Publisher's Synopsis
Automated data processing is the creation and implementation of technology that automatically processes data. This technology includes computers and other communications electronics that can gather, store, manipulate, prepare and distribute data. The text Management of Automatic Data Processing Systems focuses on automatic data integration, automated learning techniques, advancements in semiconductor technology, web-based data management system, and data processing technologies. In first chapter, we employ Chen's high-level fault model in the high-level automatic test pattern generation (ATPG). Second chapter describes some of the major issues in automatic text classification and provides a brief pointer to related work done in dealing with these issues. Third chapter proposes a new look at data integration by using complex adaptive systems principles to analyze current shortcomings and propose a direction that may lead to a data integration theory. Fourth chapter presents LinksB2N, an algorithm for discovering information overlaps in RDF data repositories and performing data integration with no human intervention over data sets that partially share the same domain. In fifth chapter, we describe a new research project that addresses the problem of mapping data across institutions. In sixth chapter, we develop the data science machine, which is able to derive predictive models from raw data automatically. Seventh chapter reviews and summarizes INDOT's current standards and practices for pavement condition data collection methods and the contractor quality control practices. A web-based data management system for multi-center studies has been presented in eighth chapter. Last chapter focuses on data mining the data processing technologies for inventory management.
