Data management: Present state and future trends

In data management, both system designers and users casually resort to performance evaluation. Performance evaluation by experimentation on a real system is generally referred to as benchmarking. The aim of this chapter is to present an overview of the major past and present state-of-the-art data-centric benchmarks. This review includes the TPC standard benchmarks, but also alternative or more specialized benchmarks. Surveyed benchmarks are categorized into three families: transaction benchmarks aimed at on-line transaction processing (OLTP), decision-support benchmarks aimed at on-line analysis processing (OLAP), and big data benchmarks. Issues, tradeoffs, and future trends in data-centric benchmarking are also discussed.

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Data-Centric Benchmarking

Encyclopedia of Information Science and Technology, Fourth Edition ◽

10.4018/978-1-5225-2255-3.ch154 ◽

2018 ◽

pp. 1772-1782 ◽

Cited By ~ 1

Author(s):

Jérôme Darmont

Keyword(s):

Big Data ◽

Performance Evaluation ◽

Decision Support ◽

Data Management ◽

Present State ◽

Transaction Processing ◽

Real System ◽

Future Trends ◽

On Line ◽

Line Analysis

In data management, both system designers and users casually resort to performance evaluation. Performance evaluation by experimentation on a real system is generally referred to as benchmarking. The aim of this chapter is to present an overview of the major past and present state-of-the-art data-centric benchmarks. This review includes the TPC standard benchmarks, but also alternative or more specialized benchmarks. Surveyed benchmarks are categorized into three families: transaction benchmarks aimed at On-Line Transaction Processing (OLTP), decision-support benchmarks aimed at On-Line Analysis Processing (OLAP) and big data benchmarks. Issues, tradeoffs and future trends in data-centric benchmarking are also discussed.

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Promoting Data Reuse and Collaboration at an Academic Medical Center

International Journal of Digital Curation ◽

10.2218/ijdc.v10i1.366 ◽

2015 ◽

Vol 10 (1) ◽

pp. 260-267 ◽

Cited By ~ 1

Author(s):

Kevin Read ◽

Jessica Athens ◽

Ian Lamb ◽

Joey Nicholson ◽

Sushan Chin ◽

...

Keyword(s):

Data Management ◽

Population Health ◽

Data Sharing ◽

Present State ◽

Medical Center ◽

Academic Medical Center ◽

Data Reuse ◽

Data Description ◽

University Library ◽

Academic Medical

A need was identified by the Department of Population Health (DPH) for an academic medical center to facilitate research using large, externally funded datasets. Barriers identified included difficulty in accessing and working with the datasets, and a lack of knowledge about institutional licenses. A need to facilitate sharing and reuse of datasets generated by researchers at the institution (internal datasets) was also recognized. The library partnered with a researcher in the DPH to create a catalog of external datasets, which provided detailed metadata and access instructions. The catalog listed researchers at the medical center and the main campus with expertise in using these external datasets in order to facilitate research and cross-campus collaboration. Data description standards were reviewed to create a set of metadata to facilitate access to both externally generated datasets, as well as the internally generated datasets that would constitute the next phase of development of the catalog. Interviews with a range of investigators at the institution identified DPH researchers as most interested in data sharing, therefore targeted outreach to this group was undertaken. Initial outreach resulted in additional external datasets being described, new local experts volunteering, proposals for additional functionality, and interest from researchers in inclusion of their internal datasets in the catalog. Despite limited outreach, the catalog has had ~250 unique page views in the three months since it went live. The establishment of the catalog also led to partnerships with the medical center’s data management core and the main university library. The Data Catalog in its present state serves a direct user need from the Department of Population Health to describe large, externally funded datasets. The library will use this initial strong community of users to expand the catalog and include internally generated research datasets. Future expansion plans will include working with DataCore and the main university library.

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