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Implementing Big Data Analytics (BDA) has been widely known as a major source of competitiveness and innovation. While previous research suggests several process models and identifies critical factors for the successful implementation of BDA, there is a lack of understanding of how this organizational process is realized by its primary recipients, that is, Data Science Professionals (DSPs) whose innovation with BDA technologies stands at the core of big data-driven innovation. In particular, far less understood are the motivational and contextual factors that derive DSPs’ innovation with BDA technologies. This study proposes that commitment is the force that can attach DSPs to the BDA implementation process and motivate them to engage in innovative behaviors. It also introduces two organizational mechanisms, namely, BDA communication reciprocity and BDA leader theme-specific reputation, that can be employed to develop this constructive force in DSPs. Inspired by this, a theoretical model was developed based on the assertions of Commitment in Workplace Theory and the literature on creativity in organizations to assess the impact of DSPs’ commitment to BDA implementation and organizational context on their innovation with BDA technologies. This study theorizes that communication reciprocity and leader theme-specific reputation influence the three components of DSPs’ commitment (affective, continuance, and normative) to BDA implementation through their perceived participation in organizational decision-making and positive uncertainty, which, in turn, derive DSP’s innovation with BDA technologies. To further enrich the theorization, the moderating role of DSPs’ competency on the effect of DSPs’ components of commitment on their innovation with BDA technologies is investigated. Predictions were tested following an experimental vignette methodology with 240 subjects where the two organizational mechanisms were manipulated. Results indicate that organizational mechanisms provoke mediating psychological perceptions, though with varying strengths. In addition, results suggest that DSPs’ innovation with BDA technologies is primarily rooted in their affective and continuance commitments, and DSPs’ competency interacts with DSPs’ affective commitment to affect their innovation with BDA technologies. This research enhances the theoretical understanding of the role of commitment and organizational context in fostering DSPs’ innovation with BDA technologies. The results of this study also offer suggestions for information systems implementation practitioners on the effectiveness of organizational mechanisms that facilitate big data-driven innovation. / Thesis / Doctor of Philosophy (PhD)

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The depleting fossil fuel and environmental concerns have created a revolutionary movement towards the installation and utilization of Renewable Energy Sources (RES) such as wind and solar energy. The RES entails challenges, both in regards to the physical integration into a grid system and regarding management of the expected demand. The flexibility in energy demand can facilitate the alignment of the supply and demand to achieve a dynamic Demand Response (DR). The flexibility is often not explicitly available or provided by a user and has to be analyzed and extracted automatically from historical consumption data. The predictive analytics of consumption data can reveal interesting patterns and periodicities that facilitate the effective extraction and representation of flexibility. The device-level analysis captures the atomic flexibilities in energy demand and provides the largest possible solution space to generate demand/supply schedules. The presence of stochasticity and noise in the device-level consumption data and the unavailability of contextual information makes the analytics task challenging. Hence, it is essential to design predictive analytical techniques that work at an atomic data granularity and perform various analyses on the effectiveness of the proposed techniques. The Ph.D. study is sponsored by the TotalFlex Project (http://www.totalflex.dk/) and is part of the IT4BI-DC program with Aalborg University and TU Dresden as Home and Host University, respectively. The main objective of the TotalFlex project is to develop a cost-effective, market-based system that utilizes total flexibility in energy demand, and provide financial and environmental benefits to all involved parties. The flexibilities from various devices are modeled using a unified format called a flex-offer, which facilitates, e.g., aggregation and trading in the energy market. In this regards, this Ph.D. study focuses on the predictive analytics of the historical device operation behavior of consumers for an efficient and effective extraction of flexibilities in their energy demands. First, the thesis performs a comprehensive survey of state-of-the-art work in the literature. It presents a critical review and analysis of various previously proposed approaches, algorithms, and methods in the field of user behavior analysis, forecasting, and flexibility analysis. Then, the thesis details the flexibility and flex-offer concepts and formally discusses the terminologies used throughout the thesis. Second, the thesis contributes to a comprehensive analysis of energy consumption behavior at the device-level. The key motive of the analysis is to extract device operation patterns of users, the correlation between devices operations, and influence of external factors in device-level demands. A novel cost/benefit trade-off analysis of device flexibility is performed to categorize devices into various segments according to their flexibility potential. Moreover, device-specific data preprocessing steps are proposed to clean device-level raw data into a format suitable for flexibility analysis. Third, the thesis presents various prediction models that are specifically tuned for device-level energy demand prediction. Further, it contributes to the feature engineering aspect of generating additional features from a demand consumption timeseries that effectively capture device operation preferences and patterns. The demand predictions utilize the carefully crafted features and other contextual information to improve the performance of the prediction models. Further, various demand prediction models are evaluated to determine the model, forecast horizon, and data granularity best suited for the device-level flexibility analysis. Furthermore, the effect of the forecast accuracy on flexibility-based DR is evaluated to identify an error level a market can absorb maintaining profitability. Fourth, the thesis proposes a generalized process for automated generation and evaluation of flex-offers from the three types of household devices, namely Wet-devices, Electric Vehicles (EV), and Heat Pumps. The proposed process automatically predicts and estimates times and values of device-specific events representing flexibility in its operations. The predicted events are combined to generate flex-offers for the device future operations. Moreover, the actual flexibility potential of household devices is quantified for various contextual conditions and degree days. Fifth, the thesis presents user-comfort oriented prescriptive techniques to prescribe flex-offers schedules. The proposed scheduler considers the trade-off between both social and financial aspects during scheduling of flex-offers, i.e., maximizing the financial benefits in a market and at the same time minimizing the loss of user comfort. Moreover, it also provides a distance-aware error measure that quantifies the actual performance of forecast models designed for flex-offers generation and scheduling. Sixth, the thesis contributes to the comprehensive analysis of the financial viability of device-level flexibility for dynamic balancing of demand and supply. The thesis quantifies the financial benefits of flexibility and investigates the device type specific market that maximizes the potential of flexibility, both regarding DR and financial incentives. Henceforth, a financial analysis of each proposed technique, namely forecast model, flex-offer generation model, and flex-offer scheduling is performed. The key motive is to evaluate the usability of the proposed models in the device-level flexibility based DR scheme and their potential in generating a positive financial incentive to markets and customers. Seven, the thesis presents a benchmark platform for device-level demand prediction. The platform provides the research community with a centralized repository of device-level datasets, forecast models, and functionalities that facilitate comparisons, evaluations, and validation of device-level forecast models. The results of the thesis can contribute to the energy market in materializing the vision of utilizing consumption and production flexibility to obtain dynamic energy balance. The developed demand forecast and flex-offer generation models also contribute to the energy data analytics and data mining fields. The quantification of flexibility further contributes by demonstrating the feasibility and financial benefits of flexibility-based DR. The developed experimental platform provide researchers and practitioners with the resources required for device-level demand analytics and prediction.

News videos play an important rule in shaping our everyday communication. At the same time, news videos use narrative patterns to keep people entertained. Understanding how these patterns work and are being applied in news videos is crucial for understanding how they may affect a videos ideological message, which is an important dimension in times of fake news and disinformation campaigns. We present Zoetrope, a web-based tool that supports the discovery of narrative patterns in news videos by means of a visual exploration approach. Zoetrope integrates a number of multimodal information extraction frameworks into an interactive visualization, to allow for an efficient exploratory access to large collections of news videos

We propose the concept of spatial analytic interfaces (SAIs) as a tool for performing in-situ, everyday analytic tasks. Mobile computing is now ubiquitous and provides access to information at nearly any time or place. However, current mobile interfaces do not easily enable the type of sophisticated analytic tasks that are now well-supported by desktop computers. Conversely, desktop computers, with large available screen space to view multiple data visualizations, are not always available at the ideal time and place for a particular task. Spatial user interfaces, leveraging state-of-the-art miniature and wearable technologies, can potentially provide intuitive computer interfaces to deal with the complexity needed to support everyday analytic tasks. These interfaces can be implemented with versatile form factors that provide mobility for doing such taskwork in-situ, that is, at the ideal time and place. We explore the design of spatial analytic interfaces for in-situ analytic tasks, that leverage the benefits of an upcoming generation of light-weight, see-through, head-worn displays. We propose how such a platform can meet the five primary design requirements for personal visual analytics: mobility, integration, interpretation, multiple views and interactivity. We begin with a design framework for spatial analytic interfaces based on a survey of existing designs of spatial user interfaces. We then explore how to best meet these requirements through a series of design concepts, user studies and prototype implementations. Our result is a holistic exploration of the spatial analytic concept on a head-worn display platform. / October 2016

I den här uppsatsen kommer jag att presentera hur och varför småföretag bör använda sig av webbanalys för att utvärdera sin webbplats. Webbplatsen är en del av företagens digitalisering och möjliggör en internationell expansion, en viktig satsning för att minska beroendet av den konkurrensutsatta lokala marknaden som många företag i Sverige upplever som hämmande för sin tillväxt idag. För att samla in underlag till uppsatsen har en litteraturstudie genomförts, där fokus har varit att samla in teori från forskare och experter inom webbanalys. / In this essay, I will present how and why small businesses should use web analytics to evaluate how their website is performing. The website is a part of the company’s digitization and it enables their expansion to other countries. This is important since many Swedish companies today identifies the competitive local market as their biggest obstacle to future growth. A literature study has been conducted to gather information, focusing on collecting theory from researchers and experts in the field of web analytics.

Ce travail s’inscrit dans une problématique générale de l’analytique de l’apprentissage numérique et particulièrement dans le contexte du projet ANR HUBBLE, un observatoire national permettant le dépôt de processus d’analyse de haut niveau. Nous nous intéressons principalement à la communication des données d’analyse aux utilisateurs en mettant à leur disposition des tableaux de bord d'apprentissage (TBA). Notre problématique porte sur l’identification de structures génériques dans le but de générer dynamiquement des TBA sur mesure. Ces structures doivent être à la fois génériques et adaptables aux besoins d’utilisateurs. Les travaux existants proposent le plus souvent des TBA trop généraux ou développés de manière adhoc. Au travers du projet HUBBLE, nous souhaitons exploiter les décisions des utilisateurs pour générer dynamiquement des TBA. Nous nous sommes intéressés au domaine de l’informatique décisionnelle en raison de la place des tableaux de bord dans leur processus. La prise de décision exige une compréhension explicite des besoins des utilisateurs. C'est pourquoi nous avons adopté une approche de conception centrée sur l'utilisateur dans le but de lui fournir des TBA adaptés. Nous proposons aussi un processus de capture des besoins qui a permis l’élaboration de nos modèles (indicateur, moyens de visualisation, utilisateur, …). Ces derniers sont utilisés par un processus de génération implémenté dans un prototype de générateur dynamique. Nous avons procédé à une phase d'évaluation itérative dont l’objectif est d'affiner nos modèles et de valider l'efficacité de notre processus de génération ainsi que de démontrer l'impact de la décision sur la génération des TBA. / This work is part of a broader issue of Learning Analytics (LA). It is particularly carried out within the context of the HUBBLE project, a national observatory for the design and sharing of data analysis processes. We are interested in communicating data analysis results to users by providing LA dashboards (LAD). Our main issue is the identification of generic LAD structures in order to generate dynamically tailored LAD. These structures must be generic to ensure their reuse, and adaptable to users’ needs. Existing works proposed LAD which remains too general or developed in an adhoc way. According to the HUBBLE project, we want to use identified decisions of end-users to generate dynamically our LAD. We were interested in the business intelligence area because of the place of dashboards in the decision-making process. Decision-making requires an explicit understanding of user needs. That's why we have adopted a user-centered design (UCD) approach to generate adapted LAD. We propose a new process for capturing end-users’ needs, in order to elaborate some models (Indicator, visualization means, user, pattern, …). These models are used by a generation process implemented in a LAD dynamic generator prototype. We conducted an iterative evaluation phase. The objective is to refine our models and validate the efficiency of our generation process. The second iteration demonstrates the impact of the decision on the LAD generation. Thus, we can confirm that the decision is considered as a central element for the generation of LADs.

Arsenic is a naturally occurring toxic metal and its presence in food composites could be a potential risk to the health of both humans and animals. Arseniccontaminated groundwater is often used for food and animal consumption, irrigation of soils, which could potentially lead to arsenic entering the human food chain. Its side effects include multiple organ damage, cancers, heart disease, diabetes mellitus, hypertension, lung disease and peripheral vascular disease. Research investigations, epidemiologic surveys and total diet studies (market baskets) provide datasets, information and knowledge on arsenic content in foods. The determination of the concentration of arsenic in rice varieties is an active area of research. With the increasing capability to measure the concentration of arsenic in foods, there are volumes of varied and continuously generated datasets on arsenic in food groups. Visual analytics, which integrates techniques from information visualization and computational data analysis via interactive visual interfaces, presents an approach to enable data on arsenic concentrations to be visually represented. The goal of this doctoral research in Environmental Science is to address the need to provide visual analytical decision support tools on arsenic content in various foods with special emphasis on rice. The hypothesis of this doctoral thesis research is that software enabled visual representation and user interaction facilitated by visual interfaces will help discover hidden relationships between arsenic content and food categories. The specific objectives investigated were: (1) Provide insightful visual analytic views of compiled data on arsenic in food categories; (2) Categorize table ready foods by arsenic content; (3) Compare arsenic content in rice product categories and (4) Identify informative sentences on arsenic concentrations in rice. The overall research method is secondary data analyses using visual analytics techniques implemented through Tableau Software. Several datasets were utilized to conduct visual analytical representations of data on arsenic concentrations in foods. These consisted of (i) arsenic concentrations in 459 crop samples; (ii) arsenic concentrations in 328 table ready foods from multi-year total diet studies; (iii) estimates of daily inorganic arsenic intake for 49 food groups from multicountry total diet studies; (iv) arsenic content in rice product categories for 193 samples of rice and rice products; (v) 758 sentences extracted from PubMed abstracts on arsenic in rice. Several key insights were made in this doctoral research. The concentration of inorganic arsenic in instant rice was lower than those of other rice types. The concentration of Dimethylarsinic Acid (DMA) in wild rice, an aquatic grass, was notably lower than rice varieties (e.g. 0.0099 ppm versus 0.182 for a long grain white rice). The categorization of 328 table ready foods into 12 categories enhances the communication on arsenic concentrations. Outlier concentration of arsenic in rice were observed in views constructed for integrating data from four total diet studies. The 193 rice samples were grouped into two groups using a cut-off level of 3 mcg of inorganic arsenic per serving. The visual analytics views constructed allow users to specify cut-off levels desired. A total of 86 sentences from 53 PubMed abstracts were identified as informative for arsenic concentrations. The sentences enabled literature curation for arsenic concentration and additional supporting information such as location of the research. An informative sentence provided global “normal” range of 0.08 to 0.20 mg/kg for arsenic in rice. A visual analytics resource developed was a dashboard that facilitates the interaction with text and a connection to the knowledge base of the PubMed literature database. The research reported provides a foundation for additional investigations on visual analytics of data on arsenic concentrations in foods. Considering the massive and complex data associated with contaminants in foods, the development of visual analytics tools are needed to facilitate diverse human cognitive tasks. Visual analytics tools can provide integrated automated analysis; interaction with data; and data visualization critically needed to enhance decision making. Stakeholders that would benefit include consumers; food and health safety personnel; farmers; and food producers. Arsenic content of baby foods warrants attention because of the early life exposures that could have life time adverse health consequences. The action of microorganisms in the soil is associated with availability of arsenic species for uptake by plants. Genomic data on microbial communities presents wealth of data to identify mitigation strategies for arsenic uptake by plants. Arsenic metabolism pathways encoded in microbial genomes warrants further research. Visual analytics tasks could facilitate the discovery of biological processes for mitigating arsenic uptake from soil. The increasing availability of central resources on data from total diet studies and research investigations presents a need for personnel with diverse levels of skills in data management and analysis. Training workshops and courses on the foundations and applications of visual analytics can contribute to global workforce development in food safety and environmental health. Research investigations could determine learning gains accomplished through hardware and software for visual analytics. Finally, there is need to develop and evaluate informatics tools that have visual analytics capabilities in the domain of contaminants in foods. / Environmental Sciences / P. Phil. (Environmental Science)