Data Engineering

DATA ENGINEERING: Mining, Information, and Intelligence describes applied research aimed at the task of collecting data and distilling useful information from that data. Most of the work presented emanates from research completed through collaborations between Acxiom Corporation and its academic research partners under the aegis of the Acxiom Laboratory for Applied Research (ALAR). Chapters are roughly ordered to follow the logical sequence of the transformation of data from raw input data streams to refined information. Four discrete sections cover Data Integration and Information Quality; Grid Computing; Data Mining; and Visualization. Additionally, there are exercises at the end of each chapter.

The primary audience for this book is the broad base of anyone interested in data engineering, whether from academia, market research firms, or business-intelligence companies. The volume is ideally suited for researchers, practitioners, and postgraduate students alike. With its focus on problems arising from industry rather than a basic research perspective, combined with its intelligent organization, extensive references, and subject and author indices, it can serve the academic, research, and industrial audiences.

First volume to organize data mining, management, and warehousing into the systematic structure of Data Engineering Focuses on recent applied research results applicable to a broad industry-research-academic market End-of-chapter exercises for classroom use Includes supplementary material: sn.pub/extras

Texte du rabat

DATA ENGINEERING: Mining, Information, and Intelligence describes applied research aimed at the task of collecting data and distilling useful information from that data. Most of the work presented emanates from research completed through collaborations between Acxiom Corporation and its academic research partners under the aegis of the Acxiom Laboratory for Applied Research (ALAR). Chapters are roughly ordered to follow the logical sequence of the transformation of data from raw input data streams to refined information. Four discrete sections cover Data Integration and Information Quality; Grid Computing; Data Mining; and Visualization. Additionally, there are exercises at the end of each chapter.

The primary audience for this book is the broad base of anyone interested in data engineering, whether from academia, market research firms, or business-intelligence companies. The volume is ideally suited for researchers, practitioners, and postgraduate students alike. With its focus on problems arising from industry rather than a basic research perspective, combined with its intelligent organization, extensive references, and subject and author indices, it can serve the academic, research, and industrial audiences.

Contenu
A Declarative Approach to Entity Resolution.- Transitive Closure of Data Records: Application and Computation.- Semantic Data Matching: Principles and Performance.- Application of the Near Miss Strategy and Edit Distance to Handle Dirty Data.- A Parallel General-Purpose Synthetic Data Generator1.- A Grid Operating Environment for CDI.- Parallel File Systems.- Performance Modeling of Enterprise Grids.- Delay Characteristics of Packet Switched Networks.- Knowledge Discovery in Textual Databases: A Concept-Association Mining Approach.- Mining E-Documents to Uncover Structures.- Designing a Flexible Framework for a Table Abstraction.- Information Quality Framework for Verifiable Intelligence Products.- Interactive Visualization of Large High-Dimensional Datasets.- Image Watermarking Based on Pyramid Decomposition with CH Transform.- Immersive Visualization of Cellular Structures.- Visualization and Ontology of Geospatial Intelligence.- Looking Ahead.