This project explores an improved design for a system that tracks drive and rest time data for heavy vehicle drivers. Tracking rest and driving time data for drivers of heavy vehicles is important since it allows for the drivers to ensure that they are following the laws related to how often they must take breaks. The system built in this project is external to the vehicles. It relies on sending smaller status updates then previously, thus decreasing the data required to track the drive and rest times. The system was evaluated in terms of how much data it saves compared to a system that sends full status updates, it was also evaluated on how the cost scales with an increased load on the system.

OSINT is the collection and analysis of data from open sources such as Twitter, Instagram or Tiktok. Since most people in today's digitised society have access to mobile devices and cameras, anyone can publish sensitive material on for example troop movements. The aim of this work is to contribute on how OSINT methods can potentially be used for intelligence gathering and by increasing knowledge in this area also protect against the problem of people sharing sensitive information by mistake. This is accomplished by conducting an experiment in which three web scraping methods and three manual methods are used for the collection of potential intelligence on three different social media platforms. Due to the state of the world today, the experiment focuses on the Ukraine conflict. All methods tested during the experiment can be used for intelligence gathering. However, the methods differ in their search capabilities and how well they can filter the results of its searches. Overall, the TWINT tool and manual searches of Twitter performed best because they have the most search options and filtering capabilities. / OSINT är insamling och analys av data från öppna källor som Twitter, Instagram eller Tiktok.Eftersom de flesta i dagens digitaliserade samhälle har tillgång till mobila enheter och kamerorkan vem som helst publicera känsligt material på exempelvis truppförflyttningar. Syftet meddetta arbete är att bidra med kunskap om hur OSINT metoder kan potentiellt användas förinsamling av underrättelse och genom en ökad kunskap kring området också skydda motproblemet där personer delar med sig av känslig information av misstag. Detta genomförsgenom att utföra ett experiment där tre web scraping metoder och tre manuella metoderanvänds för insamling av potentiell underrättelse på tre olika sociala medier plattformar. Pågrund av hur läget ser ut i världen idag så fokuserar experimentet på Ukraina konflikten. Allametoder som testades under experimentets gång kan användas för inhämtning av underrättelse.Metoderna skiljer sig i dock sina sökfunktioner och hur väl de kan filtrera resultaten från desssökningar. Överlag så presterade verktyget TWINT och manuella sökningar av Twitter bäst föratt de har flest sökalternativ och filtreringsmöjligheter.

Recent advances in reliable distributed computing have made it possible to provide high availability and scalability to traditional systems and thus serve them as reliable services. For some systems, their parallel nature in addition to weak consistency requirements allowed a more trivial transision such as distributed storage, online data analysis, batch processing and distributed stream processing. On the other hand, systems such as Complex Event Processing (CEP) still maintain a monolithic architecture, being able to offer high expressiveness at the expense of low distribution. In this work, we address the main challenges of providing a highly-available Distributed CEP service with a focus on reliability, since it is the most crucial and untouched aspect of that transition. The experimental solution presented targets low average detection latency and leverages event delegation mechanisms present on existing stream execution platforms and in-memory logging to provide availability of any complex event processing abstraction on top via redundancy and partial recovery.

Anomaly detection in computer systems operating within complex environments,such as cyber-physical systems (CPS), has become increasingly popularduring these last years due to useful insights this process can provide aboutcomputer systems’ health conditions against known reference nominal states.As performance anomalies lead degraded service delivery, and, eventually,system-wide failures, promptly detecting such anomalies may trigger timelyrecovery responses. In this thesis, Process Mining, a discipline aiming at connectingdata science with process science, is broadly explored and employedfor detecting performance anomalies in complex computer systems, proposinga methodology for connecting event data to high-level process models forvalidating functional and non-functional requirements, evaluating system performances,and detecting anomalies. The proposed methodology is appliedto the industry-relevant European Rail Traffic Management System/EuropeanTrain Control System (ERTMS/ETCS) case-study. Experimental results sampledfrom an ERTMS/ETCS system Demonstrator implementing one of thescenarios the standard prescribe have shown Process Mining allows characterizingnominal system performances and detect deviations from such nominalconditions, opening the opportunity to apply recovery routines for steeringsystem performances to acceptable levels.

This dissertation presents the GDI (Goal Directed Interface) approach to the user interface for office systems. The primary objectives of the approach are to create an interface that (1) requires little user training and (2) tries to perform higher level task activities (ex. 'schedule a meeting') that have been excluded from computerization in the past. The GDI technique (1) postulates a simple model of the office environment consisting of persons, things, and processes, and a decomposable goal set, (2) represents knowledge in the office environment through rules, frames, and scripts, and object-oriented programming techniques, (3) creates an iconic visual representation consisting of persons, things, and processes that closely mimics the user's 'mental model' of the office world, (4) requires that the user's own 'person icon' be present for all interactions so that actions appear to take place in a user controllable context (the user's icon is, literally, in the interface), (5) provides a 'selection window' through which the user communicates his/her goal by grouping relevant icons, (6) uses a rule-based expert system to examine an icon configuration and, through its expertise, derives a user goal (despite ambiguous or faulty icon placements), (7) attempts to complete the user goal through the use of scripts and multiple expert systems.

Thesis made openly available per email from author, August 2015.

published_or_final_version / abstract / Mechanical Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Architecture / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Computer Science / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Mechanical Engineering / Doctoral / Doctor of Philosophy