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Author: Sakti P. Ghosh
Publisher: Springer Science & Business Media
ISBN: 1461318815
Category : Science
Languages : en
Pages : 615
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Book Description
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.
Author: Sakti P. Ghosh
Publisher: Springer Science & Business Media
ISBN: 1461318815
Category : Science
Languages : en
Pages : 615
Get Book Here
Book Description
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.
Author: David B. Lomet
Publisher: Springer Science & Business Media
ISBN: 9783540573012
Category : Computers
Languages : en
Pages : 430
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Book Description
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.
Author: Avrim Blum
Publisher: Cambridge University Press
ISBN: 1108617360
Category : Computers
Languages : en
Pages : 433
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Book Description
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.
Author: Edward Curry
Publisher: Springer Nature
ISBN: 3030681769
Category : Computers
Languages : en
Pages : 399
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Book Description
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.
Author: John W. Foreman
Publisher: John Wiley & Sons
ISBN: 1118839862
Category : Business & Economics
Languages : en
Pages : 432
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Book Description
Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the "data scientist," toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know.
Author: Lena Wiese
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110433079
Category : Computers
Languages : en
Pages : 468
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Book Description
Advanced data management has always been at the core of efficient database and information systems. Recent trends like big data and cloud computing have aggravated the need for sophisticated and flexible data storage and processing solutions. This book provides a comprehensive coverage of the principles of data management developed in the last decades with a focus on data structures and query languages. It treats a wealth of different data models and surveys the foundations of structuring, processing, storing and querying data according these models. Starting off with the topic of database design, it further discusses weaknesses of the relational data model, and then proceeds to convey the basics of graph data, tree-structured XML data, key-value pairs and nested, semi-structured JSON data, columnar and record-oriented data as well as object-oriented data. The final chapters round the book off with an analysis of fragmentation, replication and consistency strategies for data management in distributed databases as well as recommendations for handling polyglot persistence in multi-model databases and multi-database architectures. While primarily geared towards students of Master-level courses in Computer Science and related areas, this book may also be of benefit to practitioners looking for a reference book on data modeling and query processing. It provides both theoretical depth and a concise treatment of open source technologies currently on the market.
Author: Dama International
Publisher:
ISBN: 9781634622349
Category : Database management
Languages : en
Pages : 628
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Book Description
Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals. DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties; The value of data can be and should be expressed in economic terms; Managing data means managing the quality of data; It takes metadata to manage data; It takes planning to manage data; Data management is cross-functional and requires a range of skills and expertise; Data management requires an enterprise perspective; Data management must account for a range of perspectives; Data management is data lifecycle management; Different types of data have different lifecycle requirements; Managing data includes managing risks associated with data; Data management requirements must drive information technology decisions; Effective data management requires leadership commitment.
Author: Elaine Svenonius
Publisher: MIT Press
ISBN: 9780262194334
Category : Computers
Languages : en
Pages : 282
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Book Description
Integrating the disparate disciplines of descriptive cataloging, subject cataloging, indexing, and classification, the book adopts a conceptual framework that views the process of organizing information as the use of a special language of description called a bibliographic language.
Author: Alan Agresti
Publisher: CRC Press
ISBN: 1000462919
Category : Business & Economics
Languages : en
Pages : 486
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Book Description
Foundations of Statistics for Data Scientists: With R and Python is designed as a textbook for a one- or two-term introduction to mathematical statistics for students training to become data scientists. It is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modeling. The book assumes knowledge of basic calculus, so the presentation can focus on "why it works" as well as "how to do it." Compared to traditional "mathematical statistics" textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python. The book also introduces modern topics that do not normally appear in mathematical statistics texts but are highly relevant for data scientists, such as Bayesian inference, generalized linear models for non-normal responses (e.g., logistic regression and Poisson loglinear models), and regularized model fitting. The nearly 500 exercises are grouped into "Data Analysis and Applications" and "Methods and Concepts." Appendices introduce R and Python and contain solutions for odd-numbered exercises. The book's website has expanded R, Python, and Matlab appendices and all data sets from the examples and exercises.
Author: S. Sumathi
Publisher: Springer
ISBN: 3540483993
Category : Computers
Languages : en
Pages : 793
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Book Description
This book provides comprehensive coverage of fundamentals of database management system. It contains a detailed description on Relational Database Management System Concepts. There are a variety of solved examples and review questions with solutions. This book is for those who require a better understanding of relational data modeling, its purpose, its nature, and the standards used in creating relational data model.
