springer international publishing aug 2012 (10 risultati)

Taschenbuch. Condizione: Neu. Neuware -Data quality is one of the most important problems in data management. A database system typically aims to support the creation, maintenance, and use of large amount of data, focusing on the quantity of data. However, real-life data are often dirty: inconsistent, duplicated, inaccurate, incomplete, or stale. Dirty data in a database routinely generate misleading or biased analytical results and decisions, and lead to loss of revenues, credibility and customers. With this comes the need for data quality management. In contrast to traditional data management tasks, data quality management enables the detection and correction of errors in the data, syntactic or semantic, in order to improve the quality of the data and hence, add value to business processes. While data quality has been a longstanding problem for decades, the prevalent use of the Web has increased the risks, on an unprecedented scale, of creating and propagating dirty data. This monograph gives an overview of fundamental issues underlying central aspects of data quality, namely, data consistency, data deduplication, data accuracy, data currency, and information completeness. We promote a uniform logical framework for dealing with these issues, based on data quality rules. The text is organized into seven chapters, focusing on relational data. Chapter One introduces data quality issues. A conditional dependency theory is developed in Chapter Two, for capturing data inconsistencies. It is followed by practical techniques in Chapter 2b for discovering conditional dependencies, and for detecting inconsistencies and repairing data based on conditional dependencies. Matching dependencies are introduced in Chapter Three, as matching rules for data deduplication. A theory of relative information completeness is studied in Chapter Four, revising the classical Closed World Assumption and the Open World Assumption, to characterize incomplete information in the real world. A data currency model is presented in Chapter Five, to identify the current values of entities in a database and to answer queries with the current values, in the absence of reliable timestamps. Finally, interactions between these data quality issues are explored in Chapter Six. Important theoretical results and practical algorithms are covered, but formal proofs are omitted. The bibliographical notes contain pointers to papers in which the results were presented and proven, as well as references to materials for further reading. This text is intended for a seminar course at the graduate level. It is also to serve as a useful resource for researchers and practitioners who are interested in the study of data quality. The fundamental research on data quality draws on several areas, including mathematical logic, computational complexity and database theory. It has raised as many questions as it has answered, and is a rich source of questions and vitality. Table of Contents: Data Quality: An Overview / Conditional Dependencies / Cleaning Data with Conditional Dependencies / Data Deduplication / Information Completeness / Data Currency / Interactions between Data Quality IssuesSpringer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 220 pp. Englisch.

Taschenbuch. Condizione: Neu. Neuware -In 1991, a group of researchers chose the term digital libraries to describe an emerging field of research, development, and practice. Since then, Virginia Tech has had funded research in this area, largely through its Digital Library Research Laboratory. This book is the first in a four book series that reports our key findings and current research investigations. Underlying this book series are six completed dissertations (Gonçalves, Kozievitch, Leidig, Murthy, Shen, Torres), eight dissertations underway, and many masters theses. These reflect our experience with a long string of prototype or production systems developed in the lab, such as CITIDEL, CODER, CTRnet, Ensemble, ETANA, ETD-db, MARIAN, and Open Digital Libraries. There are hundreds of related publications, presentations, tutorials, and reports. We have built upon that work so this book, and the others in the series, will address digital library related needs in many computer science, information science, and library science (e.g., LIS) courses, as well as the requirements of researchers, developers, and practitioners. Much of the early work in the digital library field struck a balance between addressing real-world needs, integrating methods from related areas, and advancing an ever-expanding research agenda. Our work has fit in with these trends, but simultaneously has been driven by a desire to provide a firm conceptual and formal basis for the field.Our aim has been to move from engineering to science. We claim that our 5S (Societies, Scenarios, Spaces, Structures, Streams) framework, discussed in publications dating back to at least 1998, provides a suitable basis. This book introduces 5S, and the key theoretical and formal aspects of the 5S framework. While the 5S framework may be used to describe many types of information systems, and is likely to have even broader utility and appeal, we focus here on digital libraries. Our view of digital libraries is broad, so further generalization should be straightforward. We have connected with related fields, including hypertext/hypermedia, information storage and retrieval, knowledge management, machine learning, multimedia, personal information management, and Web 2.0. Applications have included managing not only publications, but also archaeological information, educational resources, fish images, scientific datasets, and scientific experiments/ simulations. Table of Contents: Introduction / Exploration / Mathematical Preliminaries / Minimal Digital Library / Archaeological Digital Libraries / 5S Results: Lemmas, Proofs, and 5SSuite / Glossary / Bibliography / Authors' Biographies / IndexSpringer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 184 pp. Englisch.

Taschenbuch. Condizione: Neu. Neuware -The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose 'queries,' usually in the form of unlabeled data instances to be labeled by an 'oracle' (e.g., a human annotator) that already understands the nature of the problem. This sort of approach is well-motivated in many modern machine learning and data mining applications, where unlabeled data may be abundant or easy to come by, but training labels are difficult, time-consuming, or expensive to obtain. This book is a general introduction to active learning. It outlines several scenarios in which queries might be formulated, and details many query selection algorithms which have been organized into four broad categories, or 'query selection frameworks.' We also touch on some of the theoretical foundations of active learning, and conclude with an overview of the strengths and weaknesses of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities. Table of Contents: Automating Inquiry / Uncertainty Sampling / Searching Through the Hypothesis Space / Minimizing Expected Error and Variance / Exploiting Structure in Data / Theory / Practical ConsiderationsSpringer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 116 pp. Englisch.

Taschenbuch. Condizione: Neu. Neuware -The chase has long been used as a central tool to analyze dependencies and their effect on queries. It has been applied to different relevant problems in database theory such as query optimization, query containment and equivalence, dependency implication, and database schema design. Recent years have seen a renewed interest in the chase as an important tool in several database applications, such as data exchange and integration, query answering in incomplete data, and many others. It is well known that the chase algorithm might be non-terminating and thus, in order for it to find practical applicability, it is crucial to identify cases where its termination is guaranteed. Another important aspect to consider when dealing with the chase is that it can introduce null values into the database, thereby leading to incomplete data. Thus, in several scenarios where the chase is used the problem of dealing with data dependencies and incomplete data arises. This book discusses fundamental issues concerning data dependencies and incomplete data with a particular focus on the chase and its applications in different database areas. We report recent results about the crucial issue of identifying conditions that guarantee the chase termination. Different database applications where the chase is a central tool are discussed with particular attention devoted to query answering in the presence of data dependencies and database schema design. Table of Contents: Introduction / Relational Databases / Incomplete Databases / The Chase Algorithm / Chase Termination / Data Dependencies and Normal Forms / Universal Repairs / Chase and Database ApplicationsSpringer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 124 pp. Englisch.

Taschenbuch. Condizione: Neu. Neuware -The study of what can be computed by a team of autonomous mobile robots, originally started in robotics and AI, has become increasingly popular in theoretical computer science (especially in distributed computing), where it is now an integral part of the investigations on computability by mobile entities. The robots are identical computational entities located and able to move in a spatial universe; they operate without explicit communication and are usually unable to remember the past; they are extremely simple, with limited resources, and individually quite weak. However, collectively the robots are capable of performing complex tasks, and form a system with desirable fault-tolerant and self-stabilizing properties. The research has been concerned with the computational aspects of such systems. In particular, the focus has been on the minimal capabilities that the robots should have in order to solve a problem. This book focuses on the recent algorithmic results in the field of distributed computing by oblivious mobile robots (unable to remember the past). After introducing the computational model with its nuances, we focus on basic coordination problems: pattern formation, gathering, scattering, leader election, as well as on dynamic tasks such as flocking. For each of these problems, we provide a snapshot of the state of the art, reviewing the existing algorithmic results. In doing so, we outline solution techniques, and we analyze the impact of the different assumptions on the robots' computability power. Table of Contents: Introduction / Computational Models / Gathering and Convergence / Pattern Formation / Scatterings and Coverings / Flocking / Other DirectionsSpringer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 188 pp. Englisch.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Data quality is one of the most important problems in data management. A database system typically aims to support the creation, maintenance, and use of large amount of data, focusing on the quantity of data. However, real-life data are often dirty: inconsistent, duplicated, inaccurate, incomplete, or stale. Dirty data in a database routinely generate misleading or biased analytical results and decisions, and lead to loss of revenues, credibility and customers. With this comes the need for data quality management. In contrast to traditional data management tasks, data quality management enables the detection and correction of errors in the data, syntactic or semantic, in order to improve the quality of the data and hence, add value to business processes. While data quality has been a longstanding problem for decades, the prevalent use of the Web has increased the risks, on an unprecedented scale, of creating and propagating dirty data. This monograph gives an overview of fundamental issues underlying central aspects of data quality, namely, data consistency, data deduplication, data accuracy, data currency, and information completeness. We promote a uniform logical framework for dealing with these issues, based on data quality rules. The text is organized into seven chapters, focusing on relational data. Chapter One introduces data quality issues. A conditional dependency theory is developed in Chapter Two, for capturing data inconsistencies. It is followed by practical techniques in Chapter 2b for discovering conditional dependencies, and for detecting inconsistencies and repairing data based on conditional dependencies. Matching dependencies are introduced in Chapter Three, as matching rules for data deduplication. A theory of relative information completeness is studied in Chapter Four, revising the classical Closed World Assumption and the Open World Assumption, to characterize incomplete information in the real world. A data currency model is presented in Chapter Five, to identify the current values of entities in a database and to answer queries with the current values, in the absence of reliable timestamps. Finally, interactions between these data quality issues are explored in Chapter Six. Important theoretical results and practical algorithms are covered, but formal proofs are omitted. The bibliographical notes contain pointers to papers in which the results were presented and proven, as well as references to materials for further reading. This text is intended for a seminar course at the graduate level. It is also to serve as a useful resource for researchers and practitioners who are interested in the study of data quality. The fundamental research on data quality draws on several areas, including mathematical logic, computational complexity and database theory. It has raised as many questions as it has answered, and is a rich source of questions and vitality. Table of Contents: Data Quality: An Overview / Conditional Dependencies / Cleaning Data with Conditional Dependencies / Data Deduplication / Information Completeness / Data Currency / Interactions between Data Quality Issues 220 pp. Englisch.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In 1991, a group of researchers chose the term digital libraries to describe an emerging field of research, development, and practice. Since then, Virginia Tech has had funded research in this area, largely through its Digital Library Research Laboratory. This book is the first in a four book series that reports our key findings and current research investigations. Underlying this book series are six completed dissertations (Gonçalves, Kozievitch, Leidig, Murthy, Shen, Torres), eight dissertations underway, and many masters theses. These reflect our experience with a long string of prototype or production systems developed in the lab, such as CITIDEL, CODER, CTRnet, Ensemble, ETANA, ETD-db, MARIAN, and Open Digital Libraries. There are hundreds of related publications, presentations, tutorials, and reports. We have built upon that work so this book, and the others in the series, will address digital library related needs in many computer science, information science, and library science (e.g., LIS) courses, as well as the requirements of researchers, developers, and practitioners. Much of the early work in the digital library field struck a balance between addressing real-world needs, integrating methods from related areas, and advancing an ever-expanding research agenda. Our work has fit in with these trends, but simultaneously has been driven by a desire to provide a firm conceptual and formal basis for the field.Our aim has been to move from engineering to science. We claim that our 5S (Societies, Scenarios, Spaces, Structures, Streams) framework, discussed in publications dating back to at least 1998, provides a suitable basis. This book introduces 5S, and the key theoretical and formal aspects of the 5S framework. While the 5S framework may be used to describe many types of information systems, and is likely to have even broader utility and appeal, we focus here on digital libraries. Our view of digital libraries is broad, so further generalization should be straightforward. We have connected with related fields, including hypertext/hypermedia, information storage and retrieval, knowledge management, machine learning, multimedia, personal information management, and Web 2.0. Applications have included managing not only publications, but also archaeological information, educational resources, fish images, scientific datasets, and scientific experiments/ simulations. Table of Contents: Introduction / Exploration / Mathematical Preliminaries / Minimal Digital Library / Archaeological Digital Libraries / 5S Results: Lemmas, Proofs, and 5SSuite / Glossary / Bibliography / Authors' Biographies / Index 184 pp. Englisch.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose 'queries,' usually in the form of unlabeled data instances to be labeled by an 'oracle' (e.g., a human annotator) that already understands the nature of the problem. This sort of approach is well-motivated in many modern machine learning and data mining applications, where unlabeled data may be abundant or easy to come by, but training labels are difficult, time-consuming, or expensive to obtain. This book is a general introduction to active learning. It outlines several scenarios in which queries might be formulated, and details many query selection algorithms which have been organized into four broad categories, or 'query selection frameworks.' We also touch on some of the theoretical foundations of active learning, and conclude with an overview of the strengths and weaknesses of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities. Table of Contents: Automating Inquiry / Uncertainty Sampling / Searching Through the Hypothesis Space / Minimizing Expected Error and Variance / Exploiting Structure in Data / Theory / Practical Considerations 116 pp. Englisch.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The chase has long been used as a central tool to analyze dependencies and their effect on queries. It has been applied to different relevant problems in database theory such as query optimization, query containment and equivalence, dependency implication, and database schema design. Recent years have seen a renewed interest in the chase as an important tool in several database applications, such as data exchange and integration, query answering in incomplete data, and many others. It is well known that the chase algorithm might be non-terminating and thus, in order for it to find practical applicability, it is crucial to identify cases where its termination is guaranteed. Another important aspect to consider when dealing with the chase is that it can introduce null values into the database, thereby leading to incomplete data. Thus, in several scenarios where the chase is used the problem of dealing with data dependencies and incomplete data arises. This book discusses fundamental issues concerning data dependencies and incomplete data with a particular focus on the chase and its applications in different database areas. We report recent results about the crucial issue of identifying conditions that guarantee the chase termination. Different database applications where the chase is a central tool are discussed with particular attention devoted to query answering in the presence of data dependencies and database schema design. Table of Contents: Introduction / Relational Databases / Incomplete Databases / The Chase Algorithm / Chase Termination / Data Dependencies and Normal Forms / Universal Repairs / Chase and Database Applications 124 pp. Englisch.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The study of what can be computed by a team of autonomous mobile robots, originally started in robotics and AI, has become increasingly popular in theoretical computer science (especially in distributed computing), where it is now an integral part of the investigations on computability by mobile entities. The robots are identical computational entities located and able to move in a spatial universe; they operate without explicit communication and are usually unable to remember the past; they are extremely simple, with limited resources, and individually quite weak. However, collectively the robots are capable of performing complex tasks, and form a system with desirable fault-tolerant and self-stabilizing properties. The research has been concerned with the computational aspects of such systems. In particular, the focus has been on the minimal capabilities that the robots should have in order to solve a problem. This book focuses on the recent algorithmic results in the field of distributed computing by oblivious mobile robots (unable to remember the past). After introducing the computational model with its nuances, we focus on basic coordination problems: pattern formation, gathering, scattering, leader election, as well as on dynamic tasks such as flocking. For each of these problems, we provide a snapshot of the state of the art, reviewing the existing algorithmic results. In doing so, we outline solution techniques, and we analyze the impact of the different assumptions on the robots' computability power. Table of Contents: Introduction / Computational Models / Gathering and Convergence / Pattern Formation / Scatterings and Coverings / Flocking / Other Directions 188 pp. Englisch.