Distributed simulations require efficient communication to represent complex scenarios, which presents a great challenge. This paper investigates the use of message-oriented middleware (MOM) to address this challenge by integrating the flight simulator X-Plane with the tactical simulator TACSI and evaluating the performance of different data transfer approaches. The study assesses performance by measuring the maximum sustainable throughput (MST) and the latency of a publish-subscribe-based MOM system. Two data distribution methods are compared: single-topic publishing and publishing to multiple subtopics. The results show that single-topic publishing achieves higher MST and lower latency when transmitting the same data volume. These findings provide valuable insights for deciding the state distribution method for publish-subscribe MOM systems. Additionally, this study highlights the limitations of manual determination of MST and underlines the need for accurate performance measurement techniques. / Distribuerade system kräver effektiv kommunikation för att representera komplexa scenarion, vilket utgör en betydande utmaning. Denna rapport använder meddelandeorienterad mellanprogramvara (MOM) för att angripa denna utmaning genom att integrera flygsimulatorn X-Plane med den taktiska simulatorn TACSI och sedan utvärdera prestandan av olika dataöverföringsmetoder. Studien utvärderar prestandan genom att mäta den maximala genomströmningskapaciteten och latensen på ett publish-subscribe-baserat MOM-system. Två dataöverföringsmetoder jämförs: single-topic publicering och publicering på flera subtopics. Resultatet visar att single-topic publicering ger högre maximal genomströmningskapacitet och lägre latens vid samma mängd data. Dessa upptäckter ger värdefulla insikter när man ska bestämma metod för dataöverföring i publish-subscribe-baserade MOM-system. Slutligen visar denna studie på begränsningarna med att manuellt bestämma MST och behovet av mer noggranna tekniker för att mäta maximal genomströmningskapacitet.

The amount of available text data has increased rapidly in the latest years, making it difficult for an everyday user to find relevant information. To solve this, NLP and visualisation methods have been developed for extracting valuable information from text and presenting it to the user. The aim of this project is to implement a proof-of-concept visualisation prototype for exploring a large amount of Swedish news articles with related metadata and investigate the temporal and relational aspects of the data. The project was divided into three major parts. In the first part, sketches of the visualisation were designed and evaluated through user tests. The second part consisted of designing and implementing a NLP pipeline, using BERTopic, where both Dynamic Topic Modeling (DTM) and Hierarchical Topic Modeling (HTM) were used. Some parameters of the pipeline were evaluated using evaluation metrics and through visual inspection, for instance a Swedish sentence transformer. The final part consisted of implementing and evaluating the visualisation prototype. The project resulted in a web-based visualisation, presenting the NLP results, with two different views: a top 10 topics view and a hierarchical view containing all topics. The prototype has various features, e.g., clicking and hovering for details-on-demand and options for changing and altering the view. The prototype was then evaluated through an internal case study and user tests. For the user tests, there were two groups of participants: people working in the journalism field and people working closely to the NLP field. Both groups thought there was more value in viewing the top 10 topics view than the hierarchical view. Furthermore, the quality of the top 10 topics view was considered higher overall compared to the hierarchical view. In the end, the result of this project is a proof-of-concept visualisation prototype presenting topics of Swedish news articles, over time and in relation to each other. A few possible improvement possibilities include improving the hierarchical relations between the topics and the run time of the topic model and prototype. Also, the prototype may be further improved with additional features, e.g., real-time data, a map, the full text of the articles and a search function. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

Software produced by big companies such as SAP is often feature rich, very expensive and thus only affordable by other big companies. It usually takes months and special trained consultants to install and manage such software. However as vendors move to other market segments, featuring smaller companies, different requirements arise. It is not possible for medium or small sized companies to spend as much money for business software solutions as big companies do. They especially cannot afford to hire expensive consultants. It is on the other hand not economic for the vendor to provide the personnel free of charge. One solution to this dilemma is bundling all customer support cases on special Web platforms, such as customer support forums. SAP for example has the SAP Community Network1. This has the additional benefit that customers may help each other. (...)

The consumer behaviour is adapting within industries due to new technologies such as smart phones. As consumer behaviour changes so do companies by adapting their way of engaging and interacting with their customers. This provides potential to innovate new service offerings. Successfully launching new services which provide value for the customer is faced with risk of failure. To mitigate risks associated with failure, a clear understanding of the customer can aid with understanding what value a service offering should provide to be successfully adopted by the market. Due to customer experience being unique for each individual, personalization is a technique which could be used within software to improve the customer experience. Challenges could arise in terms of scarcity of data which can impact the performance negatively of a data driven algorithm. However, veracity is another aspect of data known to be associated with the potential to improve performance. Based on these two issues, this study conducted a sequential mixed methods study consisting of an etnographic study on Instagram to better understand the customer experience within nightlife. Furthermore, the netnographic study enabled the construction of a gold standard, which were used while conducting a GSDMM topic modelling experiment with the purpose to evaluate what topics required further pre-processing due to high ambiguity of the text content. Findings from the netnographic study and its implication for customer experience was discussed from the point of view of a software service offering. This study suggests software offerings within nightlife to improve customer experience during the pre-purchasing phase by considering aspects related to age, interests in atmosphere, type of activity, preferred music genres, spending time with friends or facilitating escapism. The discussed service has negligible control during the post-purchasing stage suggesting that the firm could innovate controlled touchpoints, such experiences can be related to anticipation, joy, celebration, social adventures, memory of previous nights out (stories), current music preferences or new desires occurring spontaneously. Upon adopting a service dominant logic, this study suggests that software services can facilitate the customer experience within nightlife through co-creation, since with the proper usage of data, network effects could occur between the customer and an organizer or venue within nightlife, but also between customer to customer. A future study is proposed to investigate how the coordination could be conducted through crowd-sourced based interactions where the software functions as an overseer of a multi-actor setting to provide further insights regarding how such coordination impacts the co-creation of value. / Konsumentbeteende förändras inom industrier mot bakgrund av att nya teknologier introduceras, till exempel smarttelefoner. Då konsumentbeteendet förändras, gör även företagen förändringar i hur de involverar och interagerar med kunder. Dessa förändringar ger möjligheter för att utveckla eller ta fram nya tjänster. Samtidigt finns utmaningar vid lansering av nya tjänster. För att minska riskerna vid lansering av nya tjänster kan en god förståelse av konsumenten tydliggöra vilket värde en tjänst bör erbjuda för att bemötas positivt av marknaden. Då kundupplevelse är unikt för varje person, kan individualiseringstekniker inom mjukvara tillämpas för att förbättra kundupplevelsen. Det kan däremot uppstå problem när det är bristfälligt med data som algoritmen kan använda sig av. Kvalité och valt fokus på data kan dock förbättra algoritmens prestationer. Mot bakgrund av de två redogjorda problemen, genomfördes en sekventiellt blandad metodstudie bestående av en nätnografisk studie på Instragram för att utöka förståelsen av kundupplevelsen inom nattlivet. Resultatet från nätnografistudien har därefter använts för att konstruera en guldstandard vilket tillämpades på en ämnesklassificerare vid namn GSDMM. Syftet med ämnesklassifikationsexperimenten var att förstå vilka ämnen som skrivs med en hög grad av tvetydighet och därför komma att kräva en mer gedigen förbehandling av den textbaserade informationen. För att tillägga, har insikter från nätnografistudien diskuterats och dess betydelse för kundupplevelsen utifrån en mjukvarutjänsts perspektiv. Studien tyder på att mjukvarutjänster inom nattlivet kan förbättra kundupplevelsen i förköpsstadiet genom att beakta aspekter relaterat till ålder, föredragen stämning, typ av aktivitet, föredragna musikgenrer, att vara med vänner eller framhävning av eskapism. Den diskuterade tjänsten har försumbar kontroll av kundupplevelsen i efterköpsstadiet, därför föreslås införandet av kontrollerbara interaktioner med tjänsten. Sådana upplevelser bör fokusera på att spänna förväntningar, glädje, firande, sociala äventyr, minnen från tidigare utgångar (berättelser), föredragen musik i stunden eller nya önskemål som uppstår spontant under utgången. Vid tillämpning av tjänstedominantlogik indikerar studien att mjukvarutjänster kan förbättra kundupplevelsen genom samskapande, eftersom vid korrekt användning av data, kan nätverkseffekter förekomma mellan dels kund och organisatör eller lokal inom nattlivet, men även mellan kund och kund. Fortsatta studier föreslås forska om hur samverkan kan koordineras genom crowdsource-baserade interaktioner där en mjukvarutjänst fungerar som kontrollant/moderator av en multi-aktörkonstellation. En sådan studie kan ge förståelse om hur koordinationen påverkar värdeskapandet under samverkan.

This study was conducted in partnership with RISE and Google, within the initiative “Academy of Lifelong Learning”, aiming to propel the digital transformation in the Swedish public sector. The study investigated the digital maturity of 18 authorities in terms of maturity level (early, developing maturing), and within the driving areas (1) Citizen Centricity, (2) Leadership, (3) Digital Toolbox and (4) Security and Sustainability. Further, it explored how citizens’ sentiment towards public authorities relates to the organizations’ digital maturity scores. The results of a digital maturity survey showed that 16 of the 18 contributing organizations were developing, whereas two scored just enough to be classified as maturing. The organizations performed best within Security and Sustainability, and the worst within the category Digital Toolbox—where the biggest competence gaps were also identified. To unlock citizens’ sentiment towards the authorities, sentiment analysis was conducted on Facebook data. In a correlation analysis, a significant negative relationship was surprisingly found between (i) maturity score and (ii) sentiment score, as well as between (i) maturity score and (ii) positive comments. Presumably, this can be explained by citizens interacting the most with the more mature organizations and thus expressing their dissatisfaction more. However, more analysis is needed to draw conclusions. / Studien genomfördes i samarbete med RISE och Google inom initiativet ”Akademin för livslångt lärande” (Academy of Lifelong Learning), som syftar till att driva på den digitala transformationen i den svenska offentliga sektorn. Studien undersökte 18 myndigheters digitala mognad med fokus på mognadsnivå (early, developing maturing), och inom de drivande områdena (1) medborgarperspektivet, (2) ledarskap, (3) digitala verktygslådan och (4) säkerhet och hållbarhet. Vidare undersöktes medborgarnas attityder gentemot offentliga myndigheter i relation till organisationernas digitala mognad. Resultatet från mognadsundersökningen visade att 16 av de 18 medverkande organisationerna var developing, medan två organisationer precis kunde klassificeras som mature. Organisationerna presterade bäst inom säkerhet och hållbarhet och sämst inom kategorin digitala verktygslådan—där de största kompetensbristerna även identifierades. För att utvärdera medborgarnas attityder gentemot myndigheterna genomfördes en sentimentanalys baserat på data från Facebook. I en korrelationsanalys hittades överraskande nog en signifikant negativt samband mellan (i) digital mognad och (ii) sentimentpoäng, samt mellan (i) digital mognad och (ii) positiva kommentarer. Detta kan antagligen förklaras med att medborgarna interagerar mer med de mest mogna organisationerna och därmed är mer benägna att utrycka sitt missnöje gentemot dem. Ytterligare analys behövs dock för att kunna dra sådana slutsatser och förklara resultatet.

The aim of this study was to examine how American news media covered Donald Trump's presidential campaign in the election of 2016, as well as discussing the possible consequences of such reporting on the election results. Using mixed methods, 3652 digital news articles were studied by discourse analysis and topic modeling through MALLET. The study found that a substantial number of articles were dedicated to such non-political news reporting as scandals, portraying an image of Trump as someone who can get away with doing whatever he wants. Furthermore, the results of the study found that media helped to convey Trump’s views of minorities, doing so in particularly by citing him. The media also relied largely on polls. Comparison of the candidates through these polls enhanced the image of the election campaign as nothing more than a horse race, as well as turning up Trumps entertainment value. As the campaign continued, the reporting got more aggressive towards Trump. At the same time there was an element of wanting to balance the critical articles about him by simultaneously writing negatively about other candidates. The study concludes that all of the non-political new stories might have directed focus away from the important policy issues, leading to people voting for candidates without  the proper insight into their politics.

Master of Science / Department of Computing and Information Sciences / Doina Caragea / The idea of socializing with other people of different backgrounds and cultures excites the web surfers. Today, there are hundreds of Social Networking sites on the web with millions of users connected with relationships such as "friend", "follow", "fan", forming a huge graph structure. The amount of data associated with the users in these Social Networking sites has resulted in opportunities for interesting data mining problems including friendship link and interest predictions, tag recommendations among others. In this work, we consider the friendship link prediction problem and study a topic modeling approach to this problem. Topic models are among the most effective approaches to latent topic analysis and mining of text data. In particular, Probabilistic Topic models are based upon the idea that documents can be seen as mixtures of topics and topics can be seen as mixtures of words. Latent Dirichlet Allocation (LDA) is one such probabilistic model which is generative in nature and is used for collections of discrete data such as text corpora. For our link prediction problem, users in the dataset are treated as "documents" and their interests as the document contents. The topic probabilities obtained by modeling users and interests using LDA provide an explicit representation for each user. User pairs are treated as examples and are represented using a feature vector constructed from the topic probabilities obtained with LDA. This vector will only capture information contained in the interests expressed by the users. Another important source of information that is relevant to the link prediction task is given by the graph structure of the social network. Our assumption is that a user "A" might be a friend of user "B" if a) users "A" and "B" have common or similar interests b) users "A" and "B" have some common friends. While capturing similarity between interests is taken care by the topic modeling technique, we use the graph structure to find common friends. In the past, the graph structure underlying the network has proven to be a trustworthy source of information for predicting friendship links. We present a comparison of predictions from feature sets constructed using topic probabilities and the link graph separately, with a feature set constructed using both topic probabilities and link graph.

Le web est maintenant devenu une importante source d’information et de divertissement pour un grand nombre de personnes et les techniques pour accéder au contenu désiré ne cessent d’évoluer. Par exemple, en plus de la liste de pages web habituelle, certains moteurs de recherche présentent maintenant directement, lorsque possible, l’information recherchée par l’usager. Dans ce contexte, l’étude des requêtes soumises à ce type de moteur de recherche devient un outil pouvant aider à perfectionner ce genre de système et ainsi améliorer l’expérience d’utilisation de ses usagers. Dans cette optique, le présent document présentera certaines techniques qui ont été développées pour faire l’étude des requêtes de recherche web soumises à un moteur de recherche. En particulier, le travail présenté ici s’intéresse à deux problèmes distincts. Le premier porte sur la classification non-supervisée d’un ensemble de requêtes de recherche web dans le but de parvenir à regrouper ensemble les requêtes traitant d’un même sujet. Le deuxième problème porte quant à lui sur la détection non-supervisée des entités nommées contenues dans un ensemble de requêtes qui ont été soumises à un moteur de recherche. Les deux techniques proposées utilisent l’information supplémentaire apportée par la connaissance des pages web qui ont été visitées par les utilisateurs ayant émis les requêtes étudiées.

Over the last decades, there have been remarkable shifts in the area of Information Retrieval (IR) as huge amount of information is increasingly accumulated on the Web. The gigantic information explosion increases the need for discovering new tools that retrieve meaningful knowledge from various complex information sources. Thus, techniques primarily used to search and extract important information from numerous database sources have been a key challenge in current IR systems. Topic modeling is one of the most recent techniquesthat discover hidden thematic structures from large data collections without human supervision. Several topic models have been proposed in various fields of study and have been utilized extensively for many applications. Latent Dirichlet Allocation (LDA) is the most well-known topic model that generates topics from large corpus of resources, such as text, images, and audio.It has been widely used in many areas in information retrieval and data mining, providing efficient way of identifying latent topics among document collections. However, LDA has a drawback that topic cohesion within a concept is attenuated when estimating infrequently occurring words. Moreover, LDAseems not to consider the meaning of words, but rather to infer hidden topics based on a statisticalapproach. However, LDA can cause either reduction in the quality of topic words or increase in loose relations between topics. In order to solve the previous problems, we propose a domain specific topic model that combines domain concepts with LDA. Two domain specific algorithms are suggested for solving the difficulties associated with LDA. The main strength of our proposed model comes from the fact that it narrows semantic concepts from broad domain knowledge to a specific one which solves the unknown domain problem. Our proposed model is extensively tested on various applications, query expansion, classification, and summarization, to demonstrate the effectiveness of the model. Experimental results show that the proposed model significantly increasesthe performance of applications.

Latent Dirichlet Allocation (LDA), a total probability generative model, is a three-tier Bayesian model. LDA computes the latent topic structure of the data and obtains the significant information of documents. However, traditional LDA has several limitations in practical applications. LDA cannot be directly used in classification because it is a non-supervised learning model. It needs to be embedded into appropriate classification algorithms. LDA is a generative model as it normally generates the latent topics in the categories where the target documents do not belong to, producing the deviation in computation and reducing the classification accuracy. The number of topics in LDA influences the learning process of model parameters greatly. Noise samples in the training data also affect the final text classification result. And, the quality of LDA based classifiers depends on the quality of the training samples to a great extent. Although parallel LDA algorithms are proposed to deal with huge amounts of data, balancing computing loads in a computer cluster poses another challenge. This thesis presents a text classification method which combines the LDA model and Support Vector Machine (SVM) classification algorithm for an improved accuracy in classification when reducing the dimension of datasets. Based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN), the algorithm automatically optimizes the number of topics to be selected which reduces the number of iterations in computation. Furthermore, this thesis presents a noise data reduction scheme to process noise data. When the noise ratio is large in the training data set, the noise reduction scheme can always produce a high level of accuracy in classification. Finally, the thesis parallelizes LDA using the MapReduce model which is the de facto computing standard in supporting data intensive applications. A genetic algorithm based load balancing algorithm is designed to balance the workloads among computers in a heterogeneous MapReduce cluster where the computers have a variety of computing resources in terms of CPU speed, memory space and hard disk space.