scholarly journals Domain-Specific Event Abstraction

2021 ◽  
pp. 117-126
Author(s):  
Finn Klessascheck ◽  
Tom Lichtenstein ◽  
Martin Meier ◽  
Simon Remy ◽  
Jan Philipp Sachs ◽  
...  
Keyword(s):  
Real World ◽  
Domain Knowledge ◽  
Process Mining ◽  
Patient Treatment ◽  
Healthcare Data ◽  
Domain Specific ◽  
Event Logs ◽  
Treatment Data ◽  
High Level ◽  
Different Levels

Process mining aims at deriving process knowledge from event logs, which contain data recorded during process executions. Typically, event logs need to be generated from process execution data, stored in different kinds of information systems. In complex domains like healthcare, data is available only at different levels of granularity. Event abstraction techniques allow the transformation of events to a common level of granularity, which enables effective process mining. Existing event abstraction techniques do not sufficiently take into account domain knowledge and, as a result, fail to deliver suitable event logs in complex application domains.This paper presents an event abstraction method based on domain ontologies. We show that the method introduced generates semantically meaningful high-level events, suitable for process mining; it is evaluated on real-world patient treatment data of a large U.S. health system.

scholarly journals Towards a Domain-Specific Modeling Language for Extracting Event Logs from ERP Systems

2021 ◽  
Vol 11 (12) ◽  
pp. 5476
Author(s):  
Ana Pajić Simović ◽  
Slađan Babarogić ◽  
Ognjen Pantelić ◽  
Stefan Krstović
Keyword(s):  
Process Mining ◽  
Modeling Language ◽  
Sufficient Information ◽  
Event Data ◽  
Erp Systems ◽  
Domain Experts ◽  
Domain Specific ◽  
Event Logs ◽  
Domain Specific Modeling ◽  
Specific Modeling

Enterprise resource planning (ERP) systems are often seen as viable sources of data for process mining analysis. To perform most of the existing process mining techniques, it is necessary to obtain a valid event log that is fully compliant with the eXtensible Event Stream (XES) standard. In ERP systems, such event logs are not available as the concept of business activity is missing. Extracting event data from an ERP database is not a trivial task and requires in-depth knowledge of the business processes and underlying data structure. Therefore, domain experts require proper techniques and tools for extracting event data from ERP databases. In this paper, we present the full specification of a domain-specific modeling language for facilitating the extraction of appropriate event data from transactional databases by domain experts. The modeling language has been developed to support complex ambiguous cases when using ERP systems. We demonstrate its applicability using a case study with real data and show that the language includes constructs that enable a domain expert to easily model data of interest in the log extraction step. The language provides sufficient information to extract and transform data from transactional ERP databases to the XES format.

scholarly journals Creating Motivational Language Learning Experiences during the “Critical Period”

2019 ◽  
Vol 11 (3) ◽  
pp. 26
Author(s):  
Junqing Jia
Keyword(s):  
Foreign Language ◽  
Language Learning ◽  
Language Proficiency ◽  
Critical Period ◽  
Domain Knowledge ◽  
Advanced Level ◽  
Domain Specific ◽  
Language Learning Motivation ◽  
Domain Expertise ◽  
High Level

Few studies have touched upon language learning motivation of advanced-level learners of Chinese, even fewer have proposed a pedagogical framework to understand and create motivational pathways. This paper aims to fill the gap by addressing a critical period of foreign language training where students are transforming from learning the foreign language to learning domain knowledge in the foreign language. Having drawn upon Confucian concepts and contextualized curricular examples, this paper proposes a framework suggesting that learners at this stage experience a less discussed psychological complexity due to their high level of language proficiency and lack of multilingual domain capacities. They are also gradually transforming into autonomous language users who expand their social milieu through demonstrating domain expertise. As such, the pedagogical implications place an emphasis on helping advanced-level Chinese learners to establish domain-specific vision and linguistic capability so that they can perform in multicultural contexts. In particular, motivational pathways during this stage should be constructed to encourage learners to constantly reflect on their recent past self and establish visions of the future one.

scholarly journals Database-Less Extraction of Event Logs from Redo Logs

2021 ◽  
pp. 73-82
Author(s):  
Dorina Bano ◽  
Tom Lichtenstein ◽  
Finn Klessascheck ◽  
Mathias Weske
Keyword(s):  
Performance Analysis ◽  
Real World ◽  
Business Processes ◽  
Process Mining ◽  
Detailed Knowledge ◽  
System Failure ◽  
Conformance Checking ◽  
Event Logs ◽  
Event Log ◽  
And Performance

Process mining is widely adopted in organizations to gain deep insights about running business processes. This can be achieved by applying different process mining techniques like discovery, conformance checking, and performance analysis. These techniques are applied on event logs, which need to be extracted from the organization’s databases beforehand. This not only implies access to databases, but also detailed knowledge about the database schema, which is often not available. In many real-world scenarios, however, process execution data is available as redo logs. Such logs are used to bring a database into a consistent state in case of a system failure. This paper proposes a semi-automatic approach to extract an event log from redo logs alone. It does not require access to the database or knowledge of the databaseschema. The feasibility of the proposed approach is evaluated on two synthetic redo logs.

scholarly journals Mining Users' Intents from Logs

2015 ◽  
Vol 6 (2) ◽  
pp. 43-71 ◽  
Author(s):  
Ghazaleh Khodabandelou ◽  
Charlotte Hug ◽  
Camille Salinesi
Keyword(s):  
Systems Engineering ◽  
Process Modeling ◽  
Process Model ◽  
Process Mining ◽  
Process Models ◽  
Levels Of Abstraction ◽  
Engineering Research ◽  
Event Logs ◽  
Novel Approach ◽  
Different Levels

Intentions play a key role in information systems engineering. Research on process modeling has highlighted that specifying intentions can expressly mitigate many problems encountered in process modeling as lack of flexibility or adaptation. Process mining approaches mine processes in terms of tasks and branching. To identify and formalize intentions from event logs, this work presents a novel approach of process mining, called Map Miner Method (MMM). This method automates the construction of intentional process models from event logs. First, MMM estimates users' strategies (i.e., the different ways to fulfill the intentions) in terms of their activities. These estimated strategies are then used to infer users' intentions at different levels of abstraction using two tailored algorithms. MMM constructs intentional process models with respect to the Map metamodel formalism. MMM is applied on a real-world dataset, i.e. event logs of developers of Eclipse UDC (Usage Data Collector). The resulting Map process model provides a precious understanding of the processes followed by the developers, and also provide feedback on the effectiveness and demonstrate scalability of MMM.

scholarly journals Understanding Contrail Business Processes through Hierarchical Clustering: A Multi-Stage Framework

Algorithms ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 244
Author(s):  
Zeeshan Tariq ◽  
Naveed Khan ◽  
Darryl Charles ◽  
Sally McClean ◽  
Ian McChesney ◽  
...  
Keyword(s):  
Hierarchical Clustering ◽  
Real World ◽  
Business Processes ◽  
Process Mining ◽  
Hybrid Approach ◽  
Business Logic ◽  
Business Context ◽  
Multi Stage ◽  
Event Clustering ◽  
High Level

Real-world business processes are dynamic, with event logs that are generally unstructured and contain heterogeneous business classes. Process mining techniques derive useful knowledge from such logs but translating them into simplified and logical segments is crucial. Complexity is increased when dealing with business processes with a large number of events with no outcome labels. Techniques such as trace clustering and event clustering, tend to simplify the complex business logs but the resulting clusters are generally not understandable to the business users as the business aspects of the process are not considered while clustering the process log. In this paper, we provided a multi-stage hierarchical framework for business-logic driven clustering of highly variable process logs with extensively large number of events. Firstly, we introduced a term contrail processes for describing the characteristics of such complex real-world business processes and their logs presenting contrail-like models. Secondly, we proposed an algorithm Novel Hierarchical Clustering (NoHiC) to discover business-logic driven clusters from these contrail processes. For clustering, the raw event log is initially decomposed into high-level business classes, and later feature engineering is performed exclusively based on the business-context features, to support the discovery of meaningful business clusters. We used a hybrid approach which combines rule-based mining technique with a novel form of agglomerative hierarchical clustering for the experiments. A case-study of a CRM process of the UK’s renowned telecommunication firm is presented and the quality of the proposed framework is verified through several measures, such as cluster segregation, classification accuracy, and fitness of the log. We compared NoHiC technique with two trace clustering techniques using two real world process logs. The discovered clusters through NoHiC are found to have improved fitness as compared to the other techniques, and they also hold valuable information about the business context of the process log.

scholarly journals Process Discovery Enhancement with Trace Clustering and Profiling

2021 ◽  
Vol 5 (4) ◽  
pp. 1-13
Author(s):  
Muhammad Faizan ◽  
Megat F. Zuhairi ◽  
Shahrinaz Ismail
Keyword(s):  
Process Model ◽  
Process Mining ◽  
Process Models ◽  
Patient Treatment ◽  
Process Discovery ◽  
Processing Times ◽  
Data Process ◽  
Event Logs ◽  
Homogeneous Subset ◽  
Process Event

The potential in process mining is progressively growing due to the increasing amount of event-data. Process mining strategies use event-logs to automatically classify process models, recommend improvements, predict processing times, check conformance, and recognize anomalies/deviations and bottlenecks. However, proper handling of event-logs while evaluating and using them as input is crucial to any process mining technique. When process mining techniques are applied to flexible systems with a large number of decisions to take at runtime, the outcome is often unstructured or semi-structured process models that are hard to comprehend. Existing approaches are good at discovering and visualizing structured processes but often struggle with less structured ones. Surprisingly, process mining is most useful in domains where flexibility is desired. A good illustration is the "patient treatment" process in a hospital, where the ability to deviate from dealing with changing conditions is crucial. It is useful to have insights into actual operations. However, there is a significant amount of diversity, which contributes to complicated, difficult-to-understand models. Trace clustering is a method for decreasing the complexity of process models in this context while also increasing their comprehensibility and accuracy. This paper discusses process mining, event-logs, and presenting a clustering approach to pre-process event-logs, i.e., a homogeneous subset of the event-log is created. A process model is generated for each subset. These homogeneous subsets are then evaluated independently from each other, which significantly improving the quality of mining results in flexible environments. The presented approach improves the fitness and precision of a discovered model while reducing its complexity, resulting in well-structured and easily understandable process discovery results.

scholarly journals Process mining with real world financial loan applications: Improving inference on incomplete event logs

PLoS ONE ◽  
2018 ◽  
Vol 13 (12) ◽  
pp. e0207806 ◽  
Author(s):  
Catarina Moreira ◽  
Emmanuel Haven ◽  
Sandro Sozzo ◽  
Andreas Wichert
Keyword(s):  
Real World ◽  
Process Mining ◽  
Event Logs

scholarly journals Discovering high-level BPMN process models from event data

2019 ◽  
Vol 25 (5) ◽  
pp. 995-1019 ◽  
Author(s):  
Anna Kalenkova ◽  
Andrea Burattin ◽  
Massimiliano de Leoni ◽  
Wil van der Aalst ◽  
Alessandro Sperduti
Keyword(s):  
Process Model ◽  
Process Mining ◽  
Real Life ◽  
Process Models ◽  
Holistic View ◽  
Life Event ◽  
Content Type ◽  
Event Logs ◽  
Wide Range ◽  
High Level

Purpose The purpose of this paper is to demonstrate that process mining techniques can help to discover process models from event logs, using conventional high-level process modeling languages, such as Business Process Model and Notation (BPMN), leveraging their representational bias. Design/methodology/approach The integrated discovery approach presented in this work is aimed to mine: control, data and resource perspectives within one process diagram, and, if possible, construct a hierarchy of subprocesses improving the model readability. The proposed approach is defined as a sequence of steps, performed to discover a model, containing various perspectives and presenting a holistic view of a process. This approach was implemented within an open-source process mining framework called ProM and proved its applicability for the analysis of real-life event logs. Findings This paper shows that the proposed integrated approach can be applied to real-life event logs of information systems from different domains. The multi-perspective process diagrams obtained within the approach are of good quality and better than models discovered using a technique that does not consider hierarchy. Moreover, due to the decomposition methods applied, the proposed approach can deal with large event logs, which cannot be handled by methods that do not use decomposition. Originality/value The paper consolidates various process mining techniques, which were never integrated before and presents a novel approach for the discovery of multi-perspective hierarchical BPMN models. This approach bridges the gap between well-known process mining techniques and a wide range of BPMN-complaint tools.

scholarly journals Psychological factors influencing the formation of therapeutic cooperation in pulmonary tuberculosis patients undergoing in-patient treatment

2021 ◽  
Vol 98 (12) ◽  
pp. 34-40
Author(s):  
N. V. Zolotova ◽  
V. V. Streltsov ◽  
G. V. Baranova ◽  
N. Yu. Kharitonova ◽  
T. R. Bagdasaryan
Keyword(s):  
Pulmonary Tuberculosis ◽  
Comparative Study ◽  
Psychological Characteristics ◽  
Patient Treatment ◽  
Affective States ◽  
Tuberculosis Patients ◽  
Quality Life ◽  
High Level ◽  
The Comparative Study ◽  
Different Levels

The objective of the study: the comparative study of psychological characteristics of pulmonary tuberculosis patients with different levels of therapeutic cooperation during in-patient treatment.Subjects and nethods. 318 pulmonary tuberculosis patients aged 18-60 years old were enrolled in the study; they all underwent in-patient treatment in Central Tuberculosis Research Institute in 2017-2019, of them 195 (61.3%) were women and 123 (38.7%) were men. In all patients, the level of therapeutic cooperation was studied using the specially designed questionnaire; psychological characteristics were assessed using certain psychodiagnostic methods.Results. It was found that the proportion of patients with a high level of therapeutic cooperation (44.3% of cases) significantly prevailed versus patients with low (29.6% of cases) (Pearson χ2 = 9.4; p < 0.01) and moderate level of cooperation (26.1% of cases) (Pearson χ2 = 15.02; p < 0.001). A comparative study of the psychological characteristics of pulmonary tuberculosis patients demonstrating different levels of therapeutic cooperation allowed identifying psychological prognostic parameters of therapeutic cooperation, the main of which were the severity of suspicion, negative affective states (irritability, aggressiveness), distrustful-skeptical style of interaction, confrontation with others, as well as the level of quality life, primarily the emotional and social aspects of functioning. The detected psychological differences are considered as targets of psychological work focused on the formation of appropriate therapeutic cooperation during in-patient treatment.

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