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Due to its simplicity and scalability, MapReduce has become a de facto standard computing model for big data processing. Since the original MapReduce model was only appropriate for embarrassingly parallel batch processing, many follow-up studies have focused on improving the efficiency and performance of the model. Spark follows one of these recent trends by providing in-memory processing capability...
Moving object prediction and indexing have been a well studied area of research and include applications in environment monitoring, traffic prediction, advertising, and efficient routing. Spark is a cluster computing framework, which utilizes Resilient Distributed Datasets (RDD) on a cluster of several commodity machines. Spark is popularly used for parallel processing of massive datasets. The modeling...
With the explosive growth of semantic data on the Web over the past years, many large-scale RDF knowledge bases with billions of facts are generating. This poses significant challenges for the storage and query of big RDF graphs. Current systems still have many limitations in processing big RDF graphs including scalability and real-time. In this paper, we introduce the SparkRDF, an elastic discreted...
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