dc.contributor.author | Hendler, James A. | |
dc.date.accessioned | 2023-01-26T15:34:08Z | |
dc.date.available | 2023-01-26T15:34:08Z | |
dc.date.issued | 2014 | |
dc.identifier.citation | Hendler J. Data Integration for Heterogenous Datasets. Big Data. 2014 Dec 1;2(4):205-215. doi: 10.1089/big.2014.0068. PMID: 25553272; PMCID: PMC4276119. | en_US |
dc.identifier.uri | http://doi.org/10.1089/big.2014.0068 | |
dc.identifier.uri | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4276119/# | |
dc.identifier.uri | https://hdl.handle.net/20.500.13015/6423 | |
dc.description.abstract | More and more, the needs of data analysts are requiring the use of data outside the control of their own organizations. The increasing amount of data available on the Web, the new technologies for linking data across datasets, and the increasing need to integrate structured and unstructured data are all driving this trend. In this article, we provide a technical overview of the emerging “broad data” area, in which the variety of heterogeneous data being used, rather than the scale of the data being analyzed, is the limiting factor in data analysis efforts. The article explores some of the emerging themes in data discovery, data integration, linked data, and the combination of structured and unstructured data. | en_US |
dc.publisher | Mary Ann Liebert, Inc. | en_US |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.title | Data Integration for Heterogenous Datasets | en_US |
dc.type | Article | en_US |