Process mining can be the one of new strategic imperatives of corporation's conduction decisions. A variety of activities occurred in the course of the decision process can be logged and saved into a data repository, and then their explicit logged data and implicit information hided in the logs can be mined out to supervise and manage the statuses of the running processes on a certain purpose. Cloud, a newer concept for constructing various web services together within a centralized web-based interface host, involves lots of communicating and transacting process activities coming from the various web sites. To look into the absence and presence of those activities constituting the processes in a cloud, process mining may be helpful for investigating the cloud processing performance, and thereby results in a certain process model efficient for the further cloud development and its composition renewal. This paper will present a rationale-based architecture of applying process mining to compute the situations of process activities occurred in a cloud, including the discussion of its major components and technical issues. Several perspectives on such an application are summarized as well.

Perspectives on process mining within cloud computing

There is well known: whether the advantage of using a normal process model to monitor the stability of a manufacturing process can be gained lies in the model's ability used to realize its conformance to the manufacturing process trend In other words, whether a manufacturing process can be stabilized depends on how much is about the conformance level between its processing trend and the norm regulated in the process model Once there are differences between a normalized process model and a manufacturing process, it will have some interesting situations that are worthy to be investigated In this paper, a process conformance technique discussed in process mining field will be newly applied to accomplish such the investigation This technique will consider statistical process control (SPC) trending charts as the norms of normalized processes so to experimentally estimate and examine the conformance level between the SPC norms and a statistically generated manufacture running process, hereby to see how the proposed process conformance approach is to the stability of the simulated manufacturing process

Statistical process monitoring by using process mining

The running logs of a process hold valuable information about its executed activity behavior and generated activity logic structure. Theses informative logs can be extracted, analyzed and utilized to improve the efficiencies of the process's execution and conduction. One of the techniques used to accomplish the process improvement is called as process mining. To mine similar processes is such an improvement mission in process mining. Rather than directly mining similar processes using a single comparing coefficient or a complicate fitness function, this paper presents a simplified heuristic process mining algorithm with two similarity comparisons that are able to relatively conform the activity logic sequences (traces) of mining processes with those of a normalized (regularized) one. The relative process conformance is to find which of the mining processes match the required activity sequences and relationships, further for necessary and sufficient applications of the mined processes to process improvements. One similarity presented is defined by the relationships in terms of the number of similar activity sequences existing in different processes; another similarity expresses the degree of the similar (identical) activity sequences among the conforming processes. Since these two similarities are with respect to certain typical behavior (activity sequences) occurred in an entire process, the common problems, such as the inappropriateness of an absolute comparison and the incapability of an intrinsic information elicitation, which are often appeared in other process conforming techniques, can be solved by the relative process comparison presented in this paper. To demonstrate the potentiality of the proposed algorithm, a numerical example is illustrated. Keywords—process mining, process similarity, artificial intelligence, process conformance. I. PROBLEM BACKGROUND ORMALLY, there requires a number of years taken to train a novice to become a veteran; thus, how to shorten working training time becomes a paramount issue for a business running. Trying to preserve experienced knowledge about the business conduction behavior into a knowledge base may be one of the ways to achieve the goal of shortening training time. The experienced works, service and design processes are such knowledge deserving of being saved into the informative knowledge base of an enterprise. Besides, the success of reengineering a process and eliciting required knowledge hidden in the working process also contributes to enrich the Min-Hsun Kuo is a graduate student with the Industrial Engineering and Management Department, Yuan Ze University, Chung Li, Taiwan. Yun-Shiow Chen, Ph.D., is a professor with the Industrial Engineering and Management Department, Yuan Ze University, Chung Li, Taiwan. (e-mail: ieyschen@saturn.yzu.edu.tw). knowledge base and to advance the quality of the working processes through extracting the knowledge base enriched. In order to achieve these destinations, process mining techniques may be a best instrument [1]. Process mining is an emerged technique used to check the conformance of processes or to discover a new process from their historical logs in the form of distinct occurred activities (events). This conformance or discovery enables an enterprise to learn from the mined processes for making the best business conduction decisions. However, if activities are distributed or unstructured at different positions in a process, there is much more critical to correctly track their executed traces (activity sequences) for mining the best process out. An appropriate process mining whose mechanism must function with the accurate logged traces tracking [2]. This paper presents a simplified and effective process conforming algorithm, in which two similarity coefficients of the relative processes are designed to perform the task of tracking traces in logs of the processes, and shows its potentiality through the illustration of an example of process mining. The proposed algorithm uses the activity sequences (traces) of processes as an assistance of gathering structural activity logs and further to define the two similarities related to the processes being mined. The first similarity defines the relationship between processes; the second expresses the degree of the identical (or similar) relationships among the mining processes. The activity sequences can be used to draw a parallel between various processes for finding the deviations from a certain required (experienced) process. How similar are the two or more processes in terms of activity sequences? 30%, 80% or 100%, is the major goal to be investigated in this paper. Traditionally, the bi-similarity or activity trace equivalence was used to parallel a normalized process with other processes [3]. Its yes/no binary solution (i.e., two processes are completely same or not) to a process mining problem, however, seems not to be plausible in real applications because (1) different process configurations may not allude to different activity sequences, and (2) the same set of activity sequences generated by different processes should not be concluded only by counting the frequency of activity sequences. Thereby, conforming or comparing processes should be performed in accordance of the process's gradualness in terms of devious activity (sub)sequences. In this paper, the aforementioned two process similarity coefficients are formulated to get the answers to the above two problems by respectively considering the following two aspects: (1) how similar is the gradualness of the mining processes to the A Simplified and Effective Algorithm Used to Mine Similar Processes: An Illustrated Example Min-Hsun Kuo and Yun-Shiow Chen N World Academy of Science, Engineering and Technology International Journal of Computer and Information Engineering Vol:3, No:6, 2009 1635 International Scholarly and Scientific Research & Innovation 3(6) 2009 ISNI:0000000091950263 O pe n Sc ie nc e In de x, C om pu te r an d In fo rm at io n E ng in ee ri ng V ol :3 , N o: 6, 2 00 9 pu bl ic at io ns .w as et .o rg /6 74 5/ pd f experienced one, and (2) how much of the gradualness in the event log can be generated by the mined process. The rest of this paper is organized as follows. Section 2 introduces the characteristics of the process mining. Section 3 reviews the process mining literatures where the main idea of this study was inspired from. Section 4 proposes the simplified process mining algorithm in detail. Section 5 explains the numerical results by illustrating an example published elsewhere. Finally, Section 6 concludes the findings of the proposed algorithm. I

A Simplified and Effective Algorithm Used to Mine Similar Processes: An Illustrated Example

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Min-Hsun Kuo

Author Tools

Chat about Author

Papers

A Simplified and Effective Algorithm Used to Mine Similar Processes: An Illustrated Example