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The Four Generations of Entity Resolution
Entity Resolution (ER) lies at the core of data integration and cleaning and, thus, a bulk of the research examines ways for improving its effectiveness and time efficiency. The initial ER methods primarily target Veracity in the context of structured (relational) data that are described by a schema of well-known quality and meaning. To achieve high effectiveness, they leverage schema, expert, and/or external knowledge. Part of these methods are extended to address Volume, processing large datasets through multi-core or massive parallelization approaches, such as the MapReduce paradigm. However, these early schema-based approaches are inapplicable to Web Data, which abound in voluminous, noi...
Foundations of Data Quality Management
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...
An Introduction to Duplicate Detection
With the ever increasing volume of data, data quality problems abound. Multiple, yet different representations of the same real-world objects in data, duplicates, are one of the most intriguing data quality problems. The effects of such duplicates are detrimental; for instance, bank customers can obtain duplicate identities, inventory levels are monitored incorrectly, catalogs are mailed multiple times to the same household, etc. Automatically detecting duplicates is difficult: First, duplicate representations are usually not identical but slightly differ in their values. Second, in principle all pairs of records should be compared, which is infeasible for large volumes of data. This lecture...
Conceptual Modeling - ER 2004
This book constitutes the refereed proceedings of the 23rd International Conference on Conceptual Modeling, ER 2004, held in Shanghai, China, in November 2004. The 57 revised full papers presented together with three invited contributions and 8 demonstration and poster papers were carefully reviewed and selected from 295 submissions. The papers are organized in topical sections on conceptual modeling, datawarehouses, schema integration, data classification and mining, web-based information systems, query processing, web services, schema evolution, conceptual modeling applications, UML, XML modeling, and industrial presentations.
Proceedings 2004 VLDB Conference
Proceedings of the 30th Annual International Conference on Very Large Data Bases held in Toronto, Canada on August 31 - September 3 2004. Organized by the VLDB Endowment, VLDB is the premier international conference on database technology.
Advances in Web-Age Information Management
Contains the proceedings of the 7th International Conference on Web-Age Information Management, WAIM 2006. The papers are organized in topical sections on, indexing, XML query processing, information retrieval, sensor networks and grid computing, peer-to-peer systems, Web services, Web searching, caching and moving objects, clustering, and more. This book constitutes the refereed proceedings of the 7th International Conference on Web-Age Information Management, WAIM 2006, held in Hong Kong, China in June 2006. The 50 revised full papers presented were carefully reviewed and selected from 290 submissions. The papers are organized in topical sections on, indexing, XML query processing, information retrieval, sensor networks and grid computing, peer-to-peer systems, Web services, Web searching, caching and moving objects, temporal database, clustering, clustering and classification, data mining, data stream processing, XML and semistructured data, data distribution and query processing, and advanced applications
Principles of Data Integration
How do you approach answering queries when your data is stored in multiple databases that were designed independently by different people? This is first comprehensive book on data integration and is written by three of the most respected experts in the field. This book provides an extensive introduction to the theory and concepts underlying today's data integration techniques, with detailed, instruction for their application using concrete examples throughout to explain the concepts. Data integration is the problem of answering queries that span multiple data sources (e.g., databases, web pages). Data integration problems surface in multiple contexts, including enterprise information integration, query processing on the Web, coordination between government agencies and collaboration between scientists. In some cases, data integration is the key bottleneck to making progress in a field. The authors provide a working knowledge of data integration concepts and techniques, giving you the tools you need to develop a complete and concise package of algorithms and applications.
Data Revolution
Data has become a factor of production, like labor and steel, and is driving a new data-centered economy. The Data rEvolution is about data volume, variety, velocity and value. It is about new ways to organize and manage data for rapid processing using tools like Hadoop and MapReduce. It is about the explosion of new tools for "connecting the dots" and increasing knowledge, including link analysis, temporal analysis and predictive analytics. It is about a vision of "analytics for everyone" that puts sophisticated statistics into the hands of all. And, it is about using visual analytics to parse the data and literally see new relationships and insights on the fly. As the data and tools become democratized, we will see a new world of experimentation and creative problem-solving, where data comes from both inside and outside the organization. Your own data is not enough. This report is a must-read for IT and business leaders who want to maximize the value of data for their organization.
On Transactional Concurrency Control
This book contains a number of chapters on transactional database concurrency control. This volume's entire sequence of chapters can summarized as follows: A two-sentence summary of the volume's entire sequence of chapters is this: traditional locking techniques can be improved in multiple dimensions, notably in lock scopes (sizes), lock modes (increment, decrement, and more), lock durations (late acquisition, early release), and lock acquisition sequence (to avoid deadlocks). Even if some of these improvements can be transferred to optimistic concurrency control, notably a fine granularity of concurrency control with serializable transaction isolation including phantom protection, pessimistic concurrency control is categorically superior to optimistic concurrency control, i.e., independent of application, workload, deployment, hardware, and software implementation.
Foundations of Query Answering in Relational Data Exchange
Relational data exchange is the problem of translating relational data according to a given specification. It is one of the many tasks that arise in information integration. A fundamental issue is how to answer queries that are posed against the result of the data exchange so that the answers are semantically consistent with the source data. For monotonic queries, the certain answers semantics by Fagin, Kolaitis, Miller, and Popa (2003) yields good answers. For many non-monotonic queries, however, this semantics was shown to yield counter-intuitive answers. This dissertation deals with the problem of computing the certain answers to monotonic queries on the one hand. On the other hand, it presents and compares semantics for answering non-monotonic queries, and investigates how hard it is to evaluate non-monotonic queries under these semantics.
