SIESTA: A Scalable Infrastructure of Sequential Pattern Analysis
Ioannis Mavroudopoulos, Anastasios Gounaris · IEEE Transactions on Big Data · 2022
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 (Scalableinfrastructureofsequential patternanalysis) 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.