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Increasingly more users are accessing database systems for interactive and exploratory data retrieval. While performing searches on these systems, users are required to use broad queries to get their desired results. Broad queries often result in too many items forcing the user to spend unnecessary time sifting through these items to find the relevant results. This problem, of finding a desired data item within many items, is referred to as "information overload". Most users experience information overload when viewing these database query results. This thesis shows that users information overload can be reduced by clustering database query results. A hierarchical agglomerative clustering algorithm is used to cluster the query results. The reduction of users information overload is evaluated using Chakrabarti et al information overload cost model. Empirical results show that users are able to find more relevant information as well as experiencing a reduction in information overload.

With the onset of the semantic web, the problem of making semantic content effectively searchable for the general public emerges. Demanding an understanding of ontologies or familiarity with a new query language would likely frustrate semantic web users and prevent widespread success. Given this need, this thesis describes AskOntos, which is a system that uses extraction ontologies to convert conjunctive, free-form queries into structured queries for semantically annotated web pages. AskOntos then executes these structured queries and provides answers as tables of extracted values. In experiments conducted AskOntos was able to translate queries with a precision of 88% and a recall of 81%.

This report describes the purpose, design, development and analysis of SQLTips, an online instructional delivery framework and set of instructional modules relating to advanced features and performance tuning of Oracle's Structured Query Language (SQL). SQLTips was developed using Wiki, server-side software that allows users to edit web pages with almost any browser. The report includes a literature review of existing SQL instructional materials and a review of instructional theory. The report also includes a description of the formative evaluation process and results. These results show that SQLTips is easy and enjoyable to use. Based on a scale of 1 to 7 with 7 being the most positive, the 10 modules comprising SQLTips averaged a 6.1 for ease of use and a 6.2 for enjoyability. Posttest results also showed an average increase of 46% upon completion of the instruction. The report also contains a critique of the project.

This Master's thesis project aims to investigate if Wikipedia's clickstream data can be used to improve the retrieval performance of information retrieval systems. The project is conducted under the assumption that a traversal between two article connects the two articles in regards to content. To extract useful terms out of the clickstream data, it needed to be structured so that it given a Wikipedia article it is possible to find all of the in-going or out-going article traversals.The project settled on using the clickstream data in an automatic query expansion approach.Two expansion methods were investigated, one based on expanding with full article title so that the context would be preserved, and the other expanded with individual terms from the article titles.The structure of the data and two proposed methods were evaluated using a set of queries and relevance judgments. The results of the evaluation shows that the method that expands with individual terms performed better than the full article title expansion method and that the individual term method managed to increase the MAP with 11.24%.  The expansion method was evaluated on two different query collections, and it was found that the proposed expansion method only improves the results where the average recall of the original queries are low.The thesis conclusion is that the clickstream can be used to improve retrieval performance for an information retrieval system. / Det här examensarbetets mål är att undersöka om Wikipedias klickströmsdata kan användas för att förbättra sökprestanda för informationsökningssystem. Arbetet har utförts under antagandet att en övergång mellan två artiklar på Wikipedia sammankopplar artiklarnas innehåll och är av intresse för användaren. För att kunna utnyttja klickströmsdatan krävs det att den struktureras på ett användbart sätt så att det givet en artikel går att se hur läsare har förflyttat sig ut eller in mot artikeln. Vi valde att utnyttja datamängden genom en automatisk sökfrågeexpansion. Två olika metoder togs fram, där den första expanderar sökfrågan med hela artikeltitlar medans den andra expanderar med enskilda ord ur en artikeltitel.Undersökningens resultat visar att den ordbaserade expansionsmetoden presterar bättre än metoden som expanderar med hela artikeltitlar. Den ordbaserade expansionsmetoden lyckades uppnå en förbättring för måttet MAP med 11.21%. Från arbetet kan man också se att expansionmetoden enbart förbättrar prestandan när täckningen för den ursprungliga sökfrågan är liten. Gällande strukturen på klickströmsdatan så presterade den utgående strukturen bättre än den ingående. Examensarbetets slutsats är att denna klickströmsdata lämpar sig bra för att förbättra sökprestanda för ett informationsökningssystem.

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