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[null,"1.","Record Nr.",null,"UNINA9910679263203321"]
[null,null,"Titolo",null,"Big data : algorithms, analytics, and applications / / edited by Kuan-Ching Li, Providence University, Taiwan; Hai Jiang, Arkansas State University, USA; Laurence T. Yang, St. Francis Xavier University, Canada; Alfredo Cuzzocrea, ICAR-CNR and Univ"]
[null,null,"Pubbl/distr/stampa",null,"Boca Raton : , : CRC Press, , [2015]"]
[{"$nulls":4},"©2015"]
[null,null,"ISBN",null,"9780367575953"]
[{"$nulls":4},"0367575957"]
[{"$nulls":4},"9780429174018"]
[{"$nulls":4},"0429174012"]
[{"$nulls":4},"9781498760409"]
[{"$nulls":4},"1498760406"]
[{"$nulls":4},"9781785398230"]
[{"$nulls":4},"1785398237"]
[{"$nulls":4},"9781482240566"]
[{"$nulls":4},"1482240564"]
[null,null,"Edizione",null,"[1st edition]"]
[null,null,"Descrizione fisica",null,"1 online resource (478 p.)"]
[null,null,"Collana",null,"Chapman & Hall/CRC Big Data Series"]
[{"$nulls":4},"Chapman & Hall Book"]
[null,null,"Classificazione",null,"COM021030COM037000MAT000000"]
[null,null,"Disciplina",null,"005.7"]
[null,null,"Soggetti",null,"Big data"]
[{"$nulls":4},"Database management"]
[{"$nulls":4},"Data mining"]
[{"$nulls":4},"Machine theory"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","A Chapman and Hall Book."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references at the end of each chapters."]
[null,null,"Nota di contenuto",null,"Front Cover; Contents; Foreword by Jack Dongarra; Preface; Editors; Contributors; 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 "]
[{"$nulls":4},"Processing : Applications and Challenges"]
[{"$nulls":4},"Chapter 6: The Art of Scheduling for Big Data ScienceChapter 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"]
[{"$nulls":4},"Chapter 13: Organic Streams : A Unified Framework for Personal Big Data Integration and Organization Towards Social Sharing and Individualized Sustainable UseChapter 14: Managing Big Trajectory Data : Online Processing of Positional Streams; Chapter 15: Personal Data Protection Aspects of Big Data; Chapter 16: Privacy-Preserving Big Data Management : The Case of OLAP; Chapter 17: Big Data in Finance; Chapter 18: Semantic-Based Heterogeneous Multimedia Big Data Retrieval; Chapter 19: Topic Modeling for Large-Scale Multimedia Analysis and Retrieval"]
[{"$nulls":4},"Chapter 20: Big Data Biometrics Processing : A Case Study of an Iris Matching Algorithm on Intel Xeon PhiChapter 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; Back Cover"]
[null,null,null,"Sommario/riassunto","Data are generated at an exponential rate all over the world. Through advanced algorithms and analytics techniques, organizations can harness this data, discover hidden patterns, and use the findings to make meaningful decisions. Containing contributions from leading experts in their respective fields, this book bridges the gap between the vastness of big data and the appropriate computational methods for scientific and social discovery. It also explores related applications in diverse sectors, covering technologies for media/data communication, elastic media/data storage, cross-network media/data fusion, SaaS, and more--"]