The emerging field of network science represents a new style of research that can unify such traditionally-diverse fields as sociology, economics, physics, biology, and computer science. It is a powerful tool in analyzing both natural and man-made systems, using the relationships between players within these networks and between the networks themselves to gain insight into the nature of each field. Until now, studies in network science have been focused on particular relationships that require varied and sometimes-incompatible datasets, which has kept it from being a truly universal discipline.

Computational Network Science seeks to unify the methods used to analyze these diverse fields. This book provides an introduction to the field of Network Science and provides the groundwork for a computational, algorithm-based approach to network and system analysis in a new and important way. This new approach would remove the need for tedious human-based analysis of different datasets and help researchers spend more time on the qualitative aspects of network science research.

Demystifies media hype regarding Network Science and serves as a fast-paced introduction to state-of-the-art concepts and systems related to network science Comprehensive coverage of Network Science algorithms, methodologies, and common problems Includes references to formative and updated developments in the field Coverage spans mathematical sociology, economics, political science, and biological networks

Learning-Based Local Visual Representation and Indexing, reviews the state-of-the-art in visual content representation and indexing, introduces cutting-edge techniques in learning based visual representation, and discusses emerging topics in visual local representation, and introduces the most recent advances in content-based visual search techniques. Discusses state-of-the-art procedures in learning-based local visual representation. Shows how to master the basic techniques needed for building a large-scale visual search engine and indexing system Provides insight into how machine learning techniques can be leveraged to refine the visual recognition system, especially in the part of visual feature representation.

Content-based 3-D object retrieval has attracted extensive attention recently and has applications in a variety of fields, such as, computer-aided design, tele-medicine,mobile multimedia, virtual reality, and entertainment. The development of efficient and effective content-based 3-D object retrieval techniques has enabled the use of fast 3-D reconstruction and model design. Recent technical progress, such as the development of camera technologies, has made it possible to capture the views of 3-D objects. As a result, view-based 3-D object retrieval has become an essential but challenging research topic.

View-based 3-D Object Retrieval introduces and discusses the fundamental challenges in view-based 3-D object retrieval, proposes a collection of selected state-of-the-art methods for accomplishing this task developed by the authors, and summarizes recent achievements in view-based 3-D object retrieval. Part I presents an Introduction to View-based 3-D Object Retrieval, Part II discusses View Extraction, Selection, and Representation, Part III provides a deep dive into View-Based 3-D Object Comparison, and Part IV looks at future research and developments including Big Data application and geographical location-based applications.

Systematically introduces view-based 3-D object retrieval, including problem definitions and settings, methodologies, and benchmark testing beds Discusses several key challenges in view-based 3-D object retrieval, and introduces the state-of-the-art solutions Presents the progression from general image retrieval techniques to view-based 3-D object retrieval Introduces future research efforts in the areas of Big Data, feature extraction, and geographical location-based applications

Data Analysis in the Cloud introduces and discusses models, methods, techniques, and systems to analyze the large number of digital data sources available on the Internet using the computing and storage facilities of the cloud.

Coverage includes scalable data mining and knowledge discovery techniques together with cloud computing concepts, models, and systems. Specific sections focus on map-reduce and NoSQL models. The book also includes techniques for conducting high-performance distributed analysis of large data on clouds. Finally, the book examines research trends such as Big Data pervasive computing, data-intensive exascale computing, and massive social network analysis.

Introduces data analysis techniques and cloud computing concepts Describes cloud-based models and systems for Big Data analytics Provides examples of the state-of-the-art in cloud data analysis Explains how to develop large-scale data mining applications on clouds Outlines the main research trends in the area of scalable Big Data analysis

Repurposing Legacy Data: Innovative Case Studies takes a look at how data scientists have re-purposed legacy data, whether their own, or legacy data that has been donated to the public domain.

Most of the data stored worldwide is legacy data-data created some time in the past, for a particular purpose, and left in obsolete formats. As with keepsakes in an attic, we retain this information thinking it may have value in the future, though we have no current use for it.

The case studies in this book, from such diverse fields as cosmology, quantum physics, high-energy physics, microbiology, psychiatry, medicine, and hospital administration, all serve to demonstrate how innovative people draw value from legacy data. By following the case examples, readers will learn how legacy data is restored, merged, and analyzed for purposes that were never imagined by the original data creators.

Discusses how combining existing data with other data sets of the same kind can produce an aggregate data set that serves to answer questions that could not be answered with any of the original data Presents a method for re-analyzing original data sets using alternate or improved methods that can provide outcomes more precise and reliable than those produced in the original analysis Explains how to integrate heterogeneous data sets for the purpose of answering questions or developing concepts that span several different scientific fields

Cloud Storage Security: A Practical Guide introduces and discusses the risks associated with cloud-based data storage from a security and privacy perspective. Gain an in-depth understanding of the risks and benefits of cloud storage illustrated using a Use-Case methodology. The authors also provide a checklist that enables the user, as well as the enterprise practitioner to evaluate what security and privacy issues need to be considered when using the cloud to store personal and sensitive information. Describes the history and the evolving nature of cloud storage and security Explores the threats to privacy and security when using free social media applications that use cloud storage Covers legal issues and laws that govern privacy, compliance, and legal responsibility for enterprise users Provides guidelines and a security checklist for selecting a cloud-storage service provider Includes case studies and best practices for securing data in the cloud Discusses the future of cloud computing

Online Social Networks: Human Cognitive Constraints in Facebook and Twitter provides new insights into the structural properties of personal online social networks and the mechanisms underpinning human online social behavior.

As the availability of digital communication data generated by social media is revolutionizing the field of social networks analysis, the text discusses the use of large- scale datasets to study the structural properties of online ego networks, to compare them with the properties of general human social networks, and to highlight additional properties.

Users will find the data collected and conclusions drawn useful during design or research service initiatives that involve online and mobile social network environments.

Provides an analysis of the structural properties of ego networks in online social networks Presents quantitative evidence of the Dunbar's number in online environments Discusses original structural and dynamic properties of human social network through OSN analysis

Text summarization has been studied for over a half century, but traditional methods process texts empirically and neglect the fundamental characteristics and principles of language use and understanding. Automatic summarization is a desirable technique for processing big data. This reference summarizes previous text summarization approaches in a multi-dimensional category space, introduces a multi-dimensional methodology for research and development, unveils the basic characteristics and principles of language use and understanding, investigates some fundamental mechanisms of summarization, studies dimensions on representations, and proposes a multi-dimensional evaluation mechanism. Investigation extends to incorporating pictures into summary and to the summarization of videos, graphs and pictures, and converges to a general summarization method. Further, some basic behaviors of summarization are studied in the complex cyber-physical-social space. Finally, a creative summarization mechanism is proposed as an effort toward the creative summarization of things, which is an open process of interactions among physical objects, data, people, and systems in cyber-physical-social space through a multi-dimensional lens of semantic computing. The author's insights can inspire research and development of many computing areas.

The first book that proposes the method for the summarization of things in cyber-physical society through a multi-dimensional lens of semantic computing. A transformation from the traditional application-driven research paradigm into a data-driven research paradigm for creative summarization through information modeling, cognitive modeling and knowledge modeling. A multi-dimensional methodology for studying, managing, creating and applying methods.

Complex Systems and Clouds: A Self-Organization and Self-Management Perspective provides insights into the intricate world of self-organizing systems. Large scale distributed computer systems have evolved into very complex systems and are at the point where they need to borrow self-adapting organizing concepts from nature.

The book explores complexity in big distributed systems and in the natural processes in physics and chemistry, building a platform for understanding how self-organization in big distributed systems can be achieved. It goes beyond the theoretical description of self-organization to present principles for designing self-organizing systems, and concludes by showing the need for a paradigm shift in the development of large-scale systems from strictly deterministic to non-deterministic and adaptive.

Analyzes the effect of self-organization applied to computer clouds Furthers research on principles of self-organization of computing and communication systems inspired by a wealth of self-organizing processes and phenomena in nature and society Presents a unique analysis of the field, with solutions and case studies

"What information do these data reveal?" "Is the information correct?" "How can I make the best use of the information?" The widespread use of computers and our reliance on the data generated by them have made these questions increasingly common and important. Computerized data may be in either digital or analog form and may be relevant to a wide range of applications that include medical monitoring and diagnosis, scientific research, engineering, quality control, seismology, meteorology, political and economic analysis and business and personal financial applications. The sources of the data may be databases that have been developed for specific purposes or may be of more general interest and include those that are accessible on the Internet.

In addition, the data may represent either single or multiple parameters. Examining data in its initial form is often very laborious and also makes it possible to "miss the forest for the trees" by failing to notice patterns in the data that are not readily apparent. To address these problems, this monograph describes several accurate and efficient methods for displaying, reviewing and analyzing digital and analog data. The methods may be used either singly or in various combinations to maximize the value of the data to those for whom it is relevant. None of the methods requires special devices and each can be used on common platforms such as personal computers, tablets and smart phones. Also, each of the methods can be easily employed utilizing widely available off-the-shelf software.

Using the methods does not require special expertise in computer science or technology, graphical design or statistical analysis. The usefulness and accuracy of all the described methods of data display, review and interpretation have been confirmed in multiple carefully performed studies using independent, objective endpoints. These studies and their results are described in the monograph. Because of their ease of use, accuracy and efficiency, the methods for displaying, reviewing and analyzing data described in this monograph can be highly useful to all who must work with computerized information and make decisions based upon it.

The reader will learn methods for easily increasing the speed and accuracy of reviewing data that are relevant to many diverse fields of endeavor This will reduce the drudgery associated with reviewing the data and simultaneously improve the reliability of the interpretations that result from the review This increased efficiency of review will make it easier to provide "full disclosure" of the data to all those responsible for making decisions based on any actionable information that might be contained in the data

The emerging field of network science represents a new style of research that can unify such traditionally-diverse fields as sociology, economics, physics, biology, and computer science. It is a powerful tool in analyzing both natural and man-made systems, using the relationships between players within these networks and between the networks themselves to gain insight into the nature of each field. Until now, studies in network science have been focused on particular relationships that require varied and sometimes-incompatible datasets, which has kept it from being a truly universal discipline.

Computational Network Science seeks to unify the methods used to analyze these diverse fields. This book provides an introduction to the field of Network Science and provides the groundwork for a computational, algorithm-based approach to network and system analysis in a new and important way. This new approach would remove the need for tedious human-based analysis of different datasets and help researchers spend more time on the qualitative aspects of network science research.

Demystifies media hype regarding Network Science and serves as a fast-paced introduction to state-of-the-art concepts and systems related to network science Comprehensive coverage of Network Science algorithms, methodologies, and common problems Includes references to formative and updated developments in the field Coverage spans mathematical sociology, economics, political science, and biological networks

Learning-Based Local Visual Representation and Indexing, reviews the state-of-the-art in visual content representation and indexing, introduces cutting-edge techniques in learning based visual representation, and discusses emerging topics in visual local representation, and introduces the most recent advances in content-based visual search techniques. Discusses state-of-the-art procedures in learning-based local visual representation. Shows how to master the basic techniques needed for building a large-scale visual search engine and indexing system Provides insight into how machine learning techniques can be leveraged to refine the visual recognition system, especially in the part of visual feature representation.

Content-based 3-D object retrieval has attracted extensive attention recently and has applications in a variety of fields, such as, computer-aided design, tele-medicine,mobile multimedia, virtual reality, and entertainment. The development of efficient and effective content-based 3-D object retrieval techniques has enabled the use of fast 3-D reconstruction and model design. Recent technical progress, such as the development of camera technologies, has made it possible to capture the views of 3-D objects. As a result, view-based 3-D object retrieval has become an essential but challenging research topic.

View-based 3-D Object Retrieval introduces and discusses the fundamental challenges in view-based 3-D object retrieval, proposes a collection of selected state-of-the-art methods for accomplishing this task developed by the authors, and summarizes recent achievements in view-based 3-D object retrieval. Part I presents an Introduction to View-based 3-D Object Retrieval, Part II discusses View Extraction, Selection, and Representation, Part III provides a deep dive into View-Based 3-D Object Comparison, and Part IV looks at future research and developments including Big Data application and geographical location-based applications.

Systematically introduces view-based 3-D object retrieval, including problem definitions and settings, methodologies, and benchmark testing beds Discusses several key challenges in view-based 3-D object retrieval, and introduces the state-of-the-art solutions Presents the progression from general image retrieval techniques to view-based 3-D object retrieval Introduces future research efforts in the areas of Big Data, feature extraction, and geographical location-based applications

Data Analysis in the Cloud introduces and discusses models, methods, techniques, and systems to analyze the large number of digital data sources available on the Internet using the computing and storage facilities of the cloud.

Coverage includes scalable data mining and knowledge discovery techniques together with cloud computing concepts, models, and systems. Specific sections focus on map-reduce and NoSQL models. The book also includes techniques for conducting high-performance distributed analysis of large data on clouds. Finally, the book examines research trends such as Big Data pervasive computing, data-intensive exascale computing, and massive social network analysis.

Introduces data analysis techniques and cloud computing concepts Describes cloud-based models and systems for Big Data analytics Provides examples of the state-of-the-art in cloud data analysis Explains how to develop large-scale data mining applications on clouds Outlines the main research trends in the area of scalable Big Data analysis

Repurposing Legacy Data: Innovative Case Studies takes a look at how data scientists have re-purposed legacy data, whether their own, or legacy data that has been donated to the public domain.

Most of the data stored worldwide is legacy data-data created some time in the past, for a particular purpose, and left in obsolete formats. As with keepsakes in an attic, we retain this information thinking it may have value in the future, though we have no current use for it.

The case studies in this book, from such diverse fields as cosmology, quantum physics, high-energy physics, microbiology, psychiatry, medicine, and hospital administration, all serve to demonstrate how innovative people draw value from legacy data. By following the case examples, readers will learn how legacy data is restored, merged, and analyzed for purposes that were never imagined by the original data creators.

Discusses how combining existing data with other data sets of the same kind can produce an aggregate data set that serves to answer questions that could not be answered with any of the original data Presents a method for re-analyzing original data sets using alternate or improved methods that can provide outcomes more precise and reliable than those produced in the original analysis Explains how to integrate heterogeneous data sets for the purpose of answering questions or developing concepts that span several different scientific fields

Cloud Storage Security: A Practical Guide introduces and discusses the risks associated with cloud-based data storage from a security and privacy perspective. Gain an in-depth understanding of the risks and benefits of cloud storage illustrated using a Use-Case methodology. The authors also provide a checklist that enables the user, as well as the enterprise practitioner to evaluate what security and privacy issues need to be considered when using the cloud to store personal and sensitive information. Describes the history and the evolving nature of cloud storage and security Explores the threats to privacy and security when using free social media applications that use cloud storage Covers legal issues and laws that govern privacy, compliance, and legal responsibility for enterprise users Provides guidelines and a security checklist for selecting a cloud-storage service provider Includes case studies and best practices for securing data in the cloud Discusses the future of cloud computing

Online Social Networks: Human Cognitive Constraints in Facebook and Twitter provides new insights into the structural properties of personal online social networks and the mechanisms underpinning human online social behavior.

As the availability of digital communication data generated by social media is revolutionizing the field of social networks analysis, the text discusses the use of large- scale datasets to study the structural properties of online ego networks, to compare them with the properties of general human social networks, and to highlight additional properties.

Users will find the data collected and conclusions drawn useful during design or research service initiatives that involve online and mobile social network environments.

Provides an analysis of the structural properties of ego networks in online social networks Presents quantitative evidence of the Dunbar's number in online environments Discusses original structural and dynamic properties of human social network through OSN analysis

Text summarization has been studied for over a half century, but traditional methods process texts empirically and neglect the fundamental characteristics and principles of language use and understanding. Automatic summarization is a desirable technique for processing big data. This reference summarizes previous text summarization approaches in a multi-dimensional category space, introduces a multi-dimensional methodology for research and development, unveils the basic characteristics and principles of language use and understanding, investigates some fundamental mechanisms of summarization, studies dimensions on representations, and proposes a multi-dimensional evaluation mechanism. Investigation extends to incorporating pictures into summary and to the summarization of videos, graphs and pictures, and converges to a general summarization method. Further, some basic behaviors of summarization are studied in the complex cyber-physical-social space. Finally, a creative summarization mechanism is proposed as an effort toward the creative summarization of things, which is an open process of interactions among physical objects, data, people, and systems in cyber-physical-social space through a multi-dimensional lens of semantic computing. The author's insights can inspire research and development of many computing areas.

The first book that proposes the method for the summarization of things in cyber-physical society through a multi-dimensional lens of semantic computing. A transformation from the traditional application-driven research paradigm into a data-driven research paradigm for creative summarization through information modeling, cognitive modeling and knowledge modeling. A multi-dimensional methodology for studying, managing, creating and applying methods.

Complex Systems and Clouds: A Self-Organization and Self-Management Perspective provides insights into the intricate world of self-organizing systems. Large scale distributed computer systems have evolved into very complex systems and are at the point where they need to borrow self-adapting organizing concepts from nature.

The book explores complexity in big distributed systems and in the natural processes in physics and chemistry, building a platform for understanding how self-organization in big distributed systems can be achieved. It goes beyond the theoretical description of self-organization to present principles for designing self-organizing systems, and concludes by showing the need for a paradigm shift in the development of large-scale systems from strictly deterministic to non-deterministic and adaptive.

Analyzes the effect of self-organization applied to computer clouds Furthers research on principles of self-organization of computing and communication systems inspired by a wealth of self-organizing processes and phenomena in nature and society Presents a unique analysis of the field, with solutions and case studies

"What information do these data reveal?" "Is the information correct?" "How can I make the best use of the information?" The widespread use of computers and our reliance on the data generated by them have made these questions increasingly common and important. Computerized data may be in either digital or analog form and may be relevant to a wide range of applications that include medical monitoring and diagnosis, scientific research, engineering, quality control, seismology, meteorology, political and economic analysis and business and personal financial applications. The sources of the data may be databases that have been developed for specific purposes or may be of more general interest and include those that are accessible on the Internet.

In addition, the data may represent either single or multiple parameters. Examining data in its initial form is often very laborious and also makes it possible to "miss the forest for the trees" by failing to notice patterns in the data that are not readily apparent. To address these problems, this monograph describes several accurate and efficient methods for displaying, reviewing and analyzing digital and analog data. The methods may be used either singly or in various combinations to maximize the value of the data to those for whom it is relevant. None of the methods requires special devices and each can be used on common platforms such as personal computers, tablets and smart phones. Also, each of the methods can be easily employed utilizing widely available off-the-shelf software.

Using the methods does not require special expertise in computer science or technology, graphical design or statistical analysis. The usefulness and accuracy of all the described methods of data display, review and interpretation have been confirmed in multiple carefully performed studies using independent, objective endpoints. These studies and their results are described in the monograph. Because of their ease of use, accuracy and efficiency, the methods for displaying, reviewing and analyzing data described in this monograph can be highly useful to all who must work with computerized information and make decisions based upon it.

The reader will learn methods for easily increasing the speed and accuracy of reviewing data that are relevant to many diverse fields of endeavor This will reduce the drudgery associated with reviewing the data and simultaneously improve the reliability of the interpretations that result from the review This increased efficiency of review will make it easier to provide "full disclosure" of the data to all those responsible for making decisions based on any actionable information that might be contained in the data