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Data exploration using example-based methods
Lissandrini M., Mottin D., Palpanas T., Velegrakis Y., Morgan&Claypool Publishers, San Rafael, CA, 2019. 164 pp. Type: Book (978-1-681734-55-2)
Date Reviewed: Nov 18 2019

This book provides a comprehensive overview and description of a broad range of search algorithms. Because the authors are experts at example-based searching methods, the book discusses the application of example-based approaches to several application fields, for example, relational databases, graphs, text, and schema matching.

A specific advantage of the book is its very useful organization of each subsection within a chapter. The typical organization of a chapter is as follows: first there is a profound survey of the algorithms, their pseudocode, and the theoretical background, along with an extensive set of references; and then there is a subsection devoted to open problems and possible research directions. The book is very useful for researchers who work in related fields.

The introductory chapter 1 outlines the book’s basic structure and topics, that is, the major content of its two parts. Chapter 2 deals with data cleaning and entity matching in relational databases through example-based methods. It also presents data exploration systems for end users to find satisfactory answers. Chapter 3 discusses searching and exploratory methods on data structures stored in large graphs. The chapter describes graph data models, including the resource description framework (RDF). There is a survey on searching by example nodes, clustering, searching among entities for similarity, and reverse engineering queries on graphs. The chapter concludes with a section devoted to graph structures that can be used in searching by examples.

Chapter 4 focuses on textual data and highlighting relevance within documents. Relation extraction methods are also presented, which have enormous significance in most recent information system architectures as data lakes and modern data warehouses dedicated to big data analytics.

Chapter 5 analyzes the selection problem of how to reconcile the data model, the conceptualized search task, and the appropriate algorithm set to find a solution that arranges all elements into a coordinated approach.

Part 2 begins with open research directions for online learning and active learning (chapter 6). In time-constrained environments, more opportunities for explore-by-example approaches exist. Chapter 7 provides a comprehensive overview of future research directions, along with an extensive set of references. Problems worth investigating include the presentation of results, visualizations, generating alternative solutions for exploration, exploring heterogeneous data, and customizing the exploration techniques for end users. Chapter 8 concludes the book.

The book is very useful for researchers who work in various areas, from searching, exploring data, and the application of searching methods in modeling, to programming, matching, and learning in the case of ontologies and graph transformations.

Reviewer:  Bálint Molnár Review #: CR146781 (2001-0006)
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