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Provides an overview of existing work in explanation support for data-driven processes. The book classifies explainability requirements across three dimensions: the target of the explanation ('What'), the audience of the explanation ('Who'), and the purpose of the explanation ('Why').

Differential privacy is a promising approach to formalizing privacy - that is, for writing down what privacy means as a mathematical equation. This book serves as an overview of the state-of-the-art in techniques for differential privacy.

Provides a thorough introduction to Graphical Processing Unit (GPU) optimized database systems especially in online analytical processing. The authors emphasis is on the breadth of the subject and cover as many publications as possible, with necessary details in some key GPU-optimized designs.

Surveys fundamental concepts and practical methods for creating and curating large knowledge bases. The book covers models and methods for discovering and curating large knowledge bases from online content, with emphasis on semi-structured web pages and unstructured text sources.

By explaining how the result of an operation was derived from its inputs, data provenance has proven to be a useful tool that is applicable in a wide variety of applications. This monograph gives a comprehensive introduction to data provenance concepts, algorithms and methodology developed in the last few decades.

Multi-tenancy is a crucial tenet for cloud dataservice providers that allows sharing of data centre resources across tenants, thereby reducing cost. In this monograph the authors review architectures of today's cloud data services, and identify trends and challenges that arise in multi-tenant cloud data services.

Shows how specialized hardware accelerators can provide an answer to the compute stagnation problem and be helpful in reducing data movement bottlenecks by placing them in the right location within the computer architecture. The focus of the book is on Field Programmable Gate Arrays.

Provides a brief introduction to three distributed learning techniques that have recently been developed: lossy communication compression, asynchronous communication, and decentralized communication. These have significant impact on the work in both the system and machine learning and mathematical optimization communities.

Surveys for a general audience the Datalog language, recursive query processing, and optimization techniques. Topics covered include the core Datalog language and various extensions, semantics, query optimizations, magic-sets optimizations, incremental view maintenance, aggregates, negation, and types.

Describes basic principles and recent developments in building approximate synopses (that is, lossy, compressed representations) of massive data. The book focuses on the four main families of synopses: random samples, histograms, wavelets, and sketches.

Describes the system architectures of web databases, and analyses the research issues related to caching and materialization in such architectures. The book also presents the state of the art in caching and materialization for web databases and organises current approaches according to fundamental questions.