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Time prediction based on process mining
- Information Systems
"... Abstract. Process mining allows for the automated discovery of pro-cess models from event logs. These models provide insights and enable various types of model-based analysis. This paper demonstrates that the discovered process models can be extended with information to pre-dict the completion time ..."
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Abstract. Process mining allows for the automated discovery of pro-cess models from event logs. These models provide insights and enable various types of model-based analysis. This paper demonstrates that the discovered process models can be extended with information to pre-dict the completion time of running instances. There are many scenarios where it is useful to have reliable time predictions. For example, when a customer phones her insurance company for information about her insurance claim, she can be given an estimate for the remaining pro-cessing time. In order to do this, we provide a configurable approach to construct a process model, augment this model with time information learned from earlier instances, and use this to predict e.g. the comple-tion time. To provide meaningful time predictions we use a configurable set of abstractions that allow for a good balance between “overfitting” and “underfitting”. The approach has been implemented in ProM and through several experiments using real-life event logs we demonstrate its applicability. 1
Discovering process models from unlabelled event logs,” in
- Reijers, Eds
, 2009
"... Abstract. Existing process mining techniques are able to discover process models from event logs where each event is known to have been produced by a given process instance. In this paper we remove this restriction and address the problem of discovering the process model when the event log is provid ..."
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Abstract. Existing process mining techniques are able to discover process models from event logs where each event is known to have been produced by a given process instance. In this paper we remove this restriction and address the problem of discovering the process model when the event log is provided as an unlabelled stream of events. Using a probabilistic approach, it is possible to estimate the model by means of an iterative Expectaction–Maximization procedure. The same procedure can be used to find the case id in unlabelled event logs. A series of experiments show how the proposed technique performs under varying conditions and in the presence of certain workflow patterns. Results are presented for a running example based on a technical support process. 1
Decomposing Petri Nets for Process Mining -- A Generic Approach
"... The practical relevance of process mining is increasing as more and more event data become available. Process mining techniques aim to discover, monitor and improve real processes by extracting knowledge from event logs. The two most prominent process mining tasks are: (i) process discovery: learni ..."
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The practical relevance of process mining is increasing as more and more event data become available. Process mining techniques aim to discover, monitor and improve real processes by extracting knowledge from event logs. The two most prominent process mining tasks are: (i) process discovery: learning a process model from example behavior recorded in an event log, and (ii) conformance checking: diagnosing and quantifying discrepancies between observed behavior and modeled behavior. The increasing volume of event data provides both opportunities and challenges for process mining. Existing process mining techniques have problems dealing with large event logs referring to many different activities. Therefore, we propose a generic approach to decompose process mining problems. The decomposition approach is generic and can be combined with different existing process discovery and conformance checking techniques. It is possible to split computationally challenging process mining problems into many smaller problems that can be analyzed easily and whose results can be combined into solutions for the original problems.
Causal Nets: A Modeling Language Tailored Towards
- Process Discovery,” in 22nd International Conference on Concurrency Theory (CONCUR 2011), ser. Lecture Notes in Computer Science
, 2011
"... Abstract. Process discovery—discovering a process model from example behavior recorded in an event log—is one of the most challenging tasks in process mining. The primary reason is that conventional modeling languages (e.g., Petri nets, BPMN, EPCs, and ULM ADs) have difficulties representing the obs ..."
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Abstract. Process discovery—discovering a process model from example behavior recorded in an event log—is one of the most challenging tasks in process mining. The primary reason is that conventional modeling languages (e.g., Petri nets, BPMN, EPCs, and ULM ADs) have difficulties representing the observed behavior properly and/or succinctly. Moreover, discovered process models tend to have deadlocks and livelocks. Therefore, we advocate a new representation more suitable for process discovery: causal nets. Causal nets are related to the representations used by several process discovery techniques (e.g., heuristic mining, fuzzy mining, and genetic mining). However, unlike existing approaches, we provide declarative semantics more suitable for process mining. To clarify these semantics and to illustrate the non-local nature of this new representation, we relate causal nets to Petri nets. 1
Managing Process Model Complexity via Abstract Syntax Modifications
"... is the author’s version of a work that was submitted/accepted for publication ..."
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Cited by 10 (0 self)
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is the author’s version of a work that was submitted/accepted for publication
Process Mining: Overview and Opportunities
, 2012
"... Over the last decade, process mining emerged as a new research field that focuses on the analysis of processes using event data. Classical data mining techniques such as classification, clustering, regression, association rule learning, and sequence/episode mining do not focus on business process mo ..."
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Cited by 9 (0 self)
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Over the last decade, process mining emerged as a new research field that focuses on the analysis of processes using event data. Classical data mining techniques such as classification, clustering, regression, association rule learning, and sequence/episode mining do not focus on business process models and are often only used to analyze a specific step in the overall process. Process mining focuses on end-to-end processes and is possible because of the growing availability of event data and new process discovery and conformance checking techniques. Process models are used for analysis (e.g., simulation and verification) and enactment by BPM/WFM systems. Previously, process models were typically made by hand without using event data. However, activities executed by people, machines, and software leave trails in so-called event logs. Process mining techniques use such logs to discover, analyze, and improve business processes. Recently, the Task Force on Process Mining released the Process Mining Manifesto. This manifesto is supported by 53 organizations and 77 process mining experts contributed to it. The active involvement of end-users, tool vendors, consultants, analysts, and researchers illustrates the growing significance of process mining as a bridge between data mining and business process modeling. The practical relevance of process mining and the interesting scientific challenges make process mining one of the “hot” topics in Business Process Management (BPM). This paper introduces process mining as a new research field and summarizes the guiding principles and challenges described in the manifesto.
Managing process model complexity via concrete syntax modifications
- IEEE Trans. on Industrial Informatics
"... Abstract—While Business Process Management (BPM) is an established discipline, the increased adoption of BPM technology in recent years has introduced new challenges. One challenge concerns dealing with the ever-growing complexity of business process models. Mechanisms for dealing with this complexi ..."
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Abstract—While Business Process Management (BPM) is an established discipline, the increased adoption of BPM technology in recent years has introduced new challenges. One challenge concerns dealing with the ever-growing complexity of business process models. Mechanisms for dealing with this complexity can be classified into two categories: i) those that are solely concerned with the visual representation of the model, and ii) those that change its inner structure. While significant attention
Using Process Mining to Generate Accurate and Interactive Business Process Maps
"... The quality of today’s digital maps is very high. This allows for new functionality as illustrated by modern car navigation systems (e.g., TomTom, Garmin, etc.), Google maps, Google Street View, Mashups using geo-tagging (e.g., Panoramio, HousingMaps, etc.), etc. People can seamlessly zoom in and o ..."
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Cited by 8 (2 self)
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The quality of today’s digital maps is very high. This allows for new functionality as illustrated by modern car navigation systems (e.g., TomTom, Garmin, etc.), Google maps, Google Street View, Mashups using geo-tagging (e.g., Panoramio, HousingMaps, etc.), etc. People can seamlessly zoom in and out using the interactive maps in such systems. Moreover, all kinds of information can be projected on these interactive maps (e.g., traffic jams, four-bedroom apartments for sale, etc.). Process models can be seen as the “maps ” describing the operational processes of organizations. Unfortunately, accurate and interactive process maps are typically missing when it comes to business process management. Either there are no good maps or the maps are static or outdated. Therefore, we propose to automatically generate business process maps using process mining techniques. By doing this, there is a close connection between these maps and the actual behavior recorded in event logs. This will allow for high-quality process models showing what
Efficient Discovery of Understandable Declarative Process Models from Event Logs
- IN: PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON ADVANCED INFORMATION SYSTEMS ENGINEERING
, 2012
"... Process mining techniques often reveal that real-life processes are more variable than anticipated. Although declarative process models are more suitable for less structured processes, most discovery techniques generate conventional procedural models. In this paper, we focus on discovering Declare m ..."
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Cited by 7 (4 self)
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Process mining techniques often reveal that real-life processes are more variable than anticipated. Although declarative process models are more suitable for less structured processes, most discovery techniques generate conventional procedural models. In this paper, we focus on discovering Declare models based on event logs. A Declare model is composed of temporal constraints. Despite the suitability of declarative process models for less structured processes, their discovery is far from trivial. Even for smaller processes there are many potential constraints. Moreover, there may be many constraints that are trivially true and that do not characterize the process well. Naively checking all possible constraints is computationally intractable and may lead to models with an excessive number of constraints. Therefore, we have developed an Apriori algorithm to reduce the search space. Moreover, we use new metrics to prune the model. As a result, we can quickly generate understandable Declare models for real-life event logs.
A Principled Approach to the Analysis of Process Mining Algorithms
"... Abstract. Process mining uses event logs to learn and reason about business process models. Existing algorithms for mining the control-flow of processes in general do not take into account the probabilistic nature of the underlying process, which affects the behaviour of algorithms and the amount of ..."
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Abstract. Process mining uses event logs to learn and reason about business process models. Existing algorithms for mining the control-flow of processes in general do not take into account the probabilistic nature of the underlying process, which affects the behaviour of algorithms and the amount of data needed for confidence in mining. We contribute a first step towards a novel probabilistic framework within which to talk about approaches to process mining, and apply it to the well-known Alpha Algorithm. We show that knowledge of model structures and algorithm behaviour can be used to predict the number of traces needed for mining.
