The RRB-tree is a confluently persistent data structure based on the persistent vector, with efficient concatenation and slicing, and effectively constant time indexing, updates and iteration. Although efficient appends have been discussed, they have not been properly studied.This thesis formally describes the persistent vector and the RRB-tree, and presents three optimisations for the RRB-tree which have been successfully used in the persistent vector. The differences between the implementations are discussed, and the performance is measured. To measure the performance, the C library librrb is implemented with the proposed optimisations.Results shows that the optimisations improves the append performance of the RRB-tree considerably, and suggests that its performance is comparable to mutable array lists in certain situations.

In a complex system such as the Norwegian railway network, there are much thatcan affect a trains punctuality. The undertakers strive to achieve higher and higherpunctuality, while the infrastructure owner, Jernbaneverket, strive for minimaldowntime on the railway network. There is collected much data for analysis aboutthe trains run and the infrastructure, in order to achieve higher punctuality and lessdowntime. The users are able to track down the source of delays and find possibleimprovements on the infrastructure, by analyzing and comparing the different datasets collected.There are many users across both different companies and internal divisionsin a company that need to cooperate, due to the size and complexity of a railwaynetwork. The different users have different needs when studying the data sets. Aarea director have the need to see the big picture over time, while a segment directorwants to see every detail within its segment.In this thesis we demonstrate a system that is aware of the different stakeholdersrequirements when presenting data. The system also takes into consideration thestakeholders need for analyzing different types of data, and comparing these.Finally, we conclude how users should be defined within a domain in order tobe aggregated over.

This master thesis looks at how clustering techniques can be appliedto a collection of scientific documents. Approximately one year of serverlogs from the CERN Document Server (CDS) are analyzed and preprocessed.Based on the findings of this analysis, and a review of thecurrent state of the art, three different clustering methods are selectedfor further work: Simple k-Means, Hierarchical Agglomerative Clustering(HAC) and Graph Partitioning. In addition, a custom, agglomerativeclustering algorithm is made in an attempt to tackle some of the problemsencountered during the experiments with k-Means and HAC. The resultsfrom k-Means and HAC are poor, but the graph partitioning methodyields some promising results.The main conclusion of this thesis is that the inherent clusters withinthe user-record relationship of a scientific collection are nebulous, butexisting. Furthermore, the most common clustering algorithms are notsuitable for this type of clustering.