Salt Lake City, Utah, United States
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Technology executive with experience in leading teams through company initiatives…

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Experience & Education

  • Acima

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Publications

  • Learning-Based Fusion for Data Deduplication: A Robust and Automated Solution

    Digital Commons @ USU

    This thesis presents two deduplication techniques that overcome the following critical and long-standing weaknesses of rule-based deduplication: (1) traditional rule-based deduplication requires significant manual tuning of the individual rules, including the selection of appropriate thresholds; (2) the accuracy of rule-based deduplication degrades when there are missing data values, significantly reducing the efficacy of the expert-defined deduplication rules.

    The first technique is a…

    This thesis presents two deduplication techniques that overcome the following critical and long-standing weaknesses of rule-based deduplication: (1) traditional rule-based deduplication requires significant manual tuning of the individual rules, including the selection of appropriate thresholds; (2) the accuracy of rule-based deduplication degrades when there are missing data values, significantly reducing the efficacy of the expert-defined deduplication rules.

    The first technique is a novel rule-level match-score fusion algorithm that employs kernel-machine-based learning to discover the decision threshold for the overall system automatically. The second is a novel clue-level match-score fusion algorithm that addresses both Problem 1 and 2. This unique solution provides robustness against missing/incomplete record data via the selection of a best-fit support vector machine. Empirical evidence shows that the combination of these two novel solutions eliminates two critical long-standing problems in deduplication, providing accurate and robust results in a critical area of rule-based deduplication.

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  • Fused Multi-modal Deduplication

    The 2009 International Conference on Data Mining

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  • Learning-Based Fusion for Data Deduplication

    IEEE

    Rule-based deduplication utilizes expert domain knowledge to identify and remove duplicate data records. Achieving high accuracy in a rule-based system requires the creation of rules containing a good combination of discriminatory clues. Unfortunately, accurate rule-based deduplication often requires significant manual tuning of both the rules and the corresponding thresholds. This need for manual tuning reduces the efficacy of rule-based deduplication and its applicability to real-world data…

    Rule-based deduplication utilizes expert domain knowledge to identify and remove duplicate data records. Achieving high accuracy in a rule-based system requires the creation of rules containing a good combination of discriminatory clues. Unfortunately, accurate rule-based deduplication often requires significant manual tuning of both the rules and the corresponding thresholds. This need for manual tuning reduces the efficacy of rule-based deduplication and its applicability to real-world data sets. No adequate solution exists for this problem. We propose a novel technique for rule-based deduplication. We apply individual deduplication rules, and combine the resultant match scores via learning-based information fusion. We show empirically that our fused deduplication technique achieves higher average accuracy than traditional rule-based deduplication. Further, our technique alleviates the need for manual tuning of the deduplication rules and corresponding thresholds.

    Other authors
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Projects

  • Health Management Platform

    - Present

    The VA Health Informatics Initiative Health Management Platform addresses the need to look across VA’s IT systems and patient populations to improve health, develops a platform to support research, registries, business and clinical predictive modeling, decision support, and other activities, and facilitate population health and achieve a healthy health system beyond the current model of one-patient-one-provider at a time.

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  • VA Informatics and Computing Infrastructure (VINCI)

    - Present

    VINCI is an initiative to improve researchers' access to VA data and to facilitate the analysis of those data while ensuring Veterans' privacy and data security. Researchers will access the data along with the tools for reporting and analysis in a secure Workspace. The Workspace is provisioned so that each study has its own project site where multiple people can collaborate using a common set of software tools and files. VINCI staff can also provide an isolated virtual machine for specific…

    VINCI is an initiative to improve researchers' access to VA data and to facilitate the analysis of those data while ensuring Veterans' privacy and data security. Researchers will access the data along with the tools for reporting and analysis in a secure Workspace. The Workspace is provisioned so that each study has its own project site where multiple people can collaborate using a common set of software tools and files. VINCI staff can also provide an isolated virtual machine for specific development activities. VINCI has a common access point using Remote Desktop Connection to connect from anywhere within the VA network.

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