Data Simplification

Data Simplification
Author: Jules J. Berman
Publsiher: Morgan Kaufmann
Total Pages: 398
Release: 2016-03-10
Genre: Computers
ISBN: 9780128038543

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Data Simplification: Taming Information With Open Source Tools addresses the simple fact that modern data is too big and complex to analyze in its native form. Data simplification is the process whereby large and complex data is rendered usable. Complex data must be simplified before it can be analyzed, but the process of data simplification is anything but simple, requiring a specialized set of skills and tools. This book provides data scientists from every scientific discipline with the methods and tools to simplify their data for immediate analysis or long-term storage in a form that can be readily repurposed or integrated with other data. Drawing upon years of practical experience, and using numerous examples and use cases, Jules Berman discusses the principles, methods, and tools that must be studied and mastered to achieve data simplification, open source tools, free utilities and snippets of code that can be reused and repurposed to simplify data, natural language processing and machine translation as a tool to simplify data, and data summarization and visualization and the role they play in making data useful for the end user. Discusses data simplification principles, methods, and tools that must be studied and mastered Provides open source tools, free utilities, and snippets of code that can be reused and repurposed to simplify data Explains how to best utilize indexes to search, retrieve, and analyze textual data Shows the data scientist how to apply ontologies, classifications, classes, properties, and instances to data using tried and true methods

Making Life Easy for Citizens and Businesses in Portugal Administrative Simplification and e Government

Making Life Easy for Citizens and Businesses in Portugal Administrative Simplification and e Government
Author: OECD
Publsiher: OECD Publishing
Total Pages: 210
Release: 2008-12-18
Genre: Electronic Book
ISBN: 9789264048263

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Analyses administrative simplification and e-government in Portugal, showing how e-government can be used as a lever for broader administrative simplification by making service delivery more coherent and efficient.

Topological Data Analysis for Scientific Visualization

Topological Data Analysis for Scientific Visualization
Author: Julien Tierny
Publsiher: Springer
Total Pages: 150
Release: 2018-01-16
Genre: Mathematics
ISBN: 9783319715070

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Combining theoretical and practical aspects of topology, this book provides a comprehensive and self-contained introduction to topological methods for the analysis and visualization of scientific data. Theoretical concepts are presented in a painstaking but intuitive manner, with numerous high-quality color illustrations. Key algorithms for the computation and simplification of topological data representations are described in detail, and their application is carefully demonstrated in a chapter dedicated to concrete use cases. With its fine balance between theory and practice, "Topological Data Analysis for Scientific Visualization" constitutes an appealing introduction to the increasingly important topic of topological data analysis for lecturers, students and researchers.

Data Abstraction and Pattern Identification in Time series Data

Data Abstraction and  Pattern Identification  in Time series Data
Author: Prithiviraj Muthumanickam
Publsiher: Linköping University Electronic Press
Total Pages: 58
Release: 2019-11-25
Genre: Electronic Book
ISBN: 9789179299651

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Data sources such as simulations, sensor networks across many application domains generate large volumes of time-series data which exhibit characteristics that evolve over time. Visual data analysis methods can help us in exploring and understanding the underlying patterns present in time-series data but, due to their ever-increasing size, the visual data analysis process can become complex. Large data sets can be handled using data abstraction techniques by transforming the raw data into a simpler format while, at the same time, preserving significant features that are important for the user. When dealing with time-series data, abstraction techniques should also take into account the underlying temporal characteristics. This thesis focuses on different data abstraction and pattern identification methods particularly in the cases of large 1D time-series and 2D spatio-temporal time-series data which exhibit spatiotemporal discontinuity. Based on the dimensionality and characteristics of the data, this thesis proposes a variety of efficient data-adaptive and user-controlled data abstraction methods that transform the raw data into a symbol sequence. The transformation of raw time-series into a symbol sequence can act as input to different sequence analysis methods from data mining and machine learning communities to identify interesting patterns of user behavior. In the case of very long duration 1D time-series, locally adaptive and user-controlled data approximation methods were presented to simplify the data, while at the same time retaining the perceptually important features. The simplified data were converted into a symbol sequence and a sketch-based pattern identification was then used to identify patterns in the symbolic data using regular expression based pattern matching. The method was applied to financial time-series and patterns such as head-and-shoulders, double and triple-top patterns were identified using hand drawn sketches in an interactive manner. Through data smoothing, the data approximation step also enables visualization of inherent patterns in the time-series representation while at the same time retaining perceptually important points. Very long duration 2D spatio-temporal eye tracking data sets that exhibit spatio-temporal discontinuity was transformed into symbolic data using scalable clustering and hierarchical cluster merging processes, each of which can be parallelized. The raw data is transformed into a symbol sequence with each symbol representing a region of interest in the eye gaze data. The identified regions of interest can also be displayed in a Space-Time Cube (STC) that captures both the temporal and contextual information. Through interactive filtering, zooming and geometric transformation, the STC representation along with linked views enables interactive data exploration. Using different sequence analysis methods, the symbol sequences are analyzed further to identify temporal patterns in the data set. Data collected from air traffic control officers from the domain of Air traffic control were used as application examples to demonstrate the results.

Automatic Text Simplification

Automatic Text Simplification
Author: Horacio Saggion
Publsiher: Springer Nature
Total Pages: 121
Release: 2022-05-31
Genre: Computers
ISBN: 9783031021664

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Thanks to the availability of texts on the Web in recent years, increased knowledge and information have been made available to broader audiences. However, the way in which a text is written—its vocabulary, its syntax—can be difficult to read and understand for many people, especially those with poor literacy, cognitive or linguistic impairment, or those with limited knowledge of the language of the text. Texts containing uncommon words or long and complicated sentences can be difficult to read and understand by people as well as difficult to analyze by machines. Automatic text simplification is the process of transforming a text into another text which, ideally conveying the same message, will be easier to read and understand by a broader audience. The process usually involves the replacement of difficult or unknown phrases with simpler equivalents and the transformation of long and syntactically complex sentences into shorter and less complex ones. Automatic text simplification, a research topic which started 20 years ago, now has taken on a central role in natural language processing research not only because of the interesting challenges it posesses but also because of its social implications. This book presents past and current research in text simplification, exploring key issues including automatic readability assessment, lexical simplification, and syntactic simplification. It also provides a detailed account of machine learning techniques currently used in simplification, describes full systems designed for specific languages and target audiences, and offers available resources for research and development together with text simplification evaluation techniques.

Digital Imaging for Cultural Heritage Preservation

Digital Imaging for Cultural Heritage Preservation
Author: Filippo Stanco,Sebastiano Battiato,Giovanni Gallo
Publsiher: CRC Press
Total Pages: 523
Release: 2011-07-28
Genre: Computers
ISBN: 9781439821732

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This edition presents the most prominent topics and applications of digital image processing, analysis, and computer graphics in the field of cultural heritage preservation. The text assumes prior knowledge of digital image processing and computer graphics fundamentals. Each chapter contains a table of contents, illustrations, and figures that elucidate the presented concepts in detail, as well as a chapter summary and a bibliography for further reading. Well-known experts cover a wide range of topics and related applications, including spectral imaging, automated restoration, computational reconstruction, digital reproduction, and 3D models.

Impact Evaluation of Business License Simplification in Peru

Impact Evaluation of Business License Simplification in Peru
Author: World Bank
Publsiher: World Bank Publications
Total Pages: 58
Release: 2012-12-01
Genre: Business & Economics
ISBN: 9780821398029

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This report assessed the effects of reforms supported by the International Finance Corporation s Business License Simplification Project in Lima, Peru, and identified the main benefits as time and cost savings for businesses.

The Design of Management Information Systems for Mental Health Organizations

The Design of Management Information Systems for Mental Health Organizations
Author: Robert L. Chapman
Publsiher: Unknown
Total Pages: 128
Release: 1980
Genre: Information storage and retrieval systems
ISBN: UOM:39015016251574

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Analytics and Big Data for Accountants

Analytics and Big Data for Accountants
Author: Jim Lindell
Publsiher: John Wiley & Sons
Total Pages: 208
Release: 2018-03-23
Genre: Computers
ISBN: 9781119512363

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Analytics is the new force driving business. Tools have been created to measure program impacts and ROI, visualize data and business processes, and uncover the relationship between key performance indicators, many using the unprecedented amount of data now flowing into organizations. Featuring updated examples and surveys, this dynamic book covers leading-edge topics in analytics and finance. It is packed with useful tips and practical guidance you can apply immediately. This book prepares accountants to: Deal with major trends in predictive analytics, optimization, correlation of metrics, and big data. Interpret and manage new trends in analytics techniques affecting your organization. Use new tools for data analytics. Critically interpret analytics reports and advise decision makers.

Benefits simplification

Benefits simplification
Author: Great Britain: Parliament: House of Commons: Work and Pensions Committee
Publsiher: The Stationery Office
Total Pages: 248
Release: 2007-07-26
Genre: Finance, Public
ISBN: 021503550X

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Benefits Simplification : Seventh report of session 2006-07, Vol. 2: Oral and written Evidence

Data Preprocessing in Data Mining

Data Preprocessing in Data Mining
Author: Salvador García,Julián Luengo,Francisco Herrera
Publsiher: Springer
Total Pages: 320
Release: 2014-08-30
Genre: Technology & Engineering
ISBN: 9783319102474

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Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data. This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given.Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering.

Pension Simplification and Expansion

Pension Simplification and Expansion
Author: United States. Congress. Senate. Committee on Finance. Subcommittee on Private Retirement Plans and Oversight of the Internal Revenue Service,United States
Publsiher: Unknown
Total Pages: 399
Release: 1992
Genre: Government publications
ISBN: PSU:000018277319

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Tax Simplification and Technical Corrections Acts of 1991

Tax Simplification and Technical Corrections Acts of 1991
Author: United States,United States. Congress. House. Committee on Ways and Means
Publsiher: Unknown
Total Pages: 838
Release: 1992
Genre: Government publications
ISBN: PSU:000018274080

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Statistical Data Cleaning with Applications in R

Statistical Data Cleaning with Applications in R
Author: Mark van der Loo,Edwin de Jonge
Publsiher: John Wiley & Sons
Total Pages: 320
Release: 2018-01-29
Genre: Computers
ISBN: 9781118897140

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A comprehensive guide to automated statistical data cleaning The production of clean data is a complex and time-consuming process that requires both technical know-how and statistical expertise. Statistical Data Cleaning brings together a wide range of techniques for cleaning textual, numeric or categorical data. This book examines technical data cleaning methods relating to data representation and data structure. A prominent role is given to statistical data validation, data cleaning based on predefined restrictions, and data cleaning strategy. Key features: Focuses on the automation of data cleaning methods, including both theory and applications written in R. Enables the reader to design data cleaning processes for either one-off analytical purposes or for setting up production systems that clean data on a regular basis. Explores statistical techniques for solving issues such as incompleteness, contradictions and outliers, integration of data cleaning components and quality monitoring. Supported by an accompanying website featuring data and R code. This book enables data scientists and statistical analysts working with data to deepen their understanding of data cleaning as well as to upgrade their practical data cleaning skills. It can also be used as material for a course in data cleaning and analyses.

Advanced Data Mining and Applications

Advanced Data Mining and Applications
Author: Xudong Luo,Jeffrey Xu Yu,Zhi Li
Publsiher: Springer
Total Pages: 741
Release: 2014-12-17
Genre: Computers
ISBN: 9783319147178

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This book constitutes the proceedings of the 10th International Conference on Advanced Data Mining and Applications, ADMA 2014, held in Guilin, China during December 2014. The 48 regular papers and 10 workshop papers presented in this volume were carefully reviewed and selected from 90 submissions. They deal with the following topics: data mining, social network and social media, recommend systems, database, dimensionality reduction, advance machine learning techniques, classification, big data and applications, clustering methods, machine learning, and data mining and database.