國立虎尾科技大學 |

Data fusion ontology : = Enabling a paradigm shift from data warehousing to crowdsourcing for accelerated pace of research.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Data fusion ontology :/
其他題名:	Enabling a paradigm shift from data warehousing to crowdsourcing for accelerated pace of research.
作者:	Raje, Satyajeet.
面頁冊數:	1 online resource (192 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-06(E), Section: A.
標題:	Information science. -
電子資源:	click for full text (PQDT)
ISBN:	9781369556452

Data fusion ontology : = Enabling a paradigm shift from data warehousing to crowdsourcing for accelerated pace of research.
Raje, Satyajeet.

Data fusion ontology :Enabling a paradigm shift from data warehousing to crowdsourcing for accelerated pace of research. - 1 online resource (192 pages)

Source: Dissertation Abstracts International, Volume: 78-06(E), Section: A.

Thesis (Ph.D.)--The Ohio State University, 2016.

Includes bibliographical references

The last decade has seen an explosion of publicly available, crowd-sourced research datasets in various basic and applied science domains. Typically, research datasets are much smaller than the industry counter-parts, but still contain rich knowledge that can be reused to maximize new scientific discoveries. This availability of data has made data-driven academic research across domain at large-scale a real possibility.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369556452Subjects--Topical Terms:

561178
Information science.
Index Terms--Genre/Form:

554714
Electronic books.

Data fusion ontology : = Enabling a paradigm shift from data warehousing to crowdsourcing for accelerated pace of research.
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500 $a Source: Dissertation Abstracts International, Volume: 78-06(E), Section: A.
500 $a Advisers: Jayashree Ramanathan; Philip Payne.
502 $a Thesis (Ph.D.)--The Ohio State University, 2016.
504 $a Includes bibliographical references
520 $a The last decade has seen an explosion of publicly available, crowd-sourced research datasets in various basic and applied science domains. Typically, research datasets are much smaller than the industry counter-parts, but still contain rich knowledge that can be reused to maximize new scientific discoveries. This availability of data has made data-driven academic research across domain at large-scale a real possibility.
520 $a However, the availability of the data has not translated to its effective usage in research. A large amount of data within the data repositories remains underutilized. This research determined that a critical factor for this inefficiency is that majority of the researchers' time is devoted to the pre-processing activities rather than actual analysis. The amount of time and resources required to complete the beginning of this progression appear to limit the efficacy of the remaining research process.
520 $a The thesis proposes research-centric "data fusion platforms" to accelerate the research process through reduction in the time required for the quantitative data analysis cycle. This is achieved by automating the critical data preprocessing steps in the workflow.
520 $a First, the thesis defines the required computational framework to enable implementation of such platforms. It conceptualizes a novel crowdsourcing solution to enable a paradigm shift from "in-advance" to "on-demand" data integration mechanisms. This is achieved through a middle-layer ontological architecture placed over a shared, crowd-sourced data repository. Highly granular semantic annotations anchor the column-level entities and relationships within the datasets to global reference ontologies. The core idea is that these annotations provide the declarative metadata that can leverage the reference ontologies. The thesis demonstrates how these annotations provide global interoperability across datasets without a predetermined schema. This enables automation of the data pre-processing operations.
520 $a The research is centered on a Data Fusion Ontology (DFO) as the required representational framework to enable the above computational solution. The DFO is designed to enable sophisticated data integration platforms with capabilities to support the research data cycle of discovery, mediation, integration and visualization.
520 $a The DFO is validated using a prototype data fusion platform that can leverage the DFO specified annotations to meet the functional requirements to accelerate the pace of research.
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