Visual exploration of large geolocation-rich data sets using formal concept analysis

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dc.contributor.advisorBritz, Katarinaen_ZA
dc.contributor.advisorFischer, Bernden_ZA
dc.contributor.authordu Toit, Tiaanen_ZA
dc.contributor.otherStellenbosch University. Faculty of Arts and Social Sciences. Dept. of Information Science.en_ZA
dc.date.accessioned2022-03-08T06:12:43Z
dc.date.accessioned2022-04-29T09:44:42Z
dc.date.available2022-03-08T06:12:43Z
dc.date.available2022-04-29T09:44:42Z
dc.date.issued2022-04
dc.descriptionThesis (MPhil)--Stellenbosch University, 2022.en_ZA
dc.description.abstractENGLISH SUMMARY: The rate at which data is being generated is ever-increasing, resulting in an abundance of very large data sets of di erent structures, all requiring improved methods of data capture, processing and storage. This in turn requires improved representation and data exploration methods. In an age of web applications, one such structure that has gained popularity is semi-structured data. Unlike relational data which can be speci cally queried, semi-structured data relies on di erent methods for data exploration and knowledge discovery. One such method is the visualisation of data beyond a purely granular textual format, in a dynamic and reactive manner. ConceptCloud, a exible interactive web application for exploring, visualising, and analysing semi-structured data sets, uses a combination of an intuitive tag cloud visualisation with an underlying formal concept lattice to provide a formal structure for navigation through a data set. It is an e ective, robust and scalable tool that allows for extension. The underlying formal concept lattice also allows for alternative possibilities of exploring data. This research describes the development and implementation of extensions made to the existing ConceptCloud tool, which are focused on improving the visualisation of, and the knowledge discovery through interaction with a data set, especially data with aspects that could be visualised in a more e ective manner. These extensions include a map based viewer for visualising geolocation data, a graph based viewer for visualising the composition of the data as well as a REST API to allow for mobile application development and further unique visualisations. These extensions are demonstrated and evaluated by visualising and exploring two semi-structured data sets in the viticultural domain, namely atmospheric measurements of grape growing regions and wine reviews. It is shown how these extensions aid in and support data exploration and knowledge discovery of these multi- faceted semi-structured data sets. These visualisations can not only be re ned and expanded upon to include other types of visualisations, but also improve data mining and knowledge discovery using ConceptCloud. This could result in further research and improvements in similar tools and processes.en_ZA
dc.description.abstractAFRIKAANSE OPSOMMING: Die pas waarteen data gegenereer word, neem daagliks toe. Dit lei tot 'n oorvloed van baie groot data stelle van verskillende strukture, wat almal beter metodes van data vaslegging, verwerking en berging vereis. Op `n soortgelyke wyse, vereis dit op sy beurt weer beter voorstelling en data ondersoek metodes. In 'n era van webtoepassings , is semi-gestruktureerde data een so 'n struktuur wat gewild geword het. Anders as verwante data wat spesi ek ondersoek kan word, maak semi-gestruktureerde data staat op verskillende metodes vir data verkenning en kennis blootlegging. Een so 'n metode is die visualisering van data buite 'n suiwer tekstuele formaat, op 'n dinamiese en reaktiewe wyse. ConceptCloud, 'n buigsame interaktiewe webtoepassing vir die verkenning, visualisering en ontleding van semi-gestruktureerde data stelle, gebruik 'n kombinasie van 'n intu tiewe merker wolk visualisering met 'n onderliggende formele konsep rooster om 'n formele struktuur vir navigasie deur 'n datastel te verskaf. Dit is 'n e ektiewe, robuuste en skaalbare hulpmiddel , wat uitbreiding moontlik maak. Die onderliggende formele konsep rooster maak ook voorsiening vir alternatiewe moontlikhede om data te verken. Hierdie navorsing beskryf die ontwikkeling en implementering van uitbreidings , wat aan die bestaande ConceptCloud instrument gemaak is. Hierdie uitbreidings is gefokus op die verbetering , die visualisering van, en die ontdekking van kennis deur interaksie met 'n data stel, veral data met aspekte wat op 'n meer e ektiewe wyse gevisualiseer kan word. Hierdie uitbreidings sluit 'n kaartgebaseerde observasie platform in, vir die visualisering van geoliggingdata , 'n gra ekgebaseerde observasie platform, vir die vertoon van die samestelling van die data , sowel as 'n REST API om voorsiening te maak vir mobiele toepassings ontwikkeling en verdere unieke visualiserings. Hierdie uitbreidings word gedemonstreer en ge evalueer deur twee semi-gestruktureerde data stelle in die wingerdbou domein te visualiseer en te verken, naamlik eerstens , atmosferiese metings van druiweverbouing streke , en tweedens, wynresensies. Dit word getoon hoe hierdie uitbreidings bydra tot , en ondersteuning van dataverkenning en kennis ontdekking van hierdie veelvlakkige semi-gestruktureerde data stelle bied. Hierdie visualiserings kan nie net verfyn en uitgebrei word om ander soorte visualiserings in te sluit nie , maar verbeter ook data ontginning en kennis blootlegging met behulp van ConceptCloud. Dit kan lei tot verdere navorsing en verbeterings in soortgelyke sagteware hulpmiddels en prosesse.af_ZA
dc.description.versionMasters
dc.format.extentxiv, 111 pages : illustrations
dc.identifier.urihttp://hdl.handle.net/10019.1/124980
dc.language.isoen_ZAen_ZA
dc.publisherStellenbosch : Stellenbosch University
dc.rights.holderStellenbosch University
dc.subjectFormal concept analysisen_ZA
dc.subjectInformation visualization -- South Africaen_ZA
dc.subjectData visualisation -- South Africaen_ZA
dc.subjectKnowledge discovery in data -- South Africaen_ZA
dc.subjectUCTD
dc.titleVisual exploration of large geolocation-rich data sets using formal concept analysisen_ZA
dc.typeThesisen_ZA
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