The master thesis will extend the students depth study. Through the master thesis the student shall study and evaluate how IT Governance, Enterprise Architecture and IT Strategy are related in StatoilHydro. The student shall also research StatoilHydro´s awareness of the concepts. The student shall propose improvements and/or changes based on this evaluation.

During the fall of 2008 a prototype of an educational multiplayer role-playing game called World of Wisdom (WoW) was developed as part of the specialization project TDT4570. WoW focuses on using knowledge for progressing through the game. The goal of this thesis was to design and develop a user-interface for teachers, that could be used to generate new content for WoW. In this thesis we described the design and implementation of such a user-interface called the WoW World Editor. The World Editor supports generating new maps, creatures, objects and questions for World of Wisdom. By making it easier to create the worlds, the course staff can focus on creating the knowledge for the game. For the students to be able to interact with the course staff while playing the game, we suggest a seperate client for the course staff. This client will then have additional functions that can be used to aid the students with problem, and to get valuable feedback from the players.

Text categorization is the task of discovering the category or class text documents belongs to, or in other words spotting the correct topic for text documents. While there today exists many machine learning schemes for building automatic classifiers, these are typically resource demanding and do not always achieve the best results when given the whole contents of the documents. A popular solution to these problems is called feature selection. The features (e.g. terms) in a document collection are given weights based on a simple scheme, and then ranked by these weights. Next, each document is represented using only the top ranked features, typically only a few percent of the features. The classifier is then built in considerably less time, and might even improve accuracy. In situations where the documents can belong to one of a series of categories, one can either build a multi-class classifier and use one feature set for all categories, or one can split the problem into a series of binary categorization tasks (deciding if documents belong to a category or not) and create one ranked feature subset for each category/classifier. Many feature selection metrics have been suggested over the last decades, including supervised methods that make use of a manually pre-categorized set of training documents, and unsupervised methods that need only training documents of the same type or collection that is to be categorized. While many of these look promising, there has been a lack of large-scale comparison experiments. Also, several methods have been proposed the last two years. Moreover, most evaluations are conducted on a set of binary tasks instead of a multi-class task as this often gives better results, although multi-class categorization with a joint feature set often is used in operational environments. In this report, we present results from the comparison of 16 feature selection methods (in addition to random selection) using various feature set sizes. Of these, 5 were unsupervised , and 11 were supervised. All methods are tested on both a Naive Bayes (NB) classifier and a Support Vector Machine (SVM) classifier. We conducted multi-class experiments using a collection with 20 non-overlapping categories, and each feature selection method produced feature sets common for all the categories. We also combined feature selection methods and evaluated their joint efforts. We found that the classical supervised methods had the best performance, including Chi Square, Information Gain and Mutual Information. The Chi Square variant GSS coefficient was also among the top performers. Odds Ratio showed excellent performance for NB, but not for SVM. The three unsupervised methods Collection Frequency, Collection Frequency Inverse Document Frequency and Term Frequency Document Frequency all showed performances close to the best group. The Bi-Normal Separation metric produced excellent results for the smallest feature subsets. The weirdness factor performed several times better than random selection, but was not among the top performing group. Some combination experiments achieved better results than each method alone, but the majority did not. The top performers Chi square and GSS coefficient classified more documents when used together than alone.Four of the five combinations that showed increase in performance included the BNS metric.

Semantic cache and distribution introduce new obstacles to how we use cache in query processing in databases. We have adapted a caching strategy called cache investment to work in a peer-to-peer database with semantic cache. Cache investment is a technique that influences the query optimizer without changing it. It suggests cache candidates based on knowledge about queries executed in the past. These queries are not only limited to the local site, but also detects locality in queries by looking at queries processed on remote sites. Our implementation of Semantic cache investment for distributed databases shows a great performance improvement, especially when multiple queries are active at the same time. To utilize cache investment we have looked into how a distributed query optimizer can be extended to use cache content in planning. This allows the query optimizer to detect and include beneficial cache content on remote sites that it otherwise would have ignored. Our implementation of a cache-aware optimizer shows an improvement in performance, but its most important task is to evaluate cache candidates provided through cache investment.

Through the research performed in this thesis, it has been shown how location awareness and maps can be exploited to facilitate the use of library resources, such as information on documents and objects. A prototype has been developed to demonstrate the feasibility of integrating several different information sources for this use. The prototype created allows for users located within the city centre of Trondheim to search for documents and to locate the library departments holding them. The user is shown a map and given information on how to travel to the nearest bus stop, as well as bus schedules on how to get to the selected library department. Several information sources for the prototype has been identified and evaluated. The prototype communicates with BIBSYS for document information retrieval, Google Maps for map generation, team-trafikk.no for bus schedules querying and Amazon.com and LibraryThing.com for book cover image downloading. To ensure data consistency some local data sources are also maintained, such as a list of all the UBiT (NTNU library) departments in Trondheim. The prototype was implemented so that it would satisfy a set of requirements. These requirements were created by applying the technique of use cases. Each requirement has been discussed and prioritised based on requests from UBiT. The most important requirements have been incorporated into the design of the prototype. This focuses on modularity and it has been discussed how the external sources best can be integrated with the prototype. The prototype is implemented using a combination of programming languages. The differences between these languages have posed a challenge, and solutions to how these can be avoided are presented. The prototype has been tested according to an extensive test plan, and the results of these tests have been document and evaluated. Each of the design decisions have been evaluated and discussed, and suggestions on how these could have been improved are given. Finally, suggestions on how the functionality of the prototype can be extended are presented. The prototype created in this thesis allows for users, familiar or unfamiliar with the city and its transportation network, to locate a document and travel to the library holding it. It demonstrates how emerging technologies such as location awareness can contribute to increased use of library services.

Several methods exists for performing similarity searches quickly using metric indexing. However, most of these methods are based on main memory indexing or require specialized disk access methods. We have described and implemented a method combining standard database access methods with the LAESA Linear Approximating Eliminating Search Algorithm to perform both range and K nearest neighbour (KNN) queries using standard database access methods and relational operators. We have studied and tested various existing implementations of R-trees, and implemented the R*-tree. We also found that some of the optimizations in R*-trees was damaging to the response time at very high dimensionality. This is mostly due to the increased CPU time removing any benefit from reducing the number of disk accesses. Further we have performed comprehensive experiments using different access methods, join operators, pivot counts and range limits for both range and nearest neighbour queries. We will also implement and experiment using a multi-threaded execution environment running on several processors.

Nowadays the computer science is extremely important in business; thanks to it you can automate tasks, streamline processes and obtain information. The best resource for the new companies is the information. I mean, today the information is very necessary with the new technologies. Employers who are responsible to decide in their companies, have begun to understand that the information helps their business and also can be one of the best critical factors that can show us if the company work is successfully or not. (1) In recent years organizations have recognized the importance of managing key resources such as the working hours and the raw materials. ERP applications are often used to standardize business processes and unify data; the importance of that software in the companies is growing every day. Although we also can say that in some cases the ERP software doesn´t solve some business problems because there are processes that aren´t standard or common. Moreover, if we would create a module for our company it has to be usable .The usability is very important because that and the automation will help to increase the performance of coordinators. This project is a study of a particular case of a telecommunications company in which it has found a problem with the production process; In This Project we will be able to solve each problem that will appear in various stages of construction our module. These steps include: a study about if there are problems in the selected area of business, whether buying or building a new module, heuristic techniques and methods to improve the usability, quality of build module using polls, usability guidelines, use case... The results have improved the production of the company and the system provides the necessary information to the coordinators but the coordinators want further improvement. The study of the usability helps users to be ready to use the software correctly.

Mobility of people and their interactions with devices and services that every day become more pervasive in our life is a valuable challenge for system engineers. Locate friends, retrieve multimedia informations from physicals objects, have medical assistance remotely is going to be a commodity for a more and more wide part of the population, including elderly people. In this scenario have an easy way for discovering and communicating with third party services and resources that we encounter in our every day life is going to make the difference between an enjoyable user experience or a frustrating ones that quickly leads to the abandon of a system. Building on the work done in the past about resources discovery and management I study a solution for user-centered interactions with resources and services dynamically discovered and used by the user in nomadic environments. The solution designed make use of embedded devices, addressing the problem encountered in the research from both end-user and developer point of views. The solution proposal make a full use of the service oriented architecture (SOA) concepts focusing on the goal of achieving the most natural human interaction with devices that the user discover on his way, keeping at the same time the framework architecture lightweight and easily extendible by third-party developers, as the SOA paradigm requires. Accessibility and extensibility are achieved on the end-user side by deploying software needed for the personal device (UbiNode) on most common smartphones and providing a easily understable Graphical User Interface; on the developer side by creating a pluggable framework based on xml and Eclipse eRCP runtimes for a fast development of multiple user interface that fits the constrains of the device in which are them deployed in. Work done consists in design and implementation of several platform components and development of prototypes that takes profit from the overall architecture. Developed modules have been deployed end tested on handled devices. UbiCollab provides a solution platform for ubiquitous collaboration scenarios and this thesis has been carried out as a contribution to it. Keywords: Ubiquitous Computing, User Centered collaboration, User Interfaces, Mobile Devices, Discovery Gestures, Object Tagging, UbiCollab.

This report covers the research, design and prototype implementation of a social application for mobile ad-hoc networks, InstantSocial. The main goals of this project has been to look at this exciting field and examine how the European scientific collaborative project MUSIC can be used to develop such an application. The project has been conducted using a Design Science approach. First the field of interest, existing similar applications and technology was examined to get a good view of the current state-of-the-art and choose a suitable prototype application. Then the design of the application was constructed, emphasizing the important features outlined in the project goals. Finally a prototype was developed and tested as a means to prove the correctness and applicability of the design. The results of the project was somewhat divided. Although the developed prototype had limited functionality, mostly because of the current limitations of the used framework, the tests that were performed where positive. Not all of the requirements of the system was successfully implemented, but this was due to limited time available or limitations in the currently available version of the framework rather than a shortage of the design. We conclude that the presented design and approach to context-adaptation is very plausible as a way in which the MUSIC project can be used to develop social application for mobile ad-hoc networks, but that much more work and testing is required before such an application can be fully realized.

System logs contain messages from a wide range of applications. They are the natural starting point when troubleshooting a system. The usual approach for analysing system logs is to write a number of regular expressionsto match specific keywords and events. When the number of expressions grows large, the analysis solution becomes unmaintainable. In addition, the use of regular expressions requires the system administrator to have extensive knowledge of the system at hand. This thesis presents methods for performing log analysis without regular expressions. This is an area of system administration that has attracted very few researchers. Therefore, little published research is available on the subject. Much effort has been put into the task of generating patterns from log file. These patterns are an important prerequisites for statistical analysis. Patterns could also be used to identify transactions for use in Markov models. None of the existing pattern mining algorithm for system logs produce satisfactory results. To solve the task at hand, a new method for mining patterns is developed. Several different approaches were tested. An approach based on inserting log lines into a tree structure turned out to be a very promising. It outputs good quality patterns and its resource use is moderate. Log analysis without prior knowledge of the system at hand have been proven difficult. This thesis shows that methods where some basic knowl- edge of systems in general is exploited, are the most promising ones. Other approaches based on Markov models and neural networks are suggested in this thesis, but they have not been tested to full extend and require some more work before being useful.