Main memory database research directions

Eich, Margaret H.

doi:10.1007/3-540-51324-8_40

Cited by 20 publications

(5 citation statements)

References 30 publications

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“…Regarding hardware solutions for processing big data, we refer the reader to [20]. Also relevant in this context is a revival of main memory or in-memory database technology, a development that was first studied in the 1980s [8] and that has finally become available in commercial products [13,17] thanks to considerable technological advances during the last 30 years. The database field has furthermore brought along not only SQL ("NoSQL") databases for coping with the requirements of big data applications such as scalability, wide distribution, and fault tolerance, which come in various flavors including key value stores (e.g., Amazon's SimpleDB or Dynamo, LinkedIn's Voldemort), column stores (e.g., Google's BigTable [5], Apache's Hbase, or Cassandra, Yahoo!…”

Section: Technology For Handling Big Datamentioning

confidence: 99%

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Big data as the new enabler in business and other intelligence

Vossen

2013

Vietnam J Comput Sci

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The term "big data" will always be remembered as the big buzzword of 2013. According to the Wikipedia, big data "is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools". In other perceptions, the "3 Vs" that characterize it (i.e., volume, velocity, and variety) or the "4 Vs" (adding veracity to the previous three) are responsible for the fact that it exceeds an organization's own data as well as its storage or compute capacity for accurate and timely decision-making. In essence, big data refers to the situation that more and more aspects and artifacts of everyday life, be it personal or professional, are available in digital form, e.g., personal or company profiles, social network and blog postings, buying histories, health records, to name just a few, that increasingly more data gets dynamically produced especially on the Internet and on the Web, and that nowadays the tools and techniques are available for evaluating and analyzing all that data in various combinations. Numerous companies already foresee the enormous business effects that analytical scenarios based on big data can have, and the impacts that it will hence have on advertising, commerce, and business intelligence (BI). This paper reviews the issues, techniques, and applications of big data, with an emphasis on future BI architectures.

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Section: Technology For Handling Big Datamentioning

confidence: 99%

“…http://www.directrelief.org/emergency/hurricane-sandy-relief-andrecovery/. 7 http://www.fitbit.com 8. http://www.nike.com/cdp/fuelband/us/en_us/ 9.…”

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confidence: 99%

Big data as the new enabler in business and other intelligence

Vossen

2013

Vietnam J Comput Sci

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“…In the extreme, main storage databases are adopted to achieve high volume TP for short transactions (as in IMS Fastpath). A discussion of various issues in main storage databases appears in [Eich89], including CC which is less of a problem in this case. We envision here a high performance centralized TP system with a large database buffer in main memory, but disk accesses are still necessary when buffer misses occur.…”

Section: Introductionmentioning

confidence: 98%

Centralized concurrency control methods for high-end TP

Thomasian

1991

SIGMOD Rec.

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“…Note that the main memory database system solution [30] which tries to maintain the (active) database in stable main memory is complementary rather than alternative. We can summarize this solution as increasing the I/O bandwidth through parallelism.…”

Section: Introductionmentioning

confidence: 99%

Parallel database systems: Open problems and new issues

Valduriez

1993

Distrib Parallel Databases

120

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Parallel database systems attempt to exploit recent multiprocessor computer architectures in order to build high-performance and high-availability database servers at a much lower price than equivalent mainframe computers. Although there are commercial SQL-based products, a number of open problems hamper the full exploitation of the capabilities of parallel systems. These problems touch on issues ranging from those of parallel processing to distributed database management. Furthermore, it is still an open issue to decide which of the various architectures among sharedmemory, shared-disk, and shared-nothing, is best for database management under various conditions. Finally, there are new issues raised by the introduction of higher functionality such as knowledge-based or object-oriented capabilities within a parallel database system.

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Main memory database research directions

Cited by 20 publications

References 30 publications

Big data as the new enabler in business and other intelligence

Big data as the new enabler in business and other intelligence

Centralized concurrency control methods for high-end TP

Parallel database systems: Open problems and new issues

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