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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...
Scaling Up Machine Learning
This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.
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...
Cost-Sensitive Machine Learning
In machine learning applications, practitioners must take into account the cost associated with the algorithm. These costs include: Cost of acquiring training dataCost of data annotation/labeling and cleaningComputational cost for model fitting, validation, and testingCost of collecting features/attributes for test dataCost of user feedback collect
The Semantic Web: Research and Applications
The books (LNCS 6643 and 6644) constitute the refereed proceedings of the 8th European Semantic Web Conference, ESWC 2011, held in Heraklion, Crete, Greece, in May/June 2011. The 57 revised full papers of the research track presented together with 7 PhD symposium papers and 14 demo papers were carefully reviewed and selected from 291 submissions. The papers are organized in topical sections on digital libraries track; inductive and probabilistic approaches track; linked open data track; mobile web track; natural language processing track; ontologies track; and reasoning track (part I); semantic data management track; semantic web in use track; sensor web track; software, services, processes and cloud computing track; social web and web science track; demo track, PhD symposium (part II).
Advances in Information Retrieval
This book constitutes the refereed proceedings of the 30th annual European Conference on Information Retrieval Research, ECIR 2008, held in Glasgow, UK, in March/April 2008. The 33 revised full papers and 19 revised short papers presented together with the abstracts of 3 invited lectures and 32 poster papers were carefully reviewed and selected from 139 full article submissions. The papers are organized in topical sections on evaluation, Web IR, social media, cross-lingual information retrieval, theory, video, representation, wikipedia and e-books, as well as expert search.
READUP BUILDUP. Thync
READUP BUILDUP. Thync is the third volume of α- instant readings put up by Florentin Smarandache. Although the style of the reading logs is uniform in the three volumes so far (rough and ready, kernel-extracting, absence of any secondary remarks, and thus masking the author-summarizator), this one address only technical issues from two topics of interest for the author (information fusion / data mining), unlike in previous eclectic books.
Financial Data Engineering
Today, investment in financial technology and digital transformation is reshaping the financial landscape and generating many opportunities. Too often, however, engineers and professionals in financial institutions lack a practical and comprehensive understanding of the concepts, problems, techniques, and technologies necessary to build a modern, reliable, and scalable financial data infrastructure. This is where financial data engineering is needed. A data engineer developing a data infrastructure for a financial product possesses not only technical data engineering skills but also a solid understanding of financial domain-specific challenges, methodologies, data ecosystems, providers, form...
Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
This book constitutes the refereed proceedings of the 4th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, CPAIOR 2007, held in Brussels, Belgium in May 2007. It covers methodological and foundational issues from AI, OR, and algorithmics as well as applications to the solution of combinatorial optimization problems in various fields via constraint programming.
Relational Data Clustering
A culmination of the authors' years of extensive research on this topic, Relational Data Clustering: Models, Algorithms, and Applications addresses the fundamentals and applications of relational data clustering. It describes theoretic models and algorithms and, through examples, shows how to apply these models and algorithms to solve real-world problems. After defining the field, the book introduces different types of model formulations for relational data clustering, presents various algorithms for the corresponding models, and demonstrates applications of the models and algorithms through extensive experimental results. The authors cover six topics of relational data clustering: Clustering on bi-type heterogeneous relational data Multi-type heterogeneous relational data Homogeneous relational data clustering Clustering on the most general case of relational data Individual relational clustering framework Recent research on evolutionary clustering This book focuses on both practical algorithm derivation and theoretical framework construction for relational data clustering. It provides a complete, self-contained introduction to advances in the field.
