Verykios

Bio: Verykios is an academic researcher. The author has contributed to research in topics: Record linkage & Data deduplication. The author has an hindex of 1, co-authored 1 publications receiving 1643 citations.

Duplicate Record Detection: A Survey

Abstract: Often, in the real world, entities have two or more representations in databases. Duplicate records do not share a common key and/or they contain errors that make duplicate matching a difficult task. Errors are introduced as the result of transcription errors, incomplete information, lack of standard formats, or any combination of these factors. In this paper, we present a thorough analysis of the literature on duplicate record detection. We cover similarity metrics that are commonly used to detect similar field entries, and we present an extensive set of duplicate detection algorithms that can detect approximately duplicate records in a database. We also cover multiple techniques for improving the efficiency and scalability of approximate duplicate detection algorithms. We conclude with coverage of existing tools and with a brief discussion of the big open problems in the area

Linked Data - the story so far

Abstract: The term “Linked Data” refers to a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the last three years, leading to the creation of a global data space containing billions of assertions— the Web of Data. In this article, the authors present the concept and technical principles of Linked Data, and situate these within the broader context of related technological developments. They describe progress to date in publishing Linked Data on the Web, review applications that have been developed to exploit the Web of Data, and map out a research agenda for the Linked Data community as it moves forward.

Ontology Matching

Abstract: Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Linked Data: Evolving the Web into a Global Data Space

Abstract: The World Wide Web has enabled the creation of a global information space comprising linked documents. As the Web becomes ever more enmeshed with our daily lives, there is a growing desire for direct access to raw data not currently available on the Web or bound up in hypertext documents. Linked Data provides a publishing paradigm in which not only documents, but also data, can be a first class citizen of the Web, thereby enabling the extension of the Web with a global data space based on open standards - the Web of Data. In this Synthesis lecture we provide readers with a detailed technical introduction to Linked Data. We begin by outlining the basic principles of Linked Data, including coverage of relevant aspects of Web architecture. The remainder of the text is based around two main themes - the publication and consumption of Linked Data. Drawing on a practical Linked Data scenario, we provide guidance and best practices on: architectural approaches to publishing Linked Data; choosing URIs and vocabularies to identify and describe resources; deciding what data to return in a description of a resource on the Web; methods and frameworks for automated linking of data sets; and testing and debugging approaches for Linked Data deployments. We give an overview of existing Linked Data applications and then examine the architectures that are used to consume Linked Data from the Web, alongside existing tools and frameworks that enable these. Readers can expect to gain a rich technical understanding of Linked Data fundamentals, as the basis for application development, research or further study.

Mastering the information age : solving problems with visual analytics

Abstract: Data Nodes, Edges Display Interactive Display Visual Analogues VisualItems in ItemRegistry User Figure 6.3: The Information Visualisation Reference Model, adapted from Heer et al.[57] 6.2 State of the Art 93 a visual analytics issue that should be better tackled by all the visualisation communities. Blending different kinds of visualisations in the same application is becoming Blending different kinds of visualisations is currently difficult more frequent. Scientific visualisation and geographic visualisation need information visualisation because they manage multi-valued data with complex topologies that can be visualised using their canonical geometry. In addition, they can also be explored with more abstract visual representations to avoid geometric artefacts. For example, census data can be visualised as a coloured map but also as a multi-dimensional dataset where the longitude and latitude are two attributes among others. Clustering this data by some similarity measure will then reveal places that can be far away in space but behave similarly in term of other attributes (e.g., level of education, level of income, size of houses etc.), similarity that would not be visible on a map. On top of these visualisation systems, a user interface allows control of the overall application. User interfaces are well understood but they can be very different in styles. 3D systems use specific types of interfaces that are very different to traditional desktop interfaces. Moreover, information visualisation systems tend to deeply embed the interaction with the visualisation, offering special kinds of controls either directly inside the visualisations (e.g., range sliders on the axes of parallel coordinates) or around it but with special kinds of widgets (e.g., range sliders for performing range-queries). Interoperability can thus be described at several levels. At the data management level, at the architecture model level and at the interface level. 6.2.2 Data Management All visual analytics applications start with data that can be either statically collected or dynamically produced. Depending on the nature of the data, visual analytics applications have used various ways of managing their storage. In order of sophistication, they are: Flat files using ad-hoc formats, Structured file formats such as XML, Specialised NoSQL systems, including Cloud Storage, Standard or extended transactional databases (SQL), Workflow or dataflow systems integrating storage, distribution and data processing. We will now consider these data storage methods, paying particular attention to Data Management for visual analytics can rely on different levels of sophistication the levels of service required by visual analytics, such as: Persistence (they all provide it by definition), Typing, Distribution, Atomic transactions, Notification, Interactive performance, Computation.

Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection

Abstract: Data matching (also known as record or data linkage, entity resolution, object identification, or field matching) is the task of identifying, matching and merging records that correspond to the same entities from several databases or even within one database. Based on research in various domains including applied statistics, health informatics, data mining, machine learning, artificial intelligence, database management, and digital libraries, significant advances have been achieved over the last decade in all aspects of the data matching process, especially on how to improve the accuracy of data matching, and its scalability to large databases. Peter Christens book is divided into three parts: Part I, Overview, introduces the subject by presenting several sample applications and their special challenges, as well as a general overview of a generic data matching process. Part II, Steps of the Data Matching Process, then details its main steps like pre-processing, indexing, field and record comparison, classification, and quality evaluation. Lastly, part III, Further Topics, deals with specific aspects like privacy, real-time matching, or matching unstructured data. Finally, it briefly describes the main features of many research and open source systems available today. By providing the reader with a broad range of data matching concepts and techniques and touching on all aspects of the data matching process, this book helps researchers as well as students specializing in data quality or data matching aspects to familiarize themselves with recent research advances and to identify open research challenges in the area of data matching. To this end, each chapter of the book includes a final section that provides pointers to further background and research material. Practitioners will better understand the current state of the art in data matching as well as the internal workings and limitations of current systems. Especially, they will learn that it is often not feasible to simply implement an existing off-the-shelf data matching system without substantial adaption and customization. Such practical considerations are discussed for each of the major steps in the data matching process.