Correlation Patterns in Service-Oriented Architectures

Lecture Notes in Computer Science

2013

Abstract. Traditionally, most process mining techniques aim at discovering procedural process models (e.g., Petri nets, BPMN, and EPCs) from event data. However, the variability present in less-structured flexible processes complicates the discovery of such procedural models. The "open world" assumption used by declarative models makes it easier to handle this variability. However, initial attempts to automatically discover declarative process models result in cluttered diagrams showing misleading constraints. Moreover, additional data attributes in event logs are not used to discover meaningful causalities. In this paper, we use correlations to prune constraints and to disambiguate event associations. As a result, the discovered process maps only show the more meaningful constraints. Moreover, the data attributes used for correlation and disambiguation are also used to find discriminatory patterns, identify outliers, and analyze bottlenecks (e.g., when do people violate constraints or miss deadlines). The approach has been implemented in ProM and experiments demonstrate the improved quality of process maps and diagnostics.

Section: Experiments and Resultsmentioning

confidence: 99%

“…Such conceptual groupings of activities can be used for guiding the discovery of Declare maps towards results that are more significant from an application domain point of view [18]. For a categorization of correlations we refer to [6]. In this paper we use:…”

Section: Correlations As a Means Of Enhancing Declare Mapsmentioning

confidence: 99%

Enhancing Declare Maps Based on Event Correlations

Bose

Maggi

Lecture Notes in Computer Science

2013

“…The binding step in our approach is related to topic of event correlation which has been investigated in the (web) services [4]. In [8] and [17] various interaction and correlation patterns are described. In [44] a technique is presented for correlating messages with the goal to visualize the execution of web services.…”

Section: Related Workmentioning

confidence: 99%

Extracting Event Data from Databases to Unleash Process Mining

Management for Professionals

2015

Abstract. Increasingly organizations are using process mining to understand the way that operational processes are executed. Process mining can be used to systematically drive innovation in a digitalized world. Next to the automated discovery of the real underlying process, there are process-mining techniques to analyze bottlenecks, to uncover hidden inefficiencies, to check compliance, to explain deviations, to predict performance, and to guide users towards "better" processes. Dozens (if not hundreds) of process-mining techniques are available and their value has been proven in many case studies. However, process mining stands or falls with the availability of event logs. Existing techniques assume that events are clearly defined and refer to precisely one case (i.e. process instance) and one activity (i.e., step in the process). Although there are systems that directly generate such event logs (e.g., BPM/WFM systems), most information systems do not record events explicitly. Cases and activities only exist implicitly. However, when creating or using process models "raw data" need to be linked to cases and activities. This paper uses a novel perspective to conceptualize a database view on event data. Starting from a class model and corresponding object models it is shown that events correspond to the creation, deletion, or modification of objects and relations. The key idea is that events leave footprints by changing the underlying database. Based on this an approach is described that scopes, binds, and classifies data to create "flat" event logs that can be analyzed using traditional process-mining techniques.

“…In [7] and [11] various interaction and correlation patterns are described. In [17] a technique is presented for correlating messages with the goal to visualize the execution of web services.…”

Section: Related Work On Service Miningmentioning

confidence: 99%

Challenges in Service Mining: Record, Check, Discover

Lecture Notes in Computer Science

2013

Abstract. Process mining aims to discover, monitor and improve real processes by extracting knowledge from event logs abundantly available in today's information systems. Although process mining has been applied in hundreds of organizations and process mining techniques have been embedded in a variety of commercial tools, to date these techniques have rarely been used for analyzing web services. One of the obvious reasons is that cross-organizational event data cannot be shared easily. However, (1) messages exchanged between services tend to be structured, (2) service-orientation continues to be the predominant implementation paradigm, and (3) the most substantial efficiency gains can often only be achieved across different organizations. Hence, there are many possible applications for service mining, i.e., applying process mining techniques to services. If messages are recorded, then one can discover a process describing interactions between services. If, in addition, descriptive or normative models are available, one can use process mining to check conformance and highlight performance problems. This extended abstract aims to provide pointers to ongoing work on service mining and lists some of the main challenges in this emerging field.