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Efficient execution of distributed database operators such as joining and aggregating is critical for the performance of big data analytics. With the increase of the compute speedup of modern CPUs, reducing the network communication time of these operators in large systems is becoming increasingly important, and also challenging current techniques. Significant performance improvements have been achieved...
Event correlation is a cornerstone for process discovery over event logs crossing multiple data sources. The computed correlation rules and process instances will greatly help us to unleash the power of process mining. However, exploring all possible event correlations over a log could be time consuming, especially when the log is large. State-of-the-art methods based on MapReduce designed to handle...
Distributed RDF data management systems become increasingly important with the growth of the Semantic Web. Regardless, current methods meet performance bottlenecks either on data loading or querying when processing large amounts of data. In this work, we propose efficient methods for processing RDF using dynamic data re-partitioning to enable rapid analysis of large datasets. Our approach adopts a...
Academia and industry are investigating novel approaches for processing vast amounts of data coming from enterprises, the Web, social media and sensor readings in an area that has come to be known as Big Data. Logic programming has traditionally focused on complex knowledge structures/programs. The question arises whether and how it can be applied in the context of Big Data. In this paper, we study...
Heterogeneous data resources in biomedicine become available both in structured and unstructured formats, such as scientific publications, healthcare guidelines, controlled vocabularies, and formal ontologies. %Increasing researches have been conducted to Bridging the gaps among these heterogeneous data is useful to discovery implicit knowledge. To make this happen, efficient computational approaches...
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