The World Wide Web has enabled the creation of a global information space comprising linked documents. As the Web becomes ever more enmeshed with our daily lives, there is a growing desire for direct access to raw data not currently available on the Web or bound up in hypertext documents. Linked Data provides a publishing paradigm in which not only documents, but also data, can be a first class citizen of the Web, thereby enabling the extension ...
Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges – from investment timing to drug discovery, and fraud detection to recommendation systems – where predictive accuracy is more vital than model interpretability. Ensemble ...
Designing Geodatabases for Transportation addresses the construction of a GIS to manage data describing the transportation facilities and services commonly organized around various modes of travel. Although details of each mode can be quite different, this book demonstrates how all modes of travel follow a basic conceptual structure consisting of an origin, a destination, a path between the two, and a conveyance that provides the abillity to mov ...
Linked Data (LD) is a well-established standard for publishing and managing structured information on the Web, gathering and bridging together knowledge from different scientific and commercial domains. The development of Linked Data Visualization techniques and tools has been followed as the primary means for the analysis of this vast amount of information by data scientists, domain experts, business users, and citizens. This book covers a w ...
Data is an increasingly important business asset and enabler for organisational activities. Data quality is a key aspect of data management and failure to understand it increases organisational risk and decreases efficiency and profitability. This book explains data quality management in practical terms, focusing on three key areas – the nature of data in enterprises, the purpose and scope of data quality management, and implementing a ...
Data quality is one of the most important problems in data management, since dirty data often leads to inaccurate data analytics results and incorrect business decisions. Poor data across businesses and the U.S. government are reported to cost trillions of dollars a year. Multiple surveys show that dirty data is the most common barrier faced by data scientists. Not surprisingly, developing effective and efficient data cleaning solutions is ch ...
