scholarly journals Process Mining of Incoming Patients with Sepsis

2019 ◽  
Vol 11 (2) ◽  
Author(s):  
Renee Hendricks
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Resource Constraints ◽  
Process Mining ◽  
Process Analysis ◽  
Analysis Method ◽  
Process Data ◽  
Large Variability ◽  
Event Logs ◽  
Area Of Interest

Data mining is a technique for analyzing large amounts of data, in various formats, often called Big Data, in order to gain knowledge about it. The healthcare industry is the next Big Data area of interest as its large variability in patients, their health status and their records which can include image scans, graphical test results, and hand-written physician notes, has been untapped for analysis. In addition to data mining, there is a newer analysis method called process mining. Process mining is similar to data mining in that large data files are reviewed and analyzed, but in this case, event logs specific to a particular process or series of processes, are analyzed. Process mining allows one to understand the initial baseline, determine any bottlenecks or resource constraints, and evaluate a recently implemented change. Process mining was conducted on a hospital event log of patients entering the emergency room with sepsis, to better understand this newer analysis method, to highlight the information discovered, and to determine its role with data mining. Not only did the analysis of the event logs provide process mapping and process analysis, but it also highlighted areas in the clinical operations in need of further investigation, including a possible relationship with patient re-admission and their release method. In addition, the data mining method of creating a histogram, of the process data, was applied, allowing data mining and process mining to be utilized complimentary.

Integration of process mining techniques in simulation results analysis

2019 ◽  
Author(s):  
Irīna Šitova ◽  
Jeļena Pečerska
Keyword(s):  
Process Mining ◽  
Process Analysis ◽  
Discrete Event ◽  
Discrete Event System ◽  
Log Analysis ◽  
Related Information ◽  
Event Logs ◽  
Event System ◽  
Simulation Results ◽  
Parameter Values

The research is carried out in the area of analysis of simulation results. The aim of this research is to explore the applicability of process mining techniques, and to introduce the process mining techniques integration into results analysis of discrete-event system simulations. As soon as the dynamic discrete-event system simulation (DESS) is based on events list or calendar, most of simulators provide the events lists. These events lists are interpreted as event logs in this research, and are used for process mining. The information from the events list is analysed to extract process-related information and perform in-depth process analysis. Event log analysis verified applicability of the proposed approach. Based on the results of this research, it can be concluded that process mining techniques in simulation results analysis provide a possibility to reveal new knowledge about the performance of the system, and to find the parameter values providing the advisable performance.

A framework for big data driven process analysis and optimization for additive manufacturing

2019 ◽  
Vol 25 (2) ◽  
pp. 308-321 ◽  
Author(s):  
Arfan Majeed ◽  
Jingxiang Lv ◽  
Tao Peng
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Additive Manufacturing ◽  
Production Efficiency ◽  
Process Analysis ◽  
Three Dimensional ◽  
Big Data Analytics ◽  
Production Performance ◽  
Content Type ◽  
Processing And Storage

Purpose This paper aims to present an overall framework of big data-based analytics to optimize the production performance of additive manufacturing (AM) process. Design/methodology/approach Four components, namely, big data application, big data sensing and acquisition, big data processing and storage, model establishing, data mining and process optimization were presented to comprise the framework. Key technologies including the big data acquisition and integration, big data mining and knowledge sharing mechanism were developed for the big data analytics for AM. Findings The presented framework was demonstrated by an application scenario from a company of three-dimensional printing solutions. The results show that the proposed framework benefited customers, manufacturers, environment and even all aspects of manufacturing phase. Research limitations/implications This study only proposed a framework, and did not include the realization of the algorithm for data analysis, such as association, classification and clustering. Practical implications The proposed framework can be used to optimize the quality, energy consumption and production efficiency of the AM process. Originality/value This paper introduces the concept of big data in the field of AM. The proposed framework can be used to make better decisions based on the big data during manufacturing process.

scholarly journals Privacy-Preserving Process Mining in Healthcare

2020 ◽  
Vol 17 (5) ◽  
pp. 1612 ◽  
Author(s):  
Anastasiia Pika ◽  
Moe T. Wynn ◽  
Stephanus Budiono ◽  
Arthur H.M. ter Hofstede ◽  
Wil M.P. van der Aalst ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Privacy ◽  
Process Mining ◽  
Personal Data ◽  
Data Transformation ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Process Data ◽  
Mining Community ◽  
Healthcare Process

Process mining has been successfully applied in the healthcare domain and has helped to uncover various insights for improving healthcare processes. While the benefits of process mining are widely acknowledged, many people rightfully have concerns about irresponsible uses of personal data. Healthcare information systems contain highly sensitive information and healthcare regulations often require protection of data privacy. The need to comply with strict privacy requirements may result in a decreased data utility for analysis. Until recently, data privacy issues did not get much attention in the process mining community; however, several privacy-preserving data transformation techniques have been proposed in the data mining community. Many similarities between data mining and process mining exist, but there are key differences that make privacy-preserving data mining techniques unsuitable to anonymise process data (without adaptations). In this article, we analyse data privacy and utility requirements for healthcare process data and assess the suitability of privacy-preserving data transformation methods to anonymise healthcare data. We demonstrate how some of these anonymisation methods affect various process mining results using three publicly available healthcare event logs. We describe a framework for privacy-preserving process mining that can support healthcare process mining analyses. We also advocate the recording of privacy metadata to capture information about privacy-preserving transformations performed on an event log.

scholarly journals Process Mining Tools Comparison

2018 ◽  
Vol 9 (34) ◽  
pp. 97-104
Author(s):  
Ufuk ÇELİK ◽  
Eyüp AKÇETİN
Keyword(s):  
Process Mining ◽  
Process Analysis ◽  
Large Data ◽  
Time Intervals ◽  
General Process ◽  
New Era ◽  
Event Logs ◽  
Special Programs ◽  
Short Time ◽  
Responsible Person

Process mining is a new era in the science of data mining and is a subset of business intelligence. Process mining analysis provides an idea about a general process by comparing each process with others in the terms of time and responsible people who deal with the process. For this reason, event logs are checked. Event logs consist of large data. Because the event logs keep all the records that occur during short time intervals. Special programs are needed to examine such data. These programs generate a process map using information such as event ID, activity, time and responsible person. Through the analysis, processes are discovered, monitored and improved. In this study, the tools named ProM, Disco, Celonis and My-Invenio used in process mining were examined and their performance according to usage features compared. According to the obtained results, the usefulness, performance and reporting features of the software used in a process analysis are revealed.

scholarly journals Process Modelling Based on Event Logs

2018 ◽  
Vol 1 (1) ◽  
pp. 385-392
Author(s):  
Edyta Brzychczy
Keyword(s):  
Process Management ◽  
Process Mining ◽  
Process Analysis ◽  
Process Modelling ◽  
Process Models ◽  
Sources Of Information ◽  
Event Logs ◽  
It Systems ◽  
Many Sources

Abstract Process modelling is a very important stage in a Business Process Management cycle enabling process analysis and its redesign. Many sources of information for process modelling purposes exist. It may be an analysis of documentation related directly or indirectly to the process being analysed, observations or participation in the process. Nowadays, for this purpose, it is increasingly proposed to use the event logs from organization’s IT systems. Event logs could be analysed with process mining techniques to create process models expressed by various notations (i.e. Petri Nets, BPMN, EPC). Process mining enables also conformance checking and enhancement analysis of the processes. In the paper issues related to process modelling and process mining are briefly discussed. A case study, an example of delivery process modelling with process mining technique is presented.

An application framework for mining online learning processes through event-logs

2019 ◽  
Vol 25 (5) ◽  
pp. 860-886
Author(s):  
Güzin Özdağoğlu ◽  
Gülin Zeynep Öztaş ◽  
Mehmet Çağliyangil
Keyword(s):  
Data Mining ◽  
Online Learning ◽  
Process Mining ◽  
General Pattern ◽  
Learning Processes ◽  
Application Framework ◽  
Main Process ◽  
Content Type ◽  
Event Logs ◽  
Event Log

Purpose Learning management systems (LMS) provide detailed information about the processes through event-logs. Process and related data-mining approaches can reveal valuable information from these files to help teachers and executives to monitor and manage their online learning processes. In this regard, the purpose of this paper is to present an overview of the current direction of the literature on educational data mining, and an application framework to analyze the educational data provided by the Moodle LMS. Design/methodology/approach The paper presents a framework to provide a decision support through the approaches existing in process and data-mining fields for analyzing the event-log data gathered from LMS platforms. In this framework, latent class analysis (LCA) and sequential pattern mining approaches were used to understand the general patterns; heuristic and fuzzy approaches were performed for process mining to obtain the workflows and statistics; finally, social-network analysis was conducted to discover the collaborations. Findings The analyses conducted in the study give clues for the process performance of the course during a semester by indicating exceptional situations, clarifying the activity flows, understanding the main process flow and revealing the students’ interactions. Findings also show that using the preliminary data analyses before process mining steps is also beneficial to understand the general pattern and expose the irregular ones. Originality/value The study highlights the benefits of analyzing event-log files of LMSs to improve the quality of online educational processes through a case study based on Moodle event-logs. The application framework covers preliminary analyses such as LCA before the use of process mining algorithms to reveal the exceptional situations.

scholarly journals Process Mining Akademik Sekolah menggunakan RapidMiner

MATICS ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 47
Author(s):  
Hafid Rizqifaluthi ◽  
Muhammad Ainul Yaqin
Keyword(s):  
Data Mining ◽  
Process Mining ◽  
Event Logs

<p><strong>Proses mining adalah sebuah ilmu yang</strong></p><strong>dikembangkan dari data mining. Proses mining mengolah data dalam bentuk event logs yang merupakan representasi dari proses bisnis. Jadi jika sebuah perusahaan atau organisasi sudah memiliki sistem informasi yang menyimpan logs secara otomatis, menerapkan proses mining akan mudah dilakukan. Event logs kemudian dipelajari, dimodelkan untuk menemukan ‘proses model’ yang sesuai dengan kejadian-kejadian yang terekam dalam events log</strong>

Efficient Implementation of Hadoop MapReduce-Based Dataflow

2018 ◽  
pp. 372-385 ◽  
Author(s):  
Ishak H. A. Meddah ◽  
Khaled Belkadi
Keyword(s):  
Business Process ◽  
Process Mining ◽  
Process Analysis ◽  
Large Data ◽  
Finite State Automaton ◽  
Hadoop Mapreduce ◽  
Event Logs ◽  
Execution Traces ◽  
Business Process Analysis ◽  
Finite State

MapReduce is a solution for the treatment of large data. With it we can analyze and process data. It does this by distributing the computation in a large set of machines. Process mining provides an important bridge between data mining and business process analysis. This technique allows for the extraction of information from event logs. Firstly, the chapter mines small patterns from log traces. Those patterns are the representation of the traces execution from a business process. The authors use existing techniques; the patterns are represented by finite state automaton; the final model is the combination of only two types of patterns that are represented by the regular expressions. Secondly, the authors compute these patterns in parallel, and then combine those patterns using MapReduce. They have two parties. The first is the Map Step. The authors mine patterns from execution traces. The second is the combination of these small patterns as reduce step. The results are promising; they show that the approach is scalable, general, and precise. It minimizes the execution time by the use of MapReduce.

Analysis of Tumor Disease Patterns Based on Medical Big Data

2021 ◽  
Vol 11 (2) ◽  
pp. 478-486
Author(s):  
Jing Zheng ◽  
Zhongjun Gao ◽  
Lixin Pu ◽  
Mingjie He ◽  
Jipeng Fan ◽  
...  
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Business Process ◽  
Data Science ◽  
Process Analysis ◽  
Practical Significance ◽  
Apriori Algorithm ◽  
Cancer Disease ◽  
Analysis And Design ◽  
Medical Big Data

Using the medical big data mining related technology, the model of tumor disease was analyzed and studied. Using data science methods as a guiding method and idea, analyzing and constructing a medical service model based on big data for oncology diseases, exploring its development strategy; using business process analysis method to analyze the business process and mapping of cancer disease medical services; using serviceoriented architecture analysis and Design methodology to build a highly flexible, configurable, and easily scalable precision medical big data platform. By analyzing the characteristics of medical big data and the shortcomings of the traditional Apriori algorithm, the Hadoop platform is used to improve and optimize the Apriori algorithm. The results show that the improved Apriori algorithm has great improvement in efficiency and performance, and can be adapted to mining medical big data. Through data mining experiments, it is concluded that there is a correlation between tumors and smoking, chronic infection, occupational pathogenic factors, etc. It has certain guiding significance for the prevention and treatment of tumors, thus also demonstrating the improved Apriori algorithm for lung tumors. Clinical research has practical significance.

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