Automated knowledge discovery or integration : a systematic review of data mining in knowledge management
| dc.contributor.advisor | Maasdorp, Christiaan H. | en_ZA |
| dc.contributor.author | Peu, Ephenia | en_ZA |
| dc.contributor.other | Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Information Science. | en_ZA |
| dc.date.accessioned | 2021-12-06T08:59:38Z | |
| dc.date.accessioned | 2021-12-22T14:28:56Z | |
| dc.date.available | 2021-12-06T08:59:38Z | |
| dc.date.available | 2021-12-22T14:28:56Z | |
| dc.date.issued | 2021-12 | |
| dc.description | Thesis (MPhil)--Stellenbosch University, 2021. | en_ZA |
| dc.description.abstract | ENGLISH SUMMARY : Data mining and knowledge management activities have been crucial for making sense of the vast amounts of data, information, and knowledge created in organisations. Data mining comprises the collection, categorisation, and analysis of data to find useful patterns and establishing solutions based on those patterns. Integrating data mining into knowledge management has had little exploration and attention. The thesis aims at this gap and investigates the role of data mining in the knowledge management literature in both quantitative and qualitative studies between 2000 to 2017. A systematic literature review identified and analysed published articles utilising data mining in knowledge management to reveal the trends in the field. The initial search was conducted on four interdisciplinary databases and an article selection process that involved inclusion and exclusion criteria and a quality assessment using a checklist yielded 54 articles for analysis. Six themes were identified in a thematic analysis where the articles were coded using Atlas.ti software: 1) technical advances improve access to and transformation of knowledge, 2) the knowledge base as the basis for improved product and service development, 3) the use of big data analytics for customer relationship management, 4) the role of data and information assets for decision support, 5) combining automation and human expertise to improve efficiency, and 6) the effectiveness of data mining applications as guided by the specificity of the knowledge management task. Finally, the themes resulting from the coding are mapped on the stages of the knowledge management process. The discovery and capture stages concern data mining techniques for knowledge discovery; the process stage uses the knowledge base and decision support to access knowledge for action; and the share and benefits stage is the domain of learning and capacity development. | en_ZA |
| dc.description.abstract | AFRIKAANSE OPSOMMING : Data-ontginning en kennisbestuursaktiwiteite is deurslaggewend om sin te maak uit die groot hoeveelhede data, inligting en kennis wat in organisasies geskep word. Data-ontginning behels die versameling, kategorisering en ontleding van data om bruikbare patrone te vind en oplossings op grond van daardie patrone te vestig. Tot op hede, is relatief min aandag gegee aan die integrasie van data-ontginning en kennisbestuur. Die tesis fokus op hierdie gaping en ondersoek die rol van data-ontginning in die kennisbestuursliteratuur in beide kwantitatiewe en kwalitatiewe studies tussen 2000 tot 2017. 'n Sistematiese literatuuroorsig het gepubliseerde artikels wat gebruik maak van data-ontginning in kennisbestuur geïdentifiseer en ontleed om die tendense in die veld te identifiseer. Die aanvanklike soektog is op vier interdissiplinêre databasisse gedoen en 'n seleksieproses met insluiting- en uitsluitingskriteria en 'n kwaliteitbeoordeling het 54 artikels vir ontleding opgelewer. Ses temas is in 'n tematiese analise, waar die artikels met behulp van Atlas.ti-sagteware gekodeer is, geïdentifiseer: 1) tegniese vooruitgang wat verbeterde toegang tot en transformasie van kennis moontlik maak, 2) die kennisbasis as oorsprong vir verbeterde produk- en diensontwikkeling, 3) die gebruik van data-analise vir kliënteverhoudingsbestuur, 4) die rol van data en inligtingsbates vir besluitsteun, 5) die kombinasie van outomatisering en menslike kundigheid vir doeltreffendheid, en 6) die bepaling van data-ontginningstoepassings deur die spesifisiteit van die kennisbestuurstaak. Laastens word die temas as resultaat van die kodering op die stadiums van die kennisbestuursproses toegepas. Die ontdekkings- en vasvangstadiums handel oor data-ontginningstegnieke vir kennisontdekking; die prosesstadium gebruik die kennisbasis en besluitsteun om aksie-kennis te verkry; en die deel- en voordelestadium is die domein van leer en kapasiteitsontwikkeling. | af_ZA |
| dc.description.version | Masters | |
| dc.format.extent | x, 170 pages ; illustrations, includes annexures | |
| dc.identifier.uri | http://hdl.handle.net/10019.1/123916 | |
| dc.language.iso | en_ZA | en_ZA |
| dc.publisher | Stellenbosch : Stellenbosch University | |
| dc.rights.holder | Stellenbosch University | |
| dc.subject | Data mining | en_ZA |
| dc.subject | Knowledge management -- Methodology | en_ZA |
| dc.subject | UCTD | |
| dc.title | Automated knowledge discovery or integration : a systematic review of data mining in knowledge management | en_ZA |
| dc.type | Thesis | en_ZA |