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Optimization And Data Requirements
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Book Synopsis Optimization for Data Analysis by : Stephen J. Wright
Download or read book Optimization for Data Analysis written by Stephen J. Wright and published by Cambridge University Press. This book was released on 2022-04-21 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: A concise text that presents and analyzes the fundamental techniques and methods in optimization that are useful in data science.
Book Synopsis Big Data Optimization: Recent Developments and Challenges by : Ali Emrouznejad
Download or read book Big Data Optimization: Recent Developments and Challenges written by Ali Emrouznejad and published by Springer. This book was released on 2016-05-26 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main objective of this book is to provide the necessary background to work with big data by introducing some novel optimization algorithms and codes capable of working in the big data setting as well as introducing some applications in big data optimization for both academics and practitioners interested, and to benefit society, industry, academia, and government. Presenting applications in a variety of industries, this book will be useful for the researchers aiming to analyses large scale data. Several optimization algorithms for big data including convergent parallel algorithms, limited memory bundle algorithm, diagonal bundle method, convergent parallel algorithms, network analytics, and many more have been explored in this book.
Book Synopsis Open Problems in Optimization and Data Analysis by : Panos M. Pardalos
Download or read book Open Problems in Optimization and Data Analysis written by Panos M. Pardalos and published by Springer. This book was released on 2018-12-04 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational and theoretical open problems in optimization, computational geometry, data science, logistics, statistics, supply chain modeling, and data analysis are examined in this book. Each contribution provides the fundamentals needed to fully comprehend the impact of individual problems. Current theoretical, algorithmic, and practical methods used to circumvent each problem are provided to stimulate a new effort towards innovative and efficient solutions. Aimed towards graduate students and researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, this book provides a broad comprehensive approach to understanding the significance of specific challenging or open problems within each discipline. The contributions contained in this book are based on lectures focused on “Challenges and Open Problems in Optimization and Data Science” presented at the Deucalion Summer Institute for Advanced Studies in Optimization, Mathematics, and Data Science in August 2016.
Book Synopsis Requirement-driven Design and Optimization of Data-intensive Flows by : Petar Jovanovic
Download or read book Requirement-driven Design and Optimization of Data-intensive Flows written by Petar Jovanovic and published by . This book was released on 2017 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data have become number one assets of today's business world. Thus, its exploitation and analysis attracted the attention of people from different fields and having different technical backgrounds. Data-intensive flows are central processes in today's business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. However, designing and optimizing such data flows, to satisfy both users' information needs and agreed quality standards, have been known as a burdensome task, typically left to the manual efforts of a BI system designer. These tasks have become even more challenging for next generation BI systems, where data flows typically need to combine data from in-house transactional storages, and data coming from external sources, in a variety of formats (e.g., social media, governmental data, news feeds). Moreover, for making an impact to business outcomes, data flows are expected to answer unanticipated analytical needs of a broader set of business users' and deliver valuable information in near real-time (i.e., at the right time). These challenges largely indicate a need for boosting the automation of the design and optimization of data-intensive flows. This PhD thesis aims at providing automatable means for managing the lifecycle of data-intensive flows. The study primarily analyzes the remaining challenges to be solved in the field of data-intensive flows, by performing a survey of current literature, and envisioning an architecture for managing the lifecycle of data-intensive flows. Following the proposed architecture, we further focus on providing automatic techniques for covering different phases of the data-intensive flows' lifecycle. In particular, the thesis first proposes an approach (CoAl) for incremental design of data-intensive flows, by means of multi-flow consolidation. CoAl not only facilitates the maintenance of data flow designs in front of changing information needs, but also supports the multi-flow optimization of data-intensive flows, by maximizing their reuse. Next, in the data warehousing (DW) context, we propose a complementary method (ORE) for incremental design of the target DW schema, along with systematically tracing the evolution metadata, which can further facilitate the design of back-end data-intensive flows (i.e., ETL processes). The thesis then studies the problem of implementing data-intensive flows into deployable formats of different execution engines, and proposes the BabbleFlow system for translating logical data-intensive flows into executable formats, spanning single or multiple execution engines. Lastly, the thesis focuses on managing the execution of data-intensive flows on distributed data processing platforms, and to this end, proposes an algorithm (H-WorD) for supporting the scheduling of data-intensive flows by workload-driven redistribution of data in computing clusters. The overall outcome of this thesis an end-to-end platform for managing the lifecycle of data-intensive flows, called Quarry. The techniques proposed in this thesis, plugged to the Quarry platform, largely facilitate the manual efforts, and assist users of different technical skills in their analytical tasks. Finally, the results of this thesis largely contribute to the field of data-intensive flows in today's BI systems, and advocate for further attention by both academia and industry to the problems of design and optimization of data-intensive flows.
Book Synopsis Data-Driven Evolutionary Optimization by : Yaochu Jin
Download or read book Data-Driven Evolutionary Optimization written by Yaochu Jin and published by Springer Nature. This book was released on 2021-06-28 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available. This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.
Download or read book Data Warehousing written by Rob Botwright and published by Rob Botwright. This book was released on 101-01-01 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introducing the "Data Warehousing: Optimizing Data Storage and Retrieval for Business Success" bundle! Unlock the full potential of your data with this comprehensive collection of four essential books: 1. Data Warehousing Fundamentals: A Beginner's Guide · Dive into the foundational principles of data warehousing and learn how to build a solid framework for storing and managing your organization's data. · Understand the importance of data modeling and gain insights into the extraction, transformation, and loading (ETL) processes essential for efficient data management. 2. Mastering Data Modeling for Data Warehousing · Take your data modeling skills to the next level with advanced techniques for conceptual, logical, and dimensional modeling. · Learn how to design scalable and efficient data warehouses that meet the evolving needs of your organization. 3. Advanced ETL Techniques for Data Warehousing Optimization · Optimize your ETL processes and streamline data extraction, transformation, and loading for maximum efficiency. · Explore advanced techniques such as incremental loading and change data capture (CDC) to ensure the smooth operation of your data warehouse. 4. Big Data Analytics: Harnessing the Power of Data Warehousing for Experts · Unlock the transformative potential of big data analytics and gain actionable insights to drive informed decision-making. · Discover how to leverage your data warehouse for real-time data processing, predictive modeling, and more. With this bundle, you'll gain the knowledge and skills needed to optimize your data storage and retrieval processes, empowering you to harness the power of data for business success. Whether you're a beginner looking to build a solid foundation or an expert seeking advanced strategies, this bundle has something for everyone. Don't miss out on this opportunity to revolutionize your approach to data warehousing and take your business to new heights!
Book Synopsis Data-Driven Optimization and Knowledge Discovery for an Enterprise Information System by : Qing Duan
Download or read book Data-Driven Optimization and Knowledge Discovery for an Enterprise Information System written by Qing Duan and published by Springer. This book was released on 2015-06-13 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive set of optimization and prediction techniques for an enterprise information system. Readers with a background in operations research, system engineering, statistics, or data analytics can use this book as a reference to derive insight from data and use this knowledge as guidance for production management. The authors identify the key challenges in enterprise information management and present results that have emerged from leading-edge research in this domain. Coverage includes topics ranging from task scheduling and resource allocation, to workflow optimization, process time and status prediction, order admission policies optimization, and enterprise service-level performance analysis and prediction. With its emphasis on the above topics, this book provides an in-depth look at enterprise information management solutions that are needed for greater automation and reconfigurability-based fault tolerance, as well as to obtain data-driven recommendations for effective decision-making.
Book Synopsis Open Problems in Optimization and Data Analysis by : Panos M. Pardalos
Download or read book Open Problems in Optimization and Data Analysis written by Panos M. Pardalos and published by Springer. This book was released on 2018-12-17 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational and theoretical open problems in optimization, computational geometry, data science, logistics, statistics, supply chain modeling, and data analysis are examined in this book. Each contribution provides the fundamentals needed to fully comprehend the impact of individual problems. Current theoretical, algorithmic, and practical methods used to circumvent each problem are provided to stimulate a new effort towards innovative and efficient solutions. Aimed towards graduate students and researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, this book provides a broad comprehensive approach to understanding the significance of specific challenging or open problems within each discipline. The contributions contained in this book are based on lectures focused on “Challenges and Open Problems in Optimization and Data Science” presented at the Deucalion Summer Institute for Advanced Studies in Optimization, Mathematics, and Data Science in August 2016.
Book Synopsis The "Orange" Model of Data Management by : Irina Steenbeek
Download or read book The "Orange" Model of Data Management written by Irina Steenbeek and published by . This book was released on 2019-10-21 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: *This book is a brief overview of the model and has only 24 pages.*Almost every data management professional, at some point in their career, has come across the following crucial questions:1. Which industry reference model should I use for the implementation of data managementfunctions?2. What are the key data management capabilities that are feasible and applicable to my company?3. How do I measure the maturity of the data management functions and compare that withthose of my peers in the industry4. What are the critical, logical steps in the implementation of data management?The "Orange" (meta)model of data management provides a collection of techniques and templates for the practical set up of data management through the design and implementation of the data and information value chain, enabled by a set of data management capabilities.This book is a toolkit for advanced data management professionals and consultants thatare involved in the data management function implementation.This book works together with the earlier published "The Data Management Toolkit". The "Orange" model assists in specifying the feasible scope of data management capabilities, that fits company's business goals and resources. "The Data Management Toolkit" is a practical implementation guide of the chosen data management capabilities.
Book Synopsis Data Democracy by : Feras A. Batarseh
Download or read book Data Democracy written by Feras A. Batarseh and published by Academic Press. This book was released on 2020-01-21 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Democracy: At the Nexus of Artificial Intelligence, Software Development, and Knowledge Engineering provides a manifesto to data democracy. After reading the chapters of this book, you are informed and suitably warned! You are already part of the data republic, and you (and all of us) need to ensure that our data fall in the right hands. Everything you click, buy, swipe, try, sell, drive, or fly is a data point. But who owns the data? At this point, not you! You do not even have access to most of it. The next best empire of our planet is one who owns and controls the world’s best dataset. If you consume or create data, if you are a citizen of the data republic (willingly or grudgingly), and if you are interested in making a decision or finding the truth through data-driven analysis, this book is for you. A group of experts, academics, data science researchers, and industry practitioners gathered to write this manifesto about data democracy. The future of the data republic, life within a data democracy, and our digital freedoms An in-depth analysis of open science, open data, open source software, and their future challenges A comprehensive review of data democracy's implications within domains such as: healthcare, space exploration, earth sciences, business, and psychology The democratization of Artificial Intelligence (AI), and data issues such as: Bias, imbalance, context, and knowledge extraction A systematic review of AI methods applied to software engineering problems
Book Synopsis Optimization and Its Applications in Control and Data Sciences by : Boris Goldengorin
Download or read book Optimization and Its Applications in Control and Data Sciences written by Boris Goldengorin and published by Springer. This book was released on 2016-09-29 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on recent research in modern optimization and its implications in control and data analysis. This book is a collection of papers from the conference “Optimization and Its Applications in Control and Data Science” dedicated to Professor Boris T. Polyak, which was held in Moscow, Russia on May 13-15, 2015. This book reflects developments in theory and applications rooted by Professor Polyak’s fundamental contributions to constrained and unconstrained optimization, differentiable and nonsmooth functions, control theory and approximation. Each paper focuses on techniques for solving complex optimization problems in different application areas and recent developments in optimization theory and methods. Open problems in optimization, game theory and control theory are included in this collection which will interest engineers and researchers working with efficient algorithms and software for solving optimization problems in market and data analysis. Theoreticians in operations research, applied mathematics, algorithm design, artificial intelligence, machine learning, and software engineering will find this book useful and graduate students will find the state-of-the-art research valuable.
Book Synopsis Data Correcting Approaches in Combinatorial Optimization by : Boris I. Goldengorin
Download or read book Data Correcting Approaches in Combinatorial Optimization written by Boris I. Goldengorin and published by Springer Science & Business Media. This book was released on 2012-10-10 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Correcting Approaches in Combinatorial Optimization focuses on algorithmic applications of the well known polynomially solvable special cases of computationally intractable problems. The purpose of this text is to design practically efficient algorithms for solving wide classes of combinatorial optimization problems. Researches, students and engineers will benefit from new bounds and branching rules in development efficient branch-and-bound type computational algorithms. This book examines applications for solving the Traveling Salesman Problem and its variations, Maximum Weight Independent Set Problem, Different Classes of Allocation and Cluster Analysis as well as some classes of Scheduling Problems. Data Correcting Algorithms in Combinatorial Optimization introduces the data correcting approach to algorithms which provide an answer to the following questions: how to construct a bound to the original intractable problem and find which element of the corrected instance one should branch such that the total size of search tree will be minimized. The PC time needed for solving intractable problems will be adjusted with the requirements for solving real world problems.
Book Synopsis Big Data Benchmarks, Performance Optimization, and Emerging Hardware by : Jianfeng Zhan
Download or read book Big Data Benchmarks, Performance Optimization, and Emerging Hardware written by Jianfeng Zhan and published by Springer. This book was released on 2014-11-10 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly revised selected papers of the 4th and 5th workshops on Big Data Benchmarks, Performance Optimization, and Emerging Hardware, BPOE 4 and BPOE 5, held respectively in Salt Lake City, in March 2014, and in Hangzhou, in September 2014. The 16 papers presented were carefully reviewed and selected from 30 submissions. Both workshops focus on architecture and system support for big data systems, such as benchmarking; workload characterization; performance optimization and evaluation; emerging hardware.
Book Synopsis Energy Efficient Servers by : Corey Gough
Download or read book Energy Efficient Servers written by Corey Gough and published by Apress. This book was released on 2015-04-07 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: Energy Efficient Servers: Blueprints for Data Center Optimization introduces engineers and IT professionals to the power management technologies and techniques used in energy efficient servers. The book includes a deep examination of different features used in processors, memory, interconnects, I/O devices, and other platform components. It outlines the power and performance impact of these features and the role firmware and software play in initialization and control. Using examples from cloud, HPC, and enterprise environments, the book demonstrates how various power management technologies are utilized across a range of server utilization. It teaches the reader how to monitor, analyze, and optimize their environment to best suit their needs. It shares optimization techniques used by data center administrators and system optimization experts at the world’s most advanced data centers.
Book Synopsis Machine Learning, Optimization, and Big Data by : Giuseppe Nicosia
Download or read book Machine Learning, Optimization, and Big Data written by Giuseppe Nicosia and published by Springer. This book was released on 2017-12-19 with total page 621 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the post-conference proceedings of the Third International Workshop on Machine Learning, Optimization, and Big Data, MOD 2017, held in Volterra, Italy, in September 2017. The 50 full papers presented were carefully reviewed and selected from 126 submissions. The papers cover topics in the field of machine learning, artificial intelligence, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications.
Book Synopsis ADVANCED OPTIMIZATION with MATLAB Using BIG DATA TECHNIQUES by : J Lopez
Download or read book ADVANCED OPTIMIZATION with MATLAB Using BIG DATA TECHNIQUES written by J Lopez and published by . This book was released on 2019-07-07 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: Global Optimization Toolbox provides functions that search for global solutions to problems that contain multiple maxima or minima. Toolbox solvers include surrogate, pattern search, genetic algorithm, particle swarm, simulated annealing, multi start, and global search. You can use these solvers for optimization problems where the objective or constraint function is continuous, discontinuous, stochastic, does not possess derivatives, or includes simulations or black-box functions. For problems with multiple objectives, you can identify a Pareto front using genetic algorithm or pattern search solvers. You can improve solver effective es by adjusting options and, for applicable solvers, customizing creation, update, and search functions. You can use custom data types with the genetic algorithm and simulated annealing solvers to represent problems not easily expressed with standard data types. The hybrid function option lets you improve a solution by applying a second solver after the first.Simulated annealing is a method for solving unconstrained and bound-constrained optimization problems. The method models the physical process of heating a material and then slowly lowering the temperature to decrease defects, thus minimizing the system energy. At each iteration of the simulated annealing algorithm, a new point is randomly generated. The distance of the new point from the current point, or the extent of the search, is based on a probability distribution with a scale proportional to the temperature. The algorithm accepts all new points that lower the objective, but also, with a certain probability, points that raise the objective. By accepting points that raise the objective, the algorithm avoids being trapped in local minima, and is able to explore globally for more possible solutions. An annealing schedule is selected to systematically decrease the temperature as the algorithm proceeds. As the temperature decreases, the algorithm reduces the extent of its search to converge to a minimum.You might need to formulate problems with more than one objective, since a single objective with several constraints may not adequately represent the problem being faced. If so, there is a vector of objectives, F(x) = [F1(x), F2(x), ..., Fm(x)], that must be traded off in some way. The relative importance of these objectives is not generally known until the system's best capabilities are determined and tradeoffs between the objectives fully understood. As the number of objectives increases, tradeoffs are likely to become complex and less easily quantified. The designer must rely on his or her intuition and ability to express preferences throughout the optimization cycle. Thus, requirements for a multiobjective design strategy must enable a natural problema formulation to be expressed, and be able to solve the problem and enter preferences into a numerically tractable and realistic design proble
Book Synopsis Multimodal and Tensor Data Analytics for Industrial Systems Improvement by : Nathan Gaw
Download or read book Multimodal and Tensor Data Analytics for Industrial Systems Improvement written by Nathan Gaw and published by Springer Nature. This book was released on with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt:
