Performance Evaluation of Query Plan Recommendation with Apache Hadoop and Apache Spark

SPARQL query processing with Apache Spark

The number and the size of linked open data graphs keep growing at a fast pace and confronts semantic RDF services with problems characterized as Big data. Distributed query processing is one of them and needs to be efficiently addressed with execution guaranteeing scalability, high availability and fault tolerance. RDF data management systems requiring these properties are rarely built from sc...

On the usability of Hadoop MapReduce, Apache Spark & Apache flink for data science

Distributed data processing platforms for cloud computing are important tools for large-scale data analytics. Apache Hadoop MapReduce has become the de facto standard in this space, though its programming interface is relatively low-level, requiring many implementation steps even for simple analysis tasks. This has led to the development of advanced dataflow oriented platforms, most prominently...

Getting Started with Apache Spark

Approximate Stream Analytics in Apache Flink and Apache Spark Streaming

Approximate computing aims for efficient execution of workflows where an approximate output is sufficient instead of the exact output. The idea behind approximate computing is to compute over a representative sample instead of the entire input dataset. Thus, approximate computing — based on the chosen sample size — can make a systematic trade-off between the output accuracy and computation effi...

Performance Comparison of Apache Spark and Tez for Entity Resolution

Entity Resolution is among the hottest topics in the field of Big data. It finds duplicates in datasets, which actually belong to same entity in the real world. Algorithms that perform Entity Resolution are computation intensive and consume a lot of time especially for large datasets. A lot of research has been conducted for improving Entity Resolution solutions. A number of algorithms are deve...