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Interactive Visualization of Big Data Leveraging Databases for Scalable Computation

Interactive Visualization of Big Data Leveraging Databases for Scalable Computation
Author: Leilani Marie Battle
Publisher:
Total Pages: 57
Release: 2013
Genre:
ISBN:
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Modern database management systems (DBMS) have been designed to efficiently store, manage and perform computations on massive amounts of data. In contrast, many existing visualization systems do not scale seamlessly from small data sets to enormous ones. We have designed a three-tiered visualization system called ScalaR to deal with this issue. ScalaR dynamically performs resolution reduction when the expected result of a DBMS query is too large to be effectively rendered on existing screen real estate. Instead of running the original query, ScalaR inserts aggregation, sampling or filtering operations to reduce the size of the result. This thesis presents the design and implementation of ScalaR, and shows results for two example applications, visualizing earthquake records and satellite imagery data, stored in SciDB as the back-end DBMS.

Image-Based Visualization

Image-Based Visualization
Author: Christophe Hurter
Publisher: Morgan & Claypool Publishers
Total Pages: 131
Release: 2015-12-01
Genre: Computers
ISBN: 1627058389
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Our society has entered a data-driven era, one in which not only are enormous amounts of data being generated daily but there are also growing expectations placed on the analysis of this data. Some data have become simply too large to be displayed and some have too short a lifespan to be handled properly with classical visualization or analysis methods. In order to address these issues, this book explores the potential solutions where we not only visualize data, but also allow users to be able to interact with it. Therefore, this book will focus on two main topics: large dataset visualization and interaction. Graphic cards and their image processing power can leverage large data visualization but they can also be of great interest to support interaction. Therefore, this book will show how to take advantage of graphic card computation power with techniques called GPGPUs (general-purpose computing on graphics processing units). As specific examples, this book details GPGPU usages to produce fast enough visualization to be interactive with improved brushing techniques, fast animations between different data representations, and view simplifications (i.e. static and dynamic bundling techniques). Since data storage and memory limitation is less and less of an issue, we will also present techniques to reduce computation time by using memory as a new tool to solve computationally challenging problems. We will investigate innovative data processing techniques: while classical algorithms are expressed in data space (e.g. computation on geographic locations), we will express them in graphic space (e.g., raster map like a screen composed of pixels). This consists of two steps: (1) a data representation is built using straightforward visualization techniques; and (2) the resulting image undergoes purely graphical transformations using image processing techniques. This type of technique is called image-based visualization. The goal of this book is to explore new computing techniques using image-based techniques to provide efficient visualizations and user interfaces for the exploration of large datasets. This book concentrates on the areas of information visualization, visual analytics, computer graphics, and human-computer interaction. This book opens up a whole field of study, including the scientific validation of these techniques, their limitations, and their generalizations to different types of datasets.

Distributed Computing in Big Data Analytics

Distributed Computing in Big Data Analytics
Author: Sourav Mazumder
Publisher: Springer
Total Pages: 166
Release: 2017-08-29
Genre: Computers
ISBN: 3319598341
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Big data technologies are used to achieve any type of analytics in a fast and predictable way, thus enabling better human and machine level decision making. Principles of distributed computing are the keys to big data technologies and analytics. The mechanisms related to data storage, data access, data transfer, visualization and predictive modeling using distributed processing in multiple low cost machines are the key considerations that make big data analytics possible within stipulated cost and time practical for consumption by human and machines. However, the current literature available in big data analytics needs a holistic perspective to highlight the relation between big data analytics and distributed processing for ease of understanding and practitioner use. This book fills the literature gap by addressing key aspects of distributed processing in big data analytics. The chapters tackle the essential concepts and patterns of distributed computing widely used in big data analytics. This book discusses also covers the main technologies which support distributed processing. Finally, this book provides insight into applications of big data analytics, highlighting how principles of distributed computing are used in those situations. Practitioners and researchers alike will find this book a valuable tool for their work, helping them to select the appropriate technologies, while understanding the inherent strengths and drawbacks of those technologies.

Big Data and Visual Analytics

Big Data and Visual Analytics
Author: Sang C. Suh
Publisher: Springer
Total Pages: 262
Release: 2018-01-15
Genre: Computers
ISBN: 331963917X
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This book provides users with cutting edge methods and technologies in the area of big data and visual analytics, as well as an insight to the big data and data analytics research conducted by world-renowned researchers in this field. The authors present comprehensive educational resources on big data and visual analytics covering state-of-the art techniques on data analytics, data and information visualization, and visual analytics. Each chapter covers specific topics related to big data and data analytics as virtual data machine, security of big data, big data applications, high performance computing cluster, and big data implementation techniques. Every chapter includes a description of an unique contribution to the area of big data and visual analytics. This book is a valuable resource for researchers and professionals working in the area of big data, data analytics, and information visualization. Advanced-level students studying computer science will also find this book helpful as a secondary textbook or reference.

Big Data for beginners

Big Data for beginners
Author: Cybellium Ltd
Publisher: Cybellium Ltd
Total Pages: 177
Release: 2023-09-26
Genre: Computers
ISBN:
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Unlock the Power of Big Data Analytics in the Modern World Are you ready to dive into the fascinating world of big data analytics? "Big Data for Beginners" is your essential guide to understanding and harnessing the potential of big data in the modern era. Whether you're new to the concept or looking to expand your knowledge, this comprehensive book equips you with the foundational knowledge and tools to navigate the complexities of big data and make informed decisions. Key Features: 1. Introduction to Big Data: Dive deep into the fundamental concepts of big data, from its definition to its significance in today's data-driven landscape. Build a strong foundation that empowers you to navigate the vast world of big data. 2. Understanding Data Sources: Navigate the diverse sources of big data, including structured, semi-structured, and unstructured data. Learn how to gather, process, and manage data from various sources to extract valuable insights. 3. Big Data Technologies: Discover the technologies that power big data analytics. Explore tools like Hadoop, Spark, and NoSQL databases, understanding their role in processing and analyzing massive datasets. 4. Data Storage and Processing: Master the art of storing and processing big data effectively. Learn about distributed file systems, data warehouses, and batch and real-time processing to ensure scalability and efficiency. 5. Data Analysis and Visualization: Uncover strategies for analyzing and visualizing big data. Explore techniques for data exploration, pattern recognition, and creating compelling visual representations that convey insights effectively. 6. Machine Learning and Predictive Analytics: Delve into the world of machine learning and predictive analytics using big data. Learn how to build models that make accurate predictions and informed decisions based on massive datasets. 7. Big Data Security and Privacy: Explore the challenges of securing and preserving privacy in the realm of big data. Learn how to implement encryption, access controls, and anonymization techniques to protect sensitive information. 8. Real-World Applications: Discover the myriad applications of big data across industries. From healthcare to finance, retail to marketing, explore how big data is transforming business operations and decision-making. 9. Challenges and Future Trends: Gain insights into the challenges posed by big data, such as data quality and scalability issues. Explore the future trends and advancements that are shaping the evolution of big data analytics. 10. Ethical Considerations: Delve into the ethical considerations surrounding big data. Learn about responsible data usage, addressing bias, and maintaining transparency in the collection and analysis of data. Who This Book Is For: "Big Data for Beginners" is an indispensable resource for individuals, students, professionals, and enthusiasts who are eager to grasp the fundamentals of big data analytics. Whether you're a beginner curious about the world of data or an experienced professional seeking to enhance your skills, this book will guide you through the intricacies and empower you to harness the potential of big data.

Scalable Big Data Architecture

Scalable Big Data Architecture
Author: Bahaaldine Azarmi
Publisher: Apress
Total Pages: 147
Release: 2015-12-31
Genre: Computers
ISBN: 1484213262
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This book highlights the different types of data architecture and illustrates the many possibilities hidden behind the term "Big Data", from the usage of No-SQL databases to the deployment of stream analytics architecture, machine learning, and governance. Scalable Big Data Architecture covers real-world, concrete industry use cases that leverage complex distributed applications , which involve web applications, RESTful API, and high throughput of large amount of data stored in highly scalable No-SQL data stores such as Couchbase and Elasticsearch. This book demonstrates how data processing can be done at scale from the usage of NoSQL datastores to the combination of Big Data distribution. When the data processing is too complex and involves different processing topology like long running jobs, stream processing, multiple data sources correlation, and machine learning, it’s often necessary to delegate the load to Hadoop or Spark and use the No-SQL to serve processed data in real time. This book shows you how to choose a relevant combination of big data technologies available within the Hadoop ecosystem. It focuses on processing long jobs, architecture, stream data patterns, log analysis, and real time analytics. Every pattern is illustrated with practical examples, which use the different open sourceprojects such as Logstash, Spark, Kafka, and so on. Traditional data infrastructures are built for digesting and rendering data synthesis and analytics from large amount of data. This book helps you to understand why you should consider using machine learning algorithms early on in the project, before being overwhelmed by constraints imposed by dealing with the high throughput of Big data. Scalable Big Data Architecture is for developers, data architects, and data scientists looking for a better understanding of how to choose the most relevant pattern for a Big Data project and which tools to integrate into that pattern.

Big Data Visualization

Big Data Visualization
Author: James D. Miller
Publisher: Packt Publishing Ltd
Total Pages: 299
Release: 2017-02-28
Genre: Computers
ISBN: 1785284169
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Learn effective tools and techniques to separate big data into manageable and logical components for efficient data visualization About This Book This unique guide teaches you how to visualize your cluttered, huge amounts of big data with ease It is rich with ample options and solid use cases for big data visualization, and is a must-have book for your shelf Improve your decision-making by visualizing your big data the right way Who This Book Is For This book is for data analysts or those with a basic knowledge of big data analysis who want to learn big data visualization in order to make their analysis more useful. You need sufficient knowledge of big data platform tools such as Hadoop and also some experience with programming languages such as R. This book will be great for those who are familiar with conventional data visualizations and now want to widen their horizon by exploring big data visualizations. What You Will Learn Understand how basic analytics is affected by big data Deep dive into effective and efficient ways of visualizing big data Get to know various approaches (using various technologies) to address the challenges of visualizing big data Comprehend the concepts and models used to visualize big data Know how to visualize big data in real time and for different use cases Understand how to integrate popular dashboard visualization tools such as Splunk and Tableau Get to know the value and process of integrating visual big data with BI tools such as Tableau Make sense of the visualization options for big data, based upon the best suited visualization techniques for big data In Detail When it comes to big data, regular data visualization tools with basic features become insufficient. This book covers the concepts and models used to visualize big data, with a focus on efficient visualizations. This book works around big data visualizations and the challenges around visualizing big data and address characteristic challenges of visualizing like speed in accessing, understanding/adding context to, improving the quality of the data, displaying results, outliers, and so on. We focus on the most popular libraries to execute the tasks of big data visualization and explore "big data oriented" tools such as Hadoop and Tableau. We will show you how data changes with different variables and for different use cases with step-through topics such as: importing data to something like Hadoop, basic analytics. The choice of visualizations depends on the most suited techniques for big data, and we will show you the various options for big data visualizations based upon industry-proven techniques. You will then learn how to integrate popular visualization tools with graphing databases to see how huge amounts of certain data. Finally, you will find out how to display the integration of visual big data with BI using Cognos BI. Style and approach With the help of insightful real-world use cases, we'll tackle data in the world of big data. The scalability and hugeness of the data makes big data visualizations different from normal data visualizations, and this book addresses all the difficulties encountered by professionals while visualizing their big data.

The Visual Organization

The Visual Organization
Author: Phil Simon
Publisher: John Wiley & Sons
Total Pages: 227
Release: 2014-02-19
Genre: Computers
ISBN: 1118858344
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The era of Big Data as arrived, and most organizations are woefully unprepared. Slowly, many are discovering that stalwarts like Excel spreadsheets, KPIs, standard reports, and even traditional business intelligence tools aren't sufficient. These old standbys can't begin to handle today's increasing streams, volumes, and types of data. Amidst all of the chaos, though, a new type of organization is emerging. In The Visual Organization, award-winning author and technology expert Phil Simon looks at how an increasingly number of organizations are embracing new dataviz tools and, more important, a new mind-set based upon data discovery and exploration. Simon adroitly shows how Amazon, Apple, Facebook, Google, Twitter, and other tech heavyweights use powerful data visualization tools to garner fascinating insights into their businesses. But make no mistake: these companies are hardly alone. Organizations of all types, industries, sizes are representing their data in new and amazing ways. As a result, they are asking better questions and making better business decisions. Rife with real-world examples and case studies, The Visual Organization is a full-color tour-de-force.

Interactive Systems for Scalable Visualization and Analysis

Interactive Systems for Scalable Visualization and Analysis
Author: Dominik Moritz
Publisher:
Total Pages: 191
Release: 2019
Genre:
ISBN:
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While computers can help us manage data, human judgment and domain expertise is what turns it into understanding. Meeting the challenges of increasingly large and complex data requires methods that richly integrate the capabilities of both people and machines. In response to these challenges, this thesis contributes new languages and models for visualization design that power interactive systems for scalable data analysis. In these languages, users can be imprecise about low-level design decisions as the system leverages this ambiguity to optimize the visual design and necessary computation. Vega-Lite is a high-level declarative language for rapidly creating interactive visualizations, while also providing a convenient yet powerful representation for tools that generate visualizations. Vega-Lite uses smart defaults to fill in low-level details to create effective designs. The declarative design facilitates optimization of the required data processing. Draco is a model of visualization design that extends Vega-Lite with shareable design guidelines, formal reasoning over the design space, and visualization recommendation. We show how we can use Draco to construct increasingly sophisticated automated visualization design and recommendation systems, including systems based on weights learned directly from the results of graphical perception experiments. We take a user-centric perspective on systems for scalable exploratory analysis. Considering both the backend and frontend concerns, we present Falcon, an interactive crossfilter application where users can interact with billions of records without latencies that negatively affect their exploration. To scale beyond billions of records, we present Pangloss, a visual analysis system that uses approximate query processing but provides eventual guarantees using Optimistic Visualization. In this concept, we treat approximate query processing as a user experience problem to address users' primary concern: trust in their exploration results. Falcon and Pangloss contribute techniques for scalable interaction and exploration of large data volumes by making principled trade-offs among people's latency tolerance, precomputation, and the level of approximation.

Handbook of Research on Big Data Storage and Visualization Techniques

Handbook of Research on Big Data Storage and Visualization Techniques
Author: Segall, Richard S.
Publisher: IGI Global
Total Pages: 1078
Release: 2018-01-05
Genre: Computers
ISBN: 1522531432
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The digital age has presented an exponential growth in the amount of data available to individuals looking to draw conclusions based on given or collected information across industries. Challenges associated with the analysis, security, sharing, storage, and visualization of large and complex data sets continue to plague data scientists and analysts alike as traditional data processing applications struggle to adequately manage big data. The Handbook of Research on Big Data Storage and Visualization Techniques is a critical scholarly resource that explores big data analytics and technologies and their role in developing a broad understanding of issues pertaining to the use of big data in multidisciplinary fields. Featuring coverage on a broad range of topics, such as architecture patterns, programing systems, and computational energy, this publication is geared towards professionals, researchers, and students seeking current research and application topics on the subject.