A guide for business enterprises on how to manage and govern big data, covering such topics as categories of data governance tools, data modeling, analytics and reporting, data security, and evaluation criteria for data governance platforms.
This book constitutes the refereed proceedings of the 32nd International Symposium on Computer and Information Sciences, ISCIS 2018, held in Poznan, Poland, in September 2018. The 29 revised full papers presented were carefully reviewed and selected from 64 submissions. The papers are dealing with the following topics: smart algorithms; data classification and processing; stochastic modelling; performance evaluation; queuing systems; wireless networks and security; image processing and computer vision.
This book constitutes the proceedings of the 14 th International Workshop on Open MP, IWOMP 2018, held in Barcelona, Spain, in September 2018. The 16 full papers presented in this volume were carefully reviewed and selected for inclusion in this book. The papers are organized in topical sections named: best paper; loops and OpenMP; OpenMP in heterogeneous systems; OpenMP improvements and innovations; OpenMP user experiences: applications and tools; and tasking evaluations.
Covering aspects from principles and limitations of statistical significance tests to topic set size design and power analysis, this book guides readers to statistically well-designed experiments. Although classical statistical significance tests are to some extent useful in information retrieval (IR) evaluation, they can harm research unless they are used appropriately with the right sample sizes and statistical power and unless the test results are reported properly. The first half of the book is mainly targeted at undergraduate students, and the second half is suitable for graduate students and researchers who regularly conduct laboratory experiments in IR, natural language processing, recommendations, and related fields. Chapters 1-5 review parametric significance tests for comparing system means, namely, t -tests and ANOVAs, and show how easily they can be conducted using Microsoft Excel or R. These chapters also discuss a few multiple comparison procedures for researchers who are interested in comparing every system pair, including a randomised version of Tukey´s Honestly Significant Difference test. The chapters then deal with known limitations of classical significance testing and provide practical guidelines for reporting research results regarding comparison of means. Chapters 6 and 7 discuss statistical power. Chapter 6 introduces topic set size design to enable test collection builders to determine an appropriate number of topics to create. Readers can easily use the author´s Excel tools for topic set size design based on the paired and two-sample t -tests, one-way ANOVA, and confidence intervals. Chapter 7 describes power-analysis-based methods for determining an appropriate sample size for a new experiment based on a similar experiment done in the past, detailing how to utilize the author´s R tools for power analysis and how to interpret the results. Case studies from IR for both Excel-based topic set size design and R-based power analysis are also provided.
Quality of Protection: Security Measurements and Metrics is an edited volume based on the Quality of Protection Workshop in Milano, Italy (September 2005). This volume discusses how security research can progress towards quality of protection in security comparable to quality of service in networking and software measurements, and metrics in empirical software engineering. Information security in the business setting has matured in the last few decades. Standards such as IS017799, the Common Criteria (ISO15408), and a number of industry certifications and risk analysis methodologies have raised the bar for good security solutions from a business perspective. Designed for a professional audience composed of researchers and practitioners in industry, Quality of Protection: Security Measurements and Metrics is also suitable for advanced-level students in computer science. Information security in the business setting has matured in the last few decades. Standards, such as IS017799, the Common Criteria s, and a number of industry and academic certifications and risk analysis methodologies, have raised the bar on what is considered good security solution, from a business perspective. Yet, the evaluation of security solutions has largely a qualitative flavor. Notions such as Security Metrics, Quality of Protection (QoP) or Protection Level Agreement (PLA) have only surfaced in the literature. Quality of Protection: Security Measurements and Metrics is an edited volume based on the Quality of Protection Workshop at ESORICS 2005, the flagship European Symposium on Research in Computer Security. This book discusses how security research can progress towards a notion of quality of protection in security, comparable to the notion of quality of service in networking and software measurements and metrics, in empirical software engineering. Quality of Protection: Security Measurements and Metrics is designed for a professional audience, composed of researchers and practitioners in industry. This book is also suitable for graduate-level students in computer science and telecommunications.
This two volume set (CCIS 901 and 902) constitutes the refereed proceedings of the 4th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2018 (originally ICYCSEE) held in Zhengzhou, China, in September 2018. The 125 revised full papers presented in these two volumes were carefully reviewed and selected from 1057 submissions. The papers cover a wide range of topics related to basic theory and techniques for data science including mathematical issues in data science, computational theory for data science, big data management and applications, data quality and data preparation, evaluation and measurement in data science, data visualization, big data mining and knowledge management, infrastructure for data science, machine learning for data science, data security and privacy, applications of data science, case study of data science, multimedia data management and analysis, data-driven scientific research, data-driven bioinformatics, data-driven healthcare, data-driven management, data-driven eGovernment, data-driven smart city/planet, data marketing and economics, social media and recommendation systems, data-driven security, data-driven business model innovation, social and/or organizational impacts of data science.
Verbessern Sie Ihre Entwicklungsprozesse und Ihre Produkte! User Story Mapping ist eine von Jeff Patton entwickelte Methode, die es Ihnen deutlich erleichtert, im Rahmen agiler Projekte eine stimmige User Experience zu schaffen. Die Idee: Die Produktentwicklung wird am Arbeitsfluss der Nutzer ausgerichtet und in flexibel anpassbaren Story Maps geplant, dokumentiert und visualisiert. Dadurch entsteht im gesamten Team - bei Product Ownern, Designern, Entwicklern und Auftraggebern - ein deutlich verbessertes gemeinsames Verständnis vom Gesamtprozess und vom zu entwickelnden Produkt. Gleichzeitig wird die Gefahr reduziert, sich in unwichtigen Details zu verzetteln oder gar ein Produkt zu entwickeln, dass dem Nutzer nicht hilft. User Story Maps statt Anforderungsdokumente In der agilen Entwicklung werden Anforderungen in User Stories heruntergebrochen. User Story Mapping geht noch weiter und stellt die Stories in einen für alle Teammitglieder nachvollziehbaren Gesamtzusammenhang. Stories als Impulse für Konversationen Eine gute User Story Map fördert Konversationen zwischen allen, die an der Entstehung eines Produkts beteiligt sind, sowie mit denen, die es letztlich anwenden. Bessere Kommunikation = bessere Produkte. User Story Maps sind wie Landkarten Mit den Maps wird die narrative Struktur von User Stories gewahrt, zugleich können einzelne Aspekte jederzeit herausgegriffen, weiterentwickelt und vertieft werden. Optimaler Outcome statt möglichst vieler Features Die Methode hilft dabei, die Features zu identifizieren, die wirklich nützlich und zudem bezahl- und realisierbar sind. Der Schlüssel: Konzentration auf das Ergebnis und die Schritte, die dorthin führen. Fortlaufend dazulernen Konversationen und Maps begleiten alle Projektschritte, z.B. Evaluation der Chancen und Risiken, Tests mit Usern und Kunden, Iterationen sowie sämtliche Schlussfolgerungen aus dem Gelernten.
Delve into your data for the key to success Data mining is quickly becoming integral to creating value and business momentum. The ability to detect unseen patterns hidden in the numbers exhaustively generated by day-to-day operations allows savvy decision-makers to exploit every tool at their disposal in the pursuit of better business. By creating models and testing whether patterns hold up, it is possible to discover new intelligence that could change your business´s entire paradigm for a more successful outcome. Data Mining for Dummies shows you why it doesn´t take a data scientist to gain this advantage, and empowers average business people to start shaping a process relevant to their business´s needs. In this book, you´ll learn the hows and whys of mining to the depths of your data, and how to make the case for heavier investment into data mining capabilities. The book explains the details of the knowledge discovery process including: * Model creation, validity testing, and interpretation * Effective communication of findings * Available tools, both paid and open-source * Data selection, transformation, and evaluation Data Mining for Dummies takes you step-by-step through a real-world data-mining project using open-source tools that allow you to get immediate hands-on experience working with large amounts of data. You´ll gain the confidence you need to start making data mining practices a routine part of your successful business. If you´re serious about doing everything you can to push your company to the top, Data Mining for Dummies is your ticket to effective data mining.
Embedded systems have long become essential in application areas in which human control is impossible or infeasible. The development of modern embedded systems is becoming increasingly difficult and challenging because of their overall system complexity, their tighter and cross-functional integration, the increasing requirements concerning safety and real-time behavior, and the need to reduce development and operation costs. This book provides a comprehensive overview of the Software Platform Embedded Systems (SPES) modeling framework and demonstrates its applicability in embedded system development in various industry domains such as automation, automotive, avionics, energy, and healthcare. In SPES 2020, twenty-one partners from academia and industry have joined forces in order to develop and evaluate in different industrial domains a modeling framework that reflects the current state of the art in embedded systems engineering. The content of this book is structured in four parts. Part I ´´Starting Point´´ discusses the status quo of embedded systems development and model-based engineering, and summarizes the key requirements faced when developing embedded systems in different application domains. Part II ´´The SPES Modeling Framework´´ describes the SPES modeling framework. Part III ´´Application and Evaluation of the SPES Modeling Framework´´ reports on the validation steps taken to ensure that the framework met the requirements discussed in Part I. Finally, Part IV ´´Impact of the SPES Modeling Framework´´ summarizes the results achieved and provides an outlook on future work. The book is mainly aimed at professionals and practitioners who deal with the development of embedded systems on a daily basis. Researchers in academia and industry may use it as a compendium for the requirements and state-of-the-art solution concepts for embedded systems development.
For undergraduate or advanced undergraduate courses in Classical Natural Language Processing, Statistical Natural Language Processing, Speech Recognition, Computational Linguistics, and Human Language Processing. An explosion of Web-based language techniques, merging of distinct fields, availability of phone-based dialogue systems, and much more make this an exciting time in speech and language processing. The first of its kind to thoroughly cover language technology - at all levels and with all modern technologies - this text takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations. The authors cover areas that traditionally are taught in different courses, to describe a unified vision of speech and language processing. Emphasis is on practical applications and scientific evaluation. An accompanying Website contains teaching materials for instructors, with pointers to language processing resources on the Web. The Second Edition offers a significant amount of new and extended material. Supplements: Click on the Resources tab to View Downloadable Files: * Solutions * Power Point Lecture Slides - Chapters 1-5, 8-10, 12-13 and 24 Now Available! * For additional resourcse visit the author website: