Foundations of data organization is a relatively new field of research in comparison to, other branches of science. It is close to twenty years old. In this short life span of this branch of computer science, it has spread to all corners of the world, which is reflected in this book. This book covers new database application areas (databases for advanced applications and CAD/VLSI databases), computational geometry, file allocation & distributed databases, database models (including non traditional database models), database machines, query processing & physical structures for relational databases, besides traditional file organization (hashing, index file organization, mathematical file organization and consecutive retrieval property), in order to identify new trends of database research. The papers in this book originally represent talks given at the International Conference on Foundations of Data Organization, which was held on May 21-24, 1985, in Kyoto, Japan. This conference was held at Kyoto University, and sponsored by the organizing committee of the International Conference on Foundations of Data Organization and the Japan Society for the Promotion of Science. The conference was in cooperation with: ACM SIGMOD, IEEE Computer Society, Information Processing Society of Japan, IBM Research, Kyushu University, Kobe University, IBM Japan, Kyoto Sangyo University and Polish Academy of Sciences. This Conference was the follow-up of the first conference, which was hosted by the Polish Academy of Sciences and held at Warsaw in 1981. The Warsaw conference focused mainly on consecutive retrieval property and it's applications.

The Third International Conference on Foundations of Data Organization and Algorithms has been organized by INRIA in Paris from June 21 to 23, 1989. Previous FODO Conferences were held in Warsaw, 1981, and in Kyoto, 1985. The goal of this year's conference is to present advances in techniques of permanent and temporary data organization in different fields. New applications such as image processing, graphics, geographic data processing, robotics, office automation, information systems, language translation, and expert systems have developed various data organizations and algorithms specific to the application requirements. The growing importance of these applications has created a need for general studies on data organization and algorithms as well as for specific studies on new database management systems and on filing services. The articles submitted for the conference were subject to the usual rigorous reviewing process and selected on that basis. They offer an excellent snapshot of the state of the art in the field and should prove invaluable for computer scientists faced by the problems of data organization which are raised by these new applications.

This volume presents the proceedings of the Fourth International Conference on Data Organization and Algorithms, FODO '93, held in Evanston, Illinois. FODO '93 reflects the maturing of the database field which hasbeen driven by the enormous growth in the range of applications for databasesystems. The "non-standard" applications of the not-so-distant past, such ashypertext, multimedia, and scientific and engineering databases, now provide some of the central motivation for the advances in hardware technology and data organizations and algorithms. The volume contains 3 invited talks, 22 contributed papers, and 2 panel papers. The contributed papers are grouped into parts on multimedia, access methods, text processing, query processing, industrial applications, physical storage, andnew directions.

This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.

Information Organization and Databases: Foundations of Data Organization provides recent developments of information organization technologies that have become crucial not only for data mining applications and information visualization, but also for treatment of semistructured data, spatio-temporal data and multimedia data that are not necessarily stored in conventional DBMSs. Information Organization and Databases: Foundations of Data Organization presents: semistructured data addressing XML, query languages and integrity constraints, focusing on advanced technologies for organizing web data for effective retrieval; multimedia database organization emphasizing video data organization and data structures for similarity retrieval; technologies for data mining and data warehousing; index organization and efficient query processing issues; spatial data access and indexing; organizing and retrieval of WWW and hypermedia. Information Organization and Databases: Foundations of Data Organization is a resource for database practitioners, database researchers, designers and administrators of multimedia information systems, and graduate-level students in the area of information retrieval and/or databases wishing to keep abreast of advances in the information organization technologies.

This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations. The book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data. The contributions are grouped into four parts: · Part I: Ecosystem Elements of Big Data Value focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders. · Part II: Research and Innovation Elements of Big Data Value details the key technical and capability challenges to be addressed for delivering big data value. · Part III: Business, Policy, and Societal Elements of Big Data Value investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society. · Part IV: Emerging Elements of Big Data Value explores the critical elements to maximizing the future potential of big data value. Overall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems. The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation.