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Management And Processing Of Complex Data Structures
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Book Synopsis Processing and Managing Complex Data for Decision Support by : Darmont, Jrme
Download or read book Processing and Managing Complex Data for Decision Support written by Darmont, Jrme and published by IGI Global. This book was released on 2006-03-31 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides an overall view of the emerging field of complex data processing, highlighting the similarities between the different data, issues and approaches"--Provided by publisher.
Download or read book NBS Special Publication written by and published by . This book was released on 1976 with total page 780 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Algorithms and Data Structures for Massive Datasets by : Dzejla Medjedovic
Download or read book Algorithms and Data Structures for Massive Datasets written by Dzejla Medjedovic and published by Simon and Schuster. This book was released on 2022-08-16 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: Massive modern datasets make traditional data structures and algorithms grind to a halt. This fun and practical guide introduces cutting-edge techniques that can reliably handle even the largest distributed datasets. In Algorithms and Data Structures for Massive Datasets you will learn: Probabilistic sketching data structures for practical problems Choosing the right database engine for your application Evaluating and designing efficient on-disk data structures and algorithms Understanding the algorithmic trade-offs involved in massive-scale systems Deriving basic statistics from streaming data Correctly sampling streaming data Computing percentiles with limited space resources Algorithms and Data Structures for Massive Datasets reveals a toolbox of new methods that are perfect for handling modern big data applications. You’ll explore the novel data structures and algorithms that underpin Google, Facebook, and other enterprise applications that work with truly massive amounts of data. These effective techniques can be applied to any discipline, from finance to text analysis. Graphics, illustrations, and hands-on industry examples make complex ideas practical to implement in your projects—and there’s no mathematical proofs to puzzle over. Work through this one-of-a-kind guide, and you’ll find the sweet spot of saving space without sacrificing your data’s accuracy. About the technology Standard algorithms and data structures may become slow—or fail altogether—when applied to large distributed datasets. Choosing algorithms designed for big data saves time, increases accuracy, and reduces processing cost. This unique book distills cutting-edge research papers into practical techniques for sketching, streaming, and organizing massive datasets on-disk and in the cloud. About the book Algorithms and Data Structures for Massive Datasets introduces processing and analytics techniques for large distributed data. Packed with industry stories and entertaining illustrations, this friendly guide makes even complex concepts easy to understand. You’ll explore real-world examples as you learn to map powerful algorithms like Bloom filters, Count-min sketch, HyperLogLog, and LSM-trees to your own use cases. What's inside Probabilistic sketching data structures Choosing the right database engine Designing efficient on-disk data structures and algorithms Algorithmic tradeoffs in massive-scale systems Computing percentiles with limited space resources About the reader Examples in Python, R, and pseudocode. About the author Dzejla Medjedovic earned her PhD in the Applied Algorithms Lab at Stony Brook University, New York. Emin Tahirovic earned his PhD in biostatistics from University of Pennsylvania. Illustrator Ines Dedovic earned her PhD at the Institute for Imaging and Computer Vision at RWTH Aachen University, Germany. Table of Contents 1 Introduction PART 1 HASH-BASED SKETCHES 2 Review of hash tables and modern hashing 3 Approximate membership: Bloom and quotient filters 4 Frequency estimation and count-min sketch 5 Cardinality estimation and HyperLogLog PART 2 REAL-TIME ANALYTICS 6 Streaming data: Bringing everything together 7 Sampling from data streams 8 Approximate quantiles on data streams PART 3 DATA STRUCTURES FOR DATABASES AND EXTERNAL MEMORY ALGORITHMS 9 Introducing the external memory model 10 Data structures for databases: B-trees, Bε-trees, and LSM-trees 11 External memory sorting
Book Synopsis Data Structures with Go by : Aditya Pratap Bhuyan
Download or read book Data Structures with Go written by Aditya Pratap Bhuyan and published by Aditya Pratap Bhuyan. This book was released on 2024-08-19 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Welcome to "Data Structures with Go: A Comprehensive Guide," your gateway to mastering data structures using the Go programming language. In today’s fast-paced software development world, a solid grasp of data structures is essential for creating efficient, scalable, and high-performance applications. This book provides a thorough exploration of data structures through Go, a language known for its simplicity, performance, and robust concurrency support. Why This Book? Data structures are fundamental to computer science and software engineering. They determine how data is organized, stored, and manipulated, significantly impacting the performance and efficiency of algorithms. With Go’s growing popularity for its clean syntax and effective concurrency model, it is an excellent choice for learning and implementing data structures. This book leverages Go’s features to offer practical insights into data structures, making it a valuable resource for developers of all skill levels. What You Will Learn Fundamentals of Data Structures: The book starts with an introduction to data structures, highlighting their importance and role in software development. You’ll explore basic data types in Go and their applications in various data structures. Arrays and Slices: Delve into arrays and slices, foundational structures in Go. Learn how to declare, initialize, and manipulate them, and understand their performance implications and practical uses. Linked Lists: Explore singly and doubly linked lists, including their structures, operations, and Go implementations. Understand how linked lists compare to arrays and slices and their advantages and limitations. Stacks and Queues: Study these essential linear data structures. Learn about stack (LIFO) and queue (FIFO) operations and their implementations in Go. The chapter also covers variants like deques and priority queues. Trees: Understand hierarchical data structures such as binary trees, binary search trees (BST), AVL trees, and Red-Black trees. Learn about tree operations, traversal techniques, and their Go implementations. Graphs: Learn about graph representations, including adjacency matrices and adjacency lists, and explore directed and undirected graphs. This chapter also covers common algorithms like Depth-First Search (DFS) and Breadth-First Search (BFS). Hashing: Discover hashing techniques, hash tables, and collision handling strategies. Implement hash tables in Go and understand their practical applications. Advanced Data Structures: Dive into specialized data structures such as heaps, tries, suffix trees, and Bloom filters. Learn about their implementations and use cases. Algorithms and Data Structures in Practice: Apply data structures to real-world problems. This chapter focuses on sorting and searching algorithms, optimization techniques, and performance profiling in Go. Real-World Applications: Explore how data structures are used in practical projects. Study case studies, best practices, and design patterns for implementing data structures in Go-based systems. Who Should Read This Book? This book caters to: Beginners: Those new to Go or data structures will find a clear, structured introduction. Intermediate Developers: Readers with some experience can deepen their knowledge and tackle advanced topics. Experienced Professionals: Those looking to explore Go or stay updated with modern practices will find valuable insights and practical examples. Learning Approach Emphasizing hands-on learning, the book includes practical examples, exercises, and real-world case studies to reinforce understanding and encourage experimentation. By working through these exercises, you will gain practical experience and a deeper grasp of data structures in Go. "Data Structures with Go: A Comprehensive Guide" is your key to mastering essential computer science principles and applying them effectively in modern applications. Dive in and discover how Go can enhance your skills in building robust, efficient, and scalable systems. Aditya
Book Synopsis Handbook of Data Structures and Applications by : Dinesh P. Mehta
Download or read book Handbook of Data Structures and Applications written by Dinesh P. Mehta and published by Taylor & Francis. This book was released on 2018-02-21 with total page 1120 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Data Structures and Applications was first published over a decade ago. This second edition aims to update the first by focusing on areas of research in data structures that have seen significant progress. While the discipline of data structures has not matured as rapidly as other areas of computer science, the book aims to update those areas that have seen advances. Retaining the seven-part structure of the first edition, the handbook begins with a review of introductory material, followed by a discussion of well-known classes of data structures, Priority Queues, Dictionary Structures, and Multidimensional structures. The editors next analyze miscellaneous data structures, which are well-known structures that elude easy classification. The book then addresses mechanisms and tools that were developed to facilitate the use of data structures in real programs. It concludes with an examination of the applications of data structures. Four new chapters have been added on Bloom Filters, Binary Decision Diagrams, Data Structures for Cheminformatics, and Data Structures for Big Data Stores, and updates have been made to other chapters that appeared in the first edition. The Handbook is invaluable for suggesting new ideas for research in data structures, and for revealing application contexts in which they can be deployed. Practitioners devising algorithms will gain insight into organizing data, allowing them to solve algorithmic problems more efficiently.
Book Synopsis Data Structures And Algorithms by : Shi-kuo Chang
Download or read book Data Structures And Algorithms written by Shi-kuo Chang and published by World Scientific. This book was released on 2003-09-29 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is an excellent, up-to-date and easy-to-use text on data structures and algorithms that is intended for undergraduates in computer science and information science. The thirteen chapters, written by an international group of experienced teachers, cover the fundamental concepts of algorithms and most of the important data structures as well as the concept of interface design. The book contains many examples and diagrams. Whenever appropriate, program codes are included to facilitate learning.This book is supported by an international group of authors who are experts on data structures and algorithms, through its website at www.cs.pitt.edu/~jung/GrowingBook/, so that both teachers and students can benefit from their expertise.
Download or read book NBS Monograph written by and published by . This book was released on 1970 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book DAMA-DMBOK written by Dama International and published by . This book was released on 2017 with total page 628 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Management written by and published by . This book was released on 1986 with total page 736 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Official Gazette of the United States Patent and Trademark Office by : United States. Patent and Trademark Office
Download or read book Official Gazette of the United States Patent and Trademark Office written by United States. Patent and Trademark Office and published by . This book was released on 1998 with total page 1128 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Official Gazette of the United States Patent and Trademark Office by :
Download or read book Official Gazette of the United States Patent and Trademark Office written by and published by . This book was released on 1998 with total page 1112 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Global ocean science report by : UNESCO
Download or read book Global ocean science report written by UNESCO and published by UNESCO Publishing. This book was released on 2017-07-04 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Global Ocean Science Report (GOSR) assesses for the first time the status and trends in ocean science capacity around the world. The report offers a global record of how, where, and by whom ocean science is conducted: generating knowledge, helping to protect ocean health, and empowering society to support sustainable ocean management in the framework of the United Nations 2030 Agenda. The GOSR identifies and quantifies the key elements of ocean science at the national, regional and global scales, including workforce, infrastructure and publications. It is the first collective attempt to systematically highlight opportunities as well as capacity gaps to advance international collaboration in ocean science and technology. This report is a resource for policy-makers, academics and other stakeholders seeking to harness the potential of ocean science to address global challenges. A comprehensive view of ocean science capacities at the national and global levels takes us closer to developing the global ocean science knowledge needed to ensure a healthy, sustainable ocean.
Book Synopsis Classification and Multivariate Analysis for Complex Data Structures by : Bernard Fichet
Download or read book Classification and Multivariate Analysis for Complex Data Structures written by Bernard Fichet and published by Springer Science & Business Media. This book was released on 2011-03-04 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: The growing capabilities in generating and collecting data has risen an urgent need of new techniques and tools in order to analyze, classify and summarize statistical information, as well as to discover and characterize trends, and to automatically bag anomalies. This volume provides the latest advances in data analysis methods for multidimensional data which can present a complex structure: The book offers a selection of papers presented at the first Joint Meeting of the Société Francophone de Classification and the Classification and Data Analysis Group of the Italian Statistical Society. Special attention is paid to new methodological contributions from both the theoretical and the applicative point of views, in the fields of Clustering, Classification, Time Series Analysis, Multidimensional Data Analysis, Knowledge Discovery from Large Datasets, Spatial Statistics.
Book Synopsis Database and Expert Systems Applications by : Sourav S. Bhowmick
Download or read book Database and Expert Systems Applications written by Sourav S. Bhowmick and published by Springer. This book was released on 2009-08-25 with total page 890 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 20th International Conference on Database and Expert Systems Applications, DEXA 2009, held in Linz, Austria, in August/September 2009. The 35 revised full papers and 35 short papers presented were carefully reviewed and selected from 202 submissions. The papers are organized in topical sections on XML and databases; Web, semantics and ontologies; temporal, spatial, and high dimensional databases; database and information system architecture, performance and security; query processing and optimisation; data and information integration and quality; data and information streams; data mining algorithms; data and information modelling; information retrieval and database systems; and database and information system architecture and performance.
Book Synopsis Secure Semantic Service-Oriented Systems by : Bhavani Thuraisingham
Download or read book Secure Semantic Service-Oriented Systems written by Bhavani Thuraisingham and published by CRC Press. This book was released on 2010-12-14 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the demand for data and information management continues to grow, so does the need to maintain and improve the security of databases, applications, and information systems. In order to effectively protect this data against evolving threats, an up-to-date understanding of the mechanisms for securing semantic Web technologies is essential. Reviewi
Book Synopsis Frontiers in Massive Data Analysis by : National Research Council
Download or read book Frontiers in Massive Data Analysis written by National Research Council and published by National Academies Press. This book was released on 2013-09-03 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.