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Discusses the main facets and directions in designing error detection and repairing techniques. The book proposes a taxonomy of current anomaly detection techniques, including error types, the automation of the detection process, and error propagation. It also sets out a taxonomy of current data repairing techniques.
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.
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.
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.
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.
There has been a recent marked increase in the amount of spatial data collected by smart phones, space telescopes, and medical devices, among others. The increased volume has brought into focus the need for specialized systems to handle big spatial data. This volume summarizes the state-of-the-art in this area.
Although several experimental studies have been conducted in recent years that compare the performance of several big graph systems, Big Graph Analytics Platforms is the first text to provide a comprehensive survey that clearly summarizes the key features and techniques developed in existing systems.
Summarizes the state of the art in crowd-powered algorithms and system design tailored to large-scale data processing; and also surveys 13 industry users and four marketplace providers of crowd work to identify how hundreds of engineers and tens of million dollars are invested in various crowdsourcing solutions.
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.
Addresses the design principles and core features of systems for analysing very large datasets using massively-parallel computation and storage techniques on large clusters of nodes. This is an ideal reference for anyone with a research or professional interest in large-scale data analytics.
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 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.
Reviews research over the past ten years on why, how, and where provenance, clarifies the relationships among notions of provenance, and describes some of their applications in confidence computation, view maintenance and update, debugging, and annotation propagation.
Deals with the automatic extraction of information from unstructured sources. The text surveys over two decades of information extraction research from various communities such as computational linguistics, machine learning, databases and information retrieval.
Presents an architectural discussion of Database Management Systems design principles, including process models, parallel architecture, storage system design, transaction system implementation, query processor and optimizer architectures, and typical shared components and utilities.
Discusses modern column-stores, their architecture and evolution, and the benefits they can bring in data analytics. There is a specific focus on three influential research prototypes, MonetDB, MonetDB/X100, and C-Store. These systems have formed the basis for several well-known commercial column-store implementations.
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.
Reviews the basics of B-trees and of B-tree indexes in databases, transactional techniques and query processing techniques related to B-trees, B-tree utilities essential for database operations, and many optimizations and improvements.
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