Validation and Verification of Temporal Knowledge as an Important Aspect of Implementing a Temporal Knowledge Base System Supporting Organizational Creativity

Mach‐Król, Maria; Michalik, Krzysztof

doi:10.15439/2015f78

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…Our future work will focus on practical assessment of process models with the time related logic specification and the possibility of validation and verification of such models [61] or compliance checking [62], especially with the existing tools which uses temporal knowledge [63], [64]. One of the possible directions is related to integration of timed process models with timed rules models for complex reasoning on contex data [65], [66].…”

Section: Discussionmentioning

confidence: 99%

Overview of Time Issues with Temporal Logics for Business Process Models

Kluza

Jobczyk

Wiśniewski

et al. 2016

Annals of Computer Science and Information Systems

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Abstract-Process models can specify various aspects of business processes. In this paper, we present an overview of the existing solutions for describing time aspects of such models. We focus on Business Process Model and Notation and provide examples of representing time patterns in this notation. As temporal issues can be specified using temporal logics, we provide a short overview of selected temporal logics which can be used to specify the time patterns in business process models.

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Section: Discussionmentioning

confidence: 99%

Overview of Time Issues with Temporal Logics for Business Process Models

Kluza

Jobczyk

Wiśniewski

et al. 2016

Annals of Computer Science and Information Systems

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“…Underlying the application of this KBS approach for CITs selection, the computational inference process aims to emulate experts¨ability to scope the context in which the design team is inserted and recognize, which techniques may address their creative needs. As creativity is primarily a team activity, a fluid information sharing helps teams to cocreate (Mach-Kr ol and Michalik, 2015). Many of the CITs selected in this research focus on allowing such information sharing, surpassing possible blockages.…”

Section: Creativity Innovation and Techniquesmentioning

confidence: 99%

An artificial intelligence approach to support knowledge management on the selection of creativity and innovation techniques

Botega

Silva

2020

JKM

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Purpose Creativity is an important skill for design teams to reach new and useful solutions. Designers often use one or more of creativity and innovation techniques (CITs) to achieve the desired creative potential during new product development (NPD). The selection of adequate CITs requires considerable expertise, given the multiple application contexts and the extensive number of techniques available. The purpose of this study is to present a creativity support system able to manage this amount of information and provide valuable knowledge to improve NPD. Design/methodology/approach This study presents a knowledge-based system prototype using artificial intelligence (AI) to support knowledge management on the selection of CITs for design. CITs assertion is modelled through a double inference process using five categories, correlating over 500 different entry scenarios to 24 implemented CITs. The techniques are classified according to: design stage, innovation focus, team relationship, execution method and difficult of use. Prototype outputs explanations on the inference process and chosen techniques information. Findings To demonstrate the system scope, two opposite design cases are presented. The system was validated by experts in knowledge management and mechanical engineering design. The validation process demonstrates relevance of the approach and improvement directions for future developments. Originality/value Though literature contains toolkits and taxonomy for CITs, no work applies AI to identify design scenarios, select best CITs and instruct about their use. Validators reported to know less than half of the available techniques, showing a clear knowledge gap among design experts.

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“…The further works over the system development should also concern requirements engineering aspects [15], [16], as well as temporal issues of the system [17], [18].…”

Section: Use Cases and System Scenariosmentioning

confidence: 99%

Context-aware and pro-active queue management systems in intelligent environments

Klimek¹

2017

Annals of Computer Science and Information Systems

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Abstract-The Ambient intelligence (AmI) paradigm refers to electronic environments which are sensitive and responsive to the presence of people. Queue systems are practically used in various institutions and commercial enterprises constituting a challenge for the intelligent environments in smart cities. The management of the customer flows guarantees elimination or reduction of the queues as well as the economic benefits which follow the clients' satisfaction of the better service quality. There has been proposed the intelligent queue management system designed as the pro-active and context-aware system basing on multiple lowlevel sensors and devices constituting the IoT (Internet of Things) network. The designed context-driven system is characterized by user friendliness, as well as the client behavior understanding to generate actions that support clients. There has been proposed the conceptual version of the system. The selected aspects of the prototype version has been simulated. This prototype can be used as the necessary experience for building the target system meeting the precise needs and assumptions typical for context-aware and pro-active system basing on IoT networks.

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Validation and Verification of Temporal Knowledge as an Important Aspect of Implementing a Temporal Knowledge Base System Supporting Organizational Creativity

Cited by 4 publications

References 6 publications

Overview of Time Issues with Temporal Logics for Business Process Models

Overview of Time Issues with Temporal Logics for Business Process Models

An artificial intelligence approach to support knowledge management on the selection of creativity and innovation techniques

Context-aware and pro-active queue management systems in intelligent environments

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