Download or read book Scientific and Statistical Database Management written by Michael Gertz and published by Springer Science & Business Media. This book was released on 2010-06-17 with total page 673 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 22nd International Conference on Scientific and Statistical Database Management, SSDBM 2010, held in Heidelberg, Germany in June/July 2010. The 30 long and 11 short papers presented were carefully reviewed and selected from 94 submissions. The topics covered are query processing; scientific data management and analysis; data mining; indexes and data representation; scientific workflow and provenance; and data stream processing.
Download or read book Scientific and Statistical Database Management written by Bertram Ludäscher and published by Springer. This book was released on 2008-07-11 with total page 631 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 20th International Conference on Scientific and Statistical Database Management, SSDBM 2008, held in Hong Kong, China, in July 2008. The 28 revised full papers, 7 revised short papers and 8 poster and demo papers presented together with 3 invited talks were carefully reviewed and selected from 84 submissions. The papers are organized in topical sections on query optimization in scientific databases, privacy, searching and mining graphs, data streams, scientific database applications, advanced indexing methods, data mining, as well as advanced queries and uncertain data.
Download or read book Proceedings of International Conference on Recent Advancement on Computer and Communication written by Basant Tiwari and published by Springer. This book was released on 2018-04-18 with total page 657 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book is a compilation of best papers presented at International Conference on Recent Advancement in Computer and Communication (ICRAC 2017) organized by IMPLab Research and Innovation Foundation, Bhopal, India. The book covers all aspects of computers and communication techniques including pervasive computing, distributed computing, cloud computing, sensor and adhoc network, image, text and speech processing, pattern recognition and pattern analysis, digital signal processing, digital electronics, telecommunication technologies, robotics, VLSI technologies, embedded system, satellite communication, digital signal processing, and digital communication. The papers included are original research works of experts from industry, government centers and academic institutions; experienced in engineering, design and research.
Download or read book Practical Statistics for Data Scientists written by Peter Bruce and published by O'Reilly Media. This book was released on 2020-04-10 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher-quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that "learn" from data Unsupervised learning methods for extracting meaning from unlabeled data
Download or read book Managing and Mining Uncertain Data written by Charu C. Aggarwal and published by Springer Science & Business Media. This book was released on 2010-07-08 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: Managing and Mining Uncertain Data, a survey with chapters by a variety of well known researchers in the data mining field, presents the most recent models, algorithms, and applications in the uncertain data mining field in a structured and concise way. This book is organized to make it more accessible to applications-driven practitioners for solving real problems. Also, given the lack of structurally organized information on this topic, Managing and Mining Uncertain Data provides insights which are not easily accessible elsewhere. Managing and Mining Uncertain Data is designed for a professional audience composed of researchers and practitioners in industry. This book is also suitable as a reference book for advanced-level students in computer science and engineering, as well as the ACM, IEEE, SIAM, INFORMS and AAAI Society groups.
Download or read book Data Analysis in Management with SPSS Software written by J.P. Verma and published by Springer Science & Business Media. This book was released on 2012-12-13 with total page 499 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides readers with a greater understanding of a variety of statistical techniques along with the procedure to use the most popular statistical software package SPSS. It strengthens the intuitive understanding of the material, thereby increasing the ability to successfully analyze data in the future. The book provides more control in the analysis of data so that readers can apply the techniques to a broader spectrum of research problems. This book focuses on providing readers with the knowledge and skills needed to carry out research in management, humanities, social and behavioural sciences by using SPSS.
Download or read book Scientific and Statistical Database Management written by Judith Bayard Cushing and published by Springer. This book was released on 2011-07-01 with total page 618 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 23rd International Conference on Scientific and Statistical Database Management, SSDBM 2011, held in Portland, OR, USA, in July 2011. The 26 long and 12 short papers presented together with 15 posters were carefully reviewed and selected from 80 submissions. The topics covered are ranked search; temporal data and queries; workflow and provenance; querying graphs; clustering and data mining; architectures and privacy; and applications and models.
Download or read book Machine Learning for Data Science Handbook written by Lior Rokach and published by Springer Nature. This book was released on 2023-08-17 with total page 975 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book organizes key concepts, theories, standards, methodologies, trends, challenges and applications of data mining and knowledge discovery in databases. It first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. It also gives in-depth descriptions of data mining applications in various interdisciplinary industries.
Download or read book Scientific and Statistical Database Management written by Marianne Winslett and published by Springer Science & Business Media. This book was released on 2009-05-22 with total page 659 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 21st International Conference on Scientific and Statistical Database Management, SSDBM 2009, held in New Orleans, LA, USA in June 2009. The 29 revised full papers and 12 revised short papers including poster and demo papers presented together with three invited presentations were carefully reviewed and selected from 76 submissions. The papers are organized in topical sections on improving the end-user experience, indexing, physical design, and energy, application experience, workflow, query processing, similarity search, mining, as well as spatial data.
Download or read book Peer to Peer Query Processing over Multidimensional Data written by Akrivi Vlachou and published by Springer Science & Business Media. This book was released on 2012-04-13 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applications that require a high degree of distribution and loosely-coupled connectivity are ubiquitous in various domains, including scientific databases, bioinformatics, and multimedia retrieval. In all these applications, data is typically voluminous and multidimensional, and support for advanced query operators is required for effective querying and efficient processing. To address this challenge, we adopt a hybrid P2P architecture and propose novel indexing and query processing algorithms. We present a scalable framework that relies on data summaries that are distributed and maintained as multidimensional routing indices. Different types of data summaries enable efficient processing of a variety of advanced query operators.
Download or read book Medicinal Plants Biodiversity Biotechnology and Conservation written by Sumita Jha and published by Springer Nature. This book was released on 2023-07-07 with total page 906 pages. Available in PDF, EPUB and Kindle. Book excerpt: This contributed volume provides a comprehensive, in-depth and subject-based reviews on the current status of active ingredients, sustainable use, biodiversity and conservation of certain endangered medicinal plants. The book also explores conventional and non-conventional biotechnological interventions for their biodiversity conservation. Medicinal plants have been used in worldwide as a major source of raw material for the traditional herbal healthcare practices as well as for drug discovery and development in pharmaceutical industry. The cumulative consequences of various human activities and environmental factors cause decline in the biodiversity of medicinal plants at an unprecedented rate worldwide. Thus, the overall understanding of ecology, species and genetic diversity along with assessment of the status of different threats and their impact on medicinal plants is crucial to sustain existing biodiversity, its utilization and conservation. All the latest advancements in the biotechnological approaches for the conservation research of endangered medicinal plants and the future perspectives have been described. This book provides comprehensive reviews spreading over about 25 chapters divided in three sections. The chapters of this book are written by recognized scientists in their respective fields which are useful to students, academicians, researchers, botanists, biotechnologists, policy makers, conservationists and industries interested in biodiversity conservation and medicinal plant research for the production of secondary metabolites.
Download or read book Federated Learning written by Heiko Ludwig and published by Springer Nature. This book was released on 2022-07-07 with total page 531 pages. Available in PDF, EPUB and Kindle. Book excerpt: Federated Learning: A Comprehensive Overview of Methods and Applications presents an in-depth discussion of the most important issues and approaches to federated learning for researchers and practitioners. Federated Learning (FL) is an approach to machine learning in which the training data are not managed centrally. Data are retained by data parties that participate in the FL process and are not shared with any other entity. This makes FL an increasingly popular solution for machine learning tasks for which bringing data together in a centralized repository is problematic, either for privacy, regulatory or practical reasons. This book explains recent progress in research and the state-of-the-art development of Federated Learning (FL), from the initial conception of the field to first applications and commercial use. To obtain this broad and deep overview, leading researchers address the different perspectives of federated learning: the core machine learning perspective, privacy and security, distributed systems, and specific application domains. Readers learn about the challenges faced in each of these areas, how they are interconnected, and how they are solved by state-of-the-art methods. Following an overview on federated learning basics in the introduction, over the following 24 chapters, the reader will dive deeply into various topics. A first part addresses algorithmic questions of solving different machine learning tasks in a federated way, how to train efficiently, at scale, and fairly. Another part focuses on providing clarity on how to select privacy and security solutions in a way that can be tailored to specific use cases, while yet another considers the pragmatics of the systems where the federated learning process will run. The book also covers other important use cases for federated learning such as split learning and vertical federated learning. Finally, the book includes some chapters focusing on applying FL in real-world enterprise settings.
Download or read book Distributed Machine Learning and Gradient Optimization written by Jiawei Jiang and published by Springer Nature. This book was released on 2022-02-23 with total page 179 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key technology, and recent research has shown gradient-based iterative optimization to be an effective solution. Focusing on methods that can speed up large-scale gradient optimization through both algorithm optimizations and careful system implementations, the book introduces three essential techniques in designing a gradient optimization algorithm to train a distributed machine learning model: parallel strategy, data compression and synchronization protocol. Written in a tutorial style, it covers a range of topics, from fundamental knowledge to a number of carefully designed algorithms and systems of distributed machine learning. It will appeal to a broad audience in the field of machine learning, artificial intelligence, big data and database management.
Download or read book Principles of Big Graph In depth Insight written by and published by Elsevier. This book was released on 2023-01-24 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: Principles of Big Graph: In-depth Insight, Volume 128 in the Advances in Computer series, highlights new advances in the field with this new volume presenting interesting chapters on a variety of topics, including CESDAM: Centered subgraph data matrix for large graph representation, Bivariate, cluster and suitability analysis of NoSQL Solutions for big graph applications, An empirical investigation on Big Graph using deep learning, Analyzing correlation between quality and accuracy of graph clustering, geneBF: Filtering protein-coded gene graph data using bloom filter, Processing large graphs with an alternative representation, MapReduce based convolutional graph neural networks: A comprehensive review. Fast exact triangle counting in large graphs using SIMD acceleration, A comprehensive investigation on attack graphs, Qubit representation of a binary tree and its operations in quantum computation, Modified ML-KNN: Role of similarity measures and nearest neighbor configuration in multi label text classification on big social network graph data, Big graph based online learning through social networks, Community detection in large-scale real-world networks, Power rank: An interactive web page ranking algorithm, GA based energy efficient modelling of a wireless sensor network, The major challenges of big graph and their solutions: A review, and An investigation on socio-cyber crime graph. Provides an update on the issues and challenges faced by current researchers Updates on future research agendas Includes advanced topics for intensive research for researchers
Download or read book Data Privacy Management Cryptocurrencies and Blockchain Technology written by Joaquin Garcia-Alfaro and published by Springer. This book was released on 2018-09-06 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed conference proceedings of the 2nd International Workshop on Cryprocurrencies and Blockchain Technology, CBT 2018, and the 13thInternational Workshop on Data Privacy Management, DPM 2018, on conjunction with the 23nd European Symposium on Research in Computer Security, ESORICS 2018, held in Barcelona, Spain, in September 2018. From the CBT Workshop 7 full and 8 short papers out of 39 submissions are included. The selected papers cover aspects of identity management, smart contracts, soft- and hardforks, proof-of-works and proof of stake as well as on network layer aspects and the application of blockchain technology for secure connect event ticketing. The DPM Workshop received 36 submissions from which 11 full and 5 short papers were selected for presentation. The papers focus on challenging problems such as translation of high-level buiness goals into system level privacy policies, administration of sensitive identifiers, data integration and privacy engineering.
Download or read book Moving Objects Management written by Xiaofeng Meng and published by Springer Science & Business Media. This book was released on 2011-01-27 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: We live in an age of rapid technological development. The Internet already affects our lives in many ways. Indeed, we continue to depend more, and more intrinsically, on the Internet, which is increasingly becoming a fundamental piece of societal infrastructure, just as water supply, electricity grids, and transportation networks have been for a long time. But while these other infrastructures are relatively static, the Internet is undergoing swift and fundamental change: Notably, the Internet is going mobile. The world has some 6.7 billion humans, 4 billion mobile phones, and 1.7 billion Internet users. The two most populous continents, Asia and Africa, have relatively low Internet penetration and hold the greatest potentials for growth. Their mobile phone users by far outnumber their Internet users, and the numbers are growing rapidly. China and India are each gaining about half a dozen million new phone users per month. Users across the globe as a whole increasingly embrace mobile Internet devices, with smart phone sales are starting to outnumber PC sales. Indeed, these and other facts suggest that the Internet stands to gain a substantial mobile component. This mega trend towards “mobile” is enabled by rapid and continuing advances in key technology areas such as mobile communication, consumer electronics, g- positioning, and computing. In short, this is the backdrop for this very timely book on moving objects by Xiaofeng Meng and Jidong Chen.
Download or read book Data Clustering written by Charu C. Aggarwal and published by CRC Press. This book was released on 2018-09-03 with total page 654 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains. The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process—including how to verify the quality of the underlying clusters—through supervision, human intervention, or the automated generation of alternative clusters.
