Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsis
Pattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types
Offering a self-contained introduction to data mining, this book presents the concepts, models and techniques for data mining in a well organized style. Both theoretical concepts and, especially, prac
Temporal and spatiotemporal data form an inherent fabric of the society as we are faced with streams of data coming from numerous sensors, data feeds, recordings associated with numerous areas of appl
With the proliferation of social media and on-line communities in networked world a large gamut of data has been collected and stored in databases. The rate at which such data is stored is growing at
This book presents a study in knowledge discovery in data with knowledge understood as a set of relations among objects and their properties. Relations in this case are implicative decision rules and
Data mining is the art and science of intelligent data analysis. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that aboun
This book covers important aspects of fundamental research in data provenance and data management(DPDM), including provenance representation and querying, as well as practical applications in such dom
This book provides a comprehensive picture of mobile big data starting from data sources to mobile data driven applications. Mobile Big Data comprises two main components: an overview of mobile
This volume presents analyses of data protection systems and of 26 jurisdictions with data protection legislation in Africa, as well as additional selected countries without comprehensive data protect
This book introduces the basic aspects of applied mathematics. It also covers such topics as information data manipulation, information coding, data approximation, data dimensionality reduction, data
Web mining aims to discover useful information and knowledge from Web hyperlinks, page contents, and usage data. Although Web mining uses many conventional data mining techniques, it is not purely an
This proceedings volume introduces recent work on the storage, retrieval and visualization of spatial Big Data, data-intensive geospatial computing and related data quality issues. Further, it address
This book highlights the use of historical data in natural hazard assessments. Different types of data, such as historical data from written documents, are linked to technically measured data such
Visualizing the data is an essential part of any data analysis. Modern computing developments have led to big improvements in graphic capabilities and there are many new possibilities for data display
This edited book focuses on the latest developments in classification, statistical learning, data analysis and related areas of data science, including statistical analysis of large datasets, big data
Owing to continuous advances in the computational power of handheld devices like smartphones and tablet computers, it has become possible to perform Big Data operations including modern data mining pr
Missing data have long plagued those conducting applied research in the social, behavioral, and health sciences. Good missing data analysis solutions are available, but practical information about im
Satellite Data Compression covers recent progress in compression techniques for multispectral, hyperspectral and ultra spectral data. A survey of recent advances in the fields of satellite communicati
This Springer Brief provides a comprehensive overview of the background and recent developments of big data. The value chain of big data is divided into four phases: data generation, data acquisition,
Data matching (also known as record or data linkage, entity resolution, object identification, or field matching) is the task of identifying, matching and merging records that correspond to the same e
This textbook provides a comprehensive introduction to the concepts and idea of multisensor data fusion. It is an extensively revised second edition of the author's successful book: "Multi-Sensor Data
The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer s
Investors and technology gurus have called big data one of the most important trends to come along in decades.Big Data Bootcamp explains what big data is and how you can use it in your company to beco
This book presents the most recent methods for analyzing and visualizing symbolic data. It generalizes classical methods of exploratory, statistical and graphical data analysis to the case of complex
This Data Handbook is a updated and largely extended new edition of the book "Semiconductors: Basic Data". The data of the former edition have been updated and a complete representation of all relevan
Web mining aims to discover useful information and knowledge from Web hyperlinks, page contents, and usage data. Although Web mining uses many conventional data mining techniques, it is not purely an
This illuminating text/reference surveys the state of the art in data science, and provides practical guidance on big data analytics. Expert perspectives are provided by authoritative researchers and
Data uncertainty is a concept closely related with most real life applications that involve data collection and interpretation. Examples can be found in data acquired with biomedical instruments or ot
Understanding sequence data, and the ability to utilize this hidden knowledge, will create a significant impact on many aspects of our society. Examples of sequence data include DNA, protein, customer
This book provides a practical introduction to analyzing ecological data using real data sets. The first part gives a largely non-mathematical introduction to data exploration, univariate methods (inc
Applied data-centric social sciences aim to develop both methodology and practical applications of various fields of social sciences and businesses with rich data. Specifically, in the social sciences
This book offers guidance for US-based IT businesses on both sides of the Atlantic when dealing with big data and government data, since transatlantic data flows are key to the success of these enterp
“We live in the age of data. In the last few years, the methodology of extracting insights from data or "data science" has emerged as a discipline in its own right. The R programming language has beco
Congestion Control in Data Transmission Networks details the modeling and control of data traffic in communication networks. It shows how various networking phenomena can be represented in a consisten
Data assimilation is a novel, versatile methodology for estimating atmospheric and oceanic variables. The estimation of a quantity of interest via data assimilation involves the combination of observa
This volume of Astrophysical Data deals with Planets and Stars; a second volume, Part II, will give data for Galaxies and the Universe. They both pro- vide basic data for use by all scientists, from t
This book considers two popular topics: fault detection and isolation (FDI) and flight data estimation using flush air data sensing (FADS) systems. Literature surveys, comparison tests, simulations an
Compared with data from general application domains, modern biological data has many unique characteristics. Biological data are often characterized as having large volumes, complex structures, high d
Data-intensive systems are a technological building block supporting Big Data and Data Science applications.This book familiarizes readers with core concepts that they should be aware of before contin
