With the proliferation of Software-as-a-Service (SaaS) offerings, it is becoming increasingly important for individual SaaS providers to operate their services at a low cost. This book investigates SaaS from the perspective of the provider and shows how operational costs can be reduced by using "multi tenancy," a technique for consolidating a large number of customers onto a small number of servers. Specifically, the book addresses multi tenancy on the database level, focusing on in-memory column databases, which are the backbone of many important new enterprise applications. For efficiently implementing multi tenancy in a farm of databases, two fundamental challenges must be addressed, (i) workload modeling and (ii) data placement. The first involves estimating the (shared) resource consumption for multi tenancy on a single in-memory database server. The second consists in assigning tenants to servers in a way that minimizes the number of required servers (and thus costs) based on the assumed workload model. This step also entails replicating tenants for performance and high availability. This book presents novel solutions to both problems.

The brief focuses on applying sublinear algorithms to manage critical big data challenges. The text offers an essential introduction to sublinear algorithms, explaining why they are vital to large scale data systems. It also demonstrates how to apply sublinear algorithms to three familiar big data applications: wireless sensor networks, big data processing in Map Reduce and smart grids. These applications present common experiences, bridging the theoretical advances of sublinear algorithms and the application domain. Sublinear Algorithms for Big Data Applications is suitable for researchers, engineers and graduate students in the computer science, communications and signal processing communities.

This book constitutes the refereed proceedings of the International Conference on Soft Computing in Data Science, SCDS 2016, held in Putrajaya, Malaysia, in September 2016. The 27 revised full papers presented were carefully reviewed and selected from 66 submissions. The papers are organized in topical sections on artificial neural networks; classification, clustering, visualization; fuzzy logic; information and sentiment analytics.

Learn the fundamental foundations and concepts of the Apache HBase (NoSQL) open source database. It covers the HBase data model, architecture, schema design, API, and administration. Apache HBase is the database for the Apache Hadoop framework. HBase is a column family based NoSQL database that provides a flexible schema model. What You'll Learn Work with the core concepts of HBase Discover the HBase data model, schema design, and architecture Use the HBase API and administration Who This Book Is For Apache HBase (NoSQL) database users, designers, developers, and admins.

This book provides a perspective on the application of machine learning-based methods in knowledge discovery from natural languages texts. By analysing various data sets, conclusions which are not normally evident, emerge and can be used for various purposes and applications. The book provides explanations of principles of time-proven machine learning algorithms applied in text mining together with step-by-step demonstrations of how to reveal the semantic contents in real-world datasets using the popular R-language with its implemented machine learning algorithms. The book is not only aimed at IT specialists, but is meant for a wider audience that needs to process big sets of text documents and has basic knowledge of the subject, e.g. e-mail service providers, online shoppers, librarians, etc. The book starts with an introduction to text-based natural language data processing and its goals and problems. It focuses on machine learning, presenting various algorithms with their use and possibilities, and reviews the positives and negatives. Beginning with the initial data pre-processing, a reader can follow the steps provided in the R-language including the subsuming of various available plug-ins into the resulting software tool. A big advantage is that R also contains many libraries implementing machine learning algorithms, so a reader can concentrate on the principal target without the need to implement the details of the algorithms her- or himself. To make sense of the results, the book also provides explanations of the algorithms, which supports the final evaluation and interpretation of the results. The examples are demonstrated using realworld data from commonly accessible Internet sources.

This book constitutes the refereed proceedings of the 9th International Symposium on Algorithmic Game Theory, SAGT 2016, held in Liverpool, UK, in September 2016.The 26 full papers presented together with 2 one-page abstracts were carefully reviewed and selected from 62 submissions. The accepted submissions cover various important aspectsof algorithmic game theory such as computational aspects of games, congestion games and networks, matching and voting, auctions and markets, and mechanism design.

This work reviews the state of the art in SVM and perceptron classifiers. A Support Vector Machine (SVM) is easily the most popular tool for dealing with a variety of machine-learning tasks, including classification. SVMs are associated with maximizing the margin between two classes. The concerned optimization problem is a convex optimization guaranteeing a globally optimal solution. The weight vector associated with SVM is obtained by a linear combination of some of the boundary and noisy vectors. Further, when the data are not linearly separable, tuning the coefficient of the regularization term becomes crucial. Even though SVMs have popularized the kernel trick, in most of the practical applications that are high-dimensional, linear SVMs are popularly used. The text examines applications to social and information networks. The work also discusses another popular linear classifier, the perceptron, and compares its performance with that of the SVM in different application areas.>

This book presents a comprehensive review of key distributed graph algorithms for computer network applications, with a particular emphasis on practical implementation. Topics and features: introduces a range of fundamental graph algorithms, covering spanning trees, graph traversal algorithms, routing algorithms, and self-stabilization; reviews graph-theoretical distributed approximation algorithms with applications in ad hoc wireless networks; describes in detail the implementation of each algorithm, with extensive use of supporting examples, and discusses their concrete network applications; examines key graph-theoretical algorithm concepts, such as dominating sets, and parameters for mobility and energy levels of nodes in wireless ad hoc networks, and provides a contemporary survey of each topic; presents a simple simulator, developed to run distributed algorithms; provides practical exercises at the end of each chapter.

This is a book about numbers and how those numbers are represented in and operated on by computers. It is crucial that developers understand this area because the numerical operations allowed by computers, and the limitations of those operations, especially in the area of floating point math, affect virtually everything people try to do with computers. This book aims to fill this gap by exploring, in sufficient but not overwhelming detail, just what it is that computers do with numbers. Divided into two parts, the first deals with standard representations of integers and floating point numbers, while the second details several other number representations. Each chapter ends with exercises to review the key points. Topics covered include interval arithmetic, fixed-point numbers, floating point numbers, big integers and rational arithmetic. This book is for anyone who develops software including software engineerings, scientists, computer science students, engineering students and anyone who programs for fun.

This book offers a selection of papers from the 2016 International Conference on Software Process Improvement (CIMPS'16), held between the 12th and 14th of October 2016 in Aguascalientes, Aguascalientes, Mexico. The CIMPS'16 is a global forum for researchers and practitioners to present and discuss the most recent innovations, trends, results, experiences and concerns in the different aspects of software engineering with a focus on, but not limited to, software processes, security in information and communication technology, and big data. The main topics covered include: organizational models, standards and methodologies, knowledge management, software systems, applications and tools, information and communication technologies and processes in non-software domains (mining, automotive, aerospace, business, health care, manufacturing, etc.) with a clear focus on software process challenges.

"If you want to learn some of the deeper explanations of deep learning and PyTorch then read this book!" - Tiklu Ganguly Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems. In Inside Deep Learning, you will learn how to: Implement deep learning with PyTorch Select the right deep learning components Train and evaluate a deep learning model Fine tune deep learning models to maximize performance Understand deep learning terminology Adapt existing PyTorch code to solve new problems Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped-you'll dive into math, theory, and practical applications. Everything is clearly explained in plain English. about the technology Deep learning isn't just for big tech companies and academics. Anyone who needs to find meaningful insights and patterns in their data can benefit from these practical techniques! The unique ability for your systems to learn by example makes deep learning widely applicable across industries and use-cases, from filtering out spam to driving cars. about the book Inside Deep Learning is a fast-paced beginners' guide to solving common technical problems with deep learning. Written for everyday developers, there are no complex mathematical proofs or unnecessary academic theory. You'll learn how deep learning works through plain language, annotated code and equations as you work through dozens of instantly useful PyTorch examples. As you go, you'll build a French-English translator that works on the same principles as professional machine translation and discover cutting-edge techniques just emerging from the latest research. Best of all, every deep learning solution in this book can run in less than fifteen minutes using free GPU hardware! about the reader For Python programmers with basic machine learning skills. about the author Edward Raff is a Chief Scientist at Booz Allen Hamilton, and the author of the JSAT machine learning library. His research includes deep learning, malware detection, reproducibility in ML, fairness/bias, and high performance computing. He is also a visiting professor at the University of Maryland, Baltimore County and teaches deep learning in the Data Science department. Dr Raff has over 40 peer reviewed publications, three best paper awards, and has presented at numerous major conferences.

The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. Current decentralized systems still focus on data and knowledge as their main resource. Feasibility of these systems relies basically on P2P (peer-to-peer) techniques and the support of agent systems with scaling and decentralized control. Synergy between grids, P2P systems, and agent technologies is the key to data- and knowledge-centered systems in large-scale environments.This volume, the 26th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, focuses on Data Warehousing and Knowledge Discovery from Big Data, and contains extended and revised versions of four papers selected as the best papers from the 16th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2014), held in Munich, Germany, during September 1-5, 2014. The papers focus on data cube computation, the construction and analysis of a data warehouse in the context of cancer epidemiology, pattern mining algorithms, and frequent item-set border approximation.

This book constitutes the refereed proceedings of the 23rd International Static Analysis Symposium, SAS 2016, held in Edinburgh, UK, in September 2016. The 21 papers presented in this volume were carefully reviewed and selected from 55 submissions. The contributions cover a variety of multi-disciplinary topics in abstract domains; abstract interpretation; abstract testing; bug detection; data flow analysis; model checking; new applications; program transformation; program verification; security analysis; theoretical frameworks; and type checking.

This book constitutes the thoroughly refereed proceedings of the 11th International Conference on Web and Internet Economics, WINE 2015, held in Amsterdam, The Netherlands, in December 2015. The 30 regular papers presented together with 8 abstracts were carefully reviewed and selected from 142 submissions and cover results on incentives and computation in theoretical computer science, artificial intelligence, and microeconomics.

A major, comprehensive professional text/reference for designing and maintaining security and reliability. From basic concepts to designing principles to deployment, all critical concepts and phases are clearly explained and presented. Includes coverage of wireless security testing techniques and prevention techniques for intrusion (attacks). An essential resource for wireless network administrators and developers.

This work highlights the importance of informal control modes on software platforms regarding their positive effects on third-party developers' behaviors and outcomes. The author presents studies in the mobile software industry, demonstrating how self-control and clan control positively affect developers' outcome performance, app quality and intentions to stay on software platforms. Moreover, the studies' findings shed light on the underlying explanatory mechanisms of why informal control modes can be exercised effectively on software platforms and how especially clan control may be facilitated through developers' social capital.

The Proceedings of SocProS 2014 serves as an academic bonanza for scientists and researchers working in the field of Soft Computing. This book contains theoretical as well as practical aspects using fuzzy logic, neural networks, evolutionary algorithms, swarm intelligence algorithms, etc., with many applications under the umbrella of 'Soft Computing'. The book is beneficial for young as well as experienced researchers dealing across complex and intricate real world problems for which finding a solution by traditional methods is a difficult task. The different application areas covered in the Proceedings are: Image Processing, Cryptanalysis, Industrial Optimization, Supply Chain Management, Newly Proposed Nature Inspired Algorithms, Signal Processing, Problems related to Medical and Healthcare, Networking Optimization Problems, etc.

This book constitutes the refereed proceedings of the 18th National Conference on Computer Engineering and Technology, NCCET 2014, held in Guiyang, China, during July/August 2014. The 18 papers presented were carefully reviewed and selected from 85 submissions. They are organized in topical sections on processor architecture; computer application and software optimization; technology on the horizon.

This book constitutes the refereed proceedings of the International Conference on Applications and Techniques in Information Security, ATIS 2015, held in Beijing, China, in November 2015. The 25 revised full papers and 10 short papers presented were carefully reviewed and selected from 103 submissions. The papers are organized in topical sections on invited speeches; cryptograph; evaluation, standards and protocols; trust computing and privacy protection; cloud security and applications; tools and methodologies; system design and implementations.

This Brief presents a new method that is based on the author and his students' shared experience in applying a structured procedure that has as its main goal the creation of a material selection technique that uses language and employs a platform that is not restricted to engineers. Based on a hybrid approach that exploits both traditional and semi-quantitative concepts, it moves forward step-by step, and uses a platform based on a Quality Function Deployment matrix framework. Candidate materials are screened out and finally assessed by two user-friendly graphic analysis tools, one based on the value curve of the product and the other on an original Bubble Maps tool. The Brief is written for all those whose aim is for a better understanding of how to integrate and speed up the entire product development process from the initial product concept and engineering design phases to design specs, manufacturability and product marketing with optimal choice of materials.

The Proceedings of SocProS 2014 serves as an academic bonanza for scientists and researchers working in the field of Soft Computing. This book contains theoretical as well as practical aspects using fuzzy logic, neural networks, evolutionary algorithms, swarm intelligence algorithms, etc., with many applications under the umbrella of 'Soft Computing'. The book is beneficial for young as well as experienced researchers dealing across complex and intricate real world problems for which finding a solution by traditional methods is a difficult task. The different application areas covered in the Proceedings are: Image Processing, Cryptanalysis, Industrial Optimization, Supply Chain Management, Newly Proposed Nature Inspired Algorithms, Signal Processing, Problems related to Medical and Healthcare, Networking Optimization Problems, etc.

This, the 25th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains five fully revised selected papers focusing on data and knowledge management systems. Topics covered include a framework consisting of two heuristics with slightly different characteristics to compute the action rating of data stores, a theoretical and experimental study of filter-based equijoins in a MapReduce environment, a constraint programming approach based on constraint reasoning to study the view selection and data placement problem given a limited amount of resources, a formalization and an approximate algorithm to tackle the problem of source selection and query decomposition in federations of SPARQL endpoints, and a matcher factory enabling the generation of a dedicated schema matcher for a given schema matching scenario.

This book constitutes the refereed proceedings of the 14th International Conference on Systems Simulation, Asia Simulation 2014, held in Kitakyushu, Japan, in October 2014. The 32 revised full papers presented were carefully reviewed and selected from 69 submissions. The papers are organized in topical sections on modeling and simulation technology; network simulation; high performance computing and cloud simulation; numerical simulation and visualization; simulation of instrumentation and control application; simulation technology in diversified higher education; general purpose simulation.

This book constitutes the refereed proceedings of the 13th International Conference on Machine Learning and Cybernetics, Lanzhou, China, in July 2014. The 45 revised full papers presented were carefully reviewed and selected from 421 submissions. The papers are organized in topical sections on classification and semi-supervised learning; clustering and kernel; application to recognition; sampling and big data; application to detection; decision tree learning; learning and adaptation; similarity and decision making; learning with uncertainty; improved learning algorithms and applications.

This, the 24th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains extended and revised versions of seven papers presented at the 25th International Conference on Database and Expert Systems Applications, DEXA 2014, held in Munich, Germany, in September 2014. Following the conference, and two further rounds of reviewing and selection, six extended papers and one invited keynote paper were chosen for inclusion in this special issue. Topics covered include systems modeling, similarity search, bioinformatics, data pricing, k-nearest neighbor querying, database replication, and data anonymization.