SIESTA: A Scalable Infrastructure of Sequential Pattern Analysis

Sequential pattern analysis has become a mature topic with a lot of techniques for a variety of sequential pattern mining-related problems. Moreover, tailored solutions for specific domains, such as business process mining, have been developed. However, there is a gap in the literature for advanced techniques for efficient detection of arbitrary sequences in large collections of activity logs. In this work, we introduce the SIESTA (Scalable infrastructure of sequential pattern analysis) solution making a threefold contribution: (i) we employ a novel architecture that relies on inverted indices during preprocessing and we introduce an advanced query processor that can detect and explore arbitrary patterns efficiently; (ii) we discuss and evaluate different configurations to optimize both the preprocessing and the querying phase; and (iii) we present evaluation results competing against representatives of the state-of-the-art with a focus on Big Data. The experimental results are particularly encouraging, e.g., when all methods are deployed in a cluster and the volume of the data is increased,SIESTA creates the indices in almost half the time compared to the state-of-the-art Elasticsearch-based solution, while also yielding faster query responses than all its competitors by up to 1 order of magnitude.