Статті в журналах: "Knowledge based data management"

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Dettmar, Harvey, Xiaohui Liu, Roger Johnson, and Alan Payne. "Knowledge-based data generation." Knowledge-Based Systems 11, no. 3-4 (November 1998): 167–77. http://dx.doi.org/10.1016/s0950-7051(98)00031-8.

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Baoan, Li. "Knowledge Management Based on Big Data Processing." Information Technology Journal 13, no. 7 (March 15, 2014): 1415–18. http://dx.doi.org/10.3923/itj.2014.1415.1418.

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Guo, Yu Dong. "Prototype System of Knowledge Management Based on Data Mining." Applied Mechanics and Materials 411-414 (September 2013): 251–54. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.251.

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Knowledge is a very crucial resource to promote economic development and society progress which includes facts, information, descriptions, or skills acquired through experience or education. With knowledge has being increasingly prominent, knowledge management has become important measure for the core competences promotion of a corporation. The paper begins with knowledge managements definition, and studies the process of knowledge discovery from databases (KDD),data mining techniques and SECI(Socialization, Externalization, Combination, Internalization) model of knowledge dimensions. Finally, a simple knowledge management prototype system was proposed which based on the KDD and data mining.

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Havlíček, J., J. Hron, and I. Tichá. "Knowledge based higher education." Agricultural Economics (Zemědělská ekonomika) 52, No. 3 (February 17, 2012): 107–16. http://dx.doi.org/10.17221/5002-agricecon.

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While data and/or information based education was built on pedagogic, psychology, philosophy of science and didactic disciplines, the new dimension of knowledge based education will involve new disciplines such as Knowledge Management, Epistemology, Systems Theory, Artificial Knowledge Management Systems, Value Theory and Theory of Measurement. It is often assumed that data, information and knowledge are depicted as a pyramid. The data, the most plentiful type, are at the bottom, information, produced from data, is above it and knowledge, produced from information through the hard work of refining or mining, above it. This schema satisfies specific needs of an organisation of warehouse data systems but it does not explain the role of these objects in the educational process. In education, the distinctions among data, information and knowledge need to be distinguished from the complex pedagogical point of view. Knowledge is the engine asking for more information and more data. Knowledge life cycle produces more information, more information asks for more data – that is: there is “just information”. Data, information and knowledge can be considered as object oriented measures assigned to real objects (entities). The following measures can be assigned to the objects: Measure of the zero order – name. Measure of the first order – data. Measure of the second order – information. Metrics of the third order – knowledge. Knowledge based curriculum involves knowledge into study plans and it considers knowledge as a distinctive part of study. Knowledge becomes the engine starting cycle of new information acquisition, reproduction and integration. The following problems have to be solved in building of knowledge based curriculum: Methodology and organisation of educational process. Technical support for knowledge based education. Evaluation and assessment of the process.

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Asrin, Fauzan, *Saide Saide, and Silvia Ratna. "Data to Knowledge-Based Transformation." International Journal of Sociotechnology and Knowledge Development 13, no. 4 (October 2021): 141–52. http://dx.doi.org/10.4018/ijskd.2021100109.

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The objectives of this study is to analyze a large amount of data that often appears to create a knowledge base that can be utilized by firm to enhance their decision support system. The authors used the association rules with rapid miner software, data mining approach, and predictive analysis that contains various data exploration scenarios. The study provides important evidence for adopting data mining methods in the industrial sector and their advantages and disadvantages. Chevron Pacific Indonesia (CPI) has a type of computer maintenance activity. Currently, a numerous errors often occur due to the accuracy in computer maintenance which has a major impact on production results. Therefore, this study focuses on association rules using growth patterns that often appear on variables that have been determined into the algorithm (FP-growth) which results in knowledge with a 100% confidence value and a 97% support value. The value results of this study has support and trust are expected to become knowledge for top management in deciding evergreen IT-business routines.

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Šuman, Sabrina, Alen Jakupović, and Francesca Gržinić Kuljanac. "Knowledge-Based Systems for Data Modelling." International Journal of Enterprise Information Systems 12, no. 2 (April 2016): 1–13. http://dx.doi.org/10.4018/ijeis.2016040101.

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Data modelling is a complex process that depends on the knowledge and experience of the designers who carry it out. The quality of created models has a significant impact on the quality of successive phases of information systems development. This paper, in short, reviews the data modelling process, the entity-relationship method (ERM) and actors in the data modelling process. Further, in more detail it presents systems, methods, and tools for the data modelling process and identifies problems that occur during the development phase of an information system. These problems also represent the authors' motivation for conducting research that aims to develop a knowledge-based system (KBS) in order to support the data modelling process by applying formal language theory (particularly translation) during the process of conceptual modelling. The paper describes the main identified characteristics of the authors' new KB system that are derived from the analysis of existing systems, methods, and tools for the data modelling process. This represents the focus of the research.

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BRADJI, Louardi, and Mahmoud BOUFAIDA. "A Rule Management System for Knowledge Based Data Cleaning." Intelligent Information Management 03, no. 06 (2011): 230–39. http://dx.doi.org/10.4236/iim.2011.36028.

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Radziszewska, Aleksandra. "Data-Driven Approach in Knowledge-Based Smart City Management." European Conference on Knowledge Management 24, no. 2 (September 5, 2023): 1090–98. http://dx.doi.org/10.34190/eckm.24.2.1600.

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The concept of smart city management is based on the implementation and use of advanced technologies, such as wireless sensors, intelligent vehicles, mobile networks, and data storage technologies. It involves integrating various information and communication technology solutions to efficiently manage a city's resources. Cities are investing in data-driven smart technologies to enhance performance and efficiency, thereby generating a large amount of data. Finding innovative ways to use this data helps improve city management and urban development. A data-driven city utilizes datafication to optimize its operations, functions, services, strategies, and policies. Datafication involves transforming various aspects of urban life into computerized data and extracting value from this information. This transformation is dependent on controlling the storage, management, processing, and analysis of the data, as well as utilizing the extracted knowledge to develop useful smart city solutions. Access to real-time data and information enables the provision of effective services that improve productivity, leading to environmental, social, and economic benefits. Both current and future smart cities have the potential to generate vast amounts of real-time data due to complex physical infrastructure and data-driven applications supported by social networks. This paper investigates how the emerging data-driven smart city is practiced and justified in terms of its innovative applied solutions. The aim of the paper is to explore the general conditions for implementing advanced data-driven technologies for smart city management, using knowledge from literature analysis and case studies. To understand this new urban phenomenon, a descriptive case study is used as a qualitative research methodology to examine and compare the possibilities of implementing data-driven approaches in knowledge-based smart city management. Seventeen case studies that use data-driven applications in real-world settings were identified from secondary sources and evaluated based on smart city indicators and related data-driven applications. Smart Cities were selected based on their rankings in the Digital Cities Index 2022, the Smart City Index 2022, and the IESE Cities in Motion Index 2022.

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Boqing Feng, Boqing Feng, Xiaolei Xu Boqing Feng, Congxu Li Xiaolei Xu, Wenbin Liu Congxu Li, and Mohan Liu Wenbin Liu. "GIS-Based Electric Service Resource Management System." 電腦學刊 34, no. 3 (June 2023): 387–97. http://dx.doi.org/10.53106/199115992023063403029.

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With the increasing investment in railway construction, China’s railway transport network is now very sound, the number of operating miles is growing, and the operating speed has also made a qualitative leap. At the same time, the safety and reliability of the operation of railway signal cables and other electrical equipment has also put forward higher requirements. At the present stage, the management of railway electrical services equipment mainly relies on manual management, which is cumbersome, inefficient and unsuitable for multi-user sharing. At the same time, the structure of railway electrical equipment is complex, and the components of the equipment are prone to aging, which can easily cause equipment failure. How to professionally manage electrical service equipment and improve the safety and reliability of electrical service equipment has become an urgent problem for railway electrical service departments. Geographic Information System (GIS) architecture uses spatial data layering technology to achieve multi-level and proportional display of equipment and facilities, which can provide visual display of professional facilities such as railway engineering, electricity and power supply, and carry out multi-source and multi-temporal intelligent analysis of data, provide geographical information service interface for various professions of engineering and electricity to meet their own functional requirements. Knowledge mapping is a key technology for acquiring knowledge and building a knowledge database in the era of big data. In order to explore the hidden information between railway electrical resources, integrate seemingly independent data into the knowledge base and apply them. In this paper, we design a GIS-based electric service resource management system in combination with knowledge mapping that can make data complete and well-structured after processing scattered and redundant information, and analyze and discuss the system’s architecture, functional requirements, key technologies and development prospects.

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Sridhar, V., and M. Narasimha Murty. "Knowledge-based clustering approach for data abstraction." Knowledge-Based Systems 7, no. 2 (June 1994): 103–13. http://dx.doi.org/10.1016/0950-7051(94)90023-x.

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Janev, Valentina, Maria-Esther Vidal, Dea Pujić, Dušan Popadić, Enrique Iglesias, Ahmad Sakor, and Andrej Čampa. "Responsible Knowledge Management in Energy Data Ecosystems." Energies 15, no. 11 (May 27, 2022): 3973. http://dx.doi.org/10.3390/en15113973.

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This paper analyzes the challenges and requirements of establishing energy data ecosystems (EDEs) as data-driven infrastructures that overcome the limitations of currently fragmented energy applications. It proposes a new data- and knowledge-driven approach for management and processing. This approach aims to extend the analytics services portfolio of various energy stakeholders and achieve two-way flows of electricity and information for optimized generation, distribution, and electricity consumption. The approach is based on semantic technologies to create knowledge-based systems that will aid machines in integrating and processing resources contextually and intelligently. Thus, a paradigm shift in the energy data value chain is proposed towards transparency and the responsible management of data and knowledge exchanged by the various stakeholders of an energy data space. The approach can contribute to innovative energy management and the adoption of new business models in future energy data spaces.

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Kalaycı, Tahir Emre, Bor Bricelj, Marko Lah, Franz Pichler, Matthias K. Scharrer, and Jelena Rubeša-Zrim. "A Knowledge Graph-Based Data Integration Framework Applied to Battery Data Management." Sustainability 13, no. 3 (February 2, 2021): 1583. http://dx.doi.org/10.3390/su13031583.

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Today, the automotive and transportation sector is undergoing a transformation process to meet the requirements of sustainable and efficient operations. This transformation mainly reveals itself by electric vehicles, hybrid electric vehicles, and electric vehicle sharing. One significant, and the most expensive, component in electric vehicles is the batteries, and the management of batteries is crucial. It is essential to perform constant monitoring of behavior changes for operational purposes and quickly adjust components and operations to these changes. Thus, to address these challenges, we propose a knowledge graph-based data integration framework for simplifying access and analysis of data accumulated through the operations of vehicles and related transportation systems. The proposed framework aims to enable the effortless analysis and navigation of integrated knowledge and the creation of additional data sets from this knowledge to use during the application of data analysis and machine learning. The knowledge graph serves as a significant component to simplify the extraction, enrichment, exploration, and generation of data in this framework. We have developed it according to the human-centered design, and various roles of the data science and machine learning life cycle can use it. Its main objective is to streamline the exploration and interaction with the integrated data to maximize human productivity. Finally, we present a battery use case to show the feasibility and benefits of the proposed framework. The use case illustrates the usage of the framework to extract knowledge from raw data, navigate and enrich it with additional knowledge, and generate data sets.

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Makani, Joyline. "Knowledge management, research data management, and university scholarship." VINE 45, no. 3 (August 10, 2015): 344–59. http://dx.doi.org/10.1108/vine-07-2014-0047.

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Purpose – The purpose of this paper is to synthesize existing research on research data management (RDM), academic scholarship and knowledge management and provide a conceptual framework for an institutional research data management support-system (RDMSS) for systems development, managerial and academic use. Design/methodology/approach – Viewing RDMSS from multiple theoretical perspectives, including data management, knowledge management, academic scholarship and the practice-based perspectives of knowledge and knowing, this paper conceptually explores the systems’ elements needed in the development of an institutional RDM service by considering the underlying data discovery and application issues, as well as the nature of academic scholarship and knowledge creation, discovery, application and sharing motivations in a university environment. Findings – The paper provides general criteria for an institutional RDMSS framework. It suggests that RDM in universities is at the very heart of the knowledge life cycle and is a central ingredient to the academic scholarships of discovery, integration, teaching, engagement and application. Research limitations/implications – This is a conceptual exploration and as a result, the research findings may lack generalisability. Researchers are therefore encouraged to further empirically examine the proposed propositions. Originality/value – The broad RDMSS framework presented in this paper can be compared with the actual situation at universities and eventually guide recommendations for adaptations and (re)design of the institutional RDM infrastructure and knowledge discovery services environment. Moreover, this paper will help to address some of the identified underlying scholarship and RDM disciplinary divides and confusion constraining the effective functioning of the modern day university’s RDM and data discovery environment.

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Neumann, Michael. "Web-Based Data, Document, and Knowledge Management in Restoration Projects." Restoration Ecology 15, no. 2 (June 2007): 326–29. http://dx.doi.org/10.1111/j.1526-100x.2007.00218.x.

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Hogan, Michael F., and Susan M. Essock. "Data and decisions: Can mental health management be knowledge-based?" Journal of Mental Health Administration 18, no. 1 (December 1991): 12–20. http://dx.doi.org/10.1007/bf02521129.

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Console, Marco, and Maurizio Lenzerini. "Epistemic Integrity Constraints for Ontology-Based Data Management." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (April 3, 2020): 2790–97. http://dx.doi.org/10.1609/aaai.v34i03.5667.

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Ontology-based data management (OBDM) is a powerful knowledge-oriented paradigm for managing data spread over multiple heterogeneous sources. In OBDM, the data sources of an information system are handled through the reconciled view provided by an ontology, i.e., the conceptualization of the underlying domain of interest expressed in some formal language. In any information systems where the basic knowledge resides in data sources, it is of paramount importance to specify the acceptable states of such information. Usually, this is done via integrity constraints, i.e., requirements that the data must satisfy formally expressed in some specific language. However, while the semantics of integrity constraints are clear in the context of databases, the presence of inferred information, typical of OBDM systems, considerably complicates the matter. In this paper, we establish a novel framework for integrity constraints in the OBDM scenarios, based on the notion of knowledge state of the information system. For integrity constraints in this framework, we define a language based on epistemic logic, and study decidability and complexity of both checking satisfaction and performing different forms of static analysis on them.

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Nomura, Toshio, and Stephen Lunn. "Integration of knowledge-based systems with data processing." Knowledge-Based Systems 1, no. 1 (December 1987): 24–31. http://dx.doi.org/10.1016/0950-7051(87)90004-9.

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Huang, Li, Yongjun Han, Aihua Yuan, Tao Xiao, Yi Yu, Lifeng Wang, Xiaomin Zhang, Hongchun Zhan, and Hanmin Zhu. "Performance Evaluation Method of Library Knowledge Management Based on Data Mining." Wireless Communications and Mobile Computing 2022 (June 21, 2022): 1–12. http://dx.doi.org/10.1155/2022/3358738.

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With the development of information technology and computer technology, the work of the library is becoming more and more digitized and networked. This article mainly studies the performance evaluation method of library knowledge management based on data mining. This paper uses attribute-oriented induction algorithm to mine generalized features. First, scan the entire data set to obtain different values of all attributes. Statistics, classification, and analysis of historical records help to understand the usage of books and periodicals and perform predictive analysis. This article uses statistical weighted weight calculation formula to calculate the library knowledge management ability evaluation index weight. The evaluation of the knowledge management ability of the library mainly adopts the questionnaire survey method, and the knowledge management status of the library is deeply understood in the form of interviews on the spot, and the obtained evaluation data and materials of the knowledge management ability of the library are organized and statistics. In order to prevent the model from remembering the patterns of the training set too deeply, to make the model more general, and to adapt to unknown data well, we use the test set to rest the model. The part of the data set that has not been used in the process of modeling and testing correction can be used to estimate the effect of the model, or to compare the effect of the model. For expert value, the value of the office is 1.02, which is approximately equal to 1, which means that the value and cost of the office are basically equal in the knowledge service of the library. The results show that the combination of knowledge management and university library management will help to form a systematic theoretical framework and behavioral model framework in the research of knowledge management models and strategies in university libraries.

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Smith, Alan D., and William T. Rupp. "Data Quality and Knowledge/Information Management in Service Operations Management." International Journal of Knowledge-Based Organizations 3, no. 3 (July 2013): 35–52. http://dx.doi.org/10.4018/ijkbo.2013070103.

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The concepts of managing data quality assurance by promoting proper safeguards to manage data quality, employee buy-in, and support from top management were illustrated in a case study of Giant Eagle, one of the largest, privately owned and family-operated companies in the U.S. and regional headquartered in Pittsburgh, PA. Specific aspects of data quality assurance, types of access, application examples (especially with its loyalty-card collection and data-mining uses that allow customers to accumulate savings specials and rewards through the fuelperks!™ and foodperks!™ incentive programs), as well as requirements for entry into knowledge-management systems were discussed through the paper. There are significant benefits, costs, and potential risks for maintaining reliable corporate data, and many organizations do not display the appropriate attitude about ensuring high-quality data, as some management may accept data errors as a cost of doing business. This present study documents the success of high volume, low profit margin grocery-based business that hinges on quality driven in product accuracy, data management, and service levels.

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Raja, K., and S. K. Srivatsa . "A Distributed Data Management in Knowledge Based Group Decision Support Systems." Journal of Applied Sciences 6, no. 1 (December 15, 2005): 27–30. http://dx.doi.org/10.3923/jas.2006.27.30.

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Rodríguez-Enríquez, Cristian Aarón, Giner Alor-Hernández, Cuauhtémoc Sánchez-Ramírez, and Guillermo Córtes-Robles. "Supply chain knowledge management: A linked data-based approach using SKOS." DYNA 82, no. 194 (December 21, 2015): 27–35. http://dx.doi.org/10.15446/dyna.v82n194.54463.

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Yoo, Sang Bong, and Yeongho Kim. "Web-based knowledge management for sharing product data in virtual enterprises." International Journal of Production Economics 75, no. 1-2 (January 2002): 173–83. http://dx.doi.org/10.1016/s0925-5273(01)00190-6.

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García-Saiz, Diego, Marta Zorrilla, and José Luis Bosque. "A clustering-based knowledge discovery process for data centre infrastructure management." Journal of Supercomputing 73, no. 1 (March 16, 2016): 215–26. http://dx.doi.org/10.1007/s11227-016-1693-z.

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Li, Yan, Manoj A. Thomas, and Kweku-Muata Osei-Bryson. "Ontology-based data mining model management for self-service knowledge discovery." Information Systems Frontiers 19, no. 4 (March 15, 2016): 925–43. http://dx.doi.org/10.1007/s10796-016-9637-y.

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Zeleny, Milan. "Integrated Knowledge Management." International Journal of Information Systems and Social Change 4, no. 4 (October 2013): 62–78. http://dx.doi.org/10.4018/jissc.2013100104.

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In this paper the author presents the inception of Integrated Knowledge Management (IKM). Knowledge management is entering its new stage, after the delays of its “definitionless”, IT-based period, when knowledge got confused with information, losing thus two decades of fruitful development. Although there now is a significant information overload, killing productivity, creativity and innovation, there can never be any knowledge overload. Knowledge is fundamentally different from information. The integration of data, information, knowledge and wisdom into a coherent and unified management support is necessary for effective transformational IKM support systems. The author draws the necessary distinction between information and knowledge and show that although it is difficult to measure the value of information, the value of knowledge can be measured simply and effectively: by the metric of added value. Several quantitative examples of knowledge measurement are also given. Once people learn how to measure knowledge, the value of the inputs of data and information can be derived. The space is thus opened for integrated knowledge management. Integrating data mining, information processing, knowledge management and wisdom attainment into a unified support system is a prerequisite for effective management in the post-crisis era of socio-economic transformation.

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Ramazanov, E. T., S. E. Sibanbaeva, and N. V. Koroleva. "Development of local knowledge cube of knowledge management system." Bulletin of the National Engineering Academy of the Republic of Kazakhstan 91, no. 1 (March 15, 2024): 103–10. http://dx.doi.org/10.47533/2024.1606-146x.11.

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The article presents the results of a study on the development of a knowledge management system in an enterprise based on the concept of knowledge management. Considered guesses on the functional scheme in the concept of the system. A scheme of the system is proposed by analogy with a local OLAP cube. A heuristic algorithm for detecting knowledge in data is constructed. A heuristic algorithm for detecting knowledge in data is constructed. A mathematical description of the algorithm based on the methods of first-order logic, the production model of knowledge representation and methods of knowledge discovery in data and intellectual analysis is given. Based on previous studies on the development of methods for extracting knowledge from data, a combined method is proposed that, when processing tables, forms a knowledge base. The inference engine checks the statement (hypothesis) in the received request from the request source. The algorithm is written in Python.

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Fitzsimmons, Sharon. "Preventing MBSE Amnesia through Role‐Based Knowledge Management." INCOSE International Symposium 33, no. 1 (July 2023): 183–96. http://dx.doi.org/10.1002/iis2.13016.

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AbstractThe problems of knowledge management (KM) have changed in the last thirty years since its introduction, as evidenced by the decrease in searches for “knowledge management” and the up‐tick in Google trends for searches on “big data.” With the introduction of Web 2.0, the amount of data available within a company has exploded, but a company's technology for navigating that data has not necessarily kept up. Consequently, companies are at high risk for organizational knowledge loss. This is particularly acute in new technologies that suffer from a labor shortage, such as Model‐Based Systems Engineering (MBSE).To reduce this risk, best practices for knowledge taxonomy and knowledge navigation are explored within this paper. A systematic literature review will generate categorization data for MBSE topics and suggest organization for a Knowledge Management System (KMS) that will facilitate user interaction. Gaps in the current research of MBSE are identified for future research. Future research may also include organizational surveys to evaluate adoption and perceived effectiveness of the recommended best practices.

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Wu, Ing-Long, and Ya-Ping Hu. "Open innovation based knowledge management implementation: a mediating role of knowledge management design." Journal of Knowledge Management 22, no. 8 (December 3, 2018): 1736–56. http://dx.doi.org/10.1108/jkm-06-2016-0238.

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Purpose Knowledge-based organizations is a new paradigm for business. Knowledge management (KM) is important for supporting core business processes. This paper aims to define an open innovation (OI)-driven KM implementation for effectively executing the support. Design/methodology/approach KM is important for supporting organizational innovation. OI plays a critical determinant role in defining the design of KM for effectively supporting OI. Further, the final goal of KM is to reach the success of OI-based KM implementation. A model is thus proposed for connecting OI as a driver to a design of KM and, in turn, KM implementation. Survey is conducted to collect data. Partial least squares is used for analysis. Findings The three processes of OI partially present significant impact on the design of KM process and, in turn, a noticeable achievement of KM implementation. The two KM processes indicate an interaction effect for reinforcement mutually. The findings provide rich evidence into the argument that OI-based KM implementation through the mediator of the design of KM process is important for a successful KM in organizations. Practical/implications While OI is a phenomenon that has increasingly become critical for the contemporary business, the design of KM mechanism needs to be adapted from the choice of OI process for guaranteeing the success of KM implementation. Originality/value Extant theories did not provide such an approach to develop an effective KM implementation in terms of the important management concept, OI, in organizations. This model empirically demonstrates its capability to work on this issue.

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Luo, Ya Ling, Shi Qiang Zhang, Su Xin Wan, and Tian Jing Zhou. "A Study of Knowledge Management System Based on Data Resources of Medical Industry." Applied Mechanics and Materials 651-653 (September 2014): 1784–89. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.1784.

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The modern hospital is a knowledge intensity organization and the knowledge management has become inevitable development trend of the hospital management. The medical profession background is the data resources of the health profession, and the quality of data resources directly influences the effect of hospital knowledge management. How to fully use the data resources of hospital for enhancing the innovation strength and the core competitive power of the hospital have already become the main focus of the health and academic professionals. The current status of medical profession data resource has been reviewed, the related concepts about data, information and knowledge have been differentiated and analyzed, and the knowledge management system based on the data resources has been constructed, which mainly includes: the hospital background, the knowledge management technology, the knowledge management mechanism, the knowledge management system and the knowledge management process etc. The primary contents of these various parts have also been outlined.

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Alijanzadeh, Ebrahim, Ali Asghar Razavi, and Safiyeh T. Ahmasebi Limuni. "Predicting Job Performance Based on Knowledge Management." Journal of Management and Accounting Studies 8, no. 4 (September 29, 2020): 34–38. http://dx.doi.org/10.24200/jmas.vol8iss4pp34-38.

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The objective of this research is to predict job performance based on knowledge management. The methodology of this research was applied according to its objective and descriptive-correlational based on the execution method. The statistical population of this research is all the librarians from public libraries of Mazandaran province with 265 members by full-census manner. 179 questionnaires were turned back. The research tool was Hosseinzadeh (2019) personal knowledge management questionnaire and Hosseini job performance questionnaire (2013). Cronbach’s alpha coefficient was used to estimate the face and content validity, and reliability of the questionnaire was estimated according to professors and specialists’ ideas which were obtained higher than 0.7 in all questionnaires. Data was analyzed using SPSS 18 software. The results of this research showed that the components of knowledge management have a positive and significant effect on job performance (P<0.01). Moreover, 37.6% of changes caused by job performance are predicted by the components of knowledge management. According to the obtained results, some suggestions are offered to improve the research variables.

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Wang, Hao, and Xianhai Meng. "BIM-Based Knowledge Management in Construction Projects." International Journal of Information Technology Project Management 9, no. 2 (April 2018): 20–37. http://dx.doi.org/10.4018/ijitpm.2018040102.

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Construction organizations have increasingly realized the importance of knowledge management (KM). They have also increasingly applied various tools and strategies to manage their knowledge. Due to the temporary nature of construction projects, however, there continues to be certain barriers and challenges of KM that are hard to overcome. This article explores the use of Building Information Modelling (BIM) to achieve better KM in UK construction organizations. First of all, why and how BIM can facilitate KM in construction projects are identified from the literature review. Secondly, a questionnaire survey in quantitative measurement is used to investigate key aspects of KM that can be improved by using BIM. The results of quantitative data analysis are further discussed with the help of literature review. It is found in this article that BIM has the potential to support KM in construction projects. In particular, BIM contributes to proactive KM, lifecycle KM, and KM processes. The findings of this article provide researchers and practitioners with a better understanding of BIM-based KM.

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Huang, Zhaoyan, and Tao Xu. "Research on Knowledge Management of Intangible Cultural Heritage Based on Linked Data." Mobile Information Systems 2022 (August 27, 2022): 1–14. http://dx.doi.org/10.1155/2022/3384391.

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At present, the protection of intangible cultural heritage has received more and more attention from all levels of society. Intangible cultural heritage is a treasure of national culture. It is an indispensable part of Chinese civilization, the crystallization of the wisdom of Chinese civilization, and represents the country’s soft power. The effective organization and management of intangible cultural heritage knowledge is the premise and foundation for the protection, dissemination, and inheritance of intangible cultural heritage. Ontology and linked data technology provide a new method and realization path for the organization and management of intangible cultural heritage knowledge. In this paper, the intangible cultural heritage knowledge is organized reasonably semantically based on the method of linked data, and the purpose is to use the structure of linked data to express the resource data of different structures in a structured manner. This paper first introduces the meaning and background of the research and analyzes the relevant research at home and abroad. Second, it introduces the related knowledge of linked data, analyzes and sorts out the elements and semantic relationship of knowledge in the field of intangible cultural heritage, and designs and constructs the ontology model of intangible cultural heritage knowledge, Finally, based on linked data technology, the process of intangible cultural heritage knowledge organization and linked data set construction is studied, including key steps such as entity to RDF, entity association, linked data storage, and publication. The application of linked data technology in the field of intangible cultural heritage knowledge organization and management can promote the standardization and standardization of intangible cultural heritage knowledge management and is of great significance to the protection and inheritance of my country’s intangible cultural heritage culture.

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Kirschfink, Heribert. "Knowledge-based system for the completion of traffic data." European Journal of Operational Research 71, no. 2 (December 1993): 247–56. http://dx.doi.org/10.1016/0377-2217(93)90052-o.

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Ngah, Rohana, and Kuan Yew Wong. "Linking knowledge management to competitive strategies of knowledge-based SMEs." Bottom Line 33, no. 1 (January 31, 2020): 42–59. http://dx.doi.org/10.1108/bl-08-2019-0105.

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Purpose This paper aims to study the effect of knowledge management in formulating competitive strategies for knowledge-based small- and medium-sized enterprises (SMEs) in Malaysia. Design/methodology/approach A quantitative approach of a survey was carried out on 135 owners and managers of knowledge-based SMEs in Malaysia. Structural equation modeling technique was used to investigate the relationship between knowledge management and competitive strategies. SmartPLS software is used to analyze the quantitative data. Only SMEs which are involved in R&D and innovation were selected to get the right respondents who meet the objective of the study. Findings The findings show mixed results. Most dimensions of knowledge management have significant relationships to differentiation strategy except for knowledge creation and knowledge acquisition, with only knowledge acquisition showing a significant relationship to cost leadership. Findings reveal that knowledge management has a positive effect on competitive strategies with more inclination toward differentiation strategy, compared to cost leadership strategy which does synchronize with their commitment in research and development and innovation. Research limitations/implications This study is only focused on knowledge-based SMEs in central Malaysia. Second, the use of a survey approach minimized the flow of information. Practical implications SMEs do have knowledge management practices but may not be exploiting it well. Mapping knowledge management practices would help SMEs identify their strengths and weaknesses to explore better business opportunities. This proves that SMEs are leveraging their resources through knowledge application, dissemination, storage and protection to be different than their competitors. However, their apparent lack of knowledge in knowledge acquisition and knowledge creation should be addressed accordingly, as it is important for their future continuous sustainability. Originality/value This paper contributes to the literature of knowledge management relating to competitive strategies in SMEs. The study offers insights on how competitive strategies were formulated through knowledge management. The mixed results reveal a new different outlook of knowledge management relating to competitive strategies.

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Hadj Sassi, Mohamed Saifeddine, Lamia Chaari Fourati, Manel Zekri, and Sadok Ben Yahia. "Knowledge Management Process for Air Quality Systems based on Data Warehouse Specification." Procedia Computer Science 192 (2021): 29–38. http://dx.doi.org/10.1016/j.procs.2021.08.004.

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Heinrichs, John H., and Jeen-Su Lim. "Integrating web-based data mining tools with business models for knowledge management." Decision Support Systems 35, no. 1 (April 2003): 103–12. http://dx.doi.org/10.1016/s0167-9236(02)00098-2.

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Mouton, A. M., B. De Baets, and P. L. M. Goethals. "Knowledge-based versus data-driven fuzzy habitat suitability models for river management." Environmental Modelling & Software 24, no. 8 (August 2009): 982–93. http://dx.doi.org/10.1016/j.envsoft.2009.02.005.

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Albert, T. M. "Knowledge-based geographic information systems (KBGIS): New analytic and data management tools." Mathematical Geology 20, no. 8 (November 1988): 1021–35. http://dx.doi.org/10.1007/bf00892977.

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Goodman, A. M., R. M. Haralick, and L. G. Shapiro. "Knowledge-based computer vision-integrated programming language and data management system design." Computer 22, no. 12 (December 1989): 43–54. http://dx.doi.org/10.1109/2.42031.

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Walsh, John N., and Jamie O’Brien. "A Knowledge-Based Framework for Service Management." Journal of Information & Knowledge Management 16, no. 04 (November 23, 2017): 1750039. http://dx.doi.org/10.1142/s0219649217500393.

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The purpose of this paper is to investigate how information and communication technologies are used for service standardisation, customisation, and modularisation by knowledge-intensive service firms through the development and empirical validation of a knowledge-based framework. This paper uses 59 in-depth interviews, observational data, and document analysis from case studies of three service-related departments in high-technology, multinational knowledge-intensive business services (KIBSs). Prior research does not conceptualise the relationships between service customisation, standardisation and modularisation. This paper seeks to overcome this gap by integrating insights from research on the role played by both knowledge and information and communication technologies (ICTs) to construct and validate a framework to deal with this gap. It outlines the implications for service firms’ use of ICT to deal with increasing knowledge intensity as well as indicating the circumstances under which service knowledge is best customised, standardised and modularised. Further testing in other industries would prove useful in extending the usefulness and applicability of the findings. The originality of the paper lies in developing and validating the first framework to outline the relationship between how service knowledge is customised, standardised or modularised and indicating the associated issues and challenges. It emphasises the role of knowledge and technology. The value of this framework increases as more firms deal with increasing knowledge intensity in the services they provide and in their use of ICTs to reap the benefits of appropriate knowledge reuse.

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Kurniawan, Yohannes. "Knowledge Management for Manufacturing Process Based on Knowledge Lifecycle to Enhance Data Sharing (Perception: Application and Benefits)." Applied Mechanics and Materials 391 (September 2013): 366–71. http://dx.doi.org/10.4028/www.scientific.net/amm.391.366.

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The importance of effective knowledge management is well recognized in a number of manufacturing companies. There is still, however, a clear need to further reinforce applications of knowledge management tools by effectively solving fundamental and specific problems related to knowledge management practice in manufacturing industry. The paper presents the concept specifically for the business case at the palm oil companies, where the tools are used to transfer the knowledge over different life cycles of the production process to enhance data sharing. This paper put forward a knowledge management operation mode for manufacturing process based on knowledge lifecycle, and research the operation mode from the points of the knowledge acquisition stage, knowledge sharing stage, and knowledge utilization stage. This paper gives a value to manufacturing companies to develop initiatives to share knowledge to achieve business objectives.

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Gulnoza, Rakhimova. "Analyzing the Impact of Distributed Data and Knowledge-Based Systems on Knowledge Management: A Systematic Mapping Study." International Journal of Advances in Applied Computational Intelligence 4, no. 2 (2023): 41–47. http://dx.doi.org/10.54216/ijaaci.040205.

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Knowledge management is a critical aspect of modern organizations seeking to harness their collective intelligence and remain competitive in a dynamic business environment. With the advent of distributed data and knowledge-based systems, the landscape of knowledge management is undergoing significant transformations. This systematic mapping study aims to provide a comprehensive overview of the current state of knowledge management within the context of distributed environments and knowledge-based systems, shedding light on the key trends and challenges that shape this evolving field. Our study employs a systematic mapping methodology to analyze a wide array of scholarly articles, conference papers, and research reports. Through a structured review process, we identify and categorize relevant publications, facilitating a holistic understanding of the relationships between distributed data and knowledge-based systems in the realm of knowledge management. By mapping the existing literature, we uncover emerging themes, gaps, and areas of interest in this interdisciplinary domain. Key findings reveal the increasing role of distributed systems in enhancing knowledge sharing, collaboration, and decision-making processes. However, challenges related to data security, interoperability, and system integration also surface as important considerations. The systematic mapping study not only offers insights into the current state of knowledge management but also provides a foundation for future research directions and practical implications for organizations striving to optimize knowledge utilization in distributed settings. In conclusion, this research contributes to a deeper understanding of how distributed data and knowledge-based systems are shaping knowledge management practices and provides a roadmap for scholars and practitioners alike to navigate this evolving terrain.

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Koltay, Tibor. "Data governance, data literacy and the management of data quality." IFLA Journal 42, no. 4 (November 30, 2016): 303–12. http://dx.doi.org/10.1177/0340035216672238.

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Data governance and data literacy are two important building blocks in the knowledge base of information professionals involved in supporting data-intensive research, and both address data quality and research data management. Applying data governance to research data management processes and data literacy education helps in delineating decision domains and defining accountability for decision making. Adopting data governance is advantageous, because it is a service based on standardised, repeatable processes and is designed to enable the transparency of data-related processes and cost reduction. It is also useful, because it refers to rules, policies, standards; decision rights; accountabilities and methods of enforcement. Therefore, although it received more attention in corporate settings and some of the skills related to it are already possessed by librarians, knowledge on data governance is foundational for research data services, especially as it appears on all levels of research data services, and is applicable to big data.

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Zhai, Jun, Jian Feng Li, and Yan Chen. "Knowledge Modeling of Product Data Based on Fuzzy Ontology." Applied Mechanics and Materials 26-28 (June 2010): 347–51. http://dx.doi.org/10.4028/www.scientific.net/amm.26-28.347.

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Ontology is the basis of knowledge modeling on the Semantic Web, and fuzzy ontology is an extension of domain ontology for solving the uncertainty problems. Ontology-base knowledge modeling of product data management (PDM) is meaningful for product design and trade etc. In order to handle fuzzy phenomenon and uncertainty of product knowledge, this paper proposes a series of fuzzy ontology models that consists of fuzzy domain ontology and fuzzy linguistic variable ontologies. Then, a fuzzy ontology framework is presented, including three parts: concepts, properties of concepts and values of properties. The application, which uses fuzzy ontology to model product knowledge, shows that these models can overcome the localization of other fuzzy ontology models, and this research facilitates the fuzzy knowledge sharing and reuse for PDM on the Semantic Web.

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R. Dhaya, R. Dhaya, R. Kanthavel R. Dhaya, and Kanagaraj Venusamy R. Kanthavel. "AI Based Learning Model Management Framework for Private Cloud Computing." 網際網路技術學刊 23, no. 7 (December 2022): 1633–42. http://dx.doi.org/10.53106/160792642022122307017.

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Artificial Intelligence (AI) systems are computational simulations that are “educated” using knowledge and individual expert participation to replicate a decision that a professional would make provided the same data. A model tries to simulate a specific decision loop that several scientists would take if they had access to all kinds of knowledge. To convey a model, you make a model asset in AI Platform Prediction, make a variant of that model and, at that point, interface the model form to the model record put away in Cloud Storage. AI and DB information sharing are essential for cutting-edge processing for DBMS innovation. The inspirations promoting their incorporation advances incorporate the requirement for admittance to a lot of data that is shared information handling, effective administration of data as information, and astute preparation of information. Notwithstanding these inspirations, the plan for a smart information base interface (IDI) was likewise spurred by the craving to save the considerable speculation spoke to by most existing data sets. A few general ways to deal with the connectivity of AI and databases and different improvements in the area of clever information bases were already examined and announced in this paper.

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Sousa, Cristóvão, Daniel Teixeira, Davide Carneiro, Diogo Nunes, and Paulo Novais. "Knowledge-based decision intelligence in street lighting management." Integrated Computer-Aided Engineering 29, no. 2 (March 14, 2022): 189–207. http://dx.doi.org/10.3233/ica-210671.

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As the availability of computational power and communication technologies increases, Humans and systems are able to tackle increasingly challenging decision problems. Taking decisions over incomplete visions of a situation is particularly challenging and calls for a set of intertwined skills that must be put into place under a clear rationale. This work addresses how to deliver autonomous decisions for the management of a public street lighting network, to optimize energy consumption without compromising light quality patterns. Our approach is grounded in an holistic methodology, combining semantic and Artificial Intelligence principles to define methods and artefacts for supporting decisions to be taken in the context of an incomplete domain. That is, a domain with absence of data and of explicit domain assertions.

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Böckenholt, I., M. Both, and W. Gaul. "A knowledge-based system for supporting data analysis problems." Decision Support Systems 5, no. 4 (December 1989): 345–54. http://dx.doi.org/10.1016/0167-9236(89)90014-6.

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Freeze, Ronald D., and Uday Kulkarni. "Knowledge management capability: defining knowledge assets." Journal of Knowledge Management 11, no. 6 (October 30, 2007): 94–109. http://dx.doi.org/10.1108/13673270710832190.

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PurposeThe purpose of this paper is to show that separate sources of knowledge are identified, described and clearly defined as organizational intangible knowledge assets. These knowledge assets are referred to as knowledge capabilities (KCs). knowledge management (KM) is utilized to leverage these assets with a view to systematic improvement in the process of achieving increased firm performance.Design/methodology/approachIn this paper knowledge capabilities are described in terms of their knowledge life cycle, tacit/implicit/explicit nature of knowledge, technology and organizational processes that encompass a firm's human capital identified as knowledge workers.FindingsThe paper finds that five knowledge capability are presented and described as expertise, lessons learned, policies and procedures, data and knowledge documents.Research limitations/implicationsThe paper shows that knowledge assets can be measured and improved in order to investigate causal relationships with identified measures of performance.Practical implicationsThe paper shows that by explicitly describing these knowledge assets, the KM activities within organizations can more effectively leverage knowledge and improve performance.Originality/valueThe paper sees that by drawing from both resource based and organizational learning literature, a knowledge management framework is presented to describe distinctly separate sources of knowledge within organizations. These knowledge sources are constructed as knowledge capabilities that can allow the assessment of organizational knowledge assets.

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Alryalat, Haroun, and Samer Al Hawari. "Towards Customer Knowledge Relationship Management: Integrating Knowledge Management and Customer Relationship Management Process." Journal of Information & Knowledge Management 07, no. 03 (September 2008): 145–57. http://dx.doi.org/10.1142/s0219649208002020.

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Due to the strong competition that exists among organisations and the rapid change in the business environment, knowledge has turned out to become a key source for organisations to enhance the competitive advantage. Integrating Knowledge Management (KM) and Customer Relationship Management (CRM) process is a new research area, therefore, scientific research and literature around it remain limited. In addition, the impact of KM process on customer acquisition, retention, and expansion to improve customer satisfaction remains under study and report. The aim of this paper is to present a conceptual framework of KM integrated with CRM called Customer Knowledge Relationship Management (CKRM) Process depending on analysis of various models presented in KM and CRM. The main highlighting is laid upon the concepts of the concept of customer knowledge (knowledge about customer, knowledge for customer, knowledge from customer). Therefore, this paper contributes to the development of KM process (Knowledge Process about Customer, Knowledge Process for Customer, and Knowledge Process from Customer). The paper investigated how the companies in Jordan developed KM process to improvement the CRM process. Based on data collected from the company, results from analysis indicated that the KM process had a positive effect on CRM process.

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Macbeth, Sam, and Jeremy V. Pitt. "Self-organising management of user-generated data and knowledge." Knowledge Engineering Review 30, no. 3 (November 19, 2014): 237–64. http://dx.doi.org/10.1017/s026988891400023x.

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AbstractThe proliferation of sensor networks, mobile and pervasive computing has provided the technological push for a new class of participatory-sensing applications, based on sensing and aggregating user-generated content, and transforming it into knowledge. However, given the power and value of both the raw data and the derived knowledge, to ensure that the generators are commensurate beneficiaries, we advocate an open approach to the data and intellectual property rights by treating user-generated content, as well as derived information and knowledge, as a common-pool resource. In this paper, we undertake an extensive review of experimental, commercial and social participatory sensory applications, from which we identify that a decentralised, community-oriented governance model is required to support this approach. Furthermore, we show that Ostrom’s institutional analysis and development framework, in conjunction with a framework for self-organising electronic institutions, can be used to give both an architecture and algorithmic base for the requisite governance model, in terms of operational and collective-choice rules specified in computational logic. This provides, we believe, the foundations for engineering knowledge commons for the next generation of participatory-sensing applications, in which the data generators are also the primary beneficiaries.

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