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Statistical Analysis of Massive Data Streams
Massive data streams, large quantities of data that arrive continuously, are becoming increasingly commonplace in many areas of science and technology. Consequently development of analytical methods for such streams is of growing importance. To address this issue, the National Security Agency asked the NRC to hold a workshop to explore methods for analysis of streams of data so as to stimulate progress in the field. This report presents the results of that workshop. It provides presentations that focused on five different research areas where massive data streams are present: atmospheric and meteorological data; high-energy physics; integrated data systems; network traffic; and mining commercial data streams. The goals of the report are to improve communication among researchers in the field and to increase relevant statistical science activity.
Frontiers in Massive Data Analysis
Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system....
Transactions on Large-Scale Data- and Knowledge-Centered Systems XLI
The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. Current decentralized systems still focus on data and knowledge as their main resource. Feasib...
Big Data Meets Survey Science
Offers a clear view of the utility and place for survey data within the broader Big Data ecosystem This book presents a collection of snapshots from two sides of the Big Data perspective. It assembles an array of tangible tools, methods, and approaches that illustrate how Big Data sources and methods are being used in the survey and social sciences to improve official statistics and estimates for human populations. It also provides examples of how survey data are being used to evaluate and improve the quality of insights derived from Big Data. Big Data Meets Survey Science: A Collection of Innovative Methods shows how survey data and Big Data are used together for the benefit of one or more ...
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Future Trends of HPC in a Disruptive Scenario
The realization that the use of components off the shelf (COTS) could reduce costs sparked the evolution of the massive parallel computing systems available today. The main problem with such systems is the development of suitable operating systems, algorithms and application software that can utilise the potential processing power of large numbers of processors. As a result, systems comprising millions of processors are still limited in the applications they can efficiently solve. Two alternative paradigms that may offer a solution to this problem are Quantum Computers (QC) and Brain Inspired Computers (BIC). This book presents papers from the 14th edition of the biennial international confe...
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Big Data Analytics
This book constitutes the proceedings of the 8th International Conference on Big Data Analytics, BDA 2020, which took place during December 15-18, 2020, in Sonepat, India. The 11 full and 3 short papers included in this volume were carefully reviewed and selected from 48 submissions; the book also contains 4 invited and 3 tutorial papers. The contributions were organized in topical sections named as follows: data science systems; data science architectures; big data analytics in healthcare; information interchange of Web data resources; and business analytics.
Big Data Analytics
This book constitutes the refereed conference proceedings of the 5th International Conference on Big Data Analytics, BDA 2017, held in Hyderabad, India, in December 2017. The 21 revised full papers were carefully reviewed and selected from 80 submissions and cover topics on big data analytics, information and knowledge management, mining of massive datasets, computational modeling, data mining and analysis.
