the book discusses recent advances in indexing and access methods for particular database applications. More specifically, issues such as external sorting, file structures for intervals, temporal access methods, spatial and spatio-temporal indexing, image and multimedia indexing, perfect external hashing methods, parallel access methods, concurrency issues in indexing and parallel external sorting are presented for the first time in a single book. Advanced Database Indexing is an excellent reference for database professionals and may be used as a text for advanced courses on the topic.

This work describes a framework for developing IP Service Platforms and emerging managed IP networks with a reference architecture from the AT&T Labs GeoPlex project. The main goal is to present basic principles that both the telecommunications industry and the Internet community can see as providing benefits for service-related network issues. Topics covered include Application Program Interfaces (APIs), reference architecture, and a model implementation. Part I begins with fundamentals of transport, and quickly transitions to modern transport and technology. Part II elucidates requirements and unifying design principles for the Internet. These fundamental principles establish the basis for consistent behaviour in view of the explosive growth underway in large-scale heterogeneous networks. Part III demonstrates and explains the resulting architecture and implementation. It should be useful for students, researchers, network professionals, software developers, system architects and technically-oriented managers involved in the definition and deployment of modern Internet platforms or services.

In our increasingly mobile world the ability to access information on demand at any time and place can satisfy people's information needs as well as confer on them a competitive advantage. The emergence of battery-operated, low-cost and portable computers such as palmtops and PDAs, coupled with the availability and exploitation of wireless networks, have made possible the potential for ubiquitous computing. Through the wireless networks, portable equipments will become an integrated part of existing distributed computing environments, and mobile users can have access to data stored at information servers located at the static portion of the network even while they are on the move.Traditionally, information is retrieved following a request-response model. However, this model is no longer adequate in a wireless computing environment. First, the wireless channel is unreliable and the bandwidth is low compared to the wired counterpart. Second, the environment is essentially asymmetric with a large number of mobile users accessing a small number of servers. Third, battery-operated portable devices can typically operate only for a short time because of the short battery lifespan. Thus, clients are expected to be disconnected most of the time. To overcome these limitations, there has been a proliferation of research efforts on designing data delivery mechanisms to support wireless computing more effectively. Data Dissemination in Wireless Computing Environments focuses on such mechanisms. The purpose is to provide a thorough and comprehensive review of recent advances on energy-efficient data delivery protocols, efficient wireless channel bandwidth utilization, reliable broadcasting and cache invalidation strategies for clients with long disconnection time. Besides surveying existing methods, this book also compares and evaluates some of the more promising schemes.

Information intermediation is the foundation stone of some of the most successful Internet companies, and is perhaps second only to the Internet Infrastructure companies. On the heels of information integration and interoperability, this book on information brokering discusses the next step in information interoperability and integration.The emerging Internet economy based on burgeoning B2B and B2C trading will soon demand semantics-based information intermediation for its feasibility and success. B2B ventures are involved in the `rationalization' of new vertical markets and construction of domain specific product catalogs. This book provides approaches for re-use of existing vocabularies and domain ontologies as a basis for this rationalization and provides a framework based on inter-ontology interoperation. Infrastructural trade-offs that identify optimizations in performance and scalability of web sites will soon give way to information based trade-offs as alternate rationalization schemes come into play and the necessity of interoperating across these schemes is realized.Information Brokering Across Heterogeneous Digital Data's intended readers are researchers, software architects and CTOs, advanced product developers dealing with information intermediation issues in the context of e-commerce (B2B and B2C), information technology professionals in various vertical markets (e.g., geo-spatial information, medicine, auto), and all librarians interested in information brokering.

Semantic Models for Multimedia Database Searching and Browsing begins with the introduction of multimedia information applications, the need for the development of the multimedia database management systems (MDBMSs), and the important issues and challenges of multimedia systems. The temporal relations, the spatial relations, the spatio-temporal relations, and several semantic models for multimedia information systems are also introduced. In addition, this book discusses recent advances in multimedia database searching and multimedia database browsing. More specifically, issues such as image/video segmentation, motion detection, object tracking, object recognition, knowledge-based event modeling, content-based retrieval, and key frame selections are presented for the first time in a single book. Two case studies consisting of two semantic models are included in the book to illustrate how to use semantic models to design multimedia information systems.Semantic Models for Multimedia Database Searching and Browsing is an excellent reference and can be used in advanced level courses for researchers, scientists, industry professionals, software engineers, students, and general readers who are interested in the issues, challenges, and ideas underlying the current practice of multimedia presentation, multimedia database searching, and multimedia browsing in multimedia information systems.

The architectural concept of a memory hierarchy has been immensely successful, making possible today's spectacular pace of technology evolution in both the volume of data and the speed of data access. Its success is difficult to understand, however, when examined within the traditional "memoryless" framework of performance analysis. The `memoryless' framework cannot properly reflect a memory hierarchy's ability to take advantage of patterns of data use that are transient. The Fractal Structure of Data Reference: Applications to the Memory Hierarchy both introduces, and justifies empirically, an alternative modeling framework in which arrivals are driven by a statistically self-similar underlying process, and are transient in nature. The substance of this book comes from the ability of the model to impose a mathematically tractable structure on important problems involving the operation and performance of a memory hierarchy. It describes events as they play out at a wide range of time scales, from the operation of file buffers and storage control cache, to a statistical view of entire disk storage applications. Striking insights are obtained about how memory hierarchies work, and how to exploit them to best advantage. The emphasis is on the practical application of such results. The Fractal Structure of Data Reference: Applications to the Memory Hierarchy will be of interest to professionals working in the area of applied computer performance and capacity planning, particularly those with a focus on disk storage. The book is also an excellent reference for those interested in database and data structure research.

Addressing the problem of large-scale data mining, this is an interdisciplinary text that describes advances in the integration of three computer-science areas: "intelligent" (machine learning-based) data-mining techniques, relational databases, and parallel processing. The basic idea is to use concepts and techniques of the last two areas, particularly parallel processing, to speed up and scale up data-mining algorithms. The book is divided into three parts, the first of which presents a comprehensive review of intelligent data-mining techniques such as rule induction, instance-based learning, neural networks and genetic algorithms. Likewise, the second part offers a detailed review of parallel processing and parallel databases. Each of these parts includes an overview of commercially-available, state-of-the-art tools. The third part deals with the application of parallel processing to data mining. The emphasis is on finding generic, cost-effective solutions for realistic data volumes. Two parallel computational environments are discussed, the first excluding the use of commercial-strength DBMS and the second using parallel DBMS servers.It is assumed that readers have a knowledge roughly equivalent to a first degree (BSc) in accurate sciences, so that they are reasonably familiar with basic concepts of statistics and computer science. The book is intended primarily for industry-data miners and practitioners in general, who would like to apply intelligent data-mining techniques to large amounts of data. It is also suitable for academic researchers and postgraduate students, particularly database researchers, who are interested in advanced, intelligent database applications, and artificial-intelligence researchers interested in industrial, real-world applications of machine learning.

The focus of Data Management for Mobile Computing is on the impact of mobile computing on data management beyond the networking level. The purpose is to provide a thorough and cohesive overview of recent advances in wireless and mobile data management. The book is written with a critical attitude. This volume probes the new issues introduced by wireless and mobile access to data and their conceptual and practical consequences. Data Management for Mobile Computing provides a single source for researchers and practitioners who want to keep abreast of the latest innovations in the field. It can also serve as a textbook for an advanced course on mobile computing or as a companion text for a variety of courses including courses on distributed systems, database management, transaction management, operating or file systems, information retrieval or dissemination, and web computing.

Knowledge base systems are an integration of conventional database systems with artificial intelligence techniques. They provide inference capabilities to the database system by encapsulating the knowledge of the application domain within the database. Knowledge is the most valuable of all corporate resources that must be captured, stored, re-used and continuously improved, in much the same way as database systems were important in the previous decade. Flexible, extensible, and yet efficient knowledge base systems are needed to capture the increasing demand for knowledge-based applications which will become a significant market in the next decade. Knowledge can be expressed in many static and dynamic forms, the most prominent being domain objects, their relationships, and their rules of evolution and transformation. It is important to express and seamlessly use all types of knowledge in a single knowledge base system. This work presents in detail features that a knowledge base system should have in order to fulfill the above requirements.It covers in detail the following topics: integration of deductive, production, and active rules in sequential database systems; integration and inter-operation of multiple rule types into the same knowledge base system; and parallel rule matching and execution, for deductive, production, and active rules, in parallel export, knowledge base, and database systems.

Database Recovery presents an in-depth discussion on all aspects of database recovery. Firstly, it introduces the topic informally to set the intuitive understanding, and then presents a formal treatment of recovery mechanism. In the past, recovery has been treated merely as a mechanism which is implemented on an ad-hoc basis. This book elevates the recovery from a mechanism to a concept, and presents its essential properties. A book on recovery is incomplete if it does not present how recovery is practiced in commercial systems. This book, therefore, presents a detailed description of recovery mechanisms as implemented on Informix, OpenIngres, Oracle, and Sybase commercial database systems. Database Recovery is suitable as a textbook for a graduate-level course on database recovery, as a secondary text for a graduate-level course on database systems, and as a reference for researchers and practitioners in industry.

One of the main uses of computer systems is the management of large amounts of symbolic information representing the state of some application domain, such as information about all the people I communicate with in my personal address database, or relevant parts of the outer space in the knowledge base of a NASA space mission. While database management systems offer only the basic services of information storage and retrieval, more powerful knowledge systems offer, in addition, a number of advanced services such as deductive and abductive reasoning for the purpose of finding explanations and diagnoses, or generating plans. In order to design and understand database and knowledge-based applications it is important to build upon well-established conceptual and mathematical foundations. What are the principles behind database and knowledge systems? What are their major components? Which are the important cases of knowledge systems? What are their limitations? Addressing these questions, and discussing the fundamental issues of information update, knowledge assimilation, integrity maintenance, and inference-based query answering, is the purpose of this book. Foundations of Databases and Knowledge Systems covers both basic and advanced topics. It may be used as the textbook of a course offering a broad introduction to databases and knowledge bases, or it may be used as an additional textbook in a course on databases or Artificial Intelligence. Professionals and researchers interested in learning about new developments will benefit from the encyclopedic character of the book, which provides organized access to many advanced concepts in the theory of databases and knowledge bases.

Information systems are the backbone of many of today's computerized applications. Distributed databases and the infrastructure needed to support them have been well studied. However, this book is the first to address distributed database interoperability by examining the successes and failures, various approaches, infrastructures, and trends of the field. A gap exists in the way that these systems have been investigated by real practitioners. This gap is more pronounced than usual, partly because of the way businesses operate, the systems they have, and the difficulties created by systems' autonomy and heterogeneity. Telecommunications firms, for example, must deal with an increased demand for automation while at the same time continuing to function at their current level. While academics are focusing on investigating differences between distributed databases, federated databases, heterogeneous databases, and, more generally, among loosely connected and tightly coupled systems, those who have to deal with real problems right away know that the only relevant research is the one that will ensure that their system works to produce reasonably correct results. Interconnecting Heterogeneous Information Systems covers the underlying principles and infrastructures needed to realize truly global information systems. The book discusses technologies related to middleware, the Web, workflows, transactions, and data warehousing. It also overviews architectures with a discussion of critical issues. The book gives an overview of systems that can be viewed as learning platforms. While these systems do not translate to successful commercial realities, they push the envelope in terms of research. Successful commercial systems have benefited from the experiments conducted in these prototypes. The book includes two case studies based on the authors' own work. Interconnecting Heterogeneous Information Systems issuitable as a textbook for a graduate-level course on Interconnecting Heterogeneous Information Systems, as well as a secondary text for a graduate-level course on database or information systems, and as a reference for researchers and practitioners in industry.

also in: THE KLUWER INTERNATIONAL SERIES ON ASIAN STUDIES IN COMPUTER AND INFORMATION SCIENCE, Volume 2