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Cloud infrastructures enable the efficient parallel execution of data-intensive tasks such as entity resolution on large datasets. We investigate challenges and possible solutions of using the MapReduce programming model for parallel entity resolution using Sorting Neighborhood blocking (SN). We propose and evaluate two efficient MapReduce-based(More)
Convergent validity of bibliometric Google Scholar data in the field of chemistry – Citation counts for papers that were accepted by Abstract Examining a comprehensive set of papers (n=1837) that were accepted for publication by the journal Angewandte Chemie International Edition (one of the prime chemistry journals in the world) or rejected by the journal(More)
The effectiveness and scalability of MapReduce-based implementations of complex data-intensive tasks depend on an even redistribution of data between map and reduce tasks. In the presence of skewed data, sophisticated redistribution approaches thus become necessary to achieve load balancing among all reduce tasks to be executed in parallel. For the complex(More)
Ontologies are heavily used in life sciences so that there is increasing value to match different ontologies in order to determine related conceptual categories. We propose a simple yet powerful methodology for instance-based ontology matching which utilizes the associations between molecular-biological objects and ontologies. The approach can build on many(More)
Entity resolution is a crucial step for data quality and data integration. Learning-based approaches show high effectiveness at the expense of poor efficiency. To reduce the typically high execution times, we investigate how learning-based entity resolution can be realized in a cloud infrastructure using MapReduce. We propose and evaluate two efficient(More)
Despite the huge amount of recent research efforts on entity resolution (matching) there has not yet been a comparative evaluation on the relative effectiveness and efficiency of alternate approaches. We therefore present such an evaluation of existing implementations on challenging real-world match tasks. We consider approaches both with and without using(More)