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A course on big data analytics
Journal of Parallel and Distributed Computing, 2018This report details a course on big data analytics designed for undergraduate junior and senior computer science students. The course is heavily focused on projects and writing code for big data processing. It is designed to help students learn parallel and distributed computing frameworks and techniques commonly used in industry.
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Interactions with big data analytics
Interactions, 2012Increasingly in the 21st century, our daily lives leave behind a detailed digital record: our shifting thoughts and opinions shared on Twitter, our social relationships, our purchasing habits, our information seeking, our photos and videos—even the movements of our bodies and cars.
Danyel Fisher +3 more
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A Framework for Big Data Analytics
Proceedings of the Eighth International C* Conference on Computer Science & Software Engineering - C3S2E '15, 2008Big Data may reside on multiple and dispersed sources and adhere to a variety of formats. Their analysis may include a range of tasks to be executed on a range of query engines. The tasks, as a whole, represent the rationale of a specific process. The users that create such a process may have various roles, like, business analysts, engineers, end-users
Verena Kantere, Maxim Filatov
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Big Data Analytics for Security
IEEE Security & Privacy, 2013Big data is changing the landscape of security tools for network monitoring, security information and event management, and forensics; however, in the eternal arms race of attack and defense, security researchers must keep exploring novel ways to mitigate and contain sophisticated attackers.
Alvaro A. Cárdenas +2 more
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Big data analytics and the LHC
Proceedings of the ACM International Conference on Computing Frontiers, 2016The Large Hadron Collider is one of the largest and most complicated pieces of scientific apparatus ever constructed. The detectors along the LHC ring see as many as 800 million proton-proton collisions per second. An event in 10 to the 11th power is new physics and there is a hierarchical series of steps to extract a tiny signal from an enormous ...
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Big Data and Analytics in Healthcare
Methods of Information in Medicine, 2015SummaryThis editorial is part of the Focus Theme of Methods of Information in Medicine on “Big Data and Analytics in Healthcare”.The amount of data being generated in the healthcare industry is growing at a rapid rate. This has generated immense interest in leveraging the availability of healthcare data (and “big data”) to improve health outcomes and ...
S S-L, Tan, G, Gao, S, Koch
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Workload management for Big Data analytics
2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013Parallel database systems and MapReduce systems are essential components of today's infrastructure for Big Data analytics. These systems process multiple concurrent workloads consisting of complex user requests, where each request is associated with an (explicit or implicit) service level objective.
Ashraf Aboulnaga, Shivnath Babu
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Big education: Opportunities for Big Data analytics
2015 IEEE International Conference on Digital Signal Processing (DSP), 2015Big Data have demonstrated significant values in extension of our insight and foresight into the world. With the rapid development of communication technologies and mobile devices, educational data have been generated at an unprecedented pace. The emerging highly flexible and scalable approaches to data processing and analysis allow us to extract new ...
Ling Cen, Dymitr Ruta, Jason W. P. Ng
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Proceedings of the 50th ACM Technical Symposium on Computer Science Education, 2019
Born from a Berkeley graduate project, the Apache Spark library has grown to be the most broadly used big data analytics platform. While Spark integrates with the older Hadoop ecosystem, it provides much more intuitive, faster, and powerful abstractions for manipulating distributed data than MapReduce.
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Born from a Berkeley graduate project, the Apache Spark library has grown to be the most broadly used big data analytics platform. While Spark integrates with the older Hadoop ecosystem, it provides much more intuitive, faster, and powerful abstractions for manipulating distributed data than MapReduce.
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An Outline on Big Data and Big Data Analytics
2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), 2018With the rapid advancements in technological applications have led to the flooding of data from various sources like web, social network data, business data, medical records, etc. over the preceding years. As compared to traditional data, big data reveals a unique characteristic from its three V’s which means big data is unstructured.
Devottam Gaurav +3 more
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