This thesis investigates a method for efficiently updating the software of an Electronic Control Unit (ECU) in a car. The patch sent to the car should be as small as possible and optimally only contained the changed part of the software. A popular algorithm for creating the patch is bsdiff. However, it is not made for filesystem images, but for binaries. Therefore, an alternative is investigated. The alternative algorithm method is based on the update engine in Android. A standalone version of the Android A/B Update is implemented and compared to bsdiff, in the aspect of the time it takes to generate the patch and the size of the patch. The result shows that bsdiff generates a slightly smaller patch. However, bsdiff is also a lot slower at generating the patch. Furthermore, the time increases linearithmic with the size of the filesystem image. This gives reason to believe that the Android A/B Update algorithm might be a better solution when updating an ECU that contains a full filesystem. However, this depends on if it is most valuable that the patch is as small as possible, or that the process of generating it is fast. / Detta examensarbete undersöker en metod för att effektivt uppdatera mjukvaran i en styrenhet i en bil. En patch som skickas till en bil ska vara så liten som möjligt och helst enbart innehålla de delar av mjukvaran som ändrats. En populär algorithm för att skapa en sådan patch är bsdiff. Den är dock inte gjord för filsystemsavbildningar, utan för binärer. Därför studeras här ett alternativ. Denna alternativa metod är baserad på Androids updateringsprocess. En fristående variant av Android A/B Update är implementerad och och jämförd med bsdiff, med avseende på tiden det tar att generera en patch och storleken av den. Resultatet visar att bsdiff genererar mindre patchar. Däremot är bsdiff också betydligt långsammare. Vidare ökar tiden linearitmisk då storleken på patchen ökar. Detta innebär att Android A/B Update kan vara en bättre lösning för att updatera en styrenhet som innehåller ett filsystem. Det beror dock på vad som värderas högst; en mindre patch eller att processen att skapa patchen ska vara snabbare.

Numerous studies on Queueing systems, such as Internet traffic flows, have shown to be bursty, self-similar and/or long-range dependent, because of the heavy (long) tails for the various distributions of interest, including intermittent intervals and queue lengths. Other studies have addressed vacation in no-customers’ queueing system or when the server fails. These patterns are important for capacity planning, performance prediction, and optimization of networks and have a negative impact on their effective functioning. Heavy-tailed distributions have been commonly used by telecommunication engineers to create workloads for simulation studies, which, regrettably, may show peculiar queueing characteristics. To cost-effectively examine the impacts of different network patterns on heavy- tailed queues, new and reliable analytical approaches need to be developed. It is decided to establish a brand-new analytical framework based on optimizing entropy functionals, such as those of Shannon, Rényi, Tsallis, and others that have been suggested within statistical physics and information theory, subject to suitable linear and non-linear system constraints. In both discrete and continuous time domains, new heavy tail analytic performance distributions will be developed, with a focus on those exhibiting the power law behaviour seen in many Internet scenarios. The exposition of two major revolutionary approaches, namely the unification of information geometry and classical queueing systems and unifying information length theory with transient queueing systems. After conclusions, open problems arising from this thesis and limitations are introduced as future work.

Bloatware är förinstallerade applikationer som i många fall kan vara svåra att avinstallera. Målet med denna studie är att undersöka om bloatware påverkar användarens upplevda prestanda av sin mobiltelefon, samt hur en upplevd försämring av mobilens prestanda påverkar en individs benägenhet att investera i en ny mobiltelefon. Tidigare forskning ger inte en universal definition av prestanda, varpå denna studie sammanställer ett ramverk för prestanda av mobiltelefoner som batteritid, responstid och lagringsutrymme. Begränsat lagringsutrymme, långsam responstid och förkortad batteritid påverkar alla telefonens prestanda negativt. Studien genomfördes med hjälp av en enkätundersökning om användares upplevelser med sina mobiltelefoner och resultaten fördjupades med hjälp av versions- och lagringsdata för bloatwareapplikationer och androidtelefoner från en sekundär källa. Studien konkluderar att bloatware påverkar den upplevda prestandan genom batteritid, responstid och lagringsutrymme, samt att detta, framförallt sett till responstid, leder till en ökad benägenhet att investera i en ny mobiltelefon. Bloatware har möjligheten att påverka användares upplevelser och investeringsvilja med sina mobiltelefoner samtidigt som det inte går att fastställa att bloatware är den enda bidragande faktorn till den upplevda prestandaförsämringen.

This item contains the agenda, minutes, and attachments for the Faculty Senate meeting on this date. There may be additional materials from the meeting available at the Faculty Center.

Fear of missing out (FoMO) is a type of internet slang used to describe the “pervasive apprehension that others might be having rewarding experiences from which one is absent” (Przybylski, Murayama, DeHaan & Gladwell, 2013, p. 1841). This pervasive fear encompasses an individual’s life and it is exacerbated by the social media updates of online counterparts. A study conducted by a South African pharmaceutical company revealed that 62% of the respondents have a constant fear of missing out on something that might be happening elsewhere. This study underscores the relevance of studying FoMO as a construct in the South African context. The current study aims to investigate the relationship between FoMO and satisfaction with life, as these two constructs have previously shown to be negatively correlated. The self-proclaimed FoMO sufferers who will be partaking in this study will be requested to complete an online questionnaire to establish the nature of the relationship between FoMO and satisfaction with life. / Psychology / M.A. (Clinical Psychology)

Responsive analytics are rapidly taking over the traditional data analytics dominated by the post-fact approaches in traditional data warehousing. Recent advancements in analytics demand placing analytical engines at the forefront of the system to react to updates occurring at high speed and detect patterns, trends and anomalies. These kinds of solutions find applications in Financial Systems, Industrial Control Systems, Business Intelligence and on-line Machine Learning among others. These applications are usually associated with Big Data and require the ability to react to constantly changing data in order to obtain timely insights and take proactive measures. Generally, these systems specify the analytical results or their basic elements in a query language, where the main task then is to maintain these results under frequent updates efficiently. The task of reacting to updates and analyzing changing data has been addressed in two ways in the literature: traditional business intelligence (BI) solutions focus on historical data analysis where the data is refreshed periodically and in batches, and stream processing solutions process streams of data from transient sources as flow (or set of flows) of data items. Both kinds of systems share the niche of reacting to updates (known as dynamic evaluation); however, they differ in architecture, query languages, and processing mechanisms. In this thesis, we investigate the possibility of a reactive and unified framework to model queries that appear in both kinds of systems. In traditional BI solutions, evaluating queries under updates has been studied under the umbrella of incremental evaluation of updates that is based on relational incremental view maintenance model and mostly focus on queries that feature equi-joins. Streaming systems, in contrast, generally follow the automaton based models to evaluate queries under updates, and they generally process queries that mostly feature comparisons of temporal attributes (e.g., timestamp attributes) along-with comparisons of non-temporal attributes over streams of bounded sizes. Temporal comparisons constitute inequality constraints, while non-temporal comparisons can either be equality or inequality constraints, hence these systems mostly process inequality joins. As starting point, we postulate the thesis that queries in streaming systems can also be evaluated efficiently based on the paradigm of incremental evaluation just like in BI systems in a main-memory model. The efficiency of such a model is measured in terms of runtime memory footprint and the update processing cost. To this end, the existing approaches of dynamic evaluation in both kind of systems present a trade-off between memory footprint and the update processing cost. More specifically, systems that avoid materialization of query (sub) results incur high update latency and systems that materialize (sub) results incur high memory footprint. We are interested in investigating the possibility to build a model that can address this trade-off. In particular, we overcome this trade-off by investigating the possibility of practical dynamic evaluation algorithm for queries that appear in both kinds of systems, and present a main-memory data representation that allows to enumerate query (sub) results without materialization and can be maintained efficiently under updates. We call this representation the Dynamic Constant Delay Linear Representation (DCLR). We devise DCLRs with the following properties: 1) they allow, without materialization, enumeration of query results with bounded-delay (and with constant delay for a sub-class of queries); 2) they allow tuple lookup in query results with logarithmic delay (and with constant delay for conjunctive queries with equi-joins only); 3) they take space linear in the size of the database; 4) they can be maintained efficiently under updates. We first study the DCLRs with the above-described properties for the class of acyclic conjunctive queries featuring equi-joins with projections and present the dynamic evaluation algorithm. Then, we present the generalization of thiw algorithm to the class of acyclic queries featuring multi-way theta-joins with projections. We devise DCLRs with the above properties for acyclic conjunctive queries, and the working of dynamic algorithms over DCLRs is based on a particular variant of join trees, called the Generalized Join Trees (GJTs) that guarantee the above-described properties of DCLRs. We define GJTs and present the algorithms to test a conjunctive query featuring theta-joins for acyclicity and to generate GJTs for such queries. To do this, we extend the classical GYO algorithm from testing a conjunctive query with equalities for acyclicity to test a conjunctive query featuring multi-way theta-joins with projections for acyclicity. We further extend the GYO algorithm to generate GJTs for queries that are acyclic. We implemented our algorithms in a query compiler that takes as input the SQL queries and generates Scala executable code – a trigger program to process queries and maintain under updates. We tested our approach against state of the art main-memory BI and CEP systems. Our evaluation results have shown that our DCLRs based approach is over an order of magnitude efficient than existing systems for both memory footprint and update processing cost. We have also shown that the enumeration of query results without materialization in DCLRs is comparable (and in some cases efficient) as compared to enumerating from materialized query results.

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