The growth of technology and the proliferation of information made modern complex systems more fragile and vulnerable. As a result, competitive advantage is no longer achieved exclusively through strategic planning but by developing an influential cadre of technical people who can efficiently manage and navigate modern complex systems. The dissertation aims to provide educators, practitioners, and organizations with a model that helps to measure individuals’ systems thinking skills, complex problem solving, personality traits, and the impacting demographic factors such as managerial and work experience, current occupation type, organizational ownership structure, and education level. The intent is to study how these skills, traits, and demographic factors can impact an individual’s abilities in working effectively with modern complex systems. These skills and traits also enable individuals to display distinctive patterns of thoughts in developing solutions that address complex technical problems. The dissertation further provides strategies for the management and enhancement of technical individuals based on assessing their performance. The model consists of three established instruments: Systems Thinking Skills, Perceived Complex Problem-Solving (PCPS), and Myers-Briggs Personality Type Indicator. These instruments are applied at the individual level to identify strengths and weak areas of improving an organization. In particular, PCPS is a researcher-developed instrument that captures the complex problem-solving perception of individuals. The different samples of the population for the dissertation come from students and practitioners.

More and more data is gathered every day and time series are a major part of it. Due to the usefulness of this type of data, it is analyzed in many application domains. While there already exists a broad variety of methods for this task, there is still a lack of approaches that address new requirements brought up by large-scale time series data like cross-domain usage or compensation of missing data. In this paper, we address these issues, by presenting novel approaches for generating and forecasting large-scale time series data.

The Dresden Database Systems Group focuses on the advancement of data management techniques from a system level as well as information management perspective. With more than 15 PhD students the research group is involved in a variety of larger research projects ranging from activities to exploit modern hardware for scalable storage engines to advancing statistical methods for large-scale time series management. The group is visible at an international level as well as actively involved in cooperations with national and regional research partners

<p>In the real world, uncertainty is a common challenging problem faced by individuals, organizations, and firms. Decision quality is highly impacted by uncertainty because decision makers lack complete information and have to leverage the loss and gain in many possible outcomes or scenarios. This study explores dynamic decision making (with known distributions) and decision learning (with unknown distributions but some samples) in not-for-profit operations and supply chain management. We first study dynamic staffing for paid workers and volunteers with uncertain supply in a nonprofit operation where the optimal policy is too complex to compute and implement. Then, we consider dynamic inventory control and pricing under both supply and demand uncertainties where unmet demand is lost leading to a challenging non-concave dynamic problem. Furthermore, we explore decision learning from limited data of focal system and available data of related but different systems by transfer learning, cross learning, and co-learning utilizing the similarities among related systems.</p>

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Projektet genomfördes i samarbete med Siemens Healthineers i syfte att utreda möjligheter till att prognostisera vårdflöden. Det genom att undersöka hur big data tillsammans med maskininlärning kan utnyttjas för prediktiv analys. Projektet utgjordes av två fallstudier med mål att, baserat på data från tidigare MRT-undersökningar, förutspå undersökningstider för kommande undersökningar respektive identifiera patienter som riskerar att missa inbokad undersökning. Fallstudierna utfördes med hjälp av programmeringsspråket R och tre olika inbyggda funktioner för maskininlärning användes för att ta fram prediktiva modeller för respektive fallstudie. Resultaten från fallstudierna gav en indikation på att det med en större datamängd av bättre kvalitet skulle vara möjligt att förutspå undersökningstider och vilka patienter som riskerar att missa sin inbokade undersökning. Det talar för att den här typen av prediktiva analyser kan användas för att prognostisera vårdflöden, något som skulle kunna bidra till ökad effektivitet och kortare väntetider i vården. / This project was performed in cooperation with Siemens Healthineers. The project aimed to investigate possibilities to forecast healthcare processes by investigating how big data and machine learning can be used for predictive analytics. The project consisted of two separate case studies. Based on data from previous MRI examinations the aim was to investigate if it is possible to predict duration of MRI examinations and identify potential no show patients. The case studies were performed with the programming language R and three machine learning methods were used to develop predictive models for each case study. The results from the case studies indicate that with a greater amount of data of better quality it would be possible to predict duration of MRI examinations and potential no show patients. The conclusion is that these types of predictive models can be used to forecast healthcare processes. This could contribute to increased effectivity and reduced waiting time in healthcare.

Since the early 2000s the space industry has undergone significant changes such as the advent of reusable launch vehicles and an increase of commercial opportunities. This new space age is characterized by a dynamic entrepreneurial climate, lowered barriers to access space and the emergence of new markets. New business models are being developed by many actors and the merging of space and other sectors continues, facilitating innovative and disruptive opportunities. Already established companies are adapting in various ways as efforts to stay relevant are gaining attention. The previous pace of development that was exclusively determined by governmental programs are now largely set by private and commercial ventures. Relating to all trends, new technologies and driving forces in the space industry is no trivial matter. By analyzing and examining identified trends and technologies the author has attempted to discern those that will have a significant impact on the industrial environment during the next decade. Market assessments have been summarized and interviews have been carried out. Discussions and conclusions relating to the services provided by the Swedish Space Corporation are presented. This report is intended to update the reader on the current status of the space industry, introduce concepts and provide relevant commentary on many important trends. / Sedan början av 2000-talet har det skett markanta förändringar inom rymdindustrin, såsom utvecklingen av återanvändningsbara raketer och en ökad mängd kommersiella möjligheter. Denna nya rymder a karaktäriseras av ett dynamiskt klimat för entreprenörer, minskande barriärer för att etablera rymdverksamhet och uppkomsten av nya marknader. Nya affärsmodeller utvecklas och integrering mellan rymden och andra industrier fortsätter, vilket ger utrymme för utveckling av innovativa och disruptiva idéer. Redan etablerade företag anpassar sig till förändringarna på olika sätt och ansträngningar för att bibehålla relevans prioriteras. Utvecklingstakten inom branschen var tidigare dominerad av statliga program men är nu alltmer influerad av privata och kommersiella satsningar. Att relatera till ny teknik, nuvarande trender och drivkrafter inom rymdindustrin är Jacob Ask is pursuing a Master of Science degree in Aerospace Engineering at KTH Royal Institute of Technology in Stockholm, Sweden. Christer Fuglesang is a professor in Space Travel, director of KTH Space Center and responsible for the Aerospace Engineering master program. He serves as the examiner for this master thesis project. Linda Lyckman is the Head of Business &amp; Technology Innovation at SSC and supervisor for this master thesis project. komplext. Genom att undersöka och analysera identifierade trender och teknologier ämnar författaren urskilja de som kan komma att påverka industrin i störst utsträckning under det kommande decenniet. Bedömningar av marknadsmöjligheter och intervjuer har genomförts och i denna rapport presenteras ¨aven diskussioner och slutsatser relaterade till den typ av tjänster som Swedish Space Corporation erbjuder. Denna rapport har för avsikt att uppdatera läsaren om delar av den aktuella nulägesanalysen inom rymdindustrin, introducera koncept och ge relevanta kommentarer om viktiga trender.

Within the architecture, engineering, and construction (AEC) domain, simulation modeling is mainly used to facilitate decision-making by enabling the assessment of different operational plans and resource arrangements, that are otherwise difficult (if not impossible), expensive, or time consuming to be evaluated in real world settings. The accuracy of such models directly affects their reliability to serve as a basis for important decisions such as project completion time estimation and resource allocation. Compared to other industries, this is particularly important in construction and infrastructure projects due to the high resource costs and the societal impacts of these projects. Discrete event simulation (DES) is a decision making tool that can benefit the process of design, control, and management of construction operations. Despite recent advancements, most DES models used in construction are created during the early planning and design stage when the lack of factual information from the project prohibits the use of realistic data in simulation modeling. The resulting models, therefore, are often built using rigid (subjective) assumptions and design parameters (e.g. precedence logic, activity durations). In all such cases and in the absence of an inclusive methodology to incorporate real field data as the project evolves, modelers rely on information from previous projects (a.k.a. secondary data), expert judgments, and subjective assumptions to generate simulations to predict future performance. These and similar shortcomings have to a large extent limited the use of traditional DES tools to preliminary studies and long-term planning of construction projects. In the realm of the business process management, process mining as a relatively new research domain seeks to automatically discover a process model by observing activity records and extracting information about processes. The research presented in this Ph.D. Dissertation was in part inspired by the prospect of construction process mining using sensory data collected from field agents. This enabled the extraction of operational knowledge necessary to generate and maintain the fidelity of simulation models. A preliminary study was conducted to demonstrate the feasibility and applicability of data-driven knowledge-based simulation modeling with focus on data collection using wireless sensor network (WSN) and rule-based taxonomy of activities. The resulting knowledge-based simulation models performed very well in properly predicting key performance measures of real construction systems. Next, a pervasive mobile data collection and mining technique was adopted and an activity recognition framework for construction equipment and worker tasks was developed. Data was collected using smartphone accelerometers and gyroscopes from construction entities to generate significant statistical time- and frequency-domain features. The extracted features served as the input of different types of machine learning algorithms that were applied to various construction activities. The trained predictive algorithms were then used to extract activity durations and calculate probability distributions to be fused into corresponding DES models. Results indicated that the generated data-driven knowledge-based simulation models outperform static models created based upon engineering assumptions and estimations with regard to compatibility of performance measure outputs to reality.

The term Big Data has been gaining importance both at the academic and at the business level. Information technology plays a critical role in shipping since there is a high demand for fast transfer and communication between the parts of a shipping contract. The development of Automatic Identification System (AIS) is intended to improve maritime safety by tracking the vessels and exchange inter-ship information.  This master’s thesis purpose was to a) investigate in which business decisions the Automatic Identification System helps the shipowners and operators (i.e., users), b) find the benefits and perils arisen from its use, and c) investigate the possible improvements based on the users’ perceptions. This master’s thesis is a qualitative study using the interpretivism paradigm. Data were collected through semi-structured interviews. A total of 6 people participated with the following criteria: a) position on technical department or DPA or shipowner, b) participating on business decisions, c) shipping company owns a fleet, and d) deals with AIS data. The Thematic Analysis led to twenty-six codes, twelve categories and five concepts. Empirical findings showed that AIS data mostly contributes to make strategic business decisions. Participants are interested in using AIS data to measure the efficiency of their fleet and ports, to estimate the fuel consumption, to reduce their costs, to protect the environment and people’s health, to analyze the trade market, to predict the time of arrival, the optimal route and speed, to maintain highest security levels and to reduce the inaccuracies due to manual input of some AIS attributes. Finally, participants mentioned some AIS challenges including technological improvement (e.g., transponders, antennas) as well as the operation of autonomous vessels.  Finally, this master’s thesis contributes using the prescriptive and descriptive theories and help stakeholders to find new decisions while researchers and developers to advance their products.

OBJECTIVE The purpose of this study was to determine how kinematic, big data can be evaluated using computational, comprehensive analysis of movement parameters in a diverse population. METHODS Retrospective data was collected, cleaned, and reviewed for further analysis of biomechanical movement in an active population using 3D collinear resistance loads. The active sample of the population involved in the study ranged from age 7 to 82 years old and respectively identified as active in 13 different sports. Moreover, a series of exercises were conducted by each participant across multiple sessions. Exercises were measured and recorded based on 6 distinct biometric movement parameters: explosiveness, velocity, power, deceleration, braking, consistency, endurance, and range of motion. Analysis and data visualization portrayed how 3D collinear resistance load impacted specific muscles and performance metrics. RESULTS The model with the highest accuracy rate was Naive Bayes and Fast Large Margin at 58.3% for future predictions considering impact for specific muscles, movement parameters, and performance metric data. The data visualization involved a proof-of-concept human-computer interface and presented each component in relation to one another within the active population database, movement parameters, and performance metrics. DISCUSSION Understanding the findings regarding 3D collinear resistance sets a precedence for future development for the active population and research in the sports analytics field. Additionally, the visual proof of concept interface promotes future development for a diverse, active population.