Automatic Extraction of Business Process; A Reverse Engineering Approach In modern day world of business, enterprise systems ERP store relevant information from executed processes in some structured form. For example workflow stores start and completion of activities, SAP processes log all transaction information and CRM systems log interactions with customers etc. Business events are usually recorded by the system as event-logs. The information can be of great value if it could be analyzed properly, but this is where shortcomings of ERP system become clear.
A set of data imperfection patterns for event logs is introduced. Abstract Process-oriented data mining process mining uses algorithms and data in the form of event logs to construct models that aim to provide insights into organisational processes.
The quality of the data both form and content presented to the modeling algorithms is critical to the success of the process mining exercise.
Cleaning event logs to address quality issues prior to conducting a process mining analysis is a necessary, but generally tedious and ad hoc task. In this paper we describe a set of data quality issues, distilled from our experiences in conducting process mining analyses, commonly found in process mining event logs or encountered while preparing event logs from raw data sources.
We show that patterns are used in a variety of domains as a means for describing commonly encountered problems and solutions. The main contributions of this article are in showing that a patterns-based approach is applicable to documenting commonly encountered event log quality issues, the formulation of a set of components for describing event log quality issues as patterns, and the description of a collection of 11 event log imperfection patterns distilled from our experiences in preparing event logs.
We postulate that a systematic approach to using such a pattern repository to identify and repair event log quality issues benefits both the process of preparing an event log and the quality of the resulting event log.
The relevance of the pattern-based approach is illustrated via application of the patterns in a case study and through an evaluation by researchers and practitioners in the field. Previous article in issue.A Two Phase Approach for Process Mining in Incomplete and noisy Logs Roya ZarehFarkhady1, Seyyed Hasan Aali2 1Department of Computer Science, Bostanabad Branch, Islamic Azad University, Bostanabad, Iran 2 Department of Computer science, Bostanabad Branch, Islamic Azad University, Bostanabad, Iran Abstract The purpose .
Process mining is an emerging machine learning methodology that aims to exploit information in event logs captured from systems and processes to explore, monitor, and improve processes [52,55].
Thus, this research attempts to develop a systematic social network mining approach that is capable of extracting implicit process information from .
Process-related threats take place when an attacker gains user access rights and performs actions, which look legitimate, but which are intended to disrupt the SCADA process. To detect such threats, we propose a semi-automated approach of .
Abstract. Abstract — Process mining techniques aim at automatically generating process models from event logs.
Many existing techniques in process mining are applicable only under very restrictive conditions about the log completeness.
In this paper, we present the first deep learning approach to aspect extraction in opinion mining. Aspect extraction is a subtask of sentiment analysis that consists in identifying opinion targets in opinionated text, i.e., in detecting the specific aspects of a product or service the opinion holder is either praising or complaining about.
Process-related threats take place when an attacker gains user access rights and performs actions, which look legitimate, but which are intended to disrupt the SCADA process.
To detect such threats, we propose a semi-automated approach of log processing.