Concept-based exploration of rich semi-structured data collections
| dc.contributor.advisor | Fischer, Bernd | en_ZA |
| dc.contributor.author | Greene, Gillian J. | en_ZA |
| dc.contributor.other | Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences (Computer Science) | en_ZA |
| dc.date.accessioned | 2017-01-26T07:35:57Z | |
| dc.date.accessioned | 2017-03-29T11:39:22Z | |
| dc.date.available | 2017-01-26T07:35:57Z | |
| dc.date.available | 2017-03-29T11:39:22Z | |
| dc.date.issued | 2017-03 | |
| dc.description | Thesis (PhD)--Stellenbosch University, 2017 | en_ZA |
| dc.description.abstract | ENGLISH ABSTRACT : Search has become one of the fundamental operations in computer science, allowing users to extract data and ultimately information from datasets. However, when users have no previous knowledge of a dataset, or have not clearly defined their search task and are therefore unable to formulate a direct query, their task becomes one of exploratory search or browsing rather than focused search or retrieval. While search and retrieval tools are widely provided, support for browsing of large and especially rich semi-structured datasets, is lacking. Semi-structured data is particularly difficult to explore and query because treating it as complete free-text causes a loss of important additional information which is encoded in the structured portions of the data while considering only the structured fields results in the loss of important free-text information. We therefore develop a framework to support exploration of semi-structured data, which is otherwise difficult to gather insights from, without requiring the user to have prior knowledge of the dataset or have formulated a specific query. Our approach uses a novel combination of tag clouds and concept lattices to facilitate data exploration, analysis and visualization by allowing the user to continuously update (i.e., add and remove) a set of keywords that the searched documents must contain. The document set is not directly provided as the result of a specific query, but aggregated information and properties of relevant documents are provided as a result. We apply our framework to data contained in software repositories, in two different ways for different goals to highlight the flexibility of the approach and the different tasks that can be supported using the same underlying dataset. We also instantiate our framework to support the exploration of a large collection of academic publication data. We evaluate the instantiations of our framework by conducting user and case studies, which indicate that our approach is usable and allows users to gather valuable information from semi-structured data archives. | en_ZA |
| dc.description.abstract | AFRIKAANSE OPSOMMING : Soektogte is een van die fundamentele operasies in rekenaarwetenskap. Dit laat gebruikers toe om data, en uiteindelik inligting, vanuit datastelle te onttrek. Wanneer gebruikers egter geen vorige kennis van ’n datastel het nie, of hul soektog nie duidelik gedefinieer het nie, en dus nie in staat is om ’n direkte navraag te formuleer nie, word hul taak een van verkennende soek, of blaai, eerder as gefokusde soek of herwinning. Terwyl soeken herwinnings-instrumente algemeen beskikbaar is, ontbreek ondersteuning vir die verkenning van groot en veral ryk semi-gestruktureerde datastelle. Semi-gestruktureerde data is veral moeilik om te verken en na te vra omdat die hantering daarvan as slegs vrye teks ’n verlies van belangrike aanvullende inligting veroorsaak wat ingebou is in die gestruktureerde gedeeltes van die data, terwyl die inagneming van slegs die gestruktureerde velde weer lei tot ’n verlies van belangrike vrye teks inligting. Ons ontwikkel dus ’n raamwerk om die verkenning van semi-gestruktureerde data te ondersteun, wat andersins moeilik is om insigte uit te verkry, sonder om van die gebruiker te vereis dat hulle voorafgaande kennis van die datastel het, of ’n spesifieke navraag reeds geformuleer het. Ons benadering maak gebruik van ’n nuwe kombinasie van etiket-wolke en konsep-roosters om data-verkenning, data-analise, en data-visualisering te fasiliteer deur die gebruiker toe te laat om voortdurend ’n stel sleutelwoorde op te dateer (m.a.w. by te voeg of te verwyder) wat bevat moet word in die dokumente waarvoor gesoek word. Die dokument-stel word nie direk verskaf as die resultaat van ’n spesifieke navraag nie, maar saamgestelde inligting en eienskappe van relevante dokumente word eerder verskaf. Ons pas ons raamwerk toe op data wat in programmatuurargiewe gestoor is op twee verskillende maniere vir verskillende doelwitte om die buigsaamheid van die benadering en die verskillende take wat ondersteun kan word met behulp van dieselfde onderliggende datastel uit te lig. Ons instansieer ook ons raamwerk om die verkenning van ’n groot versameling van akademiese publikasiedata te ondersteun. Ons evalueer die instansies van ons raamwerk deur die gebruik van gebruiker en gevallestudies, wat daarop dui dat ons benadering bruikbaar is, en gebruikers in staat stel om waardevolle inligting vanuit semi-gestruktureerde data-argiewe in te samel. | af_ZA |
| dc.format.extent | xvii, 169 pages : illustrations (chiefly colour) | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/10019.1/100859 | |
| dc.language.iso | en_ZA | en_ZA |
| dc.publisher | Stellenbosch : Stellenbosch University | en_ZA |
| dc.rights.holder | Stellenbosch University | en_ZA |
| dc.subject | Data analysis | en_ZA |
| dc.subject | Data visualization | en_ZA |
| dc.subject | Browsing (Databases) | en_ZA |
| dc.subject | Tag clouds | en_ZA |
| dc.subject | Concept lattices | en_ZA |
| dc.subject | UCTD | en_ZA |
| dc.title | Concept-based exploration of rich semi-structured data collections | en_ZA |
| dc.type | Thesis | en_ZA |