scholarly journals Temporal Logics Over Finite Traces with Uncertainty

2020 ◽  
Vol 34 (06) ◽  
pp. 10218-10225 ◽  
Author(s):  
Fabrizio M Maggi ◽  
Marco Montali ◽  
Rafael Peñaloza
Keyword(s):  
Decision Making ◽  
Temporal Logic ◽  
Business Process ◽  
Dynamic Systems ◽  
Real Life ◽  
Temporal Logics ◽  
Business Process Modelling ◽  
Process Discovery ◽  
Event Log ◽  
Computational Properties

Temporal logics over finite traces have recently seen wide application in a number of areas, from business process modelling, monitoring, and mining to planning and decision making. However, real-life dynamic systems contain a degree of uncertainty which cannot be handled with classical logics. We thus propose a new probabilistic temporal logic over finite traces using superposition semantics, where all possible evolutions are possible, until observed. We study the properties of the logic and provide automata-based mechanisms for deriving probabilistic inferences from its formulas. We then study a fragment of the logic with better computational properties. Notably, formulas in this fragment can be discovered from event log data using off-the-shelf existing declarative process discovery techniques.

scholarly journals Process Discovery of Business Processes Using Temporal Causal Relation

2019 ◽  
Vol 5 (2) ◽  
pp. 183
Author(s):  
Yutika Amelia Effendi ◽  
Nania Nuzulita
Keyword(s):  
Business Process ◽  
Business Processes ◽  
Process Mining ◽  
Causal Relation ◽  
Real Life ◽  
Process Models ◽  
Time Interval ◽  
Start Time ◽  
Process Discovery ◽  
Event Log

Background: Nowadays, enterprise computing manages business processes which has grown up rapidly. This situation triggers the production of a massive event log. One type of event log is double timestamp event log. The double timestamp has a start time and complete time of each activity executed in the business process. It also has a close relationship with temporal causal relation. The temporal causal relation is a pattern of event log that occurs from each activity performed in the process.Objective: In this paper, seven types of temporal causal relation between activities were presented as an extended version of relations used in the double timestamp event log. Since the event log was not always executed sequentially, therefore using temporal causal relation, the event log was divided into several small groups to determine the relations of activities and to mine the business process.Methods: In these experiments, the temporal causal relation based on time interval which were presented in Gantt chart also determined whether each case could be classified as sequential or parallel relations. Then to obtain the business process, each temporal causal relation was combined into one business process based on the timestamp of activity in the event log.Results: The experimental results, which were implemented in two real-life event logs, showed that using temporal causal relation and double timestamp event log could discover business process models.Conclusion: Considering the findings, this study concludes that business process models and their sequential and parallel AND, OR, XOR relations can be discovered by using temporal causal relation and double timestamp event log.Keywords:Business Process, Process Discovery, Process Mining, Temporal Causal Relation, Double Timestamp Event Log

Process Mining With Semantics

2020 ◽  
pp. 181-216
Keyword(s):  
Real Time ◽  
Business Process ◽  
Process Mining ◽  
Task Force ◽  
Process Modelling ◽  
Business Process Modelling ◽  
Process Discovery ◽  
The Individual ◽  
Semantic Process

This chapter contains the application of the semantic process mining approach in real-time. This includes the series of analysis that were performed not only to show how the method is applied in different scenarios or settings for process mining purposes, but also how to technically apply the method for semantic process mining tasks. In the first section, the work shows how the authors practically apply the current tools that supports the process mining through its participation in the Process Discovery Contest organised by the IEEE CIS Task Force on Process Mining. In the second section, the chapter shows how it expounds the results and amalgamation of the two process mining techniques, namely fuzzy miner and business process modelling notation (BPMN) approach, in order to demonstrate the capability of the proposed semantic-based fuzzy miner being able to perform a more conceptual and accurate classification of the individual traces within the process or input models.

Healthcare Process Development with BPMN

2010 ◽  
pp. 1024-1047 ◽  
Author(s):  
Elvira Rolón ◽  
Félix García ◽  
Francisco Ruíz ◽  
Mario Piattini ◽  
Luis Calahorra
Keyword(s):  
Decision Making ◽  
Business Process ◽  
Process Development ◽  
Health Management ◽  
Health Sector ◽  
Medical Assistance ◽  
Business Process Modelling ◽  
Graphical Visualization ◽  
Healthcare Process ◽  
Health Institutions

The importance of the analysis, modelling and management of a business process is not restricted to a specific enterprise sector. In the field of health management, as a result of the nature of the service offered, health institutions’ processes are also the basis for decision making which is focused on achieving their objective of providing quality medical assistance. In this work, the authors shall present the application of business process modelling to the processes of a health sector institution, using the BPMN standard notation. The objective of this work is to show the experience obtained in the creation of the conceptual models of certain hospital processes which can be used as a basis for others in collaboration with hospitals in order to model their processes using BPMN. Hospital processes are highly complex, and their graphical visualization facilitates their management and improvement by means of the understanding and detection of possible failures.

scholarly journals ACO with Intuitionistic Fuzzy Pheromone Updating Applied on Multiple-Constraint Knapsack Problem

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1456
Author(s):  
Stefka Fidanova ◽  
Krassimir Todorov Atanassov
Keyword(s):  
Decision Making ◽  
Knapsack Problem ◽  
Propositional Logic ◽  
Optimization Problems ◽  
Real Life ◽  
Combinatorial Optimization Problems ◽  
Intuitionistic Fuzzy ◽  
Life Problems ◽  
Multiple Constraint

Some of industrial and real life problems are difficult to be solved by traditional methods, because they need exponential number of calculations. As an example, we can mention decision-making problems. They can be defined as optimization problems. Ant Colony Optimization (ACO) is between the best methods, that solves combinatorial optimization problems. The method mimics behavior of the ants in the nature, when they look for a food. One of the algorithm parameters is called pheromone, and it is updated every iteration according quality of the achieved solutions. The intuitionistic fuzzy (propositional) logic was introduced as an extension of Zadeh’s fuzzy logic. In it, each proposition is estimated by two values: degree of validity and degree of non-validity. In this paper, we propose two variants of intuitionistic fuzzy pheromone updating. We apply our ideas on Multiple-Constraint Knapsack Problem (MKP) and compare achieved results with traditional ACO.

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