Relevant bibliographies by topics / Knowledge based data management

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Academic literature on the topic 'Knowledge based data management'

Author: Grafiati
Published: 20 April 2024

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Journal articles on the topic "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 (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 (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 (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 (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 (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 (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 (2023): 387–97. http://dx.doi.org/10.53106/199115992023063403029.

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

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