The current thesis investigates data-driven simulation decision-making with field-quality consumer data. This is accomplished by outlining the benefits and uses of combining machine learning and simulation in the literature and by locating barriers to the use of machine learning (ML) in the simulation subsystems at a case study organization. Additionally, an implementation is carried out to demonstrate how Scania departments can use this technology to analyze their current data and produce results that support the exploration of the simulation space and the identification of potential design issues so that preventative measures can be taken during concept development. The thesis' findings provide an overview of the literature on the relationship between machine learning and simulation technologies, as well as limitations of using machine learning in simulation systems at large scale manufacturing organizations. Support vector machines, logistic regression, and Random Forest classifiers are used to demonstrate one possible use of machine learning in simulation.

Datadriven kultur har flitigt nämnts i litteraturen som en tydlig framgångsfaktor för stora verksamheter för att skapa konkurrenskraft på marknaden.  Genom att verksamheter kan ta strategiska beslut baserat på stora mängder data förankrad i verkligheten undviks beslut som tas på magkänsla, således leder till optimering av verksamheter. Dock har små och medelstora företag (SMFs) halkat efter i utvecklingen då verksamheterna ofta saknar resurser och kompetens för att möjliggöra en datadriven kultur. Syftet med forskningen är att identifiera framgångsfaktorer speciellt inriktade på SMFs och skapa en sammanställning som SMF kan ta del av för att skapa en datadriven kultur. Den primära datainsamlingen genomfördes genom en kvalitativa ansats och fallstudie som forskningsmetod med semi-strukturerade intervjuer inriktade mot IT-branschen inom SMF som besatt på relevant kunskap inom ämnesområdet. Respondenternas svar har analyserats i jämförelse med tidigare litteratur för att generera framgångsfaktorer som möjliggör en datadriven kultur hos SMFs. Resultatet av forskningen har genererat en sammanställning på totalt fyra bekräftade huvudkategorier och sexton bekräftade underkategorier varav åtta berikande underkategorier är nya framgångsfaktorer som uppkommit från intervjuerna. De identifierade framgångsfaktorerna kan anammas av SMF för att möjliggöra den digitala transformationen mot en datadriven kultur. Resultatet av forskningen illustrerar att SMFs har stora möjligheter att öka sin konkurrenskraft, affärsvärde och produktivitet genom att tillämpa framgångsfaktorerna inom SMF och att en datadriven kultur inte är begränsade till stora verksamheter. / Data-driven culture has frequently been mentioned in the literature as a clear success factor for large enterprises (LEs) creating competitive advantages in the market. As enterprises can make strategic decisions based on large amounts of data anchored in reality, decisions are based on gut feeling, thus leading to optimization of enterprises. However, small and medium-sized enterprises (SMEs) have fallen behind in development as the enterprises often lack resources and knowledge to enable a data-driven culture. The purpose of the research is to identify success factors specifically focused on SMEs and create a compilation of which SMEs can adopt to create a data-driven culture. The primary data collection was conducted with a qualitative approach carrying out a case study with semi-structured interviews focused on the IT industry within SMEs that are obsessed with relevant knowledge in the subject area. The interviewees' responses have been analyzed in comparison with previous literature to generate success factors that enable a data-driven culture in SMEs. The results of the research have generated a compilation of a total of four confirmed main categories and sixteen confirmed subcategories, of which eight enriching subcategories are new success factors that have emerged from the interviews. The identified success factors can be adopted by SMEs to enable the digital transformation towards a data-driven culture. The results of the research illustrates that SMEs have great opportunities to increase in competitive advantages, business value and productivity by applying the success factors within SMEs and that the data-driven culture is not limited to LE.

Vehicle activated signs (VAS) display a warning message when drivers exceed a particular threshold. VAS are often installed on local roads to display a warning message depending on the speed of the approaching vehicles. VAS are usually powered by electricity; however, battery and solar powered VAS are also commonplace. This thesis investigated devel-opment of an automatic trigger speed of vehicle activated signs in order to influence driver behaviour, the effect of which has been measured in terms of reduced mean speed and low standard deviation. A comprehen-sive understanding of the effectiveness of the trigger speed of the VAS on driver behaviour was established by systematically collecting data. Specif-ically, data on time of day, speed, length and direction of the vehicle have been collected for the purpose, using Doppler radar installed at the road. A data driven calibration method for the radar used in the experiment has also been developed and evaluated. Results indicate that trigger speed of the VAS had variable effect on driv-ers’ speed at different sites and at different times of the day. It is evident that the optimal trigger speed should be set near the 85th percentile speed, to be able to lower the standard deviation. In the case of battery and solar powered VAS, trigger speeds between the 50th and 85th per-centile offered the best compromise between safety and power consump-tion. Results also indicate that different classes of vehicles report differ-ences in mean speed and standard deviation; on a highway, the mean speed of cars differs slightly from the mean speed of trucks, whereas a significant difference was observed between the classes of vehicles on lo-cal roads. A differential trigger speed was therefore investigated for the sake of completion. A data driven approach using Random forest was found to be appropriate in predicting trigger speeds respective to types of vehicles and traffic conditions. The fact that the predicted trigger speed was found to be consistently around the 85th percentile speed justifies the choice of the automatic model.

Increasing demands for accountability from both the public and the government have resulted in increasing pressure for higher education professionals to use data to support their choices. There is significant speculation that professionals at all levels of education lack the knowledge to implement data-driven decision making. However, empirical studies regarding whether or not professionals at four-year postsecondary institutions are utilizing data to guide programmatic and policy decisions are lacking. The purpose of this exploratory study was to explore the knowledge and habits of undergraduate admission professionals at four-year colleges and universities regarding their use of data in decision making. A survey instrument was disseminated and, the data collected from the instrument provided empirical information, which serves as the basis for a discussion about what specific knowledge admission professionals at four-year institutions possess and how they use data in their decision making. The instrument disseminated was designed specifically for this study. Therefore, before the research questions were addressed, Rasch analysis was utilized to evaluate the validity and reliability of the survey instrument. Data was then used to determine that undergraduate admission professionals perceived themselves as using data in their decision making. The results also indicated admission professionals feel confident in their ability to interpret and use data to in their decision making.

The use of data to drive or inform the decision making process is gaining traction in education. In response to the data driven decision making shift, an emerging group of scholars are beginning to discuss how the data movement in education may be viewed using a critical race theory (CRT) framework. With a focus on implications for racial equity, this study explores the ways and to what degree data are valued or practically applied in the decision making process in Manitoba. Participants for this qualitative research study include ten Manitoban school superintendents. Drawing attention to the ways in which data-driven practices like all other practice in education, are not neutral acts, this study looks to contribute to the growing research area on Canadian data-driven decision making and CRT. Findings from this study indicate that school divisions in the province of Manitoba are increasingly driven by data that privileges Western or colonial ways of knowing. Some recommendations for further research include, using achievement data to resist racial oppression, exploring the dangers of Gap Talk, and looking at whether data literacy includes notions of power and privilege. / February 2017

Estudar as fontes utilizadas nas notícias de jornalismo guiado por dados (JGD) é a proposta desta dissertação. Para tanto, revisita as classificações de fontes trabalhadas por teóricos da área e situa o contexto atual, derivado de transformações sociais e tecnológicas, sob a perspectiva de sociedade em rede e do jornalismo em rede. O foco do estudo está em descobrir quais fontes são acionadas em notícias do JGD, que emerge neste cenário a partir dos anos 2000. Analisa um corpus constituído por 60 notícias veiculadas nos jornais O Globo, The New York Times e La Nación, como veículos tradicionais, e Nexo, FiveThirtyEight e Chequeado, como veículos nativos. A partir do cruzamento entre a teoria e o estudo empírico, propõe a classificação de tipos de fontes nas notícias de JGD. São eles: arquivo documental, estatística e reprodução. Por meio dessa classificação, busca preencher uma lacuna no quadro teórico sobre fontes, superficialmente discutido no jornalismo até então, trazendo o uso de documentos como protagonista neste cenário. / Studying the news sources used in data-driven journalism (DDJ) practices is the proposal of this dissertation. The theoretical approach includes classifications of news sources already discussed in journalism studies. Considering the contemporary context, which is modified by social and technological transformations, this study operates from the networked society and network journalism perspectives. The main point is to detect the use of journalism sources in news developed by DDJ techniques, which emerges in this scenario during the 2000’s. It analyzes 60 news records published by O Globo, The New York Times and La Nación, as traditional media, and Nexo, FiveThirtyEight and Chequeado, as the native ones. Combining the theory and the empirical study, it proposes a classification by types of sources of DDJ news: documentary file, statistics and reproduction. Through this classification, it aims to fulfill a gap found in the theoretical sources approach, which is superficially discussed in journalism until now, bringing the use of documents as a protagonist in this scenario.

Este trabalho tem o intuito, de apresentar alguns conceitos, relativos à identifi cação de sistemás, tanto lineares quanto não linearep, além da ideia de referência virtual para, em conjunto com a teoria de projeto "de controladores baseados em dados, propor uma forrha de projeto de controladores não lineares baseados em identificação de sistemas. A utilização de referência virtual para a obtenção dos sinais necessários para a caracterização do controlador ótimo de um sistema e utilizado no método VRFT (Virtual Reference Feedback Tuning). Este método serve como base para o desenvolvimento da proposta deste trabalho que, em conjunto com a teoria de identificação de sistemas não lineares, permite a obteriçãci do controlador ótimo que leva o sistema a se comportar como especificado em malha fechada. Em especial optou-se pela caracterização do controlador utilizando estrutura de modelos racional, por esta ser uma classe bastante abrangente no que - diz respeito à quantidade de sistemas reais que ela é capaz de descrever. Fara demonstrar o potencial do método proposto para projeto de controladores, são apresentados ecemplos ilustrativos em situações onde o controlador ideal consegue ser representado pela classe de modelos, e quando isso não é possível. / This work aims to present some concepts related to linear and nonlinear system identification, as well as the •concept of virtual reference that, together with data based controller design's theory, provides design framework for nonlinear controllers. The Virtual Reference Feedback Tuning method (VRFT) is used as a basis for the current proposal, where we propose to unite nonlinear system identification algorithms and virtual reference to obtain the ideal controller: the one which makes the system behave as desired in closed loop. It was choosen to model the controller as a rational model due the wide variety of practical systems that can be represented by this model structure. For rational system identification we used an iterative algorithm which, based on the signal from input and output of the pIant, allows to identify the parameters of the pre defined controller structure with the signals obtained by virtual reference. To demonstrate the operation of the proposed identification controller methodology, illustrative examples are presented in situations where the ideal controller can be represented by the class of modeIs, and also when it is not possible.

The focus of this study was a comparison between the perceptions of school-based licensed educators in Title I and non-Title I schools in Northeast Tennessee as measured by the TELL Tennessee Survey and each school’s overall composite TVAAS score. The factor variables were professional development, instructional practices and support, teacher leadership, and school leadership. This dissertation was a quantitative study of teachers’ perceptions of data-driven professional learning and TVAAS composite scores. A one-way analysis of variance (ANOVA) was conducted to evaluate the difference between teachers’ perceptions of data-driven professional development and student TVAAS data. An independent samples t-test was used to evaluate the difference between teachers’ perceptions and poverty levels, as determined by Title I status. The dependent variable was the response to the TELL Tennessee survey questions by Northeast Tennessee school-based licensed educators. Research indicated no significant difference in Northeast Tennessee teachers’ perceptions of professional learning as measured by the TELL Tennessee survey in the dimensions of professional development, instructional practices and support, and teacher leadership as related to TVAAS composite scores. The research found a significant difference in teachers’ perceptions in the dimension of school leadership as related to TVAAS composite scores. There were no significant differences in teachers’ perceptions as measured by the TELL Tennessee survey in the dimensions of professional development, instructional practices and support, teacher leadership, and school leadership between Title I and non-Title I schools.

abstract: In the artificial intelligence literature, three forms of reasoning are commonly employed to understand agent behavior: inductive, deductive, and abductive.  More recently, data-driven approaches leveraging ideas such as machine learning, data mining, and social network analysis have gained popularity. While data-driven variants of the aforementioned forms of reasoning have been applied separately, there is little work on how data-driven approaches across all three forms relate and lend themselves to practical applications. Given an agent behavior and the percept sequence, how one can identify a specific outcome such as the likeliest explanation? To address real-world problems, it is vital to understand the different types of reasonings which can lead to better data-driven inference.   This dissertation has laid the groundwork for studying these relationships and applying them to three real-world problems. In criminal modeling, inductive and deductive reasonings are applied to early prediction of violent criminal gang members. To address this problem the features derived from the co-arrestee social network as well as geographical and temporal features are leveraged. Then, a data-driven variant of geospatial abductive inference is studied in missing person problem to locate the missing person. Finally, induction and abduction reasonings are studied for identifying pathogenic accounts of a cascade in social networks. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2019

The increasing intensity and frequency of precipitation in recent decades, combined with the human interventions in watersheds, has drastically altered the natural regimes of water and sediment transport in watersheds over the whole contiguous United States. Sediment-transport related concerns include the sustainability of aquatic biology, the stability of the river morphology, and the security and vulnerability of various riverine structures. For the present context, the concerns are related to the acceleration of upland erosion (sediment production) and in-stream sediment-transport processes that eventually lead to sediment accumulation at culverts (structures that pass streams under roadways). This nuisance has become widespread in many transportation agencies in the United States, as it has a direct bearing on maintaining normal culvert operations during extreme flows when these waterway crossings are essential for the communities they serve. Despite the prevalence of culvert sedimentation, current specifications for culvert design do not typically consider aspects of sediment transport and deposition. The overall study objective is to systematically identify the likelihood of culvert sedimentation as a function of stream and culvert geometry, along with landscape characteristics (process drivers of culvert sedimentation) in the culvert drainage area. The ideal approach for predicting sedimentation is to track sediment sources dislocated from the watershed, their overland movement, and their delivery into the streams using physical-based modeling. However, there are considerable knowledge gaps in addressing the sedimentation at culverts as an end-to-end process, especially in connecting the upland with in-stream processes and simulating the sediment deposition at culverts in non-uniform, unsteady flows, while also taking into account the vegetation growth in culverts’ vicinity. It is, therefore, no surprise that existing research, textbooks, and guidelines do not typically provide adequate information on sediment control at culverts. This dissertation presents a generalizable data-driven framework that integrates various machine-learning and visual analytics techniques with GIS in a web-based geospatial platform to explore the complex environmental processes of culvert sedimentation. The framework offers systematic procedures for (1) classifying the culvert sedimentation degree using a time-series of aerial images; (2) identifying key process-drivers from a variety of environmental and culvert structural characteristics through feature selections and interactive visual interfaces; (3) supporting human interactions to perceive empirical relationships between drivers and the culvert sedimentation degree through multivariate Geovisualization and Self-Organizing Map (SOM); and (4) forecasting culvert sedimentation potential across Iowa using machine learning algorithms. Developed using modular design and atop national datasets, the framework is generalizable and extendable, and therefore can be applied to address similar river management issues, such as habitat deterioration and water pollution, at the Contiguous US scale. The platform developed through this Ph.D. study offers a web-based problem-solving environment for a) managing inventory and retrieving culvert structural information; b) integrating diverse culvert-related datasets (e.g., culvert inventory, hydrological and land use data, and observations on the degree of sedimentation in the vicinity of culverts) in a digital repository; c) supporting culvert field inspections and real-time data collection through mobile devices; and d) hosting the data-driven framework for exploring culvert sedimentation drivers and forecasting culvert sedimentation potential across Iowa. Insights provided through the data-driven framework can be applied to support decisions for culvert management and sedimentation mitigation, as well as to provide suggestions on parameter selections for the design of these structures.