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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.
Business process-mining can be used to analyze these logs and extract an explicit process model from it besides utilizing them for other type of analyses. The main goal of process mining is to build models without appropriate knowledge, based on sequence of events; one can look for presence or absence of certain patterns and deduce some process models from it.
Business process-mining techniques are now becoming available as graphical interface-driven software tools where processes can only be represented as a flow diagram more as Visio format but in near future may be they can also be manipulated as part of mining and process analysis.
However, these processes are hidden in the code and distributed over the landscape. For instance, if the code has been developed using given business process models, then the running system only gives very little clues, which one of these models is currently under execution.
When it comes to analyze a process distributed over a landscape SAP systems are like black box to the non-SAP systems and vice versa. So far, several attempts have been made to analyze the processes spread over the landscape but not with significant success. Based on the current approach and methodology it is not easy if not impossible to achieve the task unless a proper technique and methodology is applied.
Process-Mining is the method of distilling a structured process description from a set of real execution. The real executions are captured in an event-log.
The event-log is the data basis for process mining. Thus, process mining can only yield to meaningful results if the log files contain sufficient information and if the events are logged properly.
Only events captured in the log are available for process mining. If an event is not logged, it will not be part the mined process model. Logging is by all means crucial. The following assumptions are made. Assume that is possible to record events such that Each event refers to a task i.
If the log contains events from several different process types, an additional requirement is necessary, according to the experience 4. Each process instance refers to one process type or each task refers to one process type.
Special tools are available in the market to analyze the logs within SAP system that are capable of automatically extracting business process structures from runtime artifacts of the system, namely from event logs.
The gained process structure is extremely useful, because it untangles the low-level, unreadable event log messages and transforms them into model-level, human-readable and graphical format.
The extraction of business process structures has the potential to revolutionize analysis, root cause findings, maintenance, and optimization tasks within SAP. The tool collects messages of business processes, analyzes them, identifies exceptions based on customizing and provides functionalities to solve the problem.
Although the main goal of it is to smooth the execution of business processes, it seems very interesting for process mining because of its capability to collect messages from various sources. Instead of developing reinventing a tool for process mining we focused towards what is currently possible in the market.
There are several different tools and methods available in the market where the tool ProM development of a Dutch professor W. Each node of the mined processes represents one task type.
The first line of the node description shows the WorkflowModelElement, i. The third line is the frequency of this task type in the log. The nodes are connected with directed edges. Each edge represents a mined dependency relation between the two task types.
The frequency of this relation is displayed next to the edge. The business process model automatically generated out of event-logs can help to visualize actual executions of the business processes in the deployed ERP system and over the landscape.
The potential of approach seems to be tremendous.title = "A Log Mining Approach for Process Monitoring in SCADA", abstract = "SCADA (Supervisory Control and Data Acquisition) systems are used for controlling and monitoring industrial processes.
We propose a methodology to systematically identify potential process-related threats in SCADA. The goal of process mining is to extract information (e.g., process models) from these logs, i.e., process mining describes a family of a- posteriori analysis techniques exploiting the information recorded in the event.
Process mining is a family of techniques in the field of process management that support the analysis of business processes based on event logs. During process mining, specialized data mining algorithms are applied to event log data in order to identify trends, patterns and details contained in event logs recorded by an information system.
The scenario and process of the space mining simulation. The scenario was designed with the help of space professionals with backgrounds in space engineering and . proposed approach is very robust in mining process logs with high degrees of parallelism, incompleteness and noise.
Index Terms — process mining, incomplete, noisy, log. the attention also focused on obtaining high quality event log as input for the process mining , .
The. third stream of literature has addressed t. local approach for pro. cess mining will construct business process model from local relation between.