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Process-Aware Information Systems (PAIS) is an IT system that 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,…
Process mining is a new emerging research trend over the last decade which focuses on analyzing the processes using event log and data. The raising integration of information systems for the operation of business processes provides the…
Process mining is an area of research that supports discovering information about business processes from their execution event logs. The increasing amount of event logs in organizations challenges current process mining techniques, which…
The plethora of algorithms in the research field of process mining builds on directly-follows relations. Even though various improvements have been made in the last decade, there are serious weaknesses of these relationships. Once events…
Recently, information systems like ERP, CRM and WFM record different business events or activities in a log named as event log. Process mining aims at extracting information from event logs to capture business process as it is being…
Flexible business processes can often be modelled more easily using a declarative rather than a procedural modelling approach. Process mining aims at automating the discovery of business process models. Existing declarative process mining…
Workflow technology is widely used to facilitate the business process in enterprise information systems (EIS), and it has the potential to reduce design time, enhance product quality and decrease product cost. However, significant…
Process mining is of great importance for both data-centric and process-centric systems. Process mining receives so-called process logs which are collections of partially-ordered events. An event has to possess at least three attributes,…
Process mining is a technique that performs an automatic analysis of business processes from a log of events with the promise of understanding how processes are executed in an organisation. Several models have been proposed to address this…
Process mining gains increasing popularity in business process analysis, also in heavy industry. It requires a specific data format called an event log, with the basic structure including a case identifier (case ID), activity (event) name,…
Detecting anomalies is important for identifying inefficiencies, errors, or fraud in business processes. Traditional process mining approaches focus on analyzing 'flattened', sequential, event logs based on a single case notion. However,…
Process mining has gained traction over the past decade and an impressive body of research has resulted in the introduction of a variety of process mining approaches measuring process performance. Having this set of techniques available,…
Process models generated through process mining depict the as-is state of a process. Through annotations with metrics such as the frequency or duration of activities, these models provide generic information to the process analyst. To…
The discipline of process mining deals with analyzing execution data of operational processes, extracting models from event data, checking the conformance between event data and normative models, and enhancing all aspects of processes.…
The phenomenal growth of graph data from a wide variety of real-world applications has rendered graph querying to be a problem of paramount importance. Traditional techniques use structural as well as node similarities to find matches of a…
Much time in process mining projects is spent on finding and understanding data sources and extracting the event data needed. As a result, only a fraction of time is spent actually applying techniques to discover, control and predict the…
Process mining aims to comprehend and enhance business processes by analyzing event logs. Recently, object-centric process mining has gained traction by considering multiple objects interacting with each other in a process. This…
Choosing and developing performant database solutions helps organizations optimize their operational practices and decision-making. Since graph data is becoming more common, it is crucial to develop and use them in big data with complex…
Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated…
Process discovery algorithms learn process models from executed activity sequences, describing concurrency, causality, and conflict. Concurrent activities require observing multiple permutations, increasing data requirements, especially for…