Book Description:

This book presents a language integrated query framework for big data. The continuous, rapid growth of data information to volumes of up to terabytes (1,024 gigabytes) or petabytes (1,048,576 gigabytes) means that the need for a system to manage and query information from large scale data sources is becoming more urgent. Currently available frameworks and methodologies are limited in terms of efficiency and querying compatibility between data sources due to the differences in information storage structures. For this research, the authors designed and programmed a framework based on the fundamentals of language integrated query to query existing data sources without the process of data restructuring. A web portal for the framework was also built to enable users to query protein data from the Protein Data Bank (PDB) and implement it on Microsoft Azure, a cloud computing environment known for its reliability, vast computing resources and cost-effectiveness.

Book Description:

Book Description:

Introduces the key concepts in the analysis of categoricaldata with illustrative examples and accompanying R code

This book is aimed at all those who wish to discover how to analyze categorical data without getting immersed in complicated mathematics and without needing to wade through a large amount of prose. It is aimed at researchers with their own data ready to be analyzed and at students who would like an approachable alternative view of the subject.

Each new topic in categorical data analysis is illustrated with an example that readers can apply to their own sets of data. In many cases, R code is given and excerpts from the resulting output are presented. In the context of log-linear models for cross-tabulations, two specialties of the house have been included: the use of cobweb diagrams to get visual information concerning significant interactions, and a procedure for detecting outlier category combinations. The R code used for these is available and may be freely adapted. In addition, this book:

• Uses an example to illustrate each new topic in categorical data

• Provides a clear explanation of an important subject

• Is understandable to most readers with minimal statistical and mathematical backgrounds

• Contains examples that are accompanied by R code and resulting output

• Includes starred sections that provide more background details for interested readers

Categorical Data Analysis by Example is a reference for students in statistics and researchers in other disciplines, especially the social sciences, who use categorical data. This book is also a reference for practitioners in market research, medicine, and other fields.

GRAHAM J. G. UPTON is formerly Professor of Applied Statistics, Department of Mathematical Sciences, University of Essex. Dr. Upton is author of The Analysis of Cross-tabulated Data (1978) and joint author of Spatial Data Analysis by Example (2 volumes, 1995), both published by Wiley. He is the lead author of The Oxford Dictionary of Statistics (OUP, 2014). His books have been translated into Japanese, Russian, and Welsh.

Book Description:

Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service.

This book explains the basic data algorithms without the theoretical jargon, and you’ll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark.

What you will learn

• Acquire, format, and visualize your data
• Build an image-similarity search engine
• Generate meaningful visualizations anyone can understand
• Get started with analyzing social network graphs
• Find out how to implement sentiment text analysis
• Install data analysis tools such as Pandas, MongoDB, and Apache Spark
• Get to grips with Apache Spark
• Implement machine learning algorithms such as classification or forecasting

Book Description:

Python is a multi-paradigm programming language well suited for both object-oriented application development as well as functional design patterns. Python has become the language of choice for data scientists for data analysis, visualization, and machine learning. It will give you velocity and promote high productivity.

This book will teach novices about data analysis with Python in the broadest sense possible, covering everything from data retrieval, cleaning, manipulation, visualization, and storage to complex analysis and modeling. It focuses on a plethora of open source Python modules such as NumPy, SciPy, matplotlib, pandas, IPython, Cython, scikit-learn, and NLTK. In later chapters, the book covers topics such as data visualization, signal processing, and time-series analysis, databases, predictive analytics and machine learning. This book will turn you into an ace data analyst in no time.

What You Will Learn

• Install open source Python modules on various platforms
• Get to know about the fundamentals of NumPy including arrays
• Manipulate data with pandas
• Retrieve, process, store, and visualize data
• Understand signal processing and time-series data analysis
• Work with relational and NoSQL databases
• Discover more about data modeling and machine learning
• Get to grips with interoperability and cloud computing

Book Description:

This book proposes a data-driven methodology using multi-way data analysis for the design of video-quality metrics. It also enables video- quality metrics to be created using arbitrary features. This data- driven design approach not only requires no detailed knowledge of the human visual system, but also allows a proper consideration of the temporal nature of video using a three-way prediction model, corresponding to the three-way structure of video. Using two simple example metrics, the author demonstrates not only that this purely data- driven approach outperforms state-of-the-art video-quality metrics, which are often optimized for specific properties of the human visual system, but also that multi-way data analysis methods outperform the combination of two-way data analysis methods and temporal pooling.

Book Description:

This book offers an overview of traditional big visual data analysis approaches and provides state-of-the-art solutions for several scene comprehension problems, indoor/outdoor classification, outdoor scene classification, and outdoor scene layout estimation. It is illustrated with numerous natural and synthetic color images, and extensive statistical analysis is provided to help readers visualize big visual data distribution and the associated problems. Although there has been some research on big visual data analysis, little work has been published on big image data distribution analysis using the modern statistical approach described in this book. By presenting a complete methodology on big visual data analysis with three illustrative scene comprehension problems, it provides a generic framework that can be applied to other big visual data analysis tasks.

Book Description:

QlikView is a powerful business intelligence and data discovery platform that allows people to quickly develop relevant data visualization applications for business users. The relative ease of QlikView development—including backend scripting—allows applications to be developed rapidly, and allows for more collaboration in application development for business users.

A comprehensive guide that offers QlikView developers a rich discussion of scripting topics, from basic to advanced concepts, features, and functions in a compact mini-book format. This book allows developers to quickly gain confidence in understanding and expanding their QlikView scripting knowledge, and serves as a springboard for even more advanced topics in QlikView scripting.

The book starts off by covering basic topics such as connecting to data sources, scripting, dealing with load statements, data transformations, and the concepts of the basic data model. It then dives into advanced concepts such as advanced scripting and data model optimization, the creation and use of QlikView datafiles, debugging, and essential functions and features. It also provides layout tips for developers. Qlikview Scripting is a great overview and reference guide for beginner to intermediate Qlikview developers.