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From Data to Models and Back ; 9th International Symposium, DataMod 2020, Virtual Event, October 20, 2020, Revised Selected Papers
This book constitutes the refereed proceedings of the 9th International Symposium on From Data Models and Back, DataMod 2020, held virtually, in October 2020. The 11 full papers and 3 short papers presented in this book were selected from 19 submissions. The papers are grouped in these topical sections: machine learning; simulation-based approaches, and data mining and processing related approaches.
Formal concept analysis ; Vol. 3874 ; 4th International Conference, ICFCA 2006, Dresden, Germany, Feburary 13-17, 2006, Proceedings
This book constitutes the refereed proceedings of the 4th International Conference on Formal Concept Analysis, held in February 2006. The 17 revised full papers presented together with four invited papers were carefully reviewed and selected for inclusion in the book. The papers show advances in applied lattice and order theory and in particular scientific advances related to formal concept analysis and its practical applications: data and knowledge processing including data visualization, information retrieval, machine learning, data analysis and knowledge management.
Formal concept analysis ; Vol. 3403 ; 3rd International Conference, ICFCA 2005, Lens, France, February 14-18, 2005, Proceedings
This book constitutes a comprehensive and systematic presentation of the state of the art of formal concept analysis and its applications. The first part of the book is devoted to foundational and methodological topics. The contributions in the second part demonstrate how formal concept analysis is successfully used outside of mathematics, in linguistics, text retrieval, association rule mining, data analysis, and economics. The third part presents applications in software engineering.
Formal Concept Analysis ; 5th International Conference, ICFCA 2007, Clermont-Ferrand, France, February 12-16, 2007, Proceedings
This book constitutes the refereed proceedings of the 5th International Conference on Formal Concept Analysis, ICFCA 2007. The papers comprise state of the art research from foundational to applied lattice theory and related fields, all of which involve methods and techniques of formal concept analysis such as data visualization, information retrieval, machine learning, data analysis and knowledge management.
Federation over the Web ; International Workshop, Dagstuhl Castle, Germany, May 1-6, 2005, Revised Selected Papers
The lives of people all around the world, especially in industrialized nations, continue to be changed by the presence and growth of the Internet. Its in?uence is felt at scales ranging from private lifestyles to national economies, boosting thepaceatwhichmoderninformationandcommunicationtechnologiesin?uence personal choices along with business processes and scienti?c endeavors. In addition to its billions of HTML pages, the Web can now be seen as an open repository of computing resources. These resources provide access to computational services as well as data repositories, through a rapidly growing variety of Web applications and Web services.
Fast Track to MDX
Fast Track to MDX gives you all the necessary background to let you to write useful, powerful MDX expressions and introduces the most frequently used MDX functions and constructs. No prior knowledge is assumed and examples are used throughout the book to rapidly develop your MDX skills to the point where you can solve real business problems.
Exploratory Analysis of Spatial and Temporal Data : A Systematic Approach
Exploratory data analysis (EDA) is about detecting and describing patterns, trends, and relations in data, motivated by certain purposes of investigation. As something relevant is detected in data, new questions arise, causing specific parts to be viewed in more detail. So EDA has a significant appeal: it involves hypothesis generation rather than mere hypothesis testing.
Evolving Connectionist Systems : The Knowledge Engineering Approach
Evolving Connectionist Systems is aimed at all those interested in developing and using intelligent computational models and systems to solve challenging real world problems in computer science, engineering, bioinformatics and neuroinformatics. The book challenges scientists and practitioners with open questions about future creation of new information models inspired by Nature. This edition includes new methods for adaptive, knowledge-based learning, such as online incremental feature selection, spiking neural networks, transductive neuro-fuzzy inference, adaptive data and model integration, cellular automata and artificial life systems, particle swarm optimisation, ensembles of evolving systems, and quantum inspired neural networks. New applications to gene and protein interaction modelling, brain data analysis and brain model creation, computational neuro-genetic modelling, adaptive speech, image and multimodal recognition, language modelling, adaptive robotics, modelling dynamic financial and socio-economic systems, and ecological modelling, are covered. An important new feature of the book is the attempt to connect different structural and functional levels of a complex, intelligent system, looking for inspiration from functional relationships in natural systems, such as the genetic and the brain activity.
Emerging Technologies in Knowledge Discovery and Data Mining ; PAKDD 2007 International Workshops, Nanjing, China, May 22-25, 2007, Revised Selected Papers
The objective of this volume is to offer the excellent presentations to the public, and to promote the study exchange among researchers worldwide.The first part of this volume contains industrial track. This track was organized to attract papers on new technology trends and real-world solutions in different industry sectors. The succeeding chapters include Data Mining for Biomedical Applications aimed at attracting top researchers, practitioners and students from around the world to discuss data mining applications in the field of bioinformatics
EEG signal processing for biomedical applications
Focuses on electroencephalography (EEG) signal processing in biomedical engineering applications. EEG signals are used widely in clinical and research settings to provide cognitive and emotional state information. In addition to capturing complex neural patterns at high speeds, EEG signals are a reliable and non-invasive way of measuring the electrical activity in the brain. By examining various novel analysis and signal processing methods, this collection of papers provides a better understanding of cognitive states and brain activity.
Economic Analysis of Information System Investment in Banking Industry
Explains in reahty, examines theoretically, and analyzes statistically information system investment in the banking industry with regard to the process of the information technology revolution. This kind of comprehensive research on the banking industry is the first in the world. It could be seen as an application study for Japanese financial deregulation after 1997. However, our project, the Workshop of Information System Investment, is a theoretical research venture, consisting originally, when it began in 1994, of economists and computer scientists. It aimed to measure the effect of com puter hardware and software on the modern economy, based on the microdata of each firm, and to extend the frontiers of economic science. It was, coin- dentally, the time when this project began full-scale operation, in July 1997, that the voluntary closure of Yamaichi Securities was decided. The failure of the Hokkaido Takushoku Bank was disclosed in November of the same year, and the breakdown, temporary nationalization, buying out, and mergers of several banks succeeded one another. Our research therefore suddenly got into the social spotlight on the application stage. Part I is the first history and strategic guidelines of information systems in the banking industry. Part II summarizes the economic analyses of informa tion system investment in the United States, Europe, and Japan. These parts are foundations for the statistical analyses in Part III.
Discovery science ; Vol. 3735 ; 8th International Conference, DS 2005, Singapore, October 8-11, 2005, Proceedings
This book constitutes the refereed proceedings of the 8th International Conference on Discovery Science, DS 2005, held in Singapore in October 2005, co-located with the International Conference on Algorithmic Learning Theory (ALT 2005). The 21 revised long papers and the 6 revised regular papers presented together with 9 project reports and 5 invited papers were carefully reviewed and selected from 112 submissions. The papers cover all issues in the area of automating scientific discovery or working on tools for supporting the human process of discovery in science.
Discovery Science ; 11th International Conference, DS 2008, Budapest, Hungary, October 13-16, 2008. Proceedings
This book constitutes the refereed proceedings of the 11th International Conference on Discovery Science, DS 2008, held in Budapest, Hungary, in October 2008, co-located with the 19th International Conference on Algorithmic Learning Theory, ALT 2008.
Detection of intrusions and malware, and vulnerability assessment ; 3rd International Conference, DIMVA 2006, Berlin, Germany, July 13-14, 2006, Proceedings
This book constitutes the refereed proceedings of the Third International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, DIMVA 2006, held in Berlin, Germany in July 2006.The 11 revised full papers presented were carefully reviewed and selected from 41 submissions.
Designing big data platforms : How to use, deploy, and maintain big data systems
Provides expert guidance and valuable insights on getting the most out of Big Data systems. Helps readers understand how to process large amounts of data with well-known Linux tools and database solutions, use effective techniques to collect and manage data from multiple sources, transform data into meaningful business insights, and much more. Author Yusuf Aytas, a software engineer with a vast amount of big data experience, discusses the design of the ideal Big Data platform: one that meets the needs of data analysts, data engineers, data scientists, software engineers, and a spectrum of other stakeholders across an organization. Detailed yet accessible chapters cover key topics such as stream data processing, data analytics, data science, data discovery, and data security. This real-world manual for Big Data technologies: Provides up-to-date coverage of the tools currently used in Big Data processing and management / Offers step-by-step guidance on building a data pipeline, from basic scripting to distributed systems / Highlights and explains how data is processed at scale / Includes an introduction to the foundation of a modern data platform
Data Warehousing and Knowledge Discovery ; 9th International Conference, DaWaK 2007, Regensburg, Germany, September 3-7, 2007, Proceedings
Data Warehousing and Knowledge Discovery have been widely accepted as key te- nologies for enterprises and organizations to improve their abilities in data analysis, decision support, and the automatic extraction of knowledge from data. With the exponentially growing amount of information to be included in the decision-making process, the data to be processed become more and more complex in both structure and semantics. Consequently, the process of retrieval and knowledge discovery from this huge amount of heterogeneous complex data constitutes the reality check for research in the area.
Data Warehousing and Knowledge Discovery ; 4th International Conference, DaWaK 2002, Aix-en-Provence, France, September 4-6, 2002. Proceedings
Within the last few years Data Warehousing and Knowledge Discovery technology has established itself as a key technology for enterprises that wish to improve the quality of the results obtained from data analysis, decision support, and the automatic extraction of knowledge from data. The Fourth International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2002) continues a series of successful conferences dedicated to this topic. Its main objective is to bring together researchers and practitioners to discuss research issues and experience in developing and deploying data warehousing and knowledge discovery systems, applications, and solutions.
Data Warehousing and Data Mining Techniques for Cyber Security
It provide techniques for collecting information from distributed databases and for performing data analysis. The ever expanding, tremendous amount of data collected and stored in large databases has far exceeded our human ability to comprehend--without the proper tools. There is a critical need for data analysis that can automatically analyze data, summarize it and predict future trends. In the modern age of Internet connectivity, concerns about denial of service attacks, computer viruses and worms are extremely important. Data Warehousing and Data Mining Techniques for Cyber Security contributes to the discipline of security informatics. The author discusses topics that intersect cyber security and data mining, while providing techniques for improving cyber security. Since the cost of information processing and internet accessibility is dropping, an increasing number of organizations are becoming vulnerable to cyber attacks. This volume introduces techniques for applications in the area of retail, finance, and bioinformatics, to name a few.
Data science in theory and practice : Techniques for big data analytics and complex data sets
Delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services. Written in five parts, the book examines some of the most commonly used and fundamental mathematical and statistical concepts that form the basis of data science. The authors go on to analyze various data transformation techniques useful for extracting information from raw data, long memory behavior, and predictive modeling. Readers will also learn from topics like: Analyses of foundational theoretical subjects, including the history of data science, matrix algebra and random vectors, and multivariate analysis A comprehensive examination of time series forecasting, including the different components of time series and transformations to achieve stationarity Introductions to both the R and Python programming languages, including basic data types and sample manipulations for both languages An exploration of algorithms, including how to write one and how to perform an asymptotic analysis A comprehensive discussion of several techniques for analyzing and predicting complex data sets
Data Quality : Concepts, Methodologies and Techniques
Batini and Scannapieco present a comprehensive and systematic introduction to the wide set of issues related to data quality. They start with a detailed description of different data quality dimensions, like accuracy, completeness, and consistency, and their importance in different types of data, like federated data, web data, or time-dependent data, and in different data categories classified according to frequency of change, like stable, long-term, and frequently changing data. The book's extensive description of techniques and methodologies from core data quality research as well as from related fields like data mining, probability theory, statistical data analysis, and machine learning gives an excellent overview of the current state of the art.
