Khanan : performance comparison and programming alpha algorithm in column-oriented and relational database query languages

Abstract

Process mining consists of mining business process event-logs for discovering run-time process models, process compliance verifi cation and extracting useful insights on process e efficiency. Process model discovery from event-logs is one of the most important and challenging process mining tasks. Process model discovery consists of learning a System Net (such as a Petri Net) from an event log. The -algorithm is fi rst and most widely used process discovery technique. There are several extensions proposed to -algorithm but we use the basic -algorithm as a baseline and benchmark algorithm for our study. We present a CQL (Cassandra Query Language) and SQL (Structured Query Language) implementation of the basic -algorithm (translation of -algorithm computations into CQL and SQL). Column-oriented databases have shown to improve the performance of several functions and algorithms that require analytical query processing on a large dataset. We conduct a benchmarking study consisting of a series of experiments on a large real-world dataset to compare the performance of the -algorithm CQL and SQL implementations.