Decision mining in business processes

Many companies have adopted Process-aware Information Systems (PAIS) for supporting their business processes in some form. These systems typically log events (e.g., in transaction logs or audit trails) related to the actual business process executions. Proper analysis of PAIS execution logs can yield important knowledge and help organizations improve the quality of their services. Starting from a process model as it is possible to discover by conventional process mining algorithms we analyze how data attributes influence the choices made in the process based on past process executions. Decision mining, also referred to as decision point analysis, aims at the detection of data dependencies that affect the routing of a case. In this paper we describe how machine learning techniques can be leveraged for this purpose, and discuss further challenges related to this approach. To verify the presented ideas a Decision Miner has been implemented within the ProM framework

From zero to hero: A process mining tutorial

Process mining is an emerging area that synergically combines model-based and data-oriented analysis techniques to obtain useful insights on how business processes are executed within an organization. This tutorial aims at providing an introduction to the key analysis techniques in process mining that allow decision makers to discover process models from data, compare expected and actual behaviors, and enrich models with key information about the actual process executions. In addition, the tutorial will present concrete tools and will provide practical skills for applying process mining in a variety of application domains, including the one of software development

Change Mining in Adaptive Process Management Systems

The wide-spread adoption of process-aware information systems has resulted in a bulk of computerized information about real-world processes. This data can be utilized for process performance analysis as well as for process improvement. In this context process mining offers promising perspectives. So far, existing mining techniques have been applied to operational processes, i.e., knowledge is extracted from execution logs (process discovery), or execution logs are compared with some a-priori process model (conformance checking). However, execution logs only constitute one kind of data gathered during process enactment. In particular, adaptive processes provide additional information about process changes (e.g., ad-hoc changes of single process instances) which can be used to enable organizational learning. In this paper we present an approach for mining change logs in adaptive process management systems. The change process discovered through process mining provides an aggregated overview of all changes that happened so far. This, in turn, can serve as basis for all kinds of process improvement actions, e.g., it may trigger process redesign or better control mechanisms

Semantic process mining tools: core building blocks

Process mining aims at discovering new knowledge based on information hidden in event logs. Two important enablers for such analysis are powerful process mining techniques and the omnipresence of event logs in today's information systems. Most information systems supporting (structured) business processes (e.g. ERP, CRM, and workflow systems) record events in some form (e.g. transaction logs, audit trails, and database tables). Process mining techniques use event logs for all kinds of analysis, e.g., auditing, performance analysis, process discovery, etc. Although current process mining techniques/tools are quite mature, the analysis they support is somewhat limited because it is purely based on labels in logs. This means that these techniques cannot benefit from the actual semantics behind these labels which could cater for more accurate and robust analysis techniques. Existing analysis techniques are purely syntax oriented, i.e., much time is spent on filtering, translating, interpreting, and modifying event logs given a particular question. This paper presents the core building blocks necessary to enable semantic process mining techniques/tools. Although the approach is highly generic, we focus on a particular process mining technique and show how this technique can be extended and implemented in the ProM framework tool

Runtime Monitoring of Data-Aware business rules with Integer Linear Programming

Käitusaegne seire (Runtime Compliance Monitoring) on oluline osa äriprotsesside halduse elutsüklis, mittevastavuse õigeaegses avastamises, samuti vastumeetmete korraldamises ja ennetamises. Täpsemalt on see seotud operatiivse otsuse toega, mille eesmärgiks on laiendada protsessikaeve tehnikat sidusrežiimis, käitada protsessi isendeid nii, et kõrvalekaldeid on võimalik avastada, ning on võimalik soovitada, mida võiks järgmiseks teha, ning samuti ennustada, mis hakkab juhtuma tulevaste juhtumite täitmisel. Antud magistritöö keskendub käitusaegse seire andmeteadlikele ärireeglitele. Töös kasutatakse varajaste rikkumiste tuvastamiseks lineaarset täisarvulist planeerimist (Integer Linear Programming (ILP)), mida rakendatakse kahe või enama kitsenduse koosmõjul. Töökorras toepakkujas on rakendatud protsessikaeve raamistikku ProM ja meetod on valideeritud kasutades sünteetilisi ja reaalseid logisid.Runtime Compliance Monitoring is vital building block in the Business Process Management lifecycle, in timely detection of non-compliance as well as provision of responsive and proactive countermeasures. In particular, it is linked to operational decision support, which aims at extending the application of process mining techniques to on-line, running process instances, so that deviations can be detected and it is possible to recommend what to do next and predict what will happen in the future instance execution. \n\r\n\rIn this thesis, we focus on Runtime Compliance Monitoring of data-aware business rules. In particular, we use Integer Linear Programming (ILP) for early detection of violations that occur from interplay of two or more constraints. An operational support provider has been implemented as part of process mining framework ProM and the approach has been validated using synthetic and real life logs

The perceived quality of process discovery tools

Process discovery has seen a rise in popularity in the last decade for both researchers and businesses. Recent developments mainly focused on the power and the functionalities of the discovery algorithm. While continuous improvement of these functional aspects is very important, non-functional aspects such as visualization and usability are often overlooked. However, these aspects are considered valuable for end-users and play an important part in the experience of these end-users when working with a process discovery tool. A questionnaire has been sent out to give end-users the opportunity to voice their opinion on available process discovery tools and about the state of process discovery as a domain in general. The results of 66 respondents are presented and compared with the answers of 63 respondents that were contacted through one particular software vendor's employee and customer base (i.e., Celonis)

Collaborative Virtual Enterprise Environment and Decision Mining

This paper will present some meaningful insights into the analysis and modeling phases of an Enterprise Virtual Environment (EVE) prototype. The main goal of EVE is to provide an environment for collaborative decisions using a DSS-like approach. In the second part, the proposed architecture of the system will be introduced. This system is developed primarily to simulate decision situations in the academic training of students. The second goal of the system is to provide us with user activity logs that will be the starting point of decision pattern mining process. In the third part of the paper, we will provide evidence regarding the possibility of: mining decision models from user activity logs; comparing different decision making strategies of users; and building decision reference models.Enterprise Virtual Environment, Decision Simulation, DSS Analysis and Modeling, Decision Mining, Decision Analysis, Decision Models

Predictive Monitoring of Business Processes

Modern information systems that support complex business processes generally maintain significant amounts of process execution data, particularly records of events corresponding to the execution of activities (event logs). In this paper, we present an approach to analyze such event logs in order to predictively monitor business goals during business process execution. At any point during an execution of a process, the user can define business goals in the form of linear temporal logic rules. When an activity is being executed, the framework identifies input data values that are more (or less) likely to lead to the achievement of each business goal. Unlike reactive compliance monitoring approaches that detect violations only after they have occurred, our predictive monitoring approach provides early advice so that users can steer ongoing process executions towards the achievement of business goals. In other words, violations are predicted (and potentially prevented) rather than merely detected. The approach has been implemented in the ProM process mining toolset and validated on a real-life log pertaining to the treatment of cancer patients in a large hospital

The FeaturePrediction package in ProM : correlating business process characteristics

In Process Mining, often one is not only interested in learning process models but also in answering questions such as "What do the cases that are late have in common?", "What characterizes the workers that skip this check activity?" and "Do people work faster if they have more work?". Such questions can be answered by combining process mining with classification (e.g., decision tree analysis). Several authors have proposed ad-hoc solutions for specific questions, e.g., there is work on predicting the remaining processing time and recommending activities to minimize particular risks. This paper reports on a tool, implemented as plug-in for ProM, that unifies these ideas and provide a general framework for deriving and correlating process characteristics. To demonstrate the maturity of the tool, we show the steps with the tool to answer one correlation question related to a health-care process. The answer to a second question is shown in the screencast accompanying this paper