Description
Data Management on New Hardware, 1st ed. 2017
7th International Workshop on Accelerating Data Analysis and Data Management Systems Using Modern Processor and Storage Architectures, ADMS 2016 and 4th International Workshop on In-Memory Data Management and Analytics, IMDM 2016, New Delhi, India, Septe
Information Systems and Applications, incl. Internet/Web, and HCI Series
Coordinators: Blanas Spyros, Bordawekar Rajesh, Lahiri Tirthankar, Levandoski Justin, Pavlo Andrew
Language: EnglishSubject for Data Management on New Hardware:
Keywords
data management systems; database query processing; database transaction processing; main memory engines; memory management; data layouts; database management system engines; in-memory databases; index structures; information systems; main memory; main-memory databases; robust query processing; scientific databases; tuple reconstruction
Publication date: 03-2017
Support: Print on demand
Support: Print on demand
Description
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This book contains selected papers from the 7th International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures, ADMS 2016, and the 4th International Workshop on In-Memory Data Management and Analytics, IMDM 2016, held in New Dehli, India, in September 2016. The joint Workshops were co-located with VLDB 2016. The 9 papers presented were carefully reviewed and selected from 18 submissions. They investigate opportunities in accelerating analytics/data management systems and workloads (including traditional OLTP, data warehousing/OLAP, ETL streaming/real-time, business analytics, and XML/RDF processing) running memory-only environments, using processors (e.g. commodity and specialized multi-core, GPUs and FPGAs, storage systems (e.g. storage-class memories like SSDs and phase-change memory), and hybrid programming models like CUDA, OpenCL, and Open ACC. The papers also explore the interplay between overall system design, core algorithms, query optimization strategies, programming approaches, performance modeling and evaluation, from the perspective of data management applications.
Accelerating analytics/data management systems.- Workloads (including traditional OLTP, data warehousing/OLAP, ETL streaming/real-time, business analytics, and XML/RDF processing).- Running memory-only environments, using processors (e.g. commodity and specialized multi-core, GPUs and FPGAs, storage systems (e.g. storage-class memories like SSDs and phase-change memory).- Hybrid programming models like CUDA, OpenCL, and Open ACC.- Interplay between overall system design, core algorithms, query optimization strategies, programming approaches, performance modeling and evaluation, from the perspective of data management applications.
Includes supplementary material: sn.pub/extras
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