Expressing High-Throughput Data in a Restructurable, Integrated Form for Knowledge Extraction

D. Korytina; P. A. Graf; R. King; W. B. Jones

Expressing High-Throughput Data in a Restructurable, Integrated Form for Knowledge Extraction

D. Korytina, P. A. Graf, R. King, W. B. Jones

Computational Science

University of Colorado Boulder

Research output: Contribution to conference › Paper › peer-review

2 Scopus Citations

Abstract

The growth of high-throughput and combinatorial methods in experimental materials science has pushed human-mediated data processing traditions beyond their limit. For such tools to be useful, automated data processing must become an integral part of the scientific workflow. Here, we report on components of our scientific data management system, OpenMat, and especially a core component, AutoDB, that provide a foundation for creating scientific knowledge bases to enable data mining. In particular, our system implements the extract-transformload (ETL) paradigm in a flexible way that is specifically designed to organize heterogeneous scientific data for purposes of subsequent knowledge extraction.

Original language	American English
Pages	111-115
Number of pages	5
State	Published - 2008
Event	2008 International Conference on Information and Knowledge Engineering, IKE 2008 - Las Vegas, NV, United States Duration: 14 Jul 2008 → 17 Jul 2008

Conference

Conference	2008 International Conference on Information and Knowledge Engineering, IKE 2008
Country/Territory	United States
City	Las Vegas, NV
Period	14/07/08 → 17/07/08

NREL Publication Number

NREL/CP-5900-42955

Keywords

Data integration
Data management
Data mining
Data modeling
ETL
Metadata

Cite this

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title = "Expressing High-Throughput Data in a Restructurable, Integrated Form for Knowledge Extraction",

abstract = "The growth of high-throughput and combinatorial methods in experimental materials science has pushed human-mediated data processing traditions beyond their limit. For such tools to be useful, automated data processing must become an integral part of the scientific workflow. Here, we report on components of our scientific data management system, OpenMat, and especially a core component, AutoDB, that provide a foundation for creating scientific knowledge bases to enable data mining. In particular, our system implements the extract-transformload (ETL) paradigm in a flexible way that is specifically designed to organize heterogeneous scientific data for purposes of subsequent knowledge extraction.",

keywords = "Data integration, Data management, Data mining, Data modeling, ETL, Metadata",

author = "D. Korytina and Graf, {P. A.} and R. King and Jones, {W. B.}",

year = "2008",

language = "American English",

pages = "111--115",

note = "2008 International Conference on Information and Knowledge Engineering, IKE 2008 ; Conference date: 14-07-2008 Through 17-07-2008",

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TY - CONF

T1 - Expressing High-Throughput Data in a Restructurable, Integrated Form for Knowledge Extraction

AU - Korytina, D.

AU - Graf, P. A.

AU - King, R.

AU - Jones, W. B.

PY - 2008

Y1 - 2008

N2 - The growth of high-throughput and combinatorial methods in experimental materials science has pushed human-mediated data processing traditions beyond their limit. For such tools to be useful, automated data processing must become an integral part of the scientific workflow. Here, we report on components of our scientific data management system, OpenMat, and especially a core component, AutoDB, that provide a foundation for creating scientific knowledge bases to enable data mining. In particular, our system implements the extract-transformload (ETL) paradigm in a flexible way that is specifically designed to organize heterogeneous scientific data for purposes of subsequent knowledge extraction.

AB - The growth of high-throughput and combinatorial methods in experimental materials science has pushed human-mediated data processing traditions beyond their limit. For such tools to be useful, automated data processing must become an integral part of the scientific workflow. Here, we report on components of our scientific data management system, OpenMat, and especially a core component, AutoDB, that provide a foundation for creating scientific knowledge bases to enable data mining. In particular, our system implements the extract-transformload (ETL) paradigm in a flexible way that is specifically designed to organize heterogeneous scientific data for purposes of subsequent knowledge extraction.

KW - Data integration

KW - Data management

KW - Data mining

KW - Data modeling

KW - ETL

KW - Metadata

UR - http://www.scopus.com/inward/record.url?scp=62749127248&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:62749127248

SP - 111

EP - 115

T2 - 2008 International Conference on Information and Knowledge Engineering, IKE 2008

Y2 - 14 July 2008 through 17 July 2008

ER -

Expressing High-Throughput Data in a Restructurable, Integrated Form for Knowledge Extraction

Abstract

Conference

NREL Publication Number

Keywords

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