This report is for my research of Collaboration Instance Manager of UbiCollab project. UbiCollab want to be the platform for ubiquitous collaborative active. UbiCollab project aims to develop a distributed collaborative platform which makes people in distributed space ubiquitous collaborate with friends and colleagues. Collaboration instance manager (CIM) is a core component of the UbiCollab platform, which manage such collaborative activities. My research topics of CIM include in the P2P network development by using JXME, the data synchronization through this P2P network and how to manage these synchronized date by using a local file system. The result of my research is a CIM system, which deployed as OSGI bundle. User can use that do some collaborative active. This CIM system manage the service level of data synchronization, other modules and applications can use that to handle data synchronization between each other without know the details of how to implement it. For that purpose I first reviewed the related theories of distributed systems, ubiquitous systems, mobile systems and CSCW. After that review I researched on some alternatives for developing such system and choose the candidate technologies for my prototype. Secondly I analyzed the requirements of UbiCollab and designed the prototype. Based on that design, I implemented and tested that CIM system based on agreed common scenarios and developed a simple GUI for show the utility. Finally, I evaluate the system by analysis system requirements and scenario criteria.

This report aims to describe and improve a system recovery process in large-scale storage systems. Inevitable, a recovery process results in the system being loaded with internal replication of data, and will extensively utilize several storage nodes. Such internal load can be categorized and generalized into a maintenance workload class. Obviously, a storage system will have external clients which also introduce load into the system. This can be users altering their data, uploading new content, etc. Load generated by clients can be generalized into a production workload class. When both workload classes are actively present in a system, i.e. the system is recovering while users are simultaneously accessing their data, there will be a competition of system resources between the different workload classes. The storage must ensure Quality of Service (QoS) for each workload class so that both are guaranteed system resources. We have created Dynamic Tree with Observed Metrics (DTOM), an algorithm designed to gracefully throttle resources between multiple different workload classes. DTOM can be used to enforce and ensure QoS for the variety of workloads in a system. Experimental results demonstrate that DTOM outperforms another well-known scheduling algorithm. In addition, we have designed a recovery model which aims to improve handling of critical maintenance workload. Although the model is intentionally intended for system recovery, it can also be applied to many other contexts.

XQuery is a flexible language for querying XML data across a variety of storage methods. This thesis is a part of iAD, an ongoing research effort in next generation information access solutions. iAD is hosted by Fast Search & Transfer, a company developing their next search engine platform MARS. This project seeks to investigate the utilisation of XQuery as a query language for MARS. The result of this project is a novel method of translation, dubbed “Tainting Dependencies” (TD), which seeks to avoid unecessary denormalisation of intermediate results, and is designed specifically for translation to MARS’ relational algebra. This method supports a large subset of XQuery features. Furthermore, we have developed a prototype implementation which supports basic constructs such as FLWOR and sequence construction. TD is then compared head-on to a similar method dubbed “Loop Lifting”, and the results of this comparison is evaluated through discussion. The outcome of this project is a novel and well-documented method for translation of XQuery to MQL – a method which is designed to perform equally or better than existing implementations.

Digital cameras in mobile phones have become very popular in the recent years, and it is common to have large photo collections stored in the phone. Organizing these photos on the phone is still a big problem though. This study explores different ways of utilizing the location of where the photos were taken to make it easier to manage a large photo collection. Several different positioning technologies that can be used to obtain the location of where a photo was taken are presented. Three of the application suggestions for using location information of photos were implemented as prototypes on the Android platform. Android is a new platform for mobile phones developed by Google and the Open Handset Alliance, which has been made available as a preview release for developers. A part of this study was to investigate how suitable this platform is for developing location-based software. It was found that it is very suitable, although there still are some bugs and missing features that are expected to be fixed before the final release. The three application prototypes that were implemented were called “From Photo to Map”, “From Map to Photos” and “Who Lives Here?” The “From Photo to Map” application lets the user see a map where the location of a selected photo is visualized with a marker. The “From Map to Photos” application shows a map with markers at all of the locations where the user has taken photos. When one of the markers is selected, the photos taken at that location is shown. The “Who Lives Here?” application lets the user know which of the persons in his contact list that lives where the photo was taken. A small user survey showed that the participants thought all of the applications could be useful, but they were not so sure if they would use them themselves. The survey also showed that most of the users were able to find photos faster when using map-based browsing in the “From Map to Photos” application than when browsing through a photo collection linearly, but several concerns about the implementation details and the use of an emulator make the exact efficiency gain very uncertain.

The focus of this work is querying relational data through an object-relational mapper (ORM). In Java projects, it is common to use the Hibernate ORM and write the queries using HQL and Criteria. These approaches have limitations in regard to readability and static analysis. The limitations are identified and explained in this thesis. Several possible solutions are discussed. One of the solutions is looked at in depth and implemented in a real world project. The described solution eases the construction of queries and provides a way to fully utilize the development support tools.

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Sophisticated software with malicious intentions (malware) that can easily and aggressively spread to a large set of hosts is located all over the Internet. Such software struggles to avoid malware analysts to continue its malicious actions without interruption. It is difficult for analysts to find the locations of machines infected with unknown and alien malware. Likewise, it is hard to estimate the prevalence of the outbreak of the malware. Currently, the processes are done using resource demanding manual work, or simply rough guessing. Automating these tasks is one possible way to reduce the necessary resources. This thesis presents an in-depth study of which properties such a system should have. A system design is made based on the findings, and an implementation is carried out as a proof of concept system. The final system runs (malicious) software, and at the same time observes network traffic originating from the software. A signature for intrusion detection systems (IDSes) is generated using data from the observations. When loaded in an IDS, the signature localises hosts that are infected with the same malware type, making network administrators able to find and repair the hosts. The thesis also covers a deep introductory study of the malware problem and possible countermeasures, focusing on a malware analyst's point of view.

Describing video content without watching the entire video is a challenging matter. Textual descriptions are usually inaccurate and ambiguous, and if the amount of video is large this manual task is almost endless. If the textual description is replaced with pictures from the video, this is a much more adequate method. The main challenge will then involve which pictures to pick to make sure the entire video content is covered by the description. TV stations with an appurtenant video archive would prefer to have an effective and automated method to perform this task, with focus on holding the time consumption to an absolute minimum and simultaneously get the output results as precise as possible compared with the actual video content. In this thesis, three different methods for automatic shot detection in video files have been designed and tested. The goal was to build a picture storyline from input video files, where this storyline contained one picture from each shot in the video. This task should be done in a minimum of time. Since video files actually are one long series of consecutive pictures, various image properties have been used to detect the video shots. The final evaluation has been done based both on output quality and overall time consumption. The test results show that the best method detected video shots with an average accuracy of approximately 90%, and with an overall time consumption of 8.8% of the actual video length. Combined with some additional functionality, these results may be further improved. With the solutions designed and implemented in this thesis, it is possible to detect shots in any video file, and create a picture storyline to describe the video content. Possible areas of application are TV stations and private individuals that have a need to describe a collection of video files in an effective and automated way.

This master thesis presents PORDaS, the Peer-to-peer Object Relational Database System. It is a continuation of work done in a project of fall 2005, where the foundation for the thesis was laid down. The focus of the work is on distributed query processing between autonomous databases in a structured peer-to-peer network. A great deal of effort has gone into compiling the theoretical foundation for the project, which served as a basis for assessing alternative approaches to introducing a query processor in a peer-to-peer database. The old PORDaS version was extended to include a simplified, pipelined query processor capable of joining tables. The query processor had two different execution strategies, the first was performing join operators at the requesting node and the second was performing join operators parallel among the nodes participating in the query. Experiments which ran PORDaS on a cluster of 36 computers showed that there are room for improvements even though the system was able to perform all the tests.

Today's way of life includes increasing amounts of information, and therefore handling and processing of information. Almost everything you do involves some sort of a computer somewhere, and many businesses have implemented comprehensive computer systems into their corporative structure, to serve both employees and customers. But if a new service is introduced to the users, or a new group of users are introduced to an existing service, how do you know if the performance will be satisfying? To deal with such questions, a method called The Scalability Assessment Method (SAM) has been developed. The Scalability Assessment Method is a general procedure for evaluating the scalability of a system architecture. Other projects have applied SAM to real reference systems, and their results have shown that SAM is a method that can be trusted to give credible predictions. Until recently, dedicated software tools that support the SAM method have been absent, and the researchers have been using i.a. spreadsheets in an ad hoc approach to the problems. Therefore, a SAM software package is in development. The SAM Engine (SAMe) is a Java program developed in this project, with an intuitive user interface that is enabling a non-expert user to apply the method on a desired architecture. This report documents the development of the prototype SAM Engine (SAMe), and how the program supports the SAM method. Keywords: Performance evaluation, scalability, simulation, Structure and Performance, SAM.