The Harmony Integration Workbench

Mork, Peter; Seligman, Len; Rosenthal, Arnon; Korb, Joel; Wolf, Chris

doi:10.1007/978-3-540-92148-6_3

Cited by 19 publications

(15 citation statements)

References 24 publications

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“…We have also validated that the mapping accuracy of GEM is significantly higher (by about 15% for each of precision and recall) than that achieved by two (generic) schema-matching software tools with the same datasets. These other tools are Harmony 24 and Coma++ 1 . The GEM system was designed with a focus on the Alzheimer's disease and other medical domain, and is able to maximally leverage information in the data documentation that other generic matching tools cannot.…”

Section: Resultsmentioning

confidence: 99%

Global Data Sharing in Alzheimer Disease Research

Ashish

Bhatt

Toga

2016

Alzheimer Disease &Amp; Associated Disorders

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Many investigators recognize the importance of data sharing, however they lack the capability to share data. Research efforts could be vastly expanded if Alzheimer's disease data from around the world was linked by a global infrastructure that would enable scientists to access and utilize a secure network of data with thousands of study participants at risk for or already suffering from the disease. We discuss the benefits of data sharing, impediments today and solutions to achieving this on a global scale. We introduce the Global Alzheimer's Association Interactive Network (GAAIN), a novel approach to create a global network of Alzheimer's disease data, researchers, analytical tools and computational resources to better our understanding of this debilitating condition. GAAIN has addressed the key impediments to Alzheimer's disease data sharing with its model and approach. It presents practical, promising, yet data owner sensitive data sharing solutions.

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Section: Resultsmentioning

confidence: 99%

Global Data Sharing in Alzheimer Disease Research

Ashish

Bhatt

Toga

2016

Alzheimer Disease &Amp; Associated Disorders

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“…After interviewing A2E engineers, we realized two of these tools, the Harmony Schema matcher [8] and the Affinity schema cluster visualization tool [16], significantly simplify two current A2E challenges, described in Section 4 as the first two use cases: "data migration" and "data consolidation".…”

Section: Preparationmentioning

confidence: 99%

“…Format heterogeneity has also been addressed by metadata repositories with neutral metamodels, as in the OpenII SchemaStore repository for schema metadata [7], whose extension into the BDE arena we discuss in Section 5. Using a neutral metamodel and importers / exporters from and to several popular data models is an alternative to developing a new converter for each pair of models which must interoperate [8].…”

Section: Related Workmentioning

confidence: 99%

"Big Metadata"

Smith

Seligman

Rosenthal

et al. 2014

Proceedings of Workshop on Data Analytics in the Cloud

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Current big data ecosystems lack a principled approach to metadata management. This impedes large organizations' ability to share data and data preparation and analysis code, to integrate data, and to ensure that analytic code makes compatible assumptions with the data it uses. This use-case paper describes the challenges and an in-progress effort to address them. We present a real application example, discuss requirements for "big metadata" drawn from that example as well as other U.S. government analytic applications, and briefly describe an effort to adapt an existing open source metadata manager to support the needs of big data ecosystems.

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“…A large corpus of literature is available on various matching approaches and algorithms (e.g., see [43,17]). To identify the set A of matches between two schemas s i and s j the operator A ← match(s i , s j , [d i , d j ]) can be used, which uses existing schema-, and if optional instance data is available from data sources d i and d j , instance-based matchers [16,41,3]. Examples of matchers include string based matchers using, e.g., edit distance or n-grams to determine how similar two strings are, data type matchers comparing the data types of constructs, or structure-based matchers comparing the structure of constructs within a schema such as the nesting of elements in XSD.…”

Section: Match and Infer Schematic Correspondences Between Schemasmentioning

confidence: 99%

DSToolkit: An Architecture for Flexible Dataspace Management

Hedeler

Belhajjame

et al. 2012

Transactions on Large-Scale Data- And Knowledge-Centered Systems V

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Abstract. The vision of dataspaces is to provide various of the benefits of classical data integration, but with reduced up-front costs. Combining this with opportunities for incremental refinement enables a 'pay-as-yougo' approach to data integration, resulting in simplified integrated access to distributed data. It has been speculated that model management could provide the basis for Dataspace Management, however, this has not been investigated until now. Here, we present DSToolkit, the first dataspace management system that is based on model management, and therefore, benefits from the flexibility provided by the approach for the management of schemas represented in heterogeneous models, supports the complete dataspace lifecycle, which includes automatic initialisation, maintenance and improvement of a dataspace, and allows the user to provide feedback by annotating result tuples returned as a result of queries the user has posed. The user feedback gathered is utilised for improvement by annotating, selecting and refining mappings. Without the need for additional feedback on a new data source, these techniques can also be applied to determine its perceived quality with respect to already gathered feedback and to identify the best mappings over all sources including the new one.

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The Harmony Integration Workbench

Cited by 19 publications

References 24 publications

Global Data Sharing in Alzheimer Disease Research

Global Data Sharing in Alzheimer Disease Research

"Big Metadata"

DSToolkit: An Architecture for Flexible Dataspace Management

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