Semantic process mining: A conceptual application of main tools, framework and model analysis

2020 ◽  
Vol 16 (3) ◽  
pp. 127-147
Author(s):  
Kingsley Okoye
Keyword(s):  
Business Processes ◽  
Process Mining ◽  
Mining Method ◽  
Conceptual Level ◽  
Semantic Reasoning ◽  
Abstract Knowledge ◽  
Semantic Concepts ◽  
Semantic Process ◽  
High Level ◽  
Information Semantics

Semantics has been a major challenge when applying the process mining (PM) technique to real-time business processes. The several theoretical and practical efforts to bridge the semantic gap has spanned the advanced notion of the semantic-based process mining (SPM). Fundamentally, the SPM devotes its methods to the idea of making use of existing (semantic) technologies to support the analysis of PM techniques. In principle, the semantic-based process mining method is applied through the acquisition and representation of abstract knowledge about the domain processes in question. To this effect, this paper demonstrates how the semantic concepts and process modelling (reasoning) methods are used to improve the outcomes of PM techniques from the syntactic to a more conceptual level. To do this, the study proposes an SPM-based framework that shows to be intelligent with a high level of semantic reasoning aptitudes. Technically, this paper introduces a process mining approach that uses information (semantics) about different activities that can be found in any given process to make inferences and generate rules or patterns through the method for annotation, semantic reasoning, and conceptual assertions. In turn, the method is theoretically applied to enrich the informative values of the resultant models. Also, the study conducts and systematically reviews the current tools and methods that are used to support the outcomes of the process mining as well as evaluates the results of the different methods to determine the levels of impact and its implications for process mining.

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 Automatic Labeled Dialogue Generation for Nursing Record Systems

2020 ◽  
Vol 10 (3) ◽  
pp. 62
Author(s):  
Tittaya Mairittha ◽  
Nattaya Mairittha ◽  
Sozo Inoue
Keyword(s):  
Data Augmentation ◽  
Short Term Memory ◽  
Generative Models ◽  
Abstract Knowledge ◽  
Augmentation Techniques ◽  
Nursing Record ◽  
Long Short Term Memory ◽  
The Individual ◽  
High Level ◽  
Embedding Methods

The integration of digital voice assistants in nursing residences is becoming increasingly important to facilitate nursing productivity with documentation. A key idea behind this system is training natural language understanding (NLU) modules that enable the machine to classify the purpose of the user utterance (intent) and extract pieces of valuable information present in the utterance (entity). One of the main obstacles when creating robust NLU is the lack of sufficient labeled data, which generally relies on human labeling. This process is cost-intensive and time-consuming, particularly in the high-level nursing care domain, which requires abstract knowledge. In this paper, we propose an automatic dialogue labeling framework of NLU tasks, specifically for nursing record systems. First, we apply data augmentation techniques to create a collection of variant sample utterances. The individual evaluation result strongly shows a stratification rate, with regard to both fluency and accuracy in utterances. We also investigate the possibility of applying deep generative models for our augmented dataset. The preliminary character-based model based on long short-term memory (LSTM) obtains an accuracy of 90% and generates various reasonable texts with BLEU scores of 0.76. Secondly, we introduce an idea for intent and entity labeling by using feature embeddings and semantic similarity-based clustering. We also empirically evaluate different embedding methods for learning good representations that are most suitable to use with our data and clustering tasks. Experimental results show that fastText embeddings produce strong performances both for intent labeling and on entity labeling, which achieves an accuracy level of 0.79 and 0.78 f1-scores and 0.67 and 0.61 silhouette scores, respectively.

scholarly journals Learning Analytics to Reveal Links Between Learning Design and Self-Regulated Learning

2021 ◽  
Author(s):  
Yizhou Fan ◽  
Wannisa Matcha ◽  
Nora’ayu Ahmad Uzir ◽  
Qiong Wang ◽  
Dragan Gašević
Keyword(s):  
Learning Analytics ◽  
High Performance ◽  
Process Mining ◽  
Learning Experience ◽  
Learning Design ◽  
Metacognitive Control ◽  
Self Regulated Learning ◽  
Regulated Learning ◽  
High Level ◽  
Evaluation Of Learning

AbstractThe importance of learning design in education is widely acknowledged in the literature. Should learners make effective use of opportunities provided in a learning design, especially in online environments, previous studies have shown that they need to have strong skills for self-regulated learning (SRL). The literature, which reports the use of learning analytics (LA), shows that SRL skills are best exhibited in choices of learning tactics that are reflective of metacognitive control and monitoring. However, in spite of high significance for evaluation of learning experience, the link between learning design and learning tactics has been under-explored. In order to fill this gap, this paper proposes a novel learning analytic method that combines three data analytic techniques, including a cluster analysis, a process mining technique, and an epistemic network analysis. The proposed method was applied to a dataset collected in a massive open online course (MOOC) on teaching in flipped classrooms which was offered on a Chinese MOOC platform to pre- and in-service teachers. The results showed that the application of the approach detected four learning tactics (Search oriented, Content and assessment oriented, Content oriented and Assessment oriented) which were used by MOOC learners. The analysis of tactics’ usage across learning sessions revealed that learners from different performance groups had different priorities. The study also showed that learning tactics shaped by instructional cues were embedded in different units of study in MOOC. The learners from a high-performance group showed a high level of regulation through strong alignment of the choices of learning tactics with tasks provided in the learning design. The paper also provides a discussion about implications of research and practice.

scholarly journals Methodology of synchronization among strategy and operation. A standards-based modeling approach

2017 ◽  
Vol 37 (2) ◽  
pp. 111-119
Author(s):  
VICTOR EDWIN COLLAZOS ◽  
HELGA DUARTE AMAYA
Keyword(s):  
Information Technology ◽  
Information System ◽  
Enterprise Architecture ◽  
Business Processes ◽  
Modeling Approach ◽  
Operational Level ◽  
Alignment Process ◽  
Level Design ◽  
High Level ◽  
Help Model

Enterprise Architecture (EA) has gained importance in recent years, mainly for its concept of “alignment” between the strategic and operational levels of organizations. Such alignment occurs when Information Technology (IT) is applied correctly and timely, working in synergy and harmony with strategy and the operation to achieve mutually their own goals and satisfy the organizational needs.Both the strategic and operational levels have standards that help model elements necessary to obtain desired results. In this sense, BMM and BPMN were selected because both have the support of OMG and they are fairly well known for modelling the strategic level and operational level, respectively. In addition, i* modeling goal can be used for reducing the gap between these two standards. This proposal may help both the high-level design of the information system and to the appropriate identification of the business processes that will support it.This paper presents a methodology for aligning strategy and the operation based on standards and heuristics. We have made a classification for elements of the models and, for some specific cases, an extension of the heuristics associated between them. This allows us to propose methodology, which uses above-mentioned standards and combines mappings, transformations and actions to be considered in the alignment process.

scholarly journals Role of Stochastic Petri Net (SPN) in Process Discovery for Modelling and Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Shabnam Shahzadi ◽  
Xianwen Fang ◽  
David Anekeya Alilah
Keyword(s):  
Petri Net ◽  
Business Processes ◽  
Time Perspective ◽  
Process Mining ◽  
Vital Role ◽  
Process Discovery ◽  
Stochastic Petri Net ◽  
Event Log ◽  
The Stability ◽  
Generalized Stochastic Petri Net

For exploitation and extraction of an event’s data that has vital information which is related to the process from the event log, process mining is used. There are three main basic types of process mining as explained in relation to input and output. These are process discovery, conformance checking, and enhancement. Process discovery is one of the most challenging process mining activities based on the event log. Business processes or system performance plays a vital role in modelling, analysis, and prediction. Recently, a memoryless model such as exponential distribution of the stochastic Petri net SPN has gained much attention in research and industry. This paper uses time perspective for modelling and analysis and uses stochastic Petri net to check the performance, evolution, stability, and reliability of the model. To assess the effect of time delay in firing the transition, stochastic reward net SRN model is used. The model can also be used in checking the reliability of the model, whereas the generalized stochastic Petri net GSPN is used for evaluation and checking the performance of the model. SPN is used to analyze the probability of state transition and the stability from one state to another. However, in process mining, logs are used by linking log sequence with the state and, by this, modelling can be done, and its relation with stability of the model can be established.

scholarly journals Discovering Business Processes from Email Logs using fastText and Process Mining

2020 ◽  
Author(s):  
Yaghoub rashnavadi ◽  
Sina Behzadifard ◽  
Reza Farzadnia ◽  
sina zamani
Keyword(s):  
Machine Learning ◽  
Business Processes ◽  
Oil And Gas ◽  
Process Mining ◽  
The Body ◽  
Process Models ◽  
Supervised Machine Learning ◽  
Implicit Information ◽  
Oil And Gas Sector

<p>Communication has never been more accessible than today. With the help of Instant messengers and Email Services, millions of people can transfer information with ease, and this trend has affected organizations as well. There are billions of organizational emails sent or received daily, and their main goal is to facilitate the daily operation of organizations. Behind this vast corpus of human-generated content, there is much implicit information that can be mined and used to improve or optimize the organizations’ operations. Business processes are one of those implicit knowledge areas that can be discovered from Email logs of an Organization, as most of the communications are followed inside Emails. The purpose of this research is to propose an approach to discover the process models in the Email log. In this approach, we combine two tools, supervised machine learning and process mining. With the help of supervised machine learning, fastText classifier, we classify the body text of emails to the activity-related. Then the generated log will be mined with process mining techniques to find process models. We illustrate the approach with a case study company from the oil and gas sector.</p>

Identification and Measurement of the Knowledge-Sharing Requirements in Collaborative Business Processes

2011 ◽  
pp. 215-231
Author(s):  
Farhad Daneshgar
Keyword(s):  
Knowledge Sharing ◽  
Conceptual Model ◽  
Business Process ◽  
Business Processes ◽  
Modelling Language ◽  
Collaborative Processes ◽  
Semantic Concepts ◽  
Analysis Of Knowledge ◽  
What Matters

This chapter introduces a modelling language called Awareness Net for both representation as well as measuring the knowledge-sharing requirements in collaborative business processes. It is a conceptual model that facilitates representation and analysis of knowledge-sharing requirements of the actors in collaborative business processes. The representation and measurement are handled by a set of collaborative semantic concepts and their relationships. The proposed language enforces overall specification of what matters to the actors in collaborative processes when collaborating in business process to keep them aware of the collaboration context.

Design and Implementation of an Event-Based RFID Middleware

2012 ◽  
pp. 110-131
Author(s):  
Angelo Cucinotta ◽  
Antonino Longo Minnolo ◽  
Antonio Puliafito
Keyword(s):  
Business Processes ◽  
Strong Impact ◽  
Rfid Technology ◽  
Application Development ◽  
Technological Level ◽  
Rfid Middleware ◽  
Reducing Costs ◽  
High Level ◽  
The Cost ◽  
Event Based

The downward trend in the cost of RFID technology is producing a strong impact on the industrial world that is using such powerful technology in order to rethink and optimize most of the existing business processes. In this sense, the chipless technology is playing a key role to facilitate the adoption of RFID in enterprises. All this implies the use of solutions that simplify the adoption of the continuously evolving RFID technology and allow keeping a high-level vision versus the specific technical details. In brief, it is mandatory to abstract the technological level and makes transparent the physical devices to the application level. The widespread use of the RFID technology also produces a large volume of data from many objects scattered everywhere, that have to be managed. In these complex scenarios, the RFID middleware represents an ideal solution that favors the technology integration, reducing costs for application development and introducing real benefits to the business processes. In this chapter, the authors describe the main features of our event-based RFID middleware and its powerful architecture. Their middleware is able to assure an effective process of technological abstraction, switching from a vision linked to the specific issues of interfacing devices (chipless tags, readers, sensor networks, GPS, WiFi, etc.) to the management of the event generated by each device. In brief, “event-based” means to integrate the management logic of different devices.

Awareness Matters in Virtual Communities

2004 ◽  
pp. 51-68
Author(s):  
Farhad Daneshgar
Keyword(s):  
Virtual Communities ◽  
Business Processes ◽  
Virtual Community ◽  
Community Member ◽  
Contextual Knowledge ◽  
Specific Task ◽  
Semantic Concepts

A methodology is introduced in this chapter for sharing the contextual knowledge in virtual communities. Context is represented by a set of semantic concepts and their relationships that form specific collaborative business processes within the virtual community. Two sets of objects/concepts are identified: (i) objects that make up a community member’s actual contextual knowledge, and (ii) objects that make up the contextual knowledge expected from a community member, by a specific task object that the member performs within the community, that are sufficient to enable the member to perform the task and/or to participate in relevant interactions. The excess of the objects in (ii) compared to the objects in (i) is identified. These objects need to be put within the focus of the community member in order to enable him/her to successfully get involved in various tasks within the community.

Abstractions in Intelligent Multimedia Databases

2013 ◽  
pp. 195-220
Author(s):  
Ranjan Parekh ◽  
Nalin Sharda
Keyword(s):  
Semantic Content ◽  
Multimedia Databases ◽  
Low Level ◽  
Layered Architecture ◽  
Automated Inference ◽  
Semantic Concepts ◽  
High Level ◽  
Intelligent Multimedia ◽  
Visual Keywords ◽  
Semantic Layer

Semantic characterization is necessary for developing intelligent multimedia databases, because humans tend to search for media content based on their inherent semantics. However, automated inference of semantic concepts derived from media components stored in a database is still a challenge. The aim of this chapter is to demonstrate how layered architectures and “visual keywords” can be used to develop intelligent search systems for multimedia databases. The layered architecture is used to extract meta-data from multimedia components at various layers of abstractions. While the lower layers handle physical file attributes and low-level features, the upper layers handle high-level features and attempts to remove ambiguities inherent in them. To access the various abstracted features, a query schema is presented, which provides a single point of access while establishing hierarchical pathways between feature-classes. Minimization of the semantic gap is addressed using the concept of “visual keyword” (VK). “Visual keywords” are segmented portions of images with associated low- and high-level features, implemented within a semantic layer on top of the standard low-level features layer, for characterizing semantic content in media components. Semantic information is however predominantly expressed in textual form, and hence is susceptible to the limitations of textual descriptors – viz. ambiguities related to synonyms, homonyms, hypernyms, and hyponyms. To handle such ambiguities, this chapter proposes a domain specific ontology-based layer on top of the semantic layer, to increase the effectiveness of the search process.

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