| Title |
DCMS: A data analytics and management system for molecular simulation
|
|---|---|
| Published in |
Journal of Big Data, November 2014
|
| DOI | 10.1186/s40537-014-0009-5 |
| Pubmed ID | |
| Authors |
Anand Kumar, Vladimir Grupcev, Meryem Berrada, Joseph C Fogarty, Yi-Cheng Tu, Xingquan Zhu, Sagar A Pandit, Yuni Xia |
| Abstract |
Molecular Simulation (MS) is a powerful tool for studying physical/chemical features of large systems and has seen applications in many scientific and engineering domains. During the simulation process, the experiments generate a very large number of atoms and intend to observe their spatial and temporal relationships for scientific analysis. The sheer data volumes and their intensive interactions impose significant challenges for data accessing, managing, and analysis. To date, existing MS software systems fall short on storage and handling of MS data, mainly because of the missing of a platform to support applications that involve intensive data access and analytical process. In this paper, we present the database-centric molecular simulation (DCMS) system our team developed in the past few years. The main idea behind DCMS is to store MS data in a relational database management system (DBMS) to take advantage of the declarative query interface (i.e., SQL), data access methods, query processing, and optimization mechanisms of modern DBMSs. A unique challenge is to handle the analytical queries that are often compute-intensive. For that, we developed novel indexing and query processing strategies (including algorithms running on modern co-processors) as integrated components of the DBMS. As a result, researchers can upload and analyze their data using efficient functions implemented inside the DBMS. Index structures are generated to store analysis results that may be interesting to other users, so that the results are readily available without duplicating the analysis. We have developed a prototype of DCMS based on the PostgreSQL system and experiments using real MS data and workload show that DCMS significantly outperforms existing MS software systems. We also used it as a platform to test other data management issues such as security and compression. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 2% |
| United States | 1 | 2% |
| India | 1 | 2% |
| Unknown | 51 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 30% |
| Researcher | 10 | 19% |
| Student > Master | 9 | 17% |
| Student > Bachelor | 4 | 7% |
| Student > Doctoral Student | 1 | 2% |
| Other | 6 | 11% |
| Unknown | 8 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 14 | 26% |
| Chemistry | 5 | 9% |
| Engineering | 4 | 7% |
| Materials Science | 4 | 7% |
| Business, Management and Accounting | 4 | 7% |
| Other | 13 | 24% |
| Unknown | 10 | 19% |
Attention Score in Context
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#19,017,658
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#268
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Outputs of similar age from Journal of Big Data
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