Across a wide variety of digital devices, users create, consume, and disseminate large quantities of information. While data sometimes look like a spreadsheet or network diagram, more often for everyday users their data look more like an Amazon search page, the line-up for a fantasy football team, or a set of Yelp reviews. However, interpreting these kinds of data remains a difficult task even for experts since they often feature soft or unknown constraints (e.g. ”I want some Thai food, but I also want a good bargain”) across highly multidimensional data (i.e. rating, reviews, popularity, proximity). Existing technology is largely optimized for users with hard criteria and satisfiable constraints, and consumer systems often use representations better suited for browsing than sensemaking. In this thesis I explore ways to support soft constraint decision-making and exploratory data analysis by giving users tools that show fine-grained features of the data while at the same time displaying useful contextual information. I describe approaches for representing collaborative content history and working behavior that reveal both individual and group/dataset level features. Using these approaches, I investigate general visualizations that utilize physics to help even inexperienced users find small and large trends in multivariate data. I describe the transition of physicsbased visualization from the research space into the commercial space through a startup company, and the insights that emerged both from interviews with experts in a wide variety of industries during commercialization and from a comparative lab study. Taking one core use case from commercialization, consumer search, I develop a prototype, Fractal, which helps users explore and apply constraints to Yelp data at a variety of scales by curating and representing individual-, group-, and dataset-level features. Through a user study and theoretical model I consider how the prototype can best aide users throughout the sensemaking process. My dissertation further investigates physics-based approaches for represent multivariate data, and explores how the user’s exploration process itself can help dynamically to refine the search process and visual representation. I demonstrate that selectively representing points using clusters can extend physics-based visualizations across a variety of data scales, and help users make sense of data at scales that might otherwise overload them. My model provides a framework for stitching together a model of user interest and data features, unsupervised clustering, and visual representations for exploratory data visualization. The implications from commercialization are more broad, giving insight into why research in the visualization space is/isn’t adopted by industry, a variety of real-world use cases for multivariate exploratory data analysis, and an index of common data visualization needs in industry.

In big data systems, the infrastructure is such that large amounts of data are hosted away from the users. Information security is a major challenge in such systems. From the customer’s perspective, one of the big risks in adopting big data systems is in trusting the service provider who designs and owns the infrastructure, with data security and privacy. However, big data frameworks typically focus on performance and the opportunity for including enhanced security measures is limited. In this dissertation, the problem of mitigating insider attacks is extensively investigated and several static and dynamic run-time techniques are developed. The proposed techniques are targeted at big data systems but applicable to any data system in general. First, a framework is developed to host the proposed security techniques and integrate with the underlying distributed computing environment. We endorse the idea of deploying this framework on special purpose hardware and a basic model of the software architecture for such security coprocessors is presented. Then, a set of compile-time and run-time techniques are proposed to protect user data from the perpetrators. These techniques target detection of insider attacks that exploit data and infrastructure. The compile-time intrusion detection techniques analyze the control flow by disassembling program binaries while the run-time techniques analyze the memory access patterns of processes running on the system. The proposed techniques have been implemented as prototypes and extensively tested using big data applications. Experiments were conducted on big data frameworks such as Hadoop and Spark using cloud-based services. Experimental results indicate that the proposed techniques successfully detect insider attacks in the context of data loss, data degradation, data exposure and infrastructure degradation.

By means of data-driven marketing as well as big data technology, this paper presents the investigation of a case study from travel industry implemented by a combination of propensity model and a business model “2W1H”. The business model “2W1H” represents the purchasing behavior “What to buy”, “When to buy”, and “How to buy”. This paper presents the process of building propensity models for the application in behavioral targeting in travel industry.     Combined the propensity scores from predictive analysis and logistic regression with proper marketing and CRM strategies when communicating with travelers, the business model “2W1H” can perform personalized targeting for evaluating of marketing strategy and performance. By analyzing the business model “2W1H” and the propensity model on each business model, both the validation of the model based on training model and test data set, and the validation of actual marketing activities, it has been proven that predictive analytics plays a vital role in the implementation of travelers’ purchasing behavioral targeting in marketing.

La première partie de ce travail introduit un nouveau concept d'analyse des enregistrements des essais cliniques et de la dynamique de leur évolution, aussi bien thématique que temporelle. Ce concept a été appliqué à la médecine régénérative, démontrant l'absence de corrélation entre la source de cellules souches et le champ d'application. Les pathologies odonto-stomatologiques sont très peu concernées par les essais cliniques en thérapie cellulaire par cellules souches. Pourtant les parodontites, pathologies immuno-infectieuses responsables de la destruction du tissu de soutien des dents, constituent un enjeu majeur de santé publique. Bien que les auteurs s'accordent sur la responsabilité de l'écologie immunitaire et microbienne dans la physiopathologie de la maladie, les raisons de la dysbiose, de la susceptibilité individuelle sont encore mal connues. La greffe de cellules stromales mésenchymateuses (CSM) permettrait le retour à l'homéostasie en favorisant l'activation des CSM endogènes. La deuxième partie de ce travail démontre que les parodontites ont été potentiellement associées avec 57 pathologies systémiques ; le registre des essais cliniques de l'Organisation Mondiale de la Santé ayant été analysé. L'efficacité et la sureté de l'utilisation des CSM pour la régénération parodontale dans des modèles animaux ont été également démontrées. Pourtant, les modèles utilisés souffraient de problèmes méthodologiques dont la faible représentativité physiopathologique des lésions parodontales générées. Cette deuxième partie apporte donc des données quant à l'efficacité des ASC (CSM du tissu adipeux) pour améliorer de manière quantitative et qualitative la régénération des tissus de soutien parodontaux dans un modèle murin où les lésions parodontales ont été générées par l'administration répétée de bactéries parodonto-pathogènes. Il s'agit donc d'un modèle dont la physiopathologie est plus proche de celle retrouvée chez l'Homme. Enfin, la deuxième partie démontre un effet antibactérien à large spectre des ASC dont l'effet est à la fois direct (effet macrophage-like) et indirect (via la sécrétion de facteurs antibactériens). / The first part of this work introduces a new concept of analysis of clinical trial records and the dynamics of their evolution, both thematic and temporal. This concept has been applied to regenerative medicine, showing the lack of correlation between the source of stem cells and the fields of application. The stomatognathic diseases are few involved in clinical trials for stem cells therapy. Yet periodontitis, immuno-infectious diseases responsible for the destruction of the tooth supporting tissues, are a major public health issue. While the authors agree on the responsibility of the immune and microbial ecology in the pathophysiology of the disease, the reasons for dysbiosis, individual susceptibilities, are still unclear. Graft of mesenchymal stromal cells (MSCs) would return to homeostasis by promoting the activation of endogenous MSCs. The second part of this work shows that periodontitis were potentially associated with 57 systemic diseases; the clinical trials registry of the World Health Organization have been analyzed. The efficacy and safety of the use of MSCs for periodontal regeneration in animal models have also been demonstrated. Yet the models suffered from methodological problems, periodontal lesions are few representative of the pathophysiology. This second part thus provides data on the effectiveness of ASC (CSM from adipose tissue) to improve quantitative and qualitative regeneration of periodontal supporting tissues in a mouse model where periodontal lesions were generated by repeated administration of parodonto-pathogenic bacteria. It is therefore a model whose pathophysiology is closer to that found in humans. Finally, the second part demonstrates broad antibacterial spectrum of ASC whose effect is both direct (macrophage-like effect) and indirect (via the secretion of antibacterial factors).

Computing systems are becoming increasingly data-intensive because of the explosion of data and the needs for processing the data, and storage management is critical to application performance in such data-intensive computing systems. However, existing resource management frameworks in these systems lack the support for storage management, which causes unpredictable performance degradations when applications are under I/O contention. Storage management of data-intensive systems is a challenging problem because I/O resources cannot be easily partitioned and distributed storage systems require scalable management. This dissertation presents the solutions to address these challenges for typical data-intensive systems including high-performance computing (HPC) systems and big-data systems. For HPC systems, the dissertation presents vPFS, a performance virtualization layer for parallel file system (PFS) based storage systems. It employs user-level PFS proxies to interpose and schedule parallel I/Os on a per-application basis. Based on this framework, it enables SFQ(D)+, a new proportional-share scheduling algorithm which allows diverse applications with good performance isolation and resource utilization. To manage an HPC system’s total I/O service, it also provides two complementary synchronization schemes to coordinate the scheduling of large numbers of storage nodes in a scalable manner. For big-data systems, the dissertation presents IBIS, an interposition-based big-data I/O scheduler. By interposing the different I/O phases of big-data applications, it schedules the I/Os transparently to the applications. It enables a new proportional-share scheduling algorithm, SFQ(D2), to address the dynamics of the underlying storage by adaptively adjusting the I/O concurrency. Moreover, it employs a scalable broker to coordinate the distributed I/O schedulers and provide proportional sharing of a big-data system’s total I/O service. Experimental evaluations show that these solutions have low-overhead and provide strong I/O performance isolation. For example, vPFS’ overhead is less than 3% in through- put and it delivers proportional sharing within 96% of the target for diverse workloads; and IBIS provides up to 99% better performance isolation for WordCount and 30% better proportional slowdown for TeraSort and TeraGen than native YARN.

Organizations depend upon ever greater levels of information technology (IT), such as big data and analytics, a trend which shows no sign of abating. However, not all organizations have benefited from such IT investments, resulting in mixed perceptions on the value of IT. Organizations must be knowledgeable in order to properly utilize IT tools and be able to apply that knowledge to create unique competencies in order to gain sustained advantage from IT investments. Organizational learning (OL) has been proposed as the mechanism to accomplish this task. Existing empirical research demonstrates that OL may indeed act as a mediator for the effect of IT on organizational outcomes. Yet, these studies are not consistent in their conceptualizations of the relationships involved, nor in their definitions and measurement of OL. Many use a descriptive measure of OL despite theory suggesting that a normative measure may be more appropriate. This study aims to address these concerns in a Canadian setting by using structural equation modelling (SEM) to compare the effectiveness of descriptive and normative measures of OL as mediating variables in knowledge-intensive organizations. Survey results support OL as a mediator between IT and organizational performance in addition to normative measures of OL outperforming descriptive measures. Implications for research and practice are discussed.

We propose a new ensemble algorithm: the meta-boosting algorithm. This algorithm enables the original Adaboost algorithm to improve the decisions made by different WeakLearners utilizing the meta-learning approach. Better accuracy results are achieved since this algorithm reduces both bias and variance. However, higher accuracy also brings higher computational complexity, especially on big data. We then propose the parallelized meta-boosting algorithm: Parallelized-Meta-Learning (PML) using the MapReduce programming paradigm on Hadoop. The experimental results on the Amazon EC2 cloud computing infrastructure show that PML reduces the computation complexity enormously while retaining lower error rates than the results on a single computer. As we know MapReduce has its inherent weakness that it cannot directly support iterations in an algorithm, our approach is a win-win method, since it not only overcomes this weakness, but also secures good accuracy performance. The comparison between this approach and a contemporary algorithm AdaBoost.PL is also performed.

Purpose: To investigate possible connections between an Industrial Internet of Things (IIoT) system, such as Predix, and data driven Lean practises. The aim is to examine if an IIoT platform can improve existing practises of Lean, and if so, which Lean tools are most likely influenced and how this is.Design/Methodology: The paper follows a phenomenon-based research approach. The methodology contains of a mix of primary and secondary data. The primary data was obtained through “almost unstructured” interviews with experts, while the secondary data comprises of a comprehensive review of existing literature. Moreover, a model was developed to investigate the connections between the concepts of IIoT and Lean.Findings: Findings derived from expert interviews at General Electric (GE) in Uppsala have led to the conclusion that Predix fulfils the necessary requirements to be considered an IIoT platform. However, the positive effects of the platform on the selected Lean tools could not be found. Only in one instance improved Predix the effectiveness of a Lean tool. Overall, data analytic efforts are performed and let to better in-process control. However, these efforts were independent from the Lean efforts carried out. There was no increase in data collection or analytics due to the Lean initiative and Predix is not utilised for data collection, storage, or analysis. It appears that the pharmaceutical industry is fairly slow in adapting new technologies. Firstly, the high regulatory requirements inherent within the pharmaceutical industry limit the application of cutting edge technology by demanding strict in-process control and process documentation. Secondly, the sheer size of GE itself slows down the adoption of new technology. Lastly, the pragmatic approach of the top management to align the digital strategies of the various industries and thereof resulting allocation of resources to other more technologically demanding businesses hinders the use of Predix at GE in Uppsala.

This diploma thesis presents Big Data technologies and their possible use cases and applications. Theoretical part is initially focused on definition of term Big Data and afterwards is focused on Big Data technology, particularly on Hadoop framework. There are described principles of Hadoop, such as distributed storage and data processing, and its individual components. Furthermore are presented the largest vendors of Big Data technologies. At the end of this part of the thesis are described possible use cases of Big Data technologies and also some case studies. The practical part describes implementation of demo example of Big Data technologies and it is divided into two chapters. The first chapter of the practical part deals with conceptual design of demo example, used products and architecture of the solution. Afterwards, implementation of the demo example is described in the second chapter, from preparation of demo environment to creation of applications. Goals of this thesis are description and characteristics of Big Data, presentation of the largest vendors and their Big Data products, description of possible use cases of Big Data technologies and especially implementation of demo example in Big Data tools from IBM.

The thesis covers the term for specific data analysis called Big Data. The thesis firstly defines the term Big Data and the need for its creation because of the rising need for deeper data processing and analysis tools and methods. The thesis also covers some of the technical aspects of Big Data tools, focusing on Apache Hadoop in detail. The later chapters contain Big Data market analysis and describe the biggest Big Data competitors and tools. The practical part of the thesis presents a way of using Apache Hadoop to perform data analysis with data from Twitter and the results are then visualized in Tableau.