StreamAligner: a streaming based sequence aligner on 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...

Modeling and Simulating Apache Spark Streaming Applications

Stream processing systems are used to analyze big data streams with low latency. The performance in terms of response time and throughput is crucial to ensure all arriving data are processed in time. This depends on various factors such as the complexity of used algorithms and configurations of such distributed systems and applications. To ensure a desired system behavior, performance evaluatio...

Parallel Maritime Traffic Clustering Based on Apache Spark

Maritime traffic patterns extraction is an essential part for maritime security and surveillance and DBSCANSD is a density based clustering algorithm extracting the arbitrary shapes of the normal lanes from AIS data. This paper presents a parallel DBSCANSD algorithm on top of Apache Spark. The project is an experimental research work and the results shown in this paper is preliminary. The exper...

Getting Started with Apache Spark

A comparison on scalability for batch big data processing on Apache Spark and Apache Flink

*Correspondence: [email protected] 1Department of Computer Science and Artificial Intelligence, CITIC-UGR (Research Center on Information and Communications Technology), University of Granada, Calle Periodista Daniel Saucedo Aranda, 18071 Granada, Spain Full list of author information is available at the end of the article Abstract The large amounts of data have created a need for new fram...