Since the emergence of big data, traditional business intelligence systems have been unable to meet most of the information demands in many data-driven organisations. Nowadays, big data analytics is perceived to be the solution to the challenges related to information processing of big data and decision-making of most data-driven organisations. Irrespective of the promised benefits of big data, organisations find it difficult to prove and realise the value of the investment required to develop and maintain big data analytics. The reality of big data is more complex than many organisations’ perceptions of big data. Most organisations have failed to implement big data analytics successfully, and some organisations that have implemented these systems are struggling to attain the average promised value of big data. Organisations have realised that it is impractical to migrate the entire traditional business intelligence (BI) system into big data analytics and there is a need to integrate these two types of systems. Therefore, the purpose of this study was to investigate a framework for creating a hybrid data-driven decision support system that combines components from traditional business intelligence and big data analytics systems. The study employed an interpretive qualitative research methodology to investigate research participants' understanding of the concepts related to big data, a data-driven organisation, business intelligence, and other data analytics perceptions. Semi-structured interviews were held to collect research data and thematic data analysis was used to understand the research participants’ feedback information based on their background knowledge and experiences. The application of the organisational information processing theory (OIPT) and the fit viability model (FVM) guided the interpretation of the study outcomes and the development of the proposed framework. The findings of the study suggested that data-driven organisations collect data from different data sources and process these data to transform them into information with the goal of using the information as a base of all their business decisions. Executive and senior management roles in the adoption of a data-driven decision-making culture are key to the success of the organisation. BI and big data analytics are tools and software systems that are used to assist a data-driven organisation in transforming data into information and knowledge. The suggested challenges that organisations experience when they are trying to integrate BI and big data analytics were used to guide the development of the framework that can be used to create a hybrid data-driven decision support system. The framework is divided into these elements: business motivation, information requirements, supporting mechanisms, data attributes, supporting processes and hybrid data-driven decision support system architecture. The proposed framework is created to assist data-driven organisations in assessing the components of both business intelligence and big data analytics systems and make a case-by-case decision on which components can be used to satisfy the specific data requirements of an organisation. Therefore, the study contributes to enhancing the existing literature position of the attempt to integrate business intelligence and big data analytics systems. / Dissertation (MIT (Information Systems))--University of Pretoria, 2021. / Informatics / MIT (Information Systems) / Unrestricted

Background: The rapid digitalization of business operations has transformed the decision-making process, with companies increasingly relying on Data-Driven Decision-Making (DDDM) to navigate complex business environments. However, the utilization of DDDM is not without challenges, as organizations face obstacles such as data integration issues and a lack of technical skills among managers. This study explores the integration of DDDM into organizational strategies, focusing on the experience of managers as they navigate the transition toward data-driven approaches. Problem: While the benefits of DDDM, such as enhanced operational efficiency and competitive advantage are well-documented, there is a notable gap in understanding how managers' practices and the decision-making process are integrated. Research Purpose: The purpose of the research is to examine how DDDM influences managerial practices in the decision-making process and identify the key challenges and opportunities. By examining experiences, the study aims to uncover insights that can guide organizations in refining their decision-making process and fostering data-driven approaches. Research Question: How does DDDM influence managerial practices and the decision-making process within an organization? Method: This study employs a qualitative research methodology, utilizing semi-structured interviews to gather insights from managers across various industries. It adopts a relativistic ontological stance and epistemological constructionism through a multiple case study to examine managerial practices when utilizing DDDM. Conclusion: The findings show that DDDM can significantly impact organizational efficiency, productivity, and strategic planning. However, successful DDDM requires a balance between data-driven insights and intuitive decision-making. While data can inform decisions, managers must also rely on intuition and experience, especially in complex scenarios with incomplete data.

International Telemetering Conference Proceedings / October 25-28, 1993 / Riviera Hotel and Convention Center, Las Vegas, Nevada / A data driven telemetry system is described that responds to the rapid nature in which experimental satellite telemetry content is changed during the development process. It also meets the needs of a diverse experiment in which the many phases of a mission may contain radically different types of telemetry data. The system emphasizes mechanisms for achieving high redundancy of critical data. A practical example of such an implementation, Brilliant Pebbles Flight Experiment Three (FE-3), is cited.

Dans un paysage industriel de plus en plus tourné vers le numérique, les opportunités des entreprises ne manquent pas pour innover et répondre à une demande jusqu’alors inaccessible. C’est dans ce cadre que l’internet des objets apparait comme un élan technologique a fort potentiel. Ce levier d’innovation, basé sur la valorisation de flux de données, sont par nature intangible et c’est pourquoi nous les considérons ici comme des services. Cependant, les concepteurs doivent faire face ici à un univers complexe où de nombreux domaines d’expertise et de connaissance sont engagés. C’est pourquoi nous proposons dans cette thèse un modèle méthodologique de conception mettant en scène le service, l’expertise métier et les technologies de découverte de connaissance de manière optimisé pour concevoir à l’internet des objets. Ce modèle de conception a été éprouvé chez e.l.m. leblanc, entreprise du groupe Bosch, dans le développement d’un appareil de chauffage connecté et de ses services / In a more and more numeric oriented industrial landscape, the business opportunities for companies to innovate and answer needs inaccessible yep are increasing. In this framework, the internet of things appears as a high potential technology. This innovation lever, where the value-creation is principally based on the data, is not tangible by nature and this is the reason why we conceder it as a service in this thesis. However, the designer has to face a complex universe where a high number expertise and knowledge are engaged. This is the reason why we propose in this thesis a design methodology model organizing the service, the domain knowledge and the data discovery technologies in an optimized process to design the internet of things. This model has been experienced at e.l.m. leblanc, company of the Bosch group, in the development of a connected boiler and its services.

Bridge monitoring studies indicate that the quasi-static response of a bridge, while dependent on various input forces, is affected predominantly by variations in temperature. In many structures, the quasi-static response can even be approximated as equal to its thermal response. Consequently, interpretation of measurements from quasi-static monitoring requires accounting for the thermal response in measurements. Developing solutions to this challenge, which is critical to relate measurements to decision-making and thereby realize the full potential of SHM for bridge management, is the main focus of this research. This research proposes a data-driven approach referred to as temperature-based measurement interpretation (TB-MI) approach for structural performance evaluation of bridges based on continuous bridge monitoring. The approach characterizes and predicts thermal response of structures by exploiting the relationship between temperature distributions across a bridge and measured bridge response. The TB-MI approach has two components - (i) a regression-based thermal response prediction (RBTRP) methodology and (ii) an anomaly detection methodology. The RBTRP methodology generates models to predict real-time structural response from distributed temperature measurements. The anomaly detection methodology analyses prediction error signals, which are the differences between predicted and real-time response to detect the onset of anomaly events. In order to generate realistic data-sets for evaluating the proposed TB-MI approach, this research has built a small-scale truss structure in the laboratory as a test-bed. The truss is subject to accelerated diurnal temperature cycles using a system of heating lamps. Various damage scenarios are also simulated on this structure. This research further investigates if the underlying concept of using distributed temperature measurements to predict thermal response can be implemented using physics-based models. The case study of Cleddau Bridge is considered. This research also extends the general concept of predicting bridge response from knowledge of input loads to predict structural response due to traffic loads. Starting from the TB-MI approach, it creates an integrated approach for analyzing measured response due to both thermal and vehicular loads. The proposed approaches are evaluated on measurement time-histories from a number of case studies including numerical models, laboratory-scale truss and full-scale bridges. Results illustrate that the approaches accurately predicts thermal response, and that anomaly events are detectable using signal processing techniques such as signal subtraction method and cointegration. The study demonstrates that the proposed TB-MI approach is applicable for interpreting measurements from full-scale bridges, and can be integrated within a measurement interpretation platform for continuous bridge monitoring.

This report describes the design, development, and evaluation of the Simple Linear Regression Lesson (SLRL), a web-based lesson that uses visual strategies to teach graduate students the principles of simple linear regression. The report includes a literature review on the use of graphics, animations, and data-driven animations in statistics pedagogy and instruction in general. The literature review also summarizes the pertinent instructional design and development theories that informed the creation of the lesson. Following the literature review is a description the SLRL and the methodologies used to develop it. The evaluation section of the report details the methods used during the formative and summative evaluation stages, including results from a small-group implementation of the SLRL. The report concludes with a review of the product's strengths and weaknesses and the process' strengths and weaknesses.

In this thesis, I describe research on teachers' experiences with learner-centered technology. Specifically, this research investigated teachers' experiences with adoption of the learner-centered tools available from Imagine Learning, an online elementary school literacy program. This thesis includes an extended literature review describing learner-centered classrooms, technology integration, and models of technology adoption, followed by a journal-ready article that describes teachers' experiences throughout the process of adopting Imagine Learning. Finally, I provide a description my experiences throughout this project as well as a proposal for future areas of study.

The greatest wealth is health. It is sometimes said your health is a function of what you are not doing, not what you are currently doing. The degree to which individuals can attain, process, and comprehend the necessary health information and services they need to make proper health decisions is vital for optimal health and well-being. This project documents the analysis, design, development, and implementation of a prototype web-based data-driven health & wellness system targeted for college students. The architecture for this system uses business intelligence to develop a smart online platform for real-time analysis based on inputs entered by its users. The objective is to develop modules that can be used to provide meal plan options that dietitians can recommend to students, while also providing a standard wellness health check. This also promotes constant awareness for students with specialized health diets. User-health and wellness history of each Student is collected and stored for generating progress and wellness reports for end users. The dietitian can monitor the user in real time through the data collected and stored in the data server. Users can monitor their own progress. The system incorporates user context and feedback to personalize each user's lifestyle. Implementation of this system provides a complete and easy to use integrated system that promotes the process of analyzing wellness and improving the user’s overall health. The system is designed to be in a non-clinical setting and hence more lifestyle-oriented compared to other health-oriented systems. It is thus more relevant and convenient to student’s everyday life context.

Data-Driven Pronunciation Generation for ASR Maria Obedkova In ASR systems, dictionaries are usually used to describe pronunciations of words in a language. These dictionaries are typically hand-crafted by linguists. One of the most significant drawbacks of dictionaries created this way is that linguistically motivated pronunciations are not necessarily the optimal ones for ASR. The goal of this research was to explore approaches of data-driven pro- nunciation generation for ASR. We investigated several approaches of lexicon generation and implemented the completely new data-driven solution based on the pronunciation clustering. We proposed an approach for feature extraction and researched different unsupervised methods for pronunciation clustering. We evaluated the proposed approach and compared it with the current hand-crafted dictionary. The proposed data-driven approach could beat the established base- lines but underperformed in comparison to the hand-crafted dictionary which could be due to unsatisfactory features extracted from data or insufficient fine tuning. 1

Mitochondria plays a crucial role in cells by maintaining energy metabolism and directing cell death mechanisms by buffering calcium (Ca2+ )from cytosol. Therefore, the Ca2+ overload of mitochondria due to the upregulated cytosolic Ca2+ , observed in many neurological disorders is hypothesized to be a key pathway leading to mitochondrial dysfunction and cell death. In particular, Ca2+ homeostasis disruptions due to Alzheimer’ s disease (AD)-causing presenilins (PS1/PS2) and oligomeric forms of β-amyloid peptides Aβ commonly found in AD patients are presumed to cause detrimental effects on the mitochondria and its ability to function properly. We begin by showing that Familial Alzheimer’s disease (FAD)-causing PS mutants affect intracellular Ca2+ ([Ca2+]i) homeostasis by enhancing the gating of inositol 1,4,5-trisphosphate (IP3) receptor (IP3R) Ca2+ channels on the endoplasmic reticulum (ER), leading to exaggerated Ca2+ release into the cytoplasm. Using experimental IP3R-mediated Ca2+ release data in conjunction with a computational model of mitochondrial bioenergetics, we explore how the differences in mitochondrial Ca2+ uptake in control cells and cells expressing FAD-causing PS mutants affect key variables such as ATP, reactive oxygen species (ROS), NADH, and mitochondrial Ca2+ ([Ca2+ ]m). We find that as a result of exaggerated [Ca2+]i in FAD-causing mutant PS-expressing cells, the rate of oxygen consumption increases dramatically and overcomes the Ca2+ dependent enzymes that stimulate NADH production. This leads to decreased rates of proton pumping due to diminished membrane potential (Ψm) along with less ATP and enhanced ROS production. These results show that through Ca2+ signaling disruption, mutant PS leads to mitochondrial dysfunction and potentially cell death. Next, the model for the mitochondria is expanded to include the mitochondrial uniporter (MCU) that senses Ca2+ in the microdomain formed by the close proximity of mitochondria and ER. Ca2+ concentration in the microdomain ([Ca2+] mic) depends on the distance between the cluster of IP3R channels (r) on ER and mitochondria, the number of IP3R in the cluster (nIP3R), and open-probability (Po) of IP3R. Using the same experimental results for Ca2+ release though IP3R due to FAD-causing PS mutants, in conjunction with a computational model of mitochondrial bioenergetics, a data-driven Markov chain model for IP3R gating, and a model for the dynamics of the mitochondrial permeability transition pore (PTP), we explore the difference in mitochondrial Ca2+ uptake in cells expressing wild type (PS1-WT) and FAD-causing mutant (PS1-M146L) PS. We find that increased mitochondrial [Ca2+]m due to the gain-of-function enhancement of IP3R channels in the cell expressing PS1-M146L leads to the opening of PTP in high conductance state (PTPh), where the latency of opening is inversely correlated with r and proportional to nIP3R. Furthermore, we observe diminished inner mitochondrial Ψm, [NADH], [Ca2+]m, and [ATP] when PTP opens. Additionally, we explore how parameters such as the pH gradient, inorganic phosphate concentration, and the rate of the Na+/ Ca2+ -exchanger affect the latency of PTP to open in PTPh. Intracellular accumulation of oligomeric forms of Aβ are now believed to play a key role in the early phase of AD as their rise correlates well with the early symptoms of the disease. Extensive evidence points to impaired neuronal Ca2+ homeostasis as a direct consequence of the intracellular Aβ oligomers. To study the effect of intracellular Aβ on Ca2+ signaling and the resulting mitochondrial dysfunction, we employed data-driven modeling in conjunction with total internal reflection fluorescence (TIRF) microscopy (TIRFM). High resolution fluorescence TIRFM together with detailed computational modeling provides a powerful approach towards the understanding of a wide range of Ca2+ signals mediated by the IP3R. Achieving this requires a close agreement between Ca2+ signals from computational models and TIRFM experiments. However, we found that elementary Ca2+ release events, puffs, imaged through TIRFM do not show the rapid single-channel opening and closing during x and between puffs using data-driven single channel models. TIRFM also shows a rapid equilibration of 10 ms after a channel opens or closes which is not achievable in simulation using standard Ca2+ diffusion coefficients and reaction rates between indicator dye and Ca2+. Using the widely used Ca2+ diffusion coefficients and reaction rates, our simulations show equilibration rates that are eight times slower than TIRFM imaging. We show that to get equilibrium rates consistent with observed values, the diffusion coefficients and reaction rates have to be significantly higher than the values reported in the literature. Once a close agreement between experiment and model is achieved, we use multiscale modeling in conjunction with patch-clamp electrophysiology of IP3R and fluorescence imaging of whole-cell Ca2+ response, induced by intracellular Aβ42 oligomers to show that Aβ42 inflicts cytotoxicity by impairing mitochondrial function. Driven by patch-clamp experiments, we first model the kinetics of IP3R, which is then extended to build a model for the whole-cell Ca2+ signals. The whole-cell model is then fitted to fluorescence signals to quantify the overall Ca2+ release from the ER by intracellular Aβ42 oligomers through G-protein-mediated stimulation of IP3 production. The estimated IP3 concentration as a function of intracellular Aβ42 content together with the whole-cell model allows us to show that Aβ42 oligomers impair mitochondrial function through pathological Ca2+ uptake and the resulting reduced mitochondrial inner membrane potential, leading to an overall lower ATP and increased production of reactive oxygen species and [H2O2]. We further show that mitochondrial function can be restored by the addition of Ca2+ buffer EGTA, in accordance with the observed abrogation of Aβ42 cytotoxicity by EGTA in our live cells experiments. Finally, our modeling study was extended to other pathological phenomena such as epileptic seizures and spreading depolarizations (SD) and their effects on mitochondria by incorporating conservation of particles and charge, and accounting for the energy required to restore ionic gradients to the neuron. By examining the dynamics as a function of potassium and oxygen we can account for a wide range of neuronal hyperactivity from seizures, normoxic SD, and hypoxic SD (HSD) in the model. Together with a detailed model of mitochondria xi and Ca2+ -release through the ER, we determine mitochondrial dysfunction and potential recovery mechanisms from HSD. Our results demonstrate that HSD causes detrimental mitochondrial dysfunction that can only be recovered by restoration of oxygen. Once oxygen is replenished to the neuron, organic phosphate and pH gradients along the mitochondria determine how rapid the neuron recovers from HSD.