Causality-based model checking in business process management tasks

Sergii, Chalyi; Левыкин, Игорь Викторович; Aleksandr, Petrychenko; Bogatov, Ievgen

doi:10.1109/dessert.2018.8409176

Cited by 13 publications

(16 citation statements)

References 9 publications

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“…It is necessary to select and order the sequence of events (choice) for their formation, taking into account the context of this choice. A general approach to the selection of the initial subsets of events was proposed in [15].…”

Section: Methods Of Forming Recommendations In Situation Of Cyclic Colmentioning

confidence: 99%

Method of Forming Recommendations Using Temporal Constraints in a Situation of Cyclic Cold Start of the Recommender System

Chalyi

Leshchynskyi

Leshchynska

2019

EUREKA: Physics and Engineering

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The problem of the formation of the recommended list of items in the situation of cyclic cold start of the recommendation system is considered. This problem occurs when building recommendations for occasional users. The interests of such consumers change significantly over time. These users are considered "cold" when accessing the recommendation system. A method for building recommendations in a cyclical cold start situation using temporal constraints is proposed. Temporal constraints are formed on the basis of the selection of repetitive pairs of actions for choosing the same objects at a given level of time granulation. Input data is represented by a set of user choice records. For each entry, a time stamp is indicated. The method includes the phases of the formation of temporal constraints, the addition of source data using these constraints, as well as the formation of recommendations using the collaborative filtering algorithm. The proposed method makes it possible, with the help of temporal constraints, to improve the accuracy of recommendations for "cold" users with periodic changes in their interests.

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Section: Methods Of Forming Recommendations In Situation Of Cyclic Colmentioning

confidence: 99%

Method of Forming Recommendations Using Temporal Constraints in a Situation of Cyclic Cold Start of the Recommender System

Chalyi

Leshchynskyi

Leshchynska

2019

EUREKA: Physics and Engineering

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“…Analysis of this graph allows one to represent knowledge of the business process in the form of conditions and constraints of execution of the sequence of works. In the case of more complex logs containing mixed sequences of events from several business processes, the required dependences can be obtained after filtering the log by the method proposed in [7]. In order to form such implicit dependences, application of relational nets has been proposed in [8] based on the results of analysis of the event log.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Each potentially admissible route p i new is characterized by a conjunction from a subset of the given conditions. The sum of weights of such conditions is the route potential and, according to (7), sets the probability of its realization. Table 4 Conditions for execution of the business process…”

Section: Conditions and Constraints For The Sequence Of Actions Fomentioning

confidence: 99%

Development of a method for the probabilistic inference of sequences of a business process activities to support the business process management

Левыкин

Chala

2018

EEJET

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“…When constructing the Markov logic network as the basis of the knowledge base of the information management system, it is necessary to take into account the eventual nature of the detected temporal dependencies. A general method for constructing a knowledge base using such dependencies is proposed in [16], and the approach to the formation of initial data in the form of a sequence of events is given in [17]. However, the issues of formation of weights of temporal rules taking into account the characteristics of the initial event description of the control object in this work have not been considered.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Method of Determining Weights of Temporal Rules in Markov Logic Network for Building Knowledge Base in Information Control Systems

Левыкин

Chala

2018

EUREKA: Physics and Engineering

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The problem of constructing and expanding the temporal knowledge base for the information-control system is considered. This knowledge base is formally represented by the Markov logic network. It is shown that the behavior of the control object of a given class can be reflected in the form of a set of weighted temporal rules. These rules are formed on the basis of identifying links between events that reflect known variants of the behavior of the control object. A method is proposed for calculating the weights of temporal rules in a Markov logic network for a given level of detail of the control object. The level of detail is determined by the context for executing the sequences of control actions and for weighted temporal rules is specified by selecting subsets of the event attributes. The method includes such basic phases: preparation of a subset of temporal rules for a given level of detail; finding the weights of the rules taking into account the a priori probabilities of the event traces. The method creates conditions for supporting management decisions in information management systems at various levels of detail of complex management objects. Decision support is provided by predicting the probability of success in executing a sequence of actions that implement the management function in the current situation. These probabilities are determined using the weights of the temporal rules.

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Causality-based model checking in business process management tasks

Cited by 13 publications

References 9 publications

Method of Forming Recommendations Using Temporal Constraints in a Situation of Cyclic Cold Start of the Recommender System

Method of Forming Recommendations Using Temporal Constraints in a Situation of Cyclic Cold Start of the Recommender System

Development of a method for the probabilistic inference of sequences of a business process activities to support the business process management

Method of Determining Weights of Temporal Rules in Markov Logic Network for Building Knowledge Base in Information Control Systems

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