Готові списки джерел за темами / Knowledge based data management

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Добірка наукової літератури з теми "Knowledge based data management"

Автор: Grafiati
Опубліковано: 20 квітня 2024

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Статті в журналах з теми "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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Анотація:
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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Анотація:
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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Анотація:
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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Анотація:
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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Анотація:
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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Анотація:
<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>&nbsp;</p>
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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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Дисертації з теми "Knowledge based data management"

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Andersson, Kent. "Knowledge Technology Applications for Knowledge Management." Doctoral thesis, Uppsala : Institutionen för informationsvetenskap, Univ. [distributör], 2000. http://w3.ub.uu.se/fulltext/91-506-1437-1.pdf.

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Maimone, Anthony. "Data and Knowledge Acquisition in Case-based Reasoning for Diabetes Management." Ohio University / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1156200718.

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Adam, Elena Daniela. "Knowledge management cloud-based solutions in small enterprises." Thesis, Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Informatik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-28275.

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Анотація:
Purpose – The aim of this study is to determine if adopting cloud-based knowledge management is a viable way forward for small enterprises and to investigate what are the main factors that might facilitate or inhibit these companies to adopt such solutions.Design/Methodology/Approach - In order to understand the main factors that could influence the adoption of a cloud-based knowledge management solution in small enterprises, I used a qualitative research approach, based on four semi-structured interviews with four small companies from Romania.Findings – The results of the study suggest that implementing knowledge management in the cloud is particularly beneficial for small enterprises, as a lower investment in IT infra-structure can create a competitive advantage and help them implement knowledge man-agement activities as a strategic resource. Moreover, the study suggests that relative ad-vantage, compatibility and technology readiness will influence companies in moving their knowledge to the cloud. Also, the study reveals that companies which did not adopt such a solution had already established systems for managing knowledge and failed to realize its benefits, it was not perceived as needed, they had a low level of awareness or cited security and uncertainty reasons.
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Goasdoué, François. "Knowledge Representation meets DataBases for the sake of ontology-based data management." Habilitation à diriger des recherches, Université Paris Sud - Paris XI, 2012. http://tel.archives-ouvertes.fr/tel-00759274.

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Анотація:
This Habilitation thesis outlines my research activities carried out as an Associate Professor at Univ. Paris-Sud and Inria Saclay Île-de-France. During this period, from 2003 to early 2012, my work was - and still is - at the interface between Knowledge Representation and Databases. I have mainly focused on ontology-based data management using the Semantic Web data models promoted by W3C: the Resource Description Framework (RDF) and the Web Ontology Language (OWL). In particular, my work has covered (i) the design, (ii) the optimization, and (iii) the decentralization of ontology-based data management techniques in these data models. This thesis briefly reports on the results obtained along these lines of research.
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Kairouz, Joseph. "Patient data management system medical knowledge-base evaluation." Thesis, McGill University, 1996. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=24060.

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The purpose of this thesis is to evaluate the medical data management expert system at the Pediatric Intensive Care Unit of the Montreal Children's Hospital. The objective of this study is to provide a systematic method to evaluate and, progressively improve the knowledge embedded in the medical expert system.
Following a literature survey on evaluation techniques and architecture of existing expert systems, an overview of the Patient Data Management System hardware and software components is presented. The design of the Expert Monitoring System is elaborated. Following its installation in the intensive Care Unit, the performance of the Expert Monitoring System is evaluated, operating on real vital sign data and corrections were formulated. A progressive evaluation technique, new methodology for evaluating an expert system knowledge-base is proposed for subsequent corrections and evaluations of the Expert Monitoring System.
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MILIA, GABRIELE. "Cloud-based solutions supporting data and knowledge integration in bioinformatics." Doctoral thesis, Università degli Studi di Cagliari, 2015. http://hdl.handle.net/11584/266783.

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In recent years, computer advances have changed the way the science progresses and have boosted studies in silico; as a result, the concept of “scientific research” in bioinformatics has quickly changed shifting from the idea of a local laboratory activity towards Web applications and databases provided over the network as services. Thus, biologists have become among the largest beneficiaries of the information technologies, reaching and surpassing the traditional ICT users who operate in the field of so-called "hard science" (i.e., physics, chemistry, and mathematics). Nevertheless, this evolution has to deal with several aspects (including data deluge, data integration, and scientific collaboration, just to cite a few) and presents new challenges related to the proposal of innovative approaches in the wide scenario of emergent ICT solutions. This thesis aims at facing these challenges in the context of three case studies, being each case study devoted to cope with a specific open issue by proposing proper solutions in line with recent advances in computer science. The first case study focuses on the task of unearthing and integrating information from different web resources, each having its own organization, terminology and data formats in order to provide users with flexible environment for accessing the above resources and smartly exploring their content. The study explores the potential of cloud paradigm as an enabling technology to severely curtail issues associated with scalability and performance of applications devoted to support the above task. Specifically, it presents Biocloud Search EnGene (BSE), a cloud-based application which allows for searching and integrating biological information made available by public large-scale genomic repositories. BSE is publicly available at: http://biocloud-unica.appspot.com/. The second case study addresses scientific collaboration on the Web with special focus on building a semantic network, where team members, adequately supported by easy access to biomedical ontologies, define and enrich network nodes with annotations derived from available ontologies. The study presents a cloud-based application called Collaborative Workspaces in Biomedicine (COWB) which deals with supporting users in the construction of the semantic network by organizing, retrieving and creating connections between contents of different types. Public and private workspaces provide an accessible representation of the collective knowledge that is incrementally expanded. COWB is publicly available at: http://cowb-unica.appspot.com/. Finally, the third case study concerns the knowledge extraction from very large datasets. The study investigates the performance of random forests in classifying microarray data. In particular, the study faces the problem of reducing the contribution of trees whose nodes are populated by non-informative features. Experiments are presented and results are then analyzed in order to draw guidelines about how reducing the above contribution. With respect to the previously mentioned challenges, this thesis sets out to give two contributions summarized as follows. First, the potential of cloud technologies has been evaluated for developing applications that support the access to bioinformatics resources and the collaboration by improving awareness of user's contributions and fostering users interaction. Second, the positive impact of the decision support offered by random forests has been demonstrated in order to tackle effectively the curse of dimensionality.
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White, Andrew Murray. "The application of knowledge-based techniques to constraint management in engineering databases." Thesis, Georgia Institute of Technology, 1987. http://hdl.handle.net/1853/16894.

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Gebhardt, Johan Wilhelm Ludwig. "A comparative study of the business value of computer-based mapping tools in knowledge management." Thesis, Stellenbosch : Stellenbosch University, 2008. http://hdl.handle.net/10019.1/18151.

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Thesis (MBA)--Stellenbosch University, 2008.
ENGLISH ABSTRACT: In the past decade or two companies started to realise that competitive advantage is not only achieved by optimising their business value chain, but also in managing the knowledge in the company. This led to the development of different knowledge management models and to millions of dollars being spent on knowledge management implementations across the world. Although there were huge successes, a large number of initiatives were spectacular failures - believed to be mainly caused by the linear method of capturing and presenting knowledge. Computer-based mapping tools is a new generation of personal computer (PC) based tools that allow people to present knowledge graphically. Since the focus of most research into computer-based mapping tools has been on the educational use of mapping tools, the focus of this study will be on the business use of these tools. Thus a number of common, off-the-shelf computer-based mapping tools were evaluated to determine whether they can add business value. From the evaluation a decision matrix was developed to assist knowledge workers in selecting the best tool for a specific application. The primary activities of the knowledge value chain model were investigated to select a series of business activities where the use of computer-based mapping tools could possibly generate more business value in the execution of the business activity. These activities were then measured against a set of criteria that was developed in order to evaluate the different computer-based mapping tools. It was found that the selected software applications could be clearly separated based upon their theoretical and philosophical backgrounds into concept mapping tools and mind mapping tools. It was further found that the possible business value that could be derived through the use of these tools is more dependent on the selection of the correct type of tool, than on the selection of a specific software package. Lastly it was found that concept mapping tools could be used across a broader spectrum of business activities. The research also reached the conclusion that the use of concept mapping tools will possibly add more value to a business than the use of mind mapping software.
AFRIKAANSE OPSOMMING: Gedurende die afgelope dekade of wat het maatskappye al meer begin besef dat hulle mededingingsvoordeel nie net geleë is in hoe goed hulle die besigheid se waardeketting kan optimiseer nie, maar dat die kennis in die maatskappy ook beter bestuur moet word. Dit het tot gevolg gehad dat 'n aansienlike hoeveelheid kennis bestuursmodelle ontwikkel is en dat miljoene dollar gespandeer is op die implementering van kennis bestuurstelsels. Ten spyte van groot suksesse wat behaal is, was daar ook totale mislukkings. Die vermoede bestaan dat een van die redes vir die mislukkings die liniêre manier is waarop kennis vasgevang en aangebied is. Rekenaar-gebaseerde karteringspakkette is 'n nuwe generasie van persoonlike rekenaar programmatuur wat gebruikers in staat stel om kennis grafies voor te stel. Die meeste navorsing oor die gebruik van rekenaar-gebaseerde karteringspakkette het egter op die opvoedkundige aspek daarvan gefokus. In hierdie navorsing val die fokus eerder op die besigheidsgebruik van sodanige gereedskap. 'n Aantal algemeen beskikbare, van-die-rak pakkette is ge-evalueër om vas te stel of hulle waarde tot 'n besigheid kan toevoeg. Vanuit hierdie evaluering is In keuse-matriks saamgestel om kenniswerkers in staat te stel om die beste pakket vir 'n spesifieke besigheidsaktiwiteit te kies. Die primêre aktiwiteite van die kennis waardeketting model is ondersoek ten einde 'n aantal besigheidsaktiwiteite te kan selekteer wat moontlik meer waarde tot die besigheid kan toevoeg deur die gebruik van rekenaar-gebaseerde karteringspakkette. Die geselekteerde aktiwiteite is gemeet teen 'n reeks kriteria wat ontwikkel is om die verskillende rekenaar-gebaseerde karteringspakette teen mekaar op te weeg. Die navorsing het bevind dat die geselekteerde programmatuur pakkette hoofsaaklik in twee groepe val op grond van hulle teoretiese en filosofiese funderings, naamlik konsepkaarte en gedagtekaarte. Verder is vasgestel dat meer besigheidswaarde ontsluit word deur die keuse van die regte tipe programmatuur vir 'n spesifieke aanwending as deur die keuse van In spesifieke programmatuur pakket. Laastens is bevind dat konsepkaarte oor 'n wyer verspreiding van besigheidsaktiwiteite gebruik kan word. Eventueel kan afgelei word dat die gebruik van konsepkaarte meer waarde tot 'n besigheid sal toevoeg as die gebruik van gedagtekaarte.
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Brooks, Brad Walton. "Automated Data Import and Revision Management in a Product Lifecycle Management Environment." Diss., CLICK HERE for online access, 2009. http://contentdm.lib.byu.edu/ETD/image/etd3182.pdf.

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Meng, Changping, and 蒙昌平. "Discovering meta-paths in large knowledge bases." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/209504.

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A knowledge base, such as Yago or DBpedia, can be modeled as a large graph with nodes and edges annotated with class and relationship labels. Recent work has studied how to make use of these rich information sources. In particular, meta-paths, which represent sequences of node classes and edge types between two nodes in a knowledge base, have been proposed for such tasks as information retrieval, decision making, and product recommendation. Current methods assume meta-paths are found by domain experts. However, in a large and complex knowledge base, retrieving meta-paths manually can be tedious and difficult. We thus study how to discover meta-paths automatically. Specifically, users are asked to provide example pairs of nodes that exhibit high proximity. We then investigate how to generate meta-paths that can best explain the relationship between these node pairs. Since this problem is computationally intractable, we propose a greedy algorithm to select the most relevant meta-paths. We also present a data structure to enable efficient execution of this algorithm. We further incorporate hierarchical relationships among node classes in our solutions. Finally, we propose an effective similarity join algorithm in order to generate more node pairs using these meta-paths. Extensive experiments on real knowledge bases show that our approach captures important meta-paths in an efficient and scalable manner.
published_or_final_version
Computer Science
Master
Master of Philosophy
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Книги з теми "Knowledge based data management"

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Hawryszkiewycz, I. T. Knowledge management: Organizing knowledge based enterprises. Houndmills, Basingstoke, Hampshire [England]: Palgrave Macmillan, 2010.

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Hawryszkiewycz, Igor. Knowledge management: Organizing knowledge based enterprises. New York: Palgrave Macmillan, 2009.

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Hawryszkiewycz, I. T. Knowledge management: Organizing knowledge based enterprises. Houndmills, Basingstoke, Hampshire [England]: Palgrave Macmillan, 2010.

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4

Mockler, Robert J. Knowledge-based systems for management decisions. Englewood Cliffs, N.J: Prentice Hall, 1989.

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Knowledge management: Organizing knowledge based enterprises. New York: Palgrave Macmillan, 2009.

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Knowledge-based systems for management decisions. Englewood Cliffs, N.J: Prentice Hall, 1989.

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Girard, John P., Deanna Klein, and Kristi Berg. Strategic data-based wisdom in the big data era. Hershey, PA: Information Science Reference, an imprint of IGI Global, 2015.

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8

R, Hurson A., and Pakzad S. H, eds. Parallel architectures for data/knowledge-based systems. Los Alamitos, Calif: IEEE Computer Society Press, 1995.

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Mockler, R. J. Knowledge-based systems for management decisions. Englewood Cliffs,N.J: Prentice-Hall, 1989.

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Mockler, R. J. Knowledge-based systems for management decisions. Englewood Cliffs, NJ: Prentice Hall International, 1989.

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Частини книг з теми "Knowledge based data management"

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Hirz, Mario, Wilhelm Dietrich, Anton Gfrerrer, and Johann Lang. "Knowledge-Based Engineering Data Management." In Integrated Computer-Aided Design in Automotive Development, 393–408. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-11940-8_8.

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Gómez, Jorge Marx, and Peter Cissek. "Data Warehouse Based Knowledge Management Controlling." In Communications in Computer and Information Science, 119–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16419-4_12.

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Puentes Morantes, Jorge Leonardo, Nancy Yurani Ortiz Guevara, and José Ignacio Rodriguez Molano. "Model Proposal of Knowledge Management for Technology Based Companies." In Data Mining and Big Data, 67–74. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40973-3_7.

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Dong, Jichang, K. K. Lai, and Shouyang Wang. "XML-Based Schemes for Business Project Portfolio Selection." In Data Mining and Knowledge Management, 254–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30537-8_28.

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Curé, Olivier. "Semi-automatic Data Migration in a Self-medication Knowledge-Based System." In Professional Knowledge Management, 373–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11590019_42.

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Lotov, Alexander V., Alexander A. Kistanov, and Alexander D. Zaitsev. "Visualization-Based Data Mining Tool and Its Web Application." In Data Mining and Knowledge Management, 1–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30537-8_1.

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Cheng, Junyu, and Hanhui Hu. "Development of Enterprises’ Capability Based on Cooperative Knowledge Network." In Data Mining and Knowledge Management, 187–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30537-8_20.

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Wang, Zhenyuan, Hai-Feng Guo, Yong Shi, and Kwong-Sak Leung. "A Hybrid Nonlinear Classifier Based on Generalized Choquet Integrals." In Data Mining and Knowledge Management, 34–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30537-8_4.

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Hua, Zhongsheng, and Liang Liang. "Heuristics to Scenario-Based Capacity Expansion Problem of PWB Assembly Systems." In Data Mining and Knowledge Management, 135–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30537-8_15.

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Xuan, Dung Nguyen, Ladjel Bellatreche, and Guy Pierra. "A Versioning Management Model for Ontology-Based Data Warehouses." In Data Warehousing and Knowledge Discovery, 195–206. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11823728_19.

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Тези доповідей конференцій з теми "Knowledge based data management"

1

Handley, Thomas H., and Y. Philip Li. "DataHub: Knowledge-based data management for data discovery." In The earth and space science information system (ESSIS). AIP, 1993. http://dx.doi.org/10.1063/1.44479.

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2

Ivanova-Kadiri, Ivelina. "Customer Genetic Data for Sustainability and Innovation Management." In 9th International Scientific Conference ERAZ - Knowledge Based Sustainable Development. Association of Economists and Managers of the Balkans, Belgrade, Serbia, 2023. http://dx.doi.org/10.31410/eraz.s.p.2023.169.

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Анотація:
The availability of affordable genetic testing has enabled the col­lection of vast amounts of genetic data, creating new opportunities for mar­keting management. The use of genetic data empowers companies to de­velop personalized products and services and enhance customer relation­ship management. This, in turn, creates a competitive advantage for boost­ing companies’ strategic market positioning by enhancing their sustainabil­ity and innovation policies. This review paper aims to explore how business­es can leverage genetic data for sustainability and innovation management. The framework presented outlines the integration of genetic data into differ­ent stages of sustainable product development thus allowing for precision targeting through responsible innovation management. The paper also ex­amines the potential ethical and legal implications of using genetic data in marketing management.
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Baigorri, A., J. Villadangos, J. J. Astrain, and A. Córdoba. "A medical knowledge management system based on expert tagging (MKMST)." In DATA 2013. Southampton, UK: WIT Press, 2013. http://dx.doi.org/10.2495/data130201.

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Wang, Fei, Hao Fan, and Gang Liu. "Big Data Knowledge Service Framework based on Knowledge Fusion." In 8th International Conference on Knowledge Management and Information Sharing. SCITEPRESS - Science and Technology Publications, 2016. http://dx.doi.org/10.5220/0006036301160123.

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Seman, Ali, Zainab Abu Bakar, and Azizian Mohd Sapawi. "Centre-based clustering for Y-Short Tandem Repeats (Y-STR) as numerical and categorical data." In Knowledge Management (CAMP). IEEE, 2010. http://dx.doi.org/10.1109/infrkm.2010.5466953.

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Chen, Yang, and Daisy Zhe Wang. "Knowledge expansion over probabilistic knowledge bases." In SIGMOD/PODS'14: International Conference on Management of Data. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2588555.2610516.

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Quamar, Abdul, Chuan Lei, Dorian Miller, Fatma Ozcan, Jeffrey Kreulen, Robert J. Moore, and Vasilis Efthymiou. "An Ontology-Based Conversation System for Knowledge Bases." In SIGMOD/PODS '20: International Conference on Management of Data. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3318464.3386139.

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Luo, Peng, Jiayi Chen, and Jian Li. "Enterprise big data management based on Knowledge Graph." In ICCBD 2021: 2021 4th International Conference on Computing and Big Data. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3507524.3507534.

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Zhang, Lingling, Xiao Wang, Liang Zhang, Yibing Chen, and Yong Shi. "Context-based knowledge discovery and its application." In the Data Mining and Intelligent Knowledge Management Workshop. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2462130.2462137.

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Citerne, A. "Knowledge-Based Well Data Management and Graphical Interface." In International Meeting on Petroleum Engineering. Society of Petroleum Engineers, 1988. http://dx.doi.org/10.2118/17611-ms.

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Звіти організацій з теми "Knowledge based data management"

1

Kremp, Elisabeth, and Jacques Mairesse. Knowledge Management, Innovation, and Productivity: A Firm Level Exploration Based on French Manufacturing CIS3 Data. Cambridge, MA: National Bureau of Economic Research, January 2004. http://dx.doi.org/10.3386/w10237.

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2

Kavalsky, Basil, Jose Ignacio Sembler, Monika Huppi, and Diether Beuermann. IDB-9: Knowledge Products. Inter-American Development Bank, March 2013. http://dx.doi.org/10.18235/0010524.

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Анотація:
The Inter-American Development Bank (IDB) has a diverse set of knowledge products: economic and sector work, knowledge products associated with loans, and a variety of non-reimbursable technical cooperation products (TCs) funded by either income from ordinary capital or donor trust funds. With growing emphasis on IDB's capacity to make effective non-financial contributions to its borrowers, there has been substantial focus on how to increase the development impact of TCs. This involves balancing client demand and the institution's own view of country and regional priorities. IDB has struggled to achieve this balance and to put in place supporting systems to manage and monitor TCs effectively. Since 2008 the definition and classification of TCs and guidance on how to manage them has changed almost yearly. The IDB-9 Agreement reflected both a general intention to make this area of IDB's work more effective, and a more specific mandate to look into the possibility of providing fee-based services in the future. This evaluation identified several areas in which there are continuing issues pertaining to TCs, some of which also relate to IDB's knowledge products more generally. First, the strategic alignment between TCs and lending products needs to be strengthened; it has improved somewhat in annual country programs, but needs to be strengthened in the periodic Country Strategies and overall. Second, the management of TCs is complicated by the various constraints imposed on the inter- and intrasectoral allocation of both ordinary capital and donor trust funds, and IDB Management's efforts to find ways to work around these constraints have met with limited success, as evidenced by the substantial under-utilization of available resources. Third, IDB does not have a system for monitoring and reporting on the results of TCs, and this needs to be rectified, given their importance to development effectiveness. Fourth, proposals for fee-based services have been slow to get off the ground, though some progress now appears likely. Finally, the accessibility of reports and studies is a long-standing problem, though Management has recently begun to address this more systematically with the development of a new system for data access.
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3

Arndt, Witold. A survey on research data management practices among researchers in the Helmholtz Association. HMC Office, GEOMAR Helmholtz Centre for Ocean Research Kiel, 2022. http://dx.doi.org/10.3289/hmc_publ_05.

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Анотація:
Annotation of research data with rich metadata is important to make that data findable, accessible, interoperable, and reusable (Wilkinson et al. [2016]). This ensures the conducted research data is durable. Within the Helmholtz Association, the Helmholtz Metadata Collaboration (HMC) coordinates the mission to enrich Helmholtz-based research data with metadata by providing (information about) technical solutions, advice and ensuring uniform scientific standards for the use of metadata. In 2021, HMC conducted its first community survey to align its services with the needs of Helmholtz researchers. A question catalogue with 49 (sub-)questions was designed and disseminated among researchers in all six Helmholtz research fields. The conditional succession of the questions was aligned with predetermined expertise levels ("no prior knowledge", "intermediate prior knowledge", "high level of prior knowledge"). 631 completed survey replies were obtained for analysis. The HMC Community Survey 2021 provides insight into the management of research data as well as the data publication practices of researchers in the Helmholtz Association. The characterization of research-field-dependent communities will enable HMC to further develop targeted, community-directed support for the documentation of research data with metadata.
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Borgwardt, Stefan, Ismail Ilkan Ceylan, and Thomas Lukasiewicz. Ontology-Mediated Query Answering over Log-Linear Probabilistic Data. Technische Universität Dresden, 2019. http://dx.doi.org/10.25368/2023.221.

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Large-scale knowledge bases are at the heart of modern information systems. Their knowledge is inherently uncertain, and hence they are often materialized as probabilistic databases. However, probabilistic database management systems typically lack the capability to incorporate implicit background knowledge and, consequently, fail to capture some intuitive query answers. Ontology-mediated query answering is a popular paradigm for encoding commonsense knowledge, which can provide more complete answers to user queries. We propose a new data model that integrates the paradigm of ontology-mediated query answering with probabilistic databases, employing a log-linear probability model. We compare our approach to existing proposals, and provide supporting computational results.
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5

Jia, Lili, and Steve Evans. Prevent food allergy alerts: an incentive-based approach. Food Standards Agency, February 2022. http://dx.doi.org/10.46756/sci.fsa.flm647.

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The majority of UK food recalls are due to allergen mislabelling, misleading allergen claims and/or the unintentional presence of allergens – representing a significant food safety risk and cost to industry. Labelling legislation must be followed to ensure food is safe and what it says it is, and this requires good allergen management and accurate allergen information communication down the supply chain. Distilling this information accurately, to inform labelling and/or communication of allergen information, can be particularly challenging for small to medium food businesses due to the low adoption of advanced labelling technology. In November 2018, a joint FSA/EIT (European Institute of Innovation & Technology) workshop discussed potential solutions to tackling the increase in food allergen mislabelling incidents. It was concluded that the situation could be improved by developing accessible and affordable tools for food businesses, to aid in the automation of food data collection, validation and management. As a result, the FSA are funding this initial development project that aims to develop an online system targeted at small and medium-sized food businesses, to help reduce the number of product recalls due to allergen mislabelling. The tool is also predicted to support more reliable knowledge transfer and incident tracking when things do go wrong.
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Pasquale, Joseph. Knowledge-Based Distributed Systems Management. Fort Belvoir, VA: Defense Technical Information Center, June 1986. http://dx.doi.org/10.21236/ada619313.

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Ruvinsky, Alicia, Maria Seale, R. Salter, and Natàlia Garcia-Reyero. An ontology for an epigenetics approach to prognostics and health management. Engineer Research and Development Center (U.S.), March 2023. http://dx.doi.org/10.21079/11681/46632.

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Анотація:
Techniques in prognostics and health management have advanced considerably in the last few decades, enabled by breakthroughs in computational methods and supporting technologies. These predictive models, whether data-driven or physics-based, target the modeling of a system’s aggregate performance. As such, they generalize assumptions about the modelled system’s components, and are thus limited in their ability to represent individual components and the dynamic environmental factors that affect composite system health. To address this deficiency, we have developed an epigenetics-inspired knowledge representation for engineered system state that encompasses components and environmental factors. Epigenetics is concerned with explaining how environmental factors affect the expression of an organism’s genetic material. The field has derived important in-sights into the development and progression of disease states based on how environmental factors impact genetic material, causing variations in how a gene is expressed. The health of an engineered system is similarly influenced by its environment. A foundation for a new approach to prognostics based on epigenetics must begin by representing the entities and relationships of an engineered system from the perspective of epigenetics. This paper presents an ontology for an epigenetics-inspired representation of an engineered system. An ontology describing the epigenetics of an engineered system will enable the composition of a formal model and the incremental development of a more robust, causal reasoning system.
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Bond, W., Maria Seale, and Jeffrey Hensley. A dynamic hyperbolic surface model for responsive data mining. Engineer Research and Development Center (U.S.), April 2022. http://dx.doi.org/10.21079/11681/43886.

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Анотація:
Data management systems impose structure on data via a static representation schema or data structure. Information from the data is extracted by executing queries based on predefined operators. This paradigm restricts the searchability of the data to concepts and relationships that are known or assumed to exist among the objects. While this is an effective and efficient means of retrieving simple information, we propose that such a structure severely limits the ability to derive breakthrough knowledge that exists in data under the guise of “unknown unknowns.” A dynamic system will alleviate this dependence, allowing theoretically infinite projections of the data to reveal discoverable relationships that are hidden by traditional use case-driven, static query systems. In this paper, we propose a framework for a data-responsive query algebra based on a dynamic hyperbolic surface model. Such a model could provide more intuitive access to analytics and insights from massive, aggregated datasets than existing methods. This model will significantly alter the means of addressing the underlying data by representing it as an arrangement on a dynamic, hyperbolic plane. Consequently, querying the data can be viewed as a process similar to quantum annealing, in terms of characterizing data representation as an energy minimization problem with numerous minima.
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Niederberger, Eva, and Ginger Johnson. Cholera Questions Bank: Quantitative Questions for Community Level Data Collection. Institute of Development Studies, August 2023. http://dx.doi.org/10.19088/sshap.2023.023.

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Анотація:
This questions bank resource is a menu of quantitative questions related to socio-behavioural factors and Cholera. This resource seeks to facilitate the collection of quality data on community’s capacity, behaviours, practices and perceptions in relation to cholera prevention and management. It can be used by field teams and / or local research teams working in communities with cholera transmission and those at risk adhering to safety and protection protocols. Gathering and using high quality data on social, behavioural and community dynamics in relation to cholera prevention and management is vital to: 1. Understand people’s ability, capacity and behaviour in relation to prevent and/or reduce cholera infection risks. 2. Support evidence-based decision-making on communication and engagement strategies that address people’s needs and priorities in an evolving context. 3. Provide data that decision-makers can use to adapt cholera response and preparedness strategies and activities. 4. Support public health promoters and outreach workers in engaging with local populations to strengthen community-led actions. 5. Enhance the knowledge of public health promoters and outreach workers and support them in clearly communicating on cholera prevention and control. 6. Inform the design and adaptation of information content / messages shared and discussed with the local population.
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Niederberger, Eva, and Ginger Johnson. Cholera Questions Bank: Quantitative Questions for Community Level Data Collection. Institute of Development Studies, March 2023. http://dx.doi.org/10.19088/sshap.2023.004.

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Анотація:
This questions bank resource is a menu of quantitative questions related to socio-behavioural factors and Cholera. This resource seeks to facilitate the collection of quality data on community’s capacity, behaviours, practices and perceptions in relation to cholera prevention and management. It can be used by field teams and / or local research teams working in communities with cholera transmission and those at risk adhering to safety and protection protocols. Gathering and using high quality data on social, behavioural and community dynamics in relation to cholera prevention and management is vital to: 1. Understand people’s ability, capacity and behaviour in relation to prevent and/or reduce cholera infection risks. 2. Support evidence-based decision-making on communication and engagement strategies that address people’s needs and priorities in an evolving context. 3. Provide data that decision-makers can use to adapt cholera response and preparedness strategies and activities. 4. Support public health promoters and outreach workers in engaging with local populations to strengthen community-led actions. 5. Enhance the knowledge of public health promoters and outreach workers and support them in clearly communicating on cholera prevention and control. 6. Inform the design and adaptation of information content / messages shared and discussed with the local population.
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