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Multidimensional Ranking

Multidimensional Ranking
Author: Frans A. van Vught
Publisher: Springer Science & Business Media
Total Pages: 198
Release: 2012-02-21
Genre: Education
ISBN: 9400730055
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During the last decades ranking has become one of the most controversial issues in higher education and research. It is widely recognized now that, although some of the current rankings can be severely criticized, they seem to be here to stay. In addition, rankings appear to have a great impact on decision-makers at all levels of higher education and research systems worldwide, including in universities. Rankings reflect a growing international competition among universities for talent and resources; at the same time they reinforce competition by their very results. Yet major concerns remain as to the rankings' methodological underpinnings and to their various impacts. This new book presents a comprehensive overview of the current ‘state of the art’ of ranking in higher education and research, and introduces a completely new approach called ‘multidimensional ranking’. In part 1 rankings are discussed in the broader context of quality assurance and transparency in higher education and research. In addition the many current ranking methodologies are analyzed and critized, and their impacts are explored. In part 2 a new approach to ranking is introduced, based on the basic idea that higher education and research institutions have different profiles and missions and that the performances of these institutions should reflect these differences. This multidimensional approach is operationalized in a new multidimensional and user-driven ranking tool, called U-Multirank. U-Multirank is the outcome of a pilot project, sponsored by the European Commission, in which the new ranking instrument was designed and tested at a global scale.

Advances in Computer Science for Engineering and Education VI

Advances in Computer Science for Engineering and Education VI
Author: Zhengbing Hu
Publisher: Springer Nature
Total Pages: 1166
Release: 2023-08-18
Genre: Computers
ISBN: 3031361180
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This book contains high-quality refereed research papers presented at the 6th International Conference on Computer Science, Engineering, and Education Applications (ICCSEEA2023), which took place in Warsaw, Poland, on March 17–19, 2023, and was organized by the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute”, the National Aviation University, Lviv Polytechnic National University, the Polish Operational and Systems Society, Warsaw University of Technology, and the International Research Association of Modern Education and Computer Science. The book covers a variety of topics, including cutting-edge research in computer science, artificial intelligence, engineering techniques, smart logistics, and knowledge representation with educational applications. The book is an invaluable resource for academics, graduate students, engineers, management professionals, and undergraduate students who are interested in computer science and its applications in engineering and education.

Intelligent Data Engineering and Automated Learning - IDEAL 2006

Intelligent Data Engineering and Automated Learning - IDEAL 2006
Author: Emilio Corchado
Publisher: Springer
Total Pages: 1473
Release: 2006-09-26
Genre: Computers
ISBN: 354045487X
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This book constitutes the refereed proceedings of the 7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2006. The 170 revised full papers presented were carefully selected from 557 submissions. The papers are organized in topical sections on learning and information processing, data mining, retrieval and management, bioinformatics and bio-inspired models, agents and hybrid systems, financial engineering, as well as a special session on nature-inspired date technologies.

Multidimensional Nonlinear Descriptive Analysis

Multidimensional Nonlinear Descriptive Analysis
Author: Shizuhiko Nishisato
Publisher: CRC Press
Total Pages: 336
Release: 2006-06-26
Genre: Mathematics
ISBN: 9781584886129
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Quantification of categorical, or non-numerical, data is a problem that scientists face across a wide range of disciplines. Exploring data analysis in various areas of research, such as the social sciences and biology, Multidimensional Nonlinear Descriptive Analysis presents methods for analyzing categorical data that are not necessarily sampled randomly from a normal population and often involve nonlinear relations. This reference not only provides an overview of multidimensional nonlinear descriptive analysis (MUNDA) of discrete data, it also offers new results in a variety of fields. The first part of the book covers conceptual and technical preliminaries needed to understand the data analysis in subsequent chapters. The next two parts contain applications of MUNDA to diverse data types, with each chapter devoted to one type of categorical data, a brief historical comment, and basic skills peculiar to the data types. The final part examines several problems and then concludes with suggestions for future progress. Covering both the early and later years of MUNDA research in the social sciences, psychology, ecology, biology, and statistics, this book provides a framework for potential developments in even more areas of study.

Normalization of Multidimensional Data for Multi-Criteria Decision Making Problems

Normalization of Multidimensional Data for Multi-Criteria Decision Making Problems
Author: Irik Z. Mukhametzyanov
Publisher: Springer Nature
Total Pages: 314
Release: 2023-07-25
Genre: Business & Economics
ISBN: 3031338375
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This book presents a systematic review of multidimensional normalization methods and addresses problems frequently encountered when using various methods and ways to eliminate them. The invariant properties of the linear normalization methods presented here can be used to eliminate simple problems and avoid obvious errors when choosing a normalization method. The book introduces valuable, novel techniques for the multistep normalization of multidimensional data. One of these methods involves inverting the normalized values of cost attributes into profit attributes based on the reverse sorting algorithm (ReS algorithm). Another approach presented is the IZ method, which addresses the issue of shift in normalized attribute values. Additionally, a new method for normalizing the decision matrix is proposed, called the MS method, which ensures the equalization of average values and variances of attributes. Featuring numerous illustrative examples throughout, the book helps readers to understand what difficulties can arise in multidimensional normalization, what to expect from such problems, and how to solve them. It is intended for academics and professionals in various areas of data science, computing in mathematics, and statistics, as well as decision-making and operations.

Multidimensional Similarity Structure Analysis

Multidimensional Similarity Structure Analysis
Author: I. Borg
Publisher: Springer Science & Business Media
Total Pages: 402
Release: 2012-12-06
Genre: Mathematics
ISBN: 1461247683
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Multidimensional Similarity Structure Analysis comprises a class of models that represent similarity among entities (for example, variables, items, objects, persons, etc.) in multidimensional space to permit one to grasp more easily the interrelations and patterns present in the data. The book is oriented to both researchers who have little or no previous exposure to data scaling and have no more than a high school background in mathematics and to investigators who would like to extend their analyses in the direction of hypothesis and theory testing or to more intimately understand these analytic procedures. The book is repleted with examples and illustrations of the various techniques drawn largely, but not restrictively, from the social sciences, with a heavy emphasis on the concrete, geometric or spatial aspect of the data representations.

Marketing Analytics

Marketing Analytics
Author: Wayne L. Winston
Publisher: John Wiley & Sons
Total Pages: 727
Release: 2014-01-08
Genre: Computers
ISBN: 1118417305
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Helping tech-savvy marketers and data analysts solve real-world business problems with Excel Using data-driven business analytics to understand customers and improve results is a great idea in theory, but in today's busy offices, marketers and analysts need simple, low-cost ways to process and make the most of all that data. This expert book offers the perfect solution. Written by data analysis expert Wayne L. Winston, this practical resource shows you how to tap a simple and cost-effective tool, Microsoft Excel, to solve specific business problems using powerful analytic techniques—and achieve optimum results. Practical exercises in each chapter help you apply and reinforce techniques as you learn. Shows you how to perform sophisticated business analyses using the cost-effective and widely available Microsoft Excel instead of expensive, proprietary analytical tools Reveals how to target and retain profitable customers and avoid high-risk customers Helps you forecast sales and improve response rates for marketing campaigns Explores how to optimize price points for products and services, optimize store layouts, and improve online advertising Covers social media, viral marketing, and how to exploit both effectively Improve your marketing results with Microsoft Excel and the invaluable techniques and ideas in Marketing Analytics: Data-Driven Techniques with Microsoft Excel.

Multidimensional Mining of Massive Text Data

Multidimensional Mining of Massive Text Data
Author: Chao Zhang
Publisher: Morgan & Claypool Publishers
Total Pages: 199
Release: 2019-03-21
Genre: Computers
ISBN: 1681735202
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Unstructured text, as one of the most important data forms, plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to scientific research and healthcare informatics. In many emerging applications, people's information need from text data is becoming multidimensional—they demand useful insights along multiple aspects from a text corpus. However, acquiring such multidimensional knowledge from massive text data remains a challenging task. This book presents data mining techniques that turn unstructured text data into multidimensional knowledge. We investigate two core questions. (1) How does one identify task-relevant text data with declarative queries in multiple dimensions? (2) How does one distill knowledge from text data in a multidimensional space? To address the above questions, we develop a text cube framework. First, we develop a cube construction module that organizes unstructured data into a cube structure, by discovering latent multidimensional and multi-granular structure from the unstructured text corpus and allocating documents into the structure. Second, we develop a cube exploitation module that models multiple dimensions in the cube space, thereby distilling from user-selected data multidimensional knowledge. Together, these two modules constitute an integrated pipeline: leveraging the cube structure, users can perform multidimensional, multigranular data selection with declarative queries; and with cube exploitation algorithms, users can extract multidimensional patterns from the selected data for decision making. The proposed framework has two distinctive advantages when turning text data into multidimensional knowledge: flexibility and label-efficiency. First, it enables acquiring multidimensional knowledge flexibly, as the cube structure allows users to easily identify task-relevant data along multiple dimensions at varied granularities and further distill multidimensional knowledge. Second, the algorithms for cube construction and exploitation require little supervision; this makes the framework appealing for many applications where labeled data are expensive to obtain.

Static Analysis

Static Analysis
Author: Radhia Cousot
Publisher: Springer Science & Business Media
Total Pages: 482
Release: 2010-09-09
Genre: Computers
ISBN: 3642157688
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This book constitutes the refereed proceedings of the 16th International Symposium on Static Analysis, SAS 2010, held in Perpignan, France in September 2010. The conference was co-located with 3 affiliated workshops: NSAD 2010 (Workshop on Numerical and Symbolic Abstract Domains), SASB 2010 (Workshop on Static Analysis and Systems Biology) and TAPAS 2010 (Tools for Automatic Program Analysis). The 22 revised full papers presented together with 4 invited talks were carefully reviewed and selected from 58 submissions. The papers address all aspects of static analysis including abstract domains, bug detection, data flow analysis, logic programming, systems analysis, type inference, cache analysis, flow analysis, verification, abstract testing, compiler optimization and program verification.

Analyzing Multidimensional Well-Being

Analyzing Multidimensional Well-Being
Author: Satya R. Chakravarty
Publisher: John Wiley & Sons
Total Pages: 330
Release: 2017-11-20
Genre: Business & Economics
ISBN: 1119256909
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“An indispensable reference for all researchers interested in the measurement of social welfare. . .” —François Bourguignon, Emeritus Professor at Paris School of Economics, Former Chief Economist of the World Bank. “. . .a detailed, insightful, and pedagogical presentation of the theoretical grounds of multidimensional well-being, inequality, and poverty measurement. Any student, researcher, and practitioner interested in the multidimensional approach should begin their journey into such a fascinating theme with this wonderful book.” —François Maniquet, Professor, Catholic University of Louvain, Belgium. A Review of the Multidimensional Approaches to the Measurement of Welfare, Inequality, and Poverty Analyzing Multidimensional Well-Being: A Quantitative Approach offers a comprehensive approach to the measurement of well-being that includes characteristics such as income, health, literacy, and housing. The author presents a systematic comparison of the alternative approaches to the measurement of multidimensional welfare, inequality, poverty, and vulnerability. The text contains real-life applications of some multidimensional aggregations (most of which have been designed by international organizations such as the United Nations Development Program and the Organization for Economic Co-operation and Development) that help to judge the performance of a country in the various dimensions of well-being. The text offers an evaluation of how well a society is doing with respect to achievements of all the individuals in the dimensions considered and clearly investigates how achievements in the dimensions can be evaluated from different perspectives. The author includes a detailed scrutiny of alternative techniques for setting weights to individual dimensional metrics and offers an extensive analysis into both the descriptive and welfare theoretical approaches to the concerned multi-attribute measurement and related issues. This important resource: • Contains a synthesis of multidimensional welfare, inequality, poverty, and vulnerability analysis • Examines aggregations of achievement levels in the concerned dimensions of well-being from various standpoints • Shows how to measure poverty using panel data instead of restricting attention to a single period and when we have imprecise information on dimensional achievements • Argues that multidimensional analysis is intrinsically different from marginal distributions-based analysis Written for students, teachers, researchers, and scholars, Analyzing Multidimensional Well-Being: A Quantitative Approach puts the focus on various approaches to the measurementof the many aspects of well-being and quality of life. Satya R. Chakravarty is a Professor of Economics at the Indian Statistical Institute, Kolkata, India. He is an Editor of Social Choice and Welfare and a member of the Editorial Board of Journal of Economic Inequality.