Case and Activity Identification for Mining Process Models from Middleware

Process monitoring aims to provide transparency over operational aspects of a business process. In practice, it is a challenge that traces of business process executions span across a number of diverse systems. It is cumbersome manual engineering work to identify which attributes in unstructured event data can serve as case and activity identifiers for extracting and monitoring the business process. Approaches from literature assume that these identifiers are known a priori and data is readily available in formats like eXtensible Event Stream (XES). However, in practice this is hardly the case, specifically when event data from different sources are pooled together in event stores. In this paper, we address this research gap by inferring potential case and activity identifiers in a provenance agnostic way. More specifically, we propose a semi-automatic technique for discovering event relations that are semantically relevant for business process monitoring. The results are evaluated in an industry case study with an international telecommunication provider

Extending process logs with events from supplementary sources

Since organizations typically use more than a single IT system, information about the execution of a process is rarely available in a single event log. More commonly, data is scattered across different locations and unlinked by common case identifiers. We present a method to extend an incomplete main event log with events from supplementary data sources, even though the latter lack references to the cases recorded in the main event log. We establish this correlation by using the controlflow, time, resource, and data perspectives of a process model, as well as alignment diagnostics. We evaluate our approach on a real-life event log and discuss the reliability of the correlation under different circumstances. Our evaluation shows that it is possible to correlate a large portion of the events by using our method

Technical report: Function-splitting heuristics for discovery of microservices in enterprise systems

We present heuristics that help to identify suitable consumer-oriented parts of enterprise systems which could be re-engineered as microservices. Our approach assesses the key structural and behavioural properties common to both enterprise and microservice systems, as needed to guide a microservices discovery process and coherently assess restructuring recommendations. Building upon existing business object and system structural definitions, we present heuristics for two fundamental areas of microservice discovery, namely function splitting based on object subtypes (i.e., the lowest granularity of software based on structural properties) and functional splitting based on common execution fragments across software (i.e., the lowest granularity of software based on behavioural properties). A prototype analysis tool was developed based on the defined heuristics and experiments show that it can identify microservice designs which support multiple microservice characteristics, such as high cohesion, low coupling, high scalability, availability and processing efficiency while preserving coherent features of enterprise systems. In particular, we illustrate the usefulness of this new approach by conducting a case study based on a customer management systems (SugarCRM, ChurchCRM)

Function-splitting heuristics for discovery of microservices in enterprise systems

We present heuristics that help to identify suitable consumer-oriented parts of enterprise systems which could be re-engineered as microservices. Our approach assesses the key structural and behavioural properties common to both enterprise and microservice systems, as needed to guide a microservices discovery process and coherently assess restructuring recommendations. Building upon existing business object and system structural definitions, we present heuristics for two fundamental areas of microservice discovery, namely function splitting based on object subtypes (i.e., the lowest granularity of software based on structural properties) and functional splitting based on common execution fragments across software (i.e., the lowest granularity of software based on behavioural properties). A prototype analysis tool was developed based on the defined heuristics and experiments show that it can identify microservice designs which support multiple microservice characteristics, such as high cohesion, low coupling, high scalability, high availability, and processing efficiency while preserving coherent features of enterprise systems. In particular, we illustrate the usefulness of this new approach by conducting a case study based on customer management systems: SugarCRM and ChurchCRM

Discovering microservices in enterprise systems using a business object containment heuristic

The growing impact of IoT and Blockchain platforms on business applications has increased interest in leveraging large enterprise systems as Cloud-enabled microservices. However, large and monolithic enterprise systems are unsuitable for flexible integration with such platforms. This paper presents a technique to support the re-engineering of an enterprise system based on the fundamental mechanisms for structuring its architecture, i.e., business objects managed by software functions and their relationships which influence business object interactions via the functions. The technique relies on a heuristic for deriving business object exclusive containment relationships based on analysis of source code and system logs. Furthermore, the paper provides an analysis of distributing enterprise systems based on the business object containment relationships using the NSGA II software clustering and optimization technique. The heuristics and the software clustering and optimization techniques have been validated against two open-source enterprise systems: SugarCRM and ChurchCRM. The experiments demonstrate that the proposed approach can identify microservice designs which support multiple desired microservice characteristics, such as high cohesion, low coupling, high scalability, high availability, and processing efficiency