Big Data: Algorithms, Analytics, and Applications

As today’s organizations are capturing exponentially larger amounts of data than ever, now is the time for organizations to rethink how they digest that data. Through advanced algorithms and analytics techniques, organizations can harness this data, discover hidden patterns, and use the newly acquired knowledge to achieve competitive advantages.

Presenting the contributions of leading experts in their respective fields, Big Data: Algorithms, Analytics, and Applications bridges the gap between the vastness of Big Data and the appropriate computational methods for scientific and social discovery. It covers fundamental issues about Big Data, including efficient algorithmic methods to process data, better analytical strategies to digest data, and representative applications in diverse fields, such as medicine, science, and engineering. The book is organized into five main sections:

1. Big Data Management―considers the research issues related to the management of Big Data, including indexing and scalability aspects
2. Big Data Processing―addresses the problem of processing Big Data across a wide range of resource-intensive computational settings
3. Big Data Stream Techniques and Algorithms―explores research issues regarding the management and mining of Big Data in streaming environments
4. Big Data Privacy―focuses on models, techniques, and algorithms for preserving Big Data privacy
5. Big Data Applications―illustrates practical applications of Big Data across several domains, including finance, multimedia tools, biometrics, and satellite Big Data processing

Overall, the book reports on state-of-the-art studies and achievements in algorithms, analytics, and applications of Big Data. It provides readers with the basis for further efforts in this challenging scientific field that will play a leading role in next-generation database, data warehousing, data mining, and cloud computing research. It also explores related applications in diverse sectors, covering technologies for media/data communication, elastic media/data storage, cross-network media/data fusion, and SaaS.

Table of Contents

Chapter 1: Scalable Indexing for Big Data Processing
Chapter 2: Scalability and Cost Evaluation of Incremental Data Processing Using Amazon’s Hadoop Service
Chapter 3: Singular Value Decomposition, Clustering, and Indexing for Similarity Search for Large Data Sets in High-Dimensional Spaces
Chapter 4: Multiple Sequence Alignment and Clustering with Dot Matrices, Entropy, and Genetic Algorithms
Chapter 5: Approaches for High-Performance Big Data Processing : Applications and Challenges
Chapter 6: The Art of Scheduling for Big Data Science
Chapter 7: Time–Space Scheduling in the MapReduce Framework
Chapter 8: GEMS: Graph Database Engine for Multithreaded Systems
Chapter 9: KSC-net : Community Detection for Big Data Networks
Chapter 10: Making Big Data Transparent to the Software Developers’ Community
Chapter 11: Key Technologies for Big Data Stream Computing
Chapter 12: Streaming Algorithms for Big Data Processing on Multicore Architecture
Chapter 13: Organic Streams : A Unified Framework for Personal Big Data Integration and Organization Towards Social Sharing and Individualized Sustainable Use
Chapter 14: Managing Big Trajectory Data : Online Processing of Positional Streams
Chapter 15: Personal Data Protection Aspects of Big Data
Chapter 16: Privacy-Preserving Big Data Management : The Case of OLAP
Chapter 17: Big Data in Finance
Chapter 18: Semantic-Based Heterogeneous Multimedia Big Data Retrieval
Chapter 19: Topic Modeling for Large-Scale Multimedia Analysis and Retrieval
Chapter 20: Big Data Biometrics Processing : A Case Study of an Iris Matching Algorithm on Intel Xeon Phi
Chapter 21: Storing, Managing, and Analyzing Big Satellite Data : Experiences and Lessons Learned from a Real-World Application
Chapter 22: Barriers to the Adoption of Big Data Applications in the Social Sector