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- W2400515935 abstract "The Web of Data contains a large number of different, openlyavailable datasets. In order to effectively integrate them into existing applications, meta information on statistical and structural properties is needed. Examples include information about cardinalities, value patterns, or co-occurring properties. For Linked Datasets such information is currently very limited or not available at all. Data profiling techniques are needed to compute respective statistics and meta information. However, current state of the art approaches can either not be applied to Linked Data, or exhibit considerable performance problems. We present Lodop, a framework for computing, optimizing, and benchmarking data profiling techniques based on MapReduce with Apache Pig. We implemented 15 of the most important data profiling tasks, optimized their simultaneous execution, and evaluate them with four typical datasets from the Web of Data. Our optimizations focus on reducing the amount of MapReduce jobs and minimizing the communication overhead between multiple jobs. Our evaluation shows the significant potential in optimizing the runtime costs for Linked Data profiling." @default.
- W2400515935 created "2016-06-24" @default.
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- W2400515935 date "2014-01-01" @default.
- W2400515935 modified "2023-09-27" @default.
- W2400515935 title "LODOP - Multi-Query Optimization for Linked Data Profiling Queries." @default.
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