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RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The goal of this type of analyses is to understand the response of such systems in particular with respect their probabilistic behavior, to understand their predictability and drivers or lack of thereof. Data mining capabilities are the cornerstones to perform such deep learning of system responses. For this reason static data mining capabilities were added last fiscal year (FY 15). In real applications, when dealing with complex multi-scale, multi-physics systems it seems natural that, during transients, the relevance of the different scales, and physics, would evolve over time. For these reasons the data mining capabilities have been extended allowing their application over time. In this writing it is reported a description of the new RAVEN capabilities implemented with several simple analytical tests to explain their application and highlight the proper implementation. The report concludes with the application of those newly implemented capabilities to the analysis of a simulation performed with the Bison code.

• Foreword xvii Preface to the second edition xix Preface to the first edition xxi Acknowledgments xxiii Part I PRELIMINARIES
• Chapter 1 Introduction 3 1.1 What Is Data Mining? 3 1.2 Where Is Data Mining Used? 4 1.3 Origins of Data Mining 4 1.4 Rapid Growth of Data Mining 5 1.5 Why Are There So Many Different Methods? 6 1.6 Terminology and Notation 7 1.7 Road Maps to This Book 9
• Chapter 2 Overview of the Data Mining Process 12 2.1 Introduction 12 2.2 Core Ideas in Data Mining 13 2.3 Supervised and Unsupervised Learning 15 2.4 Steps in Data Mining 15 2.5 Preliminary Steps 17 2.6 Building a Model: Example with Linear Regression 27 2.7 Using Excel for Data Mining 34 Part II DATA EXPLORATION AND DIMENSION REDUCTION
• Chapter 3 Data Visualization 43 3.1 Uses of Data Visualization 43 3.2 Data Examples 45 3.3 Basic Charts: Bar Charts, Line Graphs, and Scatterplots 45 3.4 Multidimensional Visualization 52 3.5 Specialized Visualizations 63 3.6 Summary ofMajor Visualizations and Operations, According to Data Mining Goal 67
• Chapter 4 Dimension Reduction 71 4.1 Introduction 71 4.2 Practical Considerations 72 4.3 Data Summaries 73 4.4 Correlation Analysis . 76 4.5 Reducing the Number of Categories in Categorical Variables 76 4.6 Converting a Categorical Variable to a Numerical Variable 78 4.7 Principal Components Analysis 78 4.8 Dimension Reduction Using Regression Models 87 4.9 Dimension Reduction Using Classification and Regression Trees 88 Part III PERFORMANCE EVALUATION
• Chapter 5 Evaluating Classification and Predictive Performance 93 5.1 Introduction 93 5.2 Judging Classification Performance 94 5.3 Evaluating Predictive Performance 115 Part IV PREDICTION AND CLASSIFICATION METHODS
• Chapter 6 Multiple Linear Regression 121 6.1 Introduction 121 6.2 Explanatory versus Predictive Modeling 122 6.3 Estimating the Regression Equation and Prediction 123 6.4 Variable Selection in Linear Regression 127
• Chapter 7 k-Nearest Neighbors (k-NN) 137 7.1 k-NN Classifier (Categorical Outcome) 137 7.2 k-NN for a Numerical Response 142 7.3 Advantages and Shortcomings of k-NN Algorithms 144
• Chapter 8 Naive Bayes 148 8.1 Introduction 148 8.2 Applying the Full (Exact) Bayesian Classifier 150 8.3 Advantages and Shortcomings of the Naive Bayes Classifier 159
• Chapter 9 Classification and Regression Trees 164 9.1 Introduction 164 9.2 Classification Trees 166 9.3 Measures of Impurity 169 9.4 Evaluating the Performance of a Classification Tree 173 9.5 Avoiding Overfitting 179 9.6 Classification Rules from Trees 183 9.7 Classification Trees for More Than Two Classes 185 9.8 RegressionTrees 185 9.9 Advantages, Weaknesses, and Extensions 187
• Chapter 10 Logistic Regression 192 10.1 Introduction 192 10.2 Logistic Regression Model 194 10.3 Evaluating Classification Performance 202 10.4 Example of Complete Analysis: Predicting Delayed Flights 206 10.5 Appendix: Logistic Regression for Profiling 211
• Chapter 11 Neural Nets 222 11.1 Introduction 222 11.2 Concept and Structure of a Neural Network 223 11.3 Fitting a Network to Data 223 11.4 Required User Input 237 11.5 Exploring the Relationship Between Predictors andResponse 239 11.6 Advantages and Weaknesses of Neural Networks 239
• Chapter 12 Discriminant Analysis 243 12.1 Introduction 243 12.2 Distance of an Observation from a Class 246 12.3 Fisher's Linear Classification Functions 247 12.4 Classification Performance of Discriminant Analysis 251 12.5 Prior Probabilities 252 12.6 Unequal Misclassification Costs 252 12.7 Classifying More Than Two Classes 253 12.8 Advantages and Weaknesses 254 Part V MINING RELATIONSHIPS AMONG RECORDS
• Chapter 13 Association Rules 263 13.1 Introduction 263 13.2 Discovering Association Rules in Transaction Databases 263 13.3 Generating Candidate Rules 265 13.4 Selecting Strong Rules 267 13.5 Summary 275
• Chapter 14 Cluster Analysis 279 14.1 Introduction 279 14.2 Measuring Distance Between Two Records 283 14.3 Measuring Distance Between Two Clusters 287 14.4 Hierarchical (Agglomerative) Clustering 290 14.5 Nonhierarchical Clustering: The k-Means Algorithm 295 Part VI FORECASTING TIME SERIES
• Chapter 15 Handling Time Series 305 15.1 Introduction 305 15.2 Explanatory versus Predictive Modeling 306 15.3 Popular Forecasting Methods in Business 307 15.4 Time Series Components 308 15.5 Data Partitioning 312
• Chapter 16 Regression-Based Forecasting 317 16.1 Model with Trend 317 16.2 Model with Seasonality 322 16.3 Model with Trend and Seasonality 324 16.4 Autocorrelation and ARIMA Models 324
• Chapter 17 Smoothing Methods 344 17.1 Introduction 344 17.2 MovingAverage 345 17.3 Simple Exponential Smoothing 350 17.4 Advanced Exponential Smoothing 353 Part VII CASES
• Chapter 18 Cases 367 18.1 Charles Book Club 367 18.2 German Credit 375 18.3 Tayko Software Cataloger 379 18.4 Segmenting Consumers of Bath Soap 383 18.5 Direct-MailFundraising 387 18.6 Catalog Cross Selling 389 18.7 Predicting Bankruptcy 390 18.8 Time Series Case: Forecasting Public Transportation Demand 393 References 397 Index 399.
• (source: Nielsen Book Data)

Praise for the First Edition " full of vivid and thought-provoking anecdotes needs to be read by anyone with a serious interest in research and marketing." -- Research magazine "Shmueli et al. have done a wonderful job in presenting the field of data mining a welcome addition to the literature." -- computingreviews.com Incorporating a new focus on data visualization and time series forecasting, Data Mining for Business Intelligence , Second Edition continues to supply insightful, detailed guidance on fundamental data mining techniques. This new edition guides readers through the use of the Microsoft Office Excel add-in XLMiner for developing predictive models and techniques for describing and finding patterns in data. From clustering customers into market segments and finding the characteristics of frequent flyers to learning what items are purchased with other items, the authors use interesting, real-world examples to build a theoretical and practical understanding of key data mining methods, including classification, prediction, and affinity analysis as well as data reduction, exploration, and visualization. The Second Edition now features: Three new chapters on time series forecasting, introducing popular business forecasting methods including moving average, exponential smoothing methods; regression-based models; and topics such as explanatory vs. predictive modeling, two-level models, and ensembles A revised chapter on data visualization that now features interactive visualization principles and added assignments that demonstrate interactive visualization in practice Separate chapters that each treat k-nearest neighbors and Naive Bayes methods Summaries at the start of each chapter that supply an outline of key topics The book includes access to XLMiner, allowing readers to work hands-on with the provided data. Throughout the book, applications of the discussed topics focus on the business problem as motivation and avoid unnecessary statistical theory. Each chapter concludes with exercises that allow readers to assess their comprehension of the presented material. The final chapter includes a set of cases that require use of the different data mining techniques, and a related Web site features data sets, exercise solutions, PowerPoint slides, and case solutions. Data Mining for Business Intelligence , Second Edition is an excellent book for courses on data mining, forecasting, and decision support systems at the upper-undergraduate and graduate levels. It is also a one-of-a-kind resource for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.
(source: Nielsen Book Data)

• Point Estimation Algorithms
• Applications of Bayes Theorem
• Similarity Measures
• Decision Trees
• Genetic Algorithms
• Classification: Distance Based Algorithms
• Decision Tree-Based Algorithms
• Covering (Rule-Based) Algorithms
• Clustering: An Overview
• Clustering Hierarchical Algorithms
• Clustering Partitional Algorithms
• Clustering: Large Databases
• Clustering Categorical Attributes
• Association Rules: An Overview
• Association Rules: Parallel and Distributed Algorithms
• Association Rules: Advanced Techniques and Measures
• Spatial Mining: Techniques and Algorithms.
• (source: Nielsen Book Data)

The continual explosion of information technology and the need for better data collection and management methods has made data mining an even more relevant topic of study. Books on data mining tend to be either broad and introductory or focus on some very specific technical aspect of the field.This book is a series of seventeen edited "student-authored lectures" which explore in depth the core of data mining (classification, clustering and association rules) by offering overviews that include both analysis and insight.The initial chapters lay a framework of data mining techniques by explaining some of the basics such as applications of Bayes Theorem, similarity measures, and decision trees. Before focusing on the pillars of classification, clustering and association rules, the book also considers alternative candidates such as point estimation and genetic algorithms.The book's discussion of classification includes an introduction to decision tree algorithms, rule-based algorithms (a popular alternative to decision trees) and distance-based algorithms. Five of the lecture-chapters are devoted to the concept of clustering or unsupervised classification. The functionality of hierarchical and partitional clustering algorithms is also covered as well as the efficient and scalable clustering algorithms used in large databases. The concept of association rules in terms of basic algorithms, parallel and distributive algorithms and advanced measures that help determine the value of association rules are discussed. The final chapter discusses algorithms for spatial data mining.
(source: Nielsen Book Data)

• Introduction to Data Mining
• The Data Mining Process
• Data Mining Tools and Techniques
• Managing the Data Mining Project
• Modeling Data
• Deploying the Results
• The Discovery and Delivery of Knowledge for Effective Enterprise Outcomes: Knowledge Management
• Appendices: Glossary
• References
• Web Sites
• Data Mining and Knowledge Discovery Data Sets in the Public Domain
• Microsoft Solution Providers
• Summary of Knowledge Management Case Studies and Web Locations.
• (source: Nielsen Book Data)

Microsoft Data Mining approaches data mining from the particular perspective of IT professionals using Microsoft data management technologies. The author explains the new data mining capabilities in Microsoft's SQL Server 2000 database, Commerce Server, and other products, details the Microsoft OLE DB for Data Mining standard, and gives readers best practices for using all of them. The book bridges the previously specialized field of data mining with the new technologies and methods that are quickly making it an important mainstream tool for companies of all sizes. Data mining refers to a set of technologies and techniques by which IT professionals search large databases of information (such as those contained by SQL Server) for patterns and trends. Traditionally important in finance, telecommunication, and other information-intensive fields, data mining increasingly helps companies better understand and serve their customers by revealing buying patterns and related interests. It is becoming a foundation for e-commerce and knowledge management.
(source: Nielsen Book Data)

• Swarm Intelligence in Data Mining.- Ants Constructing Rule-Based Classifiers.- Performing Feature Selection with ACO.- Simultaneous Ant Colony Optimization Algorithms for Learning Linguistic Fuzzy Rules.- Ant Colony Clustering and Feature Extraction for Anomaly Intrusion Detection.- Particle Swarm Optimization for Pattern Recognition and Image Processing.- Data and Text Mining with Hierarchical Clustering Ants.- Swarm Clustering Based on Flowers Pollination by Artificial Bees.- Computer study of the evolution of `news foragers' on the Internet.- Data Swarm Clustering.- Clustering Ensemble Using ANT and ART.
• (source: Nielsen Book Data)

This volume examines the application of swarm intelligence in data mining, addressing the issues of swarm intelligence and data mining using novel intelligent approaches. The book comprises 11 chapters including an introduction reviewing fundamental definitions and important research challenges. Important features include a detailed overview of swarm intelligence and data mining paradigms, focused coverage of timely, advanced data mining topics, state-of-the-art theoretical research and application developments and contributions by pioneers in the field.
(source: Nielsen Book Data)

• * Overview of text mining * From textual information to numerical vectors * Using text for prediction * Information retrieval and text mining * Finding structure in a document collection * Looking for information in documents * Case studies * Emerging directions * Appendix: software notes * References * Author & subject indexes.
• (source: Nielsen Book Data)

Data mining is a mature technology. The prediction problem, looking for predictive patterns in data, has been widely studied. Strong me- ods are available to the practitioner. These methods process structured numerical information, where uniform measurements are taken over a sample of data. Text is often described as unstructured information. So, it would seem, text and numerical data are different, requiring different methods. Or are they? In our view, a prediction problem can be solved by the same methods, whether the data are structured - merical measurements or unstructured text. Text and documents can be transformed into measured values, such as the presence or absence of words, and the same methods that have proven successful for pred- tive data mining can be applied to text. Yet, there are key differences. Evaluation techniques must be adapted to the chronological order of publication and to alternative measures of error. Because the data are documents, more specialized analytical methods may be preferred for text. Moreover, the methods must be modi?ed to accommodate very high dimensions: tens of thousands of words and documents. Still, the central themes are similar.
(source: Nielsen Book Data)

• Preface by Editors; Contents; List of Contributors; Exploring the Hamming Distance in Distributed Infrastructures for Similarity Search; 1 Introduction; 2 Background; 2.1 Vector Space Model; 2.2 Random Hyperplane Hashing and Hamming Similarity; 3 Literature Review; 4 Similarity Search Based on Hamming Distance; 4.1 Hamming DHT; 4.2 HCube; 5 Evaluations; 5.1 Hamming Similarity; 5.2 Hamming DHT; 5.3 HCube; 6 Conclusions and Further Research Issues; References; Data Modeling for Socially Based Routing in Opportunistic Networks; 1Introduction; 2Opportunistic Networks; 2.1Definition; 2.2Challenges.

• 2.3Use Cases3Data Routing and Dissemination; 3.1Basic Algorithms; 3.2Socially Based Algorithms; 3.3History-and Prediction-Based Algorithms; 4Potential Solutions; 4.1SPRINT; 4.2SENSE; 5Future Trends; 6Conclusions; Decision Tree Induction Methods and Their Application to Big Data; 1 Introduction; 2 Preliminary Concepts and Background; 3 Subtasks and Design Criteria for Decision Tree Induction; 4 Attribute Selection Criteria; 4.1 Information Gain Criterion and Gain Ratio; 4.2 Gini Function; 5 Discretization of Attribute Values; 5.1 Binary Discretization; 5.2 Multi-interval Discretization.

• 5.3 Discretization of Categorical or Symbolical Attributes6 Pruning; 6.1 Overview about Pruning Methods; 6.2 An Example of a Pruning Method
• Cost-Complexity Pruning; 7 Fitting Expert Knowledge into the Decision Tree Model, Improvement of Classification Performance, and Feature Subset Selection; 8 How to Interpret a Learnt Decision Tree?; 8.1 Quantitative Measures for the Quality of the Decision Tree Model; 8.2 Comparison of Two Decision Trees; 9 Conclusions; References; Sensory Data Gathering for Road TrafficMonitoring: Energy Efficiency, Reliability, and Fault Tolerance; 1 Introduction.

• 2 Literature Survey3 Convergecast Tree Management Scheme; 3.1 System Model and Assumptions; 3.2 Initialization; 3.3 Tree Maintenance; 3.4 Convergecast Controller; 4 Simulation Result; 5 Conclusion and Future Directions of Research; References; Data Aggregation and Forwarding Route Control for Efficient Data Gathering in Dense Mobile Wireless Sensor Networks; 1 Introduction; 2 Assumptions; 2.1 System Environment; 2.2 Geo-Routing; 3 Related Work; 3.1 Location-Based DataManagement in Dense MANETs; 3.2 Data Gathering Utilizing Correlation of Data in Wireless Sensor Networks.

• 4 DGUMA: Our Previous Method4.1 Mobile Agent; 4.2 Deployment of Mobile Agents; 4.3 Movement of Mobile Agent; 4.4 Transmission of Sensor Data; 5 DGUMA/DA: The Extended Method; 5.1 Outline; 5.2 Timer Setting; 5.3 Transmission of Sensor Data; 5.4 Forwarding Route Control; 5.5 Restoring Sensor Readings at the Sink; 6 Discussion; 6.1 Overhead Generated by the Forwarding Route Control; 6.2 Traffic for Data Gathering Using Lengthwise Tree in the Lengthwise Distribution; 6.3 Traffic for Data Gathering Using Crosswise Tree in the Crosswise Distribution.

This book covers the latest advances in Big Data technologies and provides the readers with a comprehensive review of the state-of-the-art in Big Data processing, analysis, analytics, and other related topics. It presents new models, algorithms, software solutions and methodologies, covering the full data cycle, from data gathering to their visualization and interaction, and includes a set of case studies and best practices. New research issues, challenges and opportunities shaping the future agenda in the field of Big Data are also identified and presented throughout the book, which is inten.

• Preliminaries
• Python language basics, IPython, and Jupyter notebook
• Built-in data structures, functions, and files
• NumPy basics : arrays and vectorized computation
• Getting started with pandas
• Data loading, storage, and file formats
• Data cleaning and preparation
• Data wrangling : join, combine, and reshape
• Plotting and visualization
• Data aggregation and group operations
• Time series
• Advanced pandas
• Introduction to modeling libraries in Python
• Data analysis examples
• Advanced NumPy
• More on the IPython system.

• Copyright; Table of Contents; Preface;
• Section 1. New for the Second Edition;
• Section 2. Conventions Used in This Book;
• Section 3. Using Code Examples;
• Section 4. O'Reilly Safari;
• Section 5. How to Contact Us;
• Section 6. Acknowledgments; In Memoriam: John D. Hunter (1968-2012); Acknowledgments for the Second Edition (2017); Acknowledgments for the First Edition (2012);
• Chapter 1. Preliminaries; 1.1 What Is This Book About?; What Kinds of Data?; 1.2 Why Python for Data Analysis?; Python as Glue; Solving the "Two-Language" Problem; Why Not Python?; 1.3 Essential Python Libraries; NumPy; pandas.

• MatplotlibIPython and Jupyter; SciPy; scikit-learn; statsmodels; 1.4 Installation and Setup; Windows; Apple (OS X, macOS); GNU/Linux; Installing or Updating Python Packages; Python 2 and Python 3; Integrated Development Environments (IDEs) and Text Editors; 1.5 Community and Conferences; 1.6 Navigating This Book; Code Examples; Data for Examples; Import Conventions; Jargon;
• Chapter 2. Python Language Basics, IPython, and Jupyter Notebooks; 2.1 The Python Interpreter; 2.2 IPython Basics; Running the IPython Shell; Running the Jupyter Notebook; Tab Completion; Introspection.

• The %run CommandExecuting Code from the Clipboard; Terminal Keyboard Shortcuts; About Magic Commands; Matplotlib Integration; 2.3 Python Language Basics; Language Semantics; Scalar Types; Control Flow;
• Chapter 3. Built-in Data Structures, Functions, and Files; 3.1 Data Structures and Sequences; Tuple; List; Built-in Sequence Functions; dict; set; List, Set, and Dict Comprehensions; 3.2 Functions; Namespaces, Scope, and Local Functions; Returning Multiple Values; Functions Are Objects; Anonymous (Lambda) Functions; Currying: Partial Argument Application; Generators.

• Errors and Exception Handling3.3 Files and the Operating System; Bytes and Unicode with Files; 3.4 Conclusion;
• Chapter 4. NumPy Basics: Arrays and Vectorized Computation; 4.1 The NumPy ndarray: A Multidimensional Array Object; Creating ndarrays; Data Types for ndarrays; Arithmetic with NumPy Arrays; Basic Indexing and Slicing; Boolean Indexing; Fancy Indexing; Transposing Arrays and Swapping Axes; 4.2 Universal Functions: Fast Element-Wise Array Functions; 4.3 Array-Oriented Programming with Arrays; Expressing Conditional Logic as Array Operations; Mathematical and Statistical Methods.

• Methods for Boolean ArraysSorting; Unique and Other Set Logic; 4.4 File Input and Output with Arrays; 4.5 Linear Algebra; 4.6 Pseudorandom Number Generation; 4.7 Example: Random Walks; Simulating Many Random Walks at Once; 4.8 Conclusion;
• Chapter 5. Getting Started with pandas; 5.1 Introduction to pandas Data Structures; Series; DataFrame; Index Objects; 5.2 Essential Functionality; Reindexing; Dropping Entries from an Axis; Indexing, Selection, and Filtering; Integer Indexes; Arithmetic and Data Alignment; Function Application and Mapping; Sorting and Ranking.

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples.
(source: Nielsen Book Data)

• to Data Mining Principles.- Data Warehousing, Data Mining, and OLAP.- Data Marts and Data Warehouse.- Evolution and Scaling of Data Mining Algorithms.- Emerging Trends and Applications of Data Mining.- Data Mining Trends and Knowledge Discovery.- Data Mining Tasks, Techniques, and Applications.- Data Mining: an Introduction - Case Study.- Data Mining & KDD.- Statistical Themes and Lessons for Data Mining.- Theoretical Frameworks for Data Mining.- Major and Privacy Issues in Data Mining and Knowledge Discovery.- Active Data Mining.- Decomposition in Data Mining - A Case Study.- Data Mining System Products and Research Prototypes.- Data Mining in Customer Value and Customer Relationship Management.- Data Mining in Business.- Data Mining in Sales Marketing and Finance.- Banking and Commercial Applications.- Data Mining for Insurance.- Data Mining in Biomedicine and Science.- Text and Web Mining.- Data Mining in Information Analysis and Delivery.- Data Mining in Telecommunications and Control.- Data Mining in Security.
• (source: Nielsen Book Data)

This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in database systems, and presents a broad, yet in-depth overview of the field of data mining. Data mining is a multidisciplinary field, drawing work from areas including database technology, artificial intelligence, machine learning, neural networks, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing and data visualization.
(source: Nielsen Book Data)

• Introduction.- Synthesizing conditional patterns in a database.- Synthesizing arbitrary Boolean expressions induced by frequent itemsets.- Measuring association among items in a database.- Mining association rules induced by item and quantity purchased.- Mining patterns different related databases.- Mining icebergs in different time-stamped data sources.-Synthesizing exceptional patterns in different data Sources.- Clustering items in time-stamped databases.- Synthesizing some extreme association rules from multiple databases.- Clustering local frequency items in multiple data sources.- Mining patterns of select items in different data sources.- Mining calendar-based periodic patterns in time-stamped data.- Measuring influence of an item in time-stamped databases.- Clustering multiple databases induced by local patterns.- Enhancing quality of patterns in multiple related databases.- Concluding remarks.
• (source: Nielsen Book Data)

This book presents recent advances in Knowledge discovery in databases (KDD) with a focus on the areas of market basket database, time-stamped databases and multiple related databases. Various interesting and intelligent algorithms are reported on data mining tasks. A large number of association measures are presented, which play significant roles in decision support applications. This book presents, discusses and contrasts new developments in mining time-stamped data, time-based data analyses, the identification of temporal patterns, the mining of multiple related databases, as well as local patterns analysis.
(source: Nielsen Book Data)

• Randomized algorithms.- Randomization in estimation, identification and filtering problems under arbitrary external noises.- Data mining.
• (source: Nielsen Book Data)

In the fields of data mining and control, the huge amount of unstructured data and the presence of uncertainty in system descriptions have always been critical issues. The book Randomized Algorithms in Automatic Control and Data Mining introduces the readers to the fundamentals of randomized algorithm applications in data mining (especially clustering) and in automatic control synthesis. The methods proposed in this book guarantee that the computational complexity of classical algorithms and the conservativeness of standard robust control techniques will be reduced. It is shown that when a problem requires "brute force" in selecting among options, algorithms based on random selection of alternatives offer good results with certain probability for a restricted time and significantly reduce the volume of operations.
(source: Nielsen Book Data)

• Analyzing Your Data for Success at Work
• Build the Core--Tableau Basics
• Go with the Flow--More Tableau Basics
• Core View Types in Tableau
• Advanced View Types in Tableau
• Take Over with Tableau--View Structure, Marks Card, Summaries, Formatting, and Titles
• Organize the Data in Your Views--Sorting, Filtering, Aggregations, Percentages, Spotlighting, Totals/Subtotals, and Motion Charts
• Essential Calculations and Models--Quick Calculations, Custom Table Calculations, Reference Lines, Trend Lines, and Forecasting
• Managing Data Is Critical for Great Results--Data Items and Data Management in Tableau
• Advanced Data Management in Tableau--Calculated Fields, Functions, and Parameters
• Advanced Data Management in Tableau--Managing Data Connections
• Share Your Insights from Tableau
• Timesaving Tips
• Build a Basic Dashboard.

Tired of boring spreadsheets and data overload from confusing graphs? Master the art of visualization with Rapid Graphs with Tableau 8! Tableau insiders Stephen and Eileen McDaniel expertly provide a hands-on case study approach and more than 225 illustrations that will teach you how to quickly explore and understand your data to make informed decisions in a wide variety of real-world situations. Rapid Graphs with Tableau 8 includes best practices of visual analytics for ideas on how to communicate your findings with audience-friendly graphs, tables and maps. "A picture is worth a thousand words" is a common saying that is more relevant today than ever as data volumes grow and the need for easy access to answers becomes more critical. This book covers the core of Tableau capabilities in easy-to-follow examples, updated and expanded for Version 8. Learn how to be successful with Tableau from the team that started the original training program as the founding Tableau Education Partner! "A must read for anyone interested in Tableau. Clear explanations, practical advice and beautiful examples!" Elissa Fink - Chief Marketing Officer, Tableau Software.
(source: Nielsen Book Data)

• New Trends in Databases and Information Systems: Contributions from ADBIS 2013 / Yamine Ait Ameur, Witold Andrzejewski, Ladjel Bellatreche, Barbara Catania [and 13 others]
• Part I: ADBIS Short Contributions
• New Ontological Alignment System Based on a Non-monotonic Description Logic / Ratiba Guebaili-Djider, Aicha Mokhtari, Farid Nouioua, Narhimene Boustia and Karima Akli Astouati
• Spatiotemporal Co-occurrence Rules / Karthik Ganesan Pillai, Rafal A. Angryk, Juan M. Banda, Tim Wylie and Michael A. Schuh
• R-Tree: An Efficient Spatial Access Method for Highly Redundant Point Data / Martin Šumák and Peter Gurský
• Labeling Association Rule Clustering through a Genetic Algorithm Approach / Renan de Padua, Veronica Oliveira de Carvalho and Adriane Beatriz de Souza Serapião
• Time Series Queries Processing with GPU Support / Piotr Przymus and Krzysztof Kaczmarski
• Rule-Based Multi-dialect Infrastructure for Conceptual Problem Solving over Heterogeneous Distributed Information Resources / Leonid Kalinichenko, Sergey Stupnikov, Alexey Vovchenko and Dmitry Kovalev
• Distributed Processing of XPath Queries Using MapReduce / Matthew Damigos, Manolis Gergatsoulis and Stathis Plitsos
• A Query Language for Workflow Instance Data / Philipp Baumgärtel, Johannes Tenschert and Richard Lenz
• When Too Similar Is Bad: A Practical Example of the Solar Dynamics Observatory Content-Based Image-Retrieval System / Juan M. Banda, Michael A. Schuh, Tim Wylie, Patrick McInerney and Rafal A. Angryk
• Viable Systems Model Based Information Flows / Marite Kirikova and Mara Pudane
• On Materializing Paths for Faster Recursive Querying / Aleksandra Boniewicz, Piotr Wiśniewski and Krzysztof Stencel
• XSLTMark II -- A Simple, Extensible and Portable XSLT Benchmark / Viktor Mašíček and Irena Holubová (Mlýnková)
• ReMoSSA: Reference Model for Specification of Self-adaptive Service-Oriented-Architecture / Sihem Cherif, Raoudha Ben Djemaa and Ikram Amous
• DSD: A DaaS Service Discovery Method in P2P Environments / Riad Mokadem, Franck Morvan, Chirine Ghedira Guegan and Djamal Benslimane.

• Part II: Special Session on Big Data: New Trends and Applications
• Designing Parallel Relational Data Warehouses: A Global, Comprehensive Approach / Soumia Benkrid, Ladjel Bellatreche and Alfredo Cuzzocrea
• Big Data New Frontiers: Mining, Search and Management of Massive Repositories of Solar Image Data and Solar Events / Juan M. Banda, Michael A. Schuh, Rafal A. Angryk, Karthik Ganesan Pillai and Patrick McInerney
• Extraction, Sentiment Analysis and Visualization of Massive Public Messages / Jacopo Farina, Mirjana Mazuran and Elisa Quintarelli
• Desidoo, a Big-Data Application to Join the Online and Real-World Marketplaces / Daniele Apiletti and Fabio Forno
• GraphDB -- Storing Large Graphs on Secondary Memory / Lucas Fonseca Navarro, Ana Paula Appel and Estevam Rafael Hruschka Junior
• Hadoop on a Low-Budget General Purpose HPC Cluster in Academia / Paolo Garza, Paolo Margara, Nicolò Nepote, Luigi Grimaudo and Elio Piccolo
• Discovering Contextual Association Rules in Relational Databases / Elisa Quintarelli and Emanuele Rabosio
• Challenges and Issues on Collecting and Analyzing Large Volumes of Network Data Measurements / Enrico Masala, Antonio Servetti, Simone Basso and Juan Carlos De Martin.

• Part III: Second International Workshop on GPUs in Databases
• GPU-Accelerated Query Selectivity Estimation Based on Data Clustering and Monte Carlo Integration Method Developed in CUDA Environment / Dariusz Rafal Augustyn and Lukasz Warchal
• Exploring the Design Space of a GPU-Aware Database Architecture / Sebastian Breß, Max Heimel, Norbert Siegmund, Ladjel Bellatreche and Gunter Saake
• Dynamic Compression Strategy for Time Series Database Using GPU / Piotr Przymus and Krzysztof Kaczmarski
• Online Document Clustering Using GPUs / Benjamin E. Teitler, Jagan Sankaranarayanan, Hanan Samet and Marco D. Adelfio.

• Part IV: Second International Workshop on Ontologies Meet Advanced Information Systems
• Using the Semantics of Texts for Information Retrieval: A Concept- and Domain Relation-Based Approach / Davide Buscaldi, Marie-Noëlle Bessagnet, Albert Royer and Christian Sallaberry
• A Latent Semantic Indexing-Based Approach to Determine Similar Clusters in Large-scale Schema Matching / Seham Moawed, Alsayed Algergawy, Amany Sarhan, Ali Eldosouky and Gunter Saake
• Poss-SROIQ(D) : Possibilistic Description Logic Extension toward an Uncertain Geographic Ontology / Safia Bal Bourai, Aicha Mokhtari and Faiza Khellaf
• Ontology-Based Context-Aware Social Networks / Maha Maalej, Achraf Mtibaa and Faïez Gargouri
• Diversity in a Semantic Recommender System / Latifa Baba-Hamed and Magloire Namber
• Ontology -- Driven Observer Pattern / Amrita Chaturvedi and Prabhakar T.V.

• Part V: First International Workshop on Social Business Intelligence: Integrating Social Content in Decision Making
• Towards a Semantic Data Infrastructure for Social Business Intelligence / Rafael Berlanga, María José Aramburu, Dolores M. Llidó and Lisette García-Moya
• Subjective Business Polarization: Sentiment Analysis Meets Predictive Modeling / Caterina Liberati and Furio Camillo
• Sentiment Analysis and City Branding / Roberto Grandi and Federico Neri
• A Case Study for a Collaborative Business Environment in Real Estate / Nicoletta Dessì and Gianfranco Garau
• OLAP on Information Networks: A New Framework for Dealing with Bibliographic Data / Wararat Jakawat, Cécile Favre and Sabine Loudcher
• Doctoral Consortium
• Spatial Indexes for Simplicial and Cellular Meshes / Riccardo Fellegara
• Mathematical Methods of Tensor Factorization Applied to Recommender Systems / Giuseppe Ricci, Marco de Gemmis and Giovanni Semeraro
• Extended Dynamic Weighted Majority Using Diversity to Handle Drifts / Parneeta Sidhu and M.P.S Bhatia.

This book reports on state-of-art research and applications in the field of databases and information systems. It includes both fourteen selected short contributions, presented at the East-European Conference on Advances in Databases and Information Systems (ADBIS 2013, September 1-4, Genova, Italy), and twenty-six papers from ADBIS 2013 satellite events. The short contributions from the main conference are collected in the first part of the book, which covers a wide range of topics, like data management, similarity searches, spatio-temporal and social network data, data mining, data warehousing, and data management on novel architectures, such as graphics processing units, parallel database management systems, cloud and MapReduce environments. In contrast, the contributions from the satellite events are organized in five different parts, according to their respective ADBIS satellite event: BiDaTA 2013 - Special Session on Big Data: New Trends and Applications); GID 2013 - The Second International Workshop on GPUs in Databases; OAIS 2013 - The Second International Workshop on Ontologies Meet Advanced Information Systems; SoBI 2013 - The First International Workshop on Social Business Intelligence: Integrating Social Content in Decision Making; and last but not least, the Doctoral Consortium, a forum for Ph. D. students. The book, which addresses academics and professionals alike, provides the readers with a comprehensive and timely overview of new trends in database and information systems research, and promotes new ideas and collaborations among the different research communities of the eastern European countries and the rest of the world.

• Communication.- Computer Engineering.
• (source: Nielsen Book Data)

This book covers diverse aspects of advanced computer and communication engineering, focusing specifically on industrial and manufacturing theory and applications of electronics, communications, computing and information technology. Experts in research, industry, and academia present the latest developments in technology, describe applications involving cutting-edge communication and computer systems and explore likely future directions. In addition, access is offered to numerous new algorithms that assist in solving computer and communication engineering problems. The book is based on presentations delivered at ICOCOE 2014, the 1st International Conference on Communication and Computer Engineering. It will appeal to a wide range of professionals in the field, including telecommunication engineers, computer engineers and scientists, researchers, academics and students.
(source: Nielsen Book Data)

• Introduction
• Preliminaries
• From Event Logs to Process Models
• Beyond Process Discovery
• Putting Process Mining to Work
• Reflection
• Epilogue.

This is the second edition of Wil van der Aalst's seminal book on process mining, which now discusses the field also in the broader context of data science and big data approaches. It includes several additions and updates, e.g. on inductive mining techniques, the notion of alignments, a considerably expanded section on software tools and a completely new chapter of process mining in the large. It is self-contained, while at the same time covering the entire process-mining spectrum from process discovery to predictive analytics. After a general introduction to data science and process mining in Part I, Part II provides the basics of business process modeling and data mining necessary to understand the remainder of the book. Next, Part III focuses on process discovery as the most important process mining task, while Part IV moves beyond discovering the control flow of processes, highlighting conformance checking, and organizational and time perspectives. Part V offers a guide to successfully applying process mining in practice, including an introduction to the widely used open-source tool ProM and several commercial products. Lastly, Part VI takes a step back, reflecting on the material presented and the key open challenges. Overall, this book provides a comprehensive overview of the state of the art in process mining. It is intended for business process analysts, business consultants, process managers, graduate students, and BPM researchers.

Query-driven visualization and analytics is a unique approach for high-performance visualization that offers new capabilities for knowledge discovery and hypothesis testing. The new capabilities akin to finding needles in haystacks are the result of combining technologies from the fields of scientific visualization and scientific data management. This approach is crucial for rapid data analysis and visualization in the petascale regime. This article describes how query-driven visualization is applied to a hero-sized network traffic analysis problem.

• Introduction.- Towards a More Adequate Description of Uncertainty.- Towards Justification of Heuristic Techniques for Processing Uncertainty.- Towards More Computationally Efficient Techniques for Processing Uncertainty.- Towards Better Ways of Extracting Information About Uncertainty from Data.
• (source: Nielsen Book Data)

On various examples ranging from geosciences to environmental sciences, this book explains how to generate an adequate description of uncertainty, how to justify semiheuristic algorithms for processing uncertainty, and how to make these algorithms more computationally efficient. It explains in what sense the existing approach to uncertainty as a combination of random and systematic components is only an approximation, presents a more adequate three-component model with an additional periodic error component, and explains how uncertainty propagation techniques can be extended to this model. The book provides a justification for a practically efficient heuristic technique (based on fuzzy decision-making). It explains how the computational complexity of uncertainty processing can be reduced. The book also shows how to take into account that in real life, the information about uncertainty is often only partially known, and, on several practical examples, explains how to extract the missing information about uncertainty from the available data.
(source: Nielsen Book Data)

• Integrated Systems Design
• Knowledge, Competence and Business Process Management
• Integrated Systems Technologies.

This book presents the results of discussions and presentation from the latest ISDT event (2014) which was dedicated to the 94th birthday anniversary of Prof. Lotfi A. Zade, father of Fuzzy logic. The book consists of three main chapters, namely: Chapter 1: Integrated Systems Design Chapter 2: Knowledge, Competence and Business Process Management Chapter 3: Integrated Systems Technologies Each article presents novel and scientific research results with respect to the target goal of improving our common understanding of KT integration.

• An Introduction to Knowledge Engineering.- Types of Knowledge-Based Systems.- Knowledge Acquisition.- Knowledge Representation and Reasoning.- Expert System Shells, Environments and Languages.- Life Cycles and Methodologies.- Uncertain Reasoning.- Hybrid Knowledge-Based Systems.
• (source: Nielsen Book Data)

An Introduction to Knowledge Engineering presents a simple but detailed exp- ration of current and established work in the ?eld of knowledge-based systems and related technologies. Its treatment of the increasing variety of such systems is designed to provide the reader with a substantial grounding in such techno- gies as expert systems, neural networks, genetic algorithms, case-based reasoning systems, data mining, intelligent agents and the associated techniques and meth- ologies. The material is reinforced by the inclusion of numerous activities that provide opportunities for the reader to engage in their own research and re?ection as they progress through the book. In addition, self-assessment questions allow the student to check their own understanding of the concepts covered. The book will be suitable for both undergraduate and postgraduate students in computing science and related disciplines such as knowledge engineering, arti?cial intelligence, intelligent systems, cognitive neuroscience, robotics and cybernetics. vii Contents Foreword vii 1 An Introduction to Knowledge Engineering. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Section 1: Data, Information and Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Section 2: Skills of a Knowledge Engineer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Section 3: An Introduction to Knowledge-Based Systems. . . . . . . . . . . . . . . . . 18 2 Types of Knowledge-Based Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Section 1: Expert Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Section 2: Neural Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Section 3: Case-Based Reasoning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Section 4: Genetic Algorithms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 Section 5: Intelligent Agents. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 Section 6: Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 3 Knowledge Acquisition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 4 Knowledge Representation and Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 Section 1: Using Knowledge. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Section 2: Logic, Rules and Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 Section 3: Developing Rule-Based Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 Section 4: Semantic Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
(source: Nielsen Book Data)