Pragamana : performance comparison and programming -miner algorithm in relational database query language and NoSQL column-oriented using apache phoenix

Abstract

Process-Aware Information Systems (PAIS) support business processes and generate large amounts of event logs from the execution of business processes. An event log is represented as a tuple of CaseID, Timestamp, Activity and Actor. Process mining is a new and emerging field that aims at analyzing the event logs to discover, enhance and improve business processes and check conformance between run time and design time. A large volume of event logs that are generated are stored in the databases such as relational, NoSQL and NewSQL. While relational databases perform well for a certain class of applications, there are a certain class of applications for which such databases create bottlenecks (like Scalability and Sharding). To handle such class of applications, NoSQL database systems have emerged. A relevant application of interest is the process mining task of discovering a process model (workflow model) from event logs. The -miner algorithm is one of the first and most widely used Process Discovery technique. Our objective is to investigate which of the databases (Relational or NoSQL) perform better for a Process Discovery application under Process Mining. We implement the -miner algorithm on relational (row-oriented) and NoSQL (column-oriented) databases in database query languages so that our algorithm is tightly coupled to the database. We do a performance benchmarking of the -miner algorithm on a row-oriented database and a NoSQL columnoriented database to compare which database can efficiently store massive event logs and analyze it in seconds to discover a process model.