Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Business Process Variant Analysis Based on Mutual Fingerprints of Event Logs

ABSTRACT: Comparing business process variants using event logs is a common use case in process mining. Existing techniques for process variant analysis detect statistically-significant differences between variants at the level of individual entities (such as process activities) and their relationships (e.g. directly-follows relations between activities). This may lead to a proliferation of differences due to the low level of granularity in which such differences are captured. This paper presents a novel approach to detect statistically-significant differences between variants at the level of entire process traces (i.e. sequences of directly-follows relations). The cornerstone of this approach is a technique to learn a directly-follows graph called mutual fingerprint from the event logs of the two variants. A mutual fingerprint is a lossless encoding of a set of traces and their duration using discrete wavelet transformation. This structure facilitates the understanding of statistical differences along the control-flow and performance dimensions. The approach has been evaluated using real-life event logs against two baselines. The results show that at a trace level, the baselines cannot always reveal the differences discovered by our approach, or can detect spurious differences.

SUBMITTER: Dustdar S

PROVIDER: S-EPMC7266464 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Encrypted Web traffic dataset: Event logs and packet traces.

Project description:We present a dataset that captures seven days of monitoring data from eight servers hosting more than 800 sites across a large campus network. The dataset contains data from network monitoring and host-based monitoring. The first set of data are packet traces collected by a probe situated on the network link in front of the web servers. The traces contain encrypted HTTP over TLS 1.2 communication between clients and web servers. The second set of data is an event log captured directly on the web servers. The events are generated by the Internet Information Services (IIS) logging and include both the IIS default features and custom features, such as client port and transferred data volume. Anonymization of all features in the dataset has been carefully carried out to prevent private information leakage while preserving the information value of the dataset. The dataset is suitable mainly for training machine learning techniques for anomaly detection and the identification of relationships between network traffic and events on web servers. We also add tools, settings, and a guide to convert the packet traces to IP flows that are often preferred for network traffic analysis.

| S-EPMC9065631 | biostudies-literature

Clone detection for business process models.

Project description:Models are key in software engineering, especially with the rise of model-driven software engineering. One such use of modeling is in business process modeling, where models are used to represent processes in enterprises. As the number of these process models grow in repositories, it leads to an increasing management and maintenance cost. Clone detection is a means that may provide various benefits such as repository management, data prepossessing, filtering, refactoring, and process family detection. In model clone detection, highly similar model fragments are mined from larger model repositories. In this study, we have extended SAMOS (Statistical Analysis of Models) framework for clone detection of business process models. The framework has been developed to support different types of analytics on models, including clone detection. We present the underlying techniques utilized in the framework, as well as our approach in extending the framework. We perform three experimental evaluations to demonstrate the effectiveness of our approach. We first compare our tool against the Apromore toolset for a pairwise model similarity using a synthetic model mutation dataset. As indicated by the results, SAMOS seems to outperform Apromore in the coverage of the metrics in pairwise similarity of models. Later, we do a comparative analysis of the tools on model clone detection using a dataset derived from the SAP Reference Model Collection. In this case, the results show a better precision for Apromore, while a higher recall measure for SAMOS. Finally, we show the additional capabilities of our approach for different model scoping styles through another set of experimental evaluations.

| S-EPMC9454782 | biostudies-literature

Discovering generative models from event logs: data-driven simulation vs deep learning.

Project description:A generative model is a statistical model capable of generating new data instances from previously observed ones. In the context of business processes, a generative model creates new execution traces from a set of historical traces, also known as an event log. Two types of generative business process models have been developed in previous work: data-driven simulation models and deep learning models. Until now, these two approaches have evolved independently, and their relative performance has not been studied. This paper fills this gap by empirically comparing a data-driven simulation approach with multiple deep learning approaches for building generative business process models. The study sheds light on the relative strengths of these two approaches and raises the prospect of developing hybrid approaches that combine these strengths.

| S-EPMC8293933 | biostudies-literature

Adaptive model to support business process reengineering.

Project description:The one constant in the world is change. The changing dynamics of business environment enforces the organizations to re-design or reengineer their business processes. The main objective of such reengineering processes is to provide services or produce products with the possible lowest cost, shortest time, and best quality. Accordingly, Business Process Re-engineering (BPR) provides a roadmap of how to efficiently achieve the operational goals in terms of enhanced flexibility and productivity, reduced cost, and improved quality of service or product. In this article, we propose an efficient model for BPR. The model specifies where the breakdowns occur in BPR implementation, justifies why such breakdowns occur, and proposes techniques to prevent their occurrence again. The proposed model has been built based on two main sections. The first section focuses on integrating Critical Success Factors (CSFs) and the performance of business processes during the reengineering processes. Additionally, it implements the association rule mining technique to investigate the relationship between CSFs and different business processes. The second section aims to measure the performance of business processes (intended success of BPR) by process time, cycle time, quality and cost before and after reengineering processes. A case study of the Egyptian Tax Authority (ETA) is used to test the efficiency of the proposed model.

| S-EPMC8093952 | biostudies-literature

Towards successful business process improvement - An extension of change acceleration process model.

Project description:Change Acceleration Process model (CAP) emerged in early 90's as a set of principles for accelerating change management efforts in organizations. Business Process Improvement (BPI) projects open avenues of opportunity and success for organizations in this highly competitive era. However, most of these projects fail due to lack of commitment, communication, scope creep and inadequate resources. This research attempts to study industry relevant factors most critical to success of a BPI Project in the highly competitive telecom sector. Modified Delphi technique employing a panel of telecom professionals was adopted in order to determine the critical success factors (CSFs) after a thorough review of the literature. Exploratory factor analysis was performed to map extracted factors to the five agents of change. Research outcome defines the relevant CSFs in terms of vision, skills, incentives, resources and action plan. A significant contribution of this research is an extended CAP model for implementation of BPI projects. Practical implications of this research are utilization of the proposed model for BPI project success.

| S-EPMC6881029 | biostudies-literature

The Five Diamond Method for Explorative Business Process Management

Project description:Explorative business process management (BPM) is attracting increasing interest in the literature and professional practice. Organizations have recognized that a focus on operational efficiency is no longer sufficient when disruptive forces can make the value proposition of entire processes obsolete. So far, however, research on how to create entirely new processes has remained largely conceptual, leaving it open how explorative BPM can be put into practice. Following the design science research paradigm and situational method engineering, we address this research gap by proposing a method called the Five Diamond Method. This method guides explorative BPM activities by supporting organizations in identifying opportunities from business and technology trends and integrating them into business processes with novel value propositions. The method is evaluated against literature-backed design objectives and competing artifacts, qualitative data gathered from BPM practitioners, as well as a pilot study and two real-world applications. This research provides two contributions. First, the Five Diamond Method broadens the scope of BPM by integrating prescriptive knowledge from innovation management. Second, the method supports capturing emerging opportunities arising from changing customer needs and digital technologies. Supplementary Information The online version contains supplementary material available at 10.1007/s12599-021-00703-1.

| S-EPMC8185488 | biostudies-literature

The event study in international business research: Opportunities, challenges, and practical solutions.

Project description:The event study or event study method (ESM) is an empirical technique for capturing investors' reaction to an event affecting one or more publicly traded firms. The ESM has been little employed in international business (IB) research despite its frequency in accounting, economics, and finance; for example, only two percent of the empirical articles in JIBS over 1970-2019 include an event study. While this scarcity could indicate a lack of demand, we argue that the field of IB studies offers many interesting and important research opportunities for an event study. We believe that the challenges arise primarily from the supply side, because conducting an event study involves overcoming a variety of data and analytical hurdles. We examine these methodological challenges and offer practical solutions designed to encourage adoption of the ESM. An online appendix with coding and examples provides additional resources.Supplementary informationThe online version contains supplementary material available at 10.1057/s41267-022-00509-7.

| S-EPMC8968242 | biostudies-literature

MIAMI: mutual information-based analysis of multiplex imaging data.

Project description:MotivationStudying the interaction or co-expression of the proteins or markers in the tumor microenvironment of cancer subjects can be crucial in the assessment of risks, such as death or recurrence. In the conventional approach, the cells need to be declared positive or negative for a marker based on its intensity. For multiple markers, manual thresholds are required for all the markers, which can become cumbersome. The performance of the subsequent analysis relies heavily on this step and thus suffers from subjectivity and lacks robustness.ResultsWe present a new method where different marker intensities are viewed as dependent random variables, and the mutual information (MI) between them is considered to be a metric of co-expression. Estimation of the joint density, as required in the traditional form of MI, becomes increasingly challenging as the number of markers increases. We consider an alternative formulation of MI which is conceptually similar but has an efficient estimation technique for which we develop a new generalization. With the proposed method, we analyzed a lung cancer dataset finding the co-expression of the markers, HLA-DR and CK to be associated with survival. We also analyzed a triple negative breast cancer dataset finding the co-expression of the immuno-regulatory proteins, PD1, PD-L1, Lag3 and IDO, to be associated with disease recurrence. We demonstrated the robustness of our method through different simulation studies.Availability and implementationThe associated R package can be found here, https://github.com/sealx017/MIAMI.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC9344855 | biostudies-literature

Conceptual framework for the mapping of management process with information technology in a business process.

Project description:This study on component framework reveals the importance of management process and technology mapping in a business environment. We defined ERP as a software tool, which has to provide business solution but not necessarily an integration of all the departments. Any business process can be classified as management process, operational process and the supportive process. We have gone through entire management process and were enable to bring influencing components to be mapped with a technology for a business solution. Governance, strategic management, and decision making are thoroughly discussed and the need of mapping these components with the ERP is clearly explained. Also we suggest that implementation of this framework might reduce the ERP failures and especially the ERP misfit was completely rectified.

| S-EPMC4377516 | biostudies-other

LoGo: Combining Local and Global Techniques for Predictive Business Process Monitoring

Project description:Predicting process behavior in terms of the next activity to be executed and/or its timestamp can be crucial, e.g., to avoid impeding compliance violations or performance problems. Basically, two prediction techniques are conceivable, i.e., global and local techniques. Global techniques consider all process behavior at once, but might suffer from noise. Local techniques consider a certain subset of the behavior, but might loose the “big picture”. A combination of both techniques is promising to balance out each others drawbacks, but exists so far only in an implicit and unsystematic way. We propose LoGo as a systematic combined approach based on a novel global technique and an extended local one. LoGo is evaluated based on real life execution logs from multiple domains, outperforming nine comparison approaches. Overall, LoGo results in explainable prediction models and high prediction quality.

| S-EPMC7266451 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data