Swedish law has established that all major companies should energy map their estateand operations every four years. Hence, a need to create a digital tool to accomplishthis work. In this project such a software tool was developed. This tool includesmethods that generate a report and calculate key figures throuth several smart formsand databases. With this, the consultant may save time.The goal was to design a general digital framework to efficiently handle complexbuildings. This digital tool should be useful when Sweco maps complex buildings inother assignments. The sub-objective of the project were to create an preliminarySweco report, create a smart framework that alerts the user if unreasonable valuesexist, import values form databases, and provide indicative information for energysurveys.The digital tool has found to be useful. It still needs some improvement but thearchitecture is more or less implemented. The database function succeeds for thecase with the customer and generates valuable results but no general solution wasfound before the project ended.

Much previous research concerning the effects of the in-store experience on customers’ decision-making has been laboratory-based. There is a need for empirical research in a real store context to determine the impact of product, service and in-store environment perceptions on customer satisfaction and behaviour. This study is based on a literature review (Project 1) and a large scale empirical study (Projects 2/3) combining two sources of secondary data from the largest retailer in the UK, Tesco, and their loyalty ‘Clubcard’ provider, Dunnhumby. Data includes customer responses to an online self-completion survey of the customers’ shopping experience combined with customer demographic and behavioural data from a loyalty card programme for the same individual. The total sample comprised n=30,696 Tesco shoppers. The online survey measured aspects of the in-store experience. These items were subjected to factor analysis to identify the influences on the in-store experience with four factors emerging: assortment, retail atmosphere, personalised customer service and checkout customer service. These factors were then matched for each individual with behavioural and demographic data collected via the Tesco Clubcard loyalty program. Regression and sensitivity analyses were then conducted to determine the relative impact of the in-store customer experience dimensions on customer behaviour. Findings include that perceptions of customer service have a strong positive impact on customers’ overall shopping satisfaction and spending behaviour. Perceptions of the in-store environment and product quality/ availability positively influence customer satisfaction but negatively influence the amount of money spent during their shopping trip. Furthermore, personalised customer service has a strong positive impact on spend and overall shopping satisfaction, which also positively influences the number of store visits the week after. However, an increase in shopping satisfaction coming from positive perceptions of the in-store environment and product quality/ availability factors helps to reduce their negative impact on spend week after. A key contribution of this study is to suggest a priority order for investment; retailers should prioritise personalised customer service and checkout customer service, followed by the in-store environment together with product quality and availability. These findings are very important in the context of the many initiatives the majority of retail operators undertake. Many retailers focus on cost-optimisation plans like implementing self-service check outs or easy to operate and clinical in-store environment. This research clearly and solidly shows which approach should be followed and what really matters for customers. That is why the findings are important for both retailers and academics, contributing to and expanding knowledge and practice on the impact of the in-store environment on the customer experience.

As data mining increasingly shapes organizational decision-making, the quality of its results must be questioned to ensure trust in the technology. Inaccuracies can mislead decision-makers and cause costly mistakes. With more data collected for analytical purposes, privacy is also a major concern. Data security policies and regulations are increasingly put in place to manage risks, but these policies and regulations often employ technologies that substitute and/or suppress sensitive details contained in the data sets being mined. Data masking and substitution and/or data encryption and suppression of sensitive attributes from data sets can limit access to important details. It is believed that the use of data masking and encryption can impact the quality of data mining results. This dissertation investigated and compared the causal effects of data masking and encryption on classification performance as a measure of the quality of knowledge discovery. A review of the literature found a gap in the body of knowledge, indicating that this problem had not been studied before in an experimental setting. The objective of this dissertation was to gain an understanding of the trade-offs between data security and utility in the field of analytics and data mining. The research used a nationally recognized cancer incidence database, to show how masking and encryption of potentially sensitive demographic attributes such as patients’ marital status, race/ethnicity, origin, and year of birth, could have a statistically significant impact on the patients’ predicted survival. Performance parameters measured by four different classifiers delivered sizable variations in the range of 9% to 10% between a control group, where the select attributes were untouched, and two experimental groups where the attributes were substituted or suppressed to simulate the effects of the data protection techniques. In practice, this represented a corroboration of the potential risk involved when basing medical treatment decisions using data mining applications where attributes in the data sets are masked or encrypted for patient privacy and security concerns.

Master thesis deals with a problem of data integration between Big Data platform and enterprise data warehouse. Main goal of this thesis is to create a complex transfer system to move data from a data warehouse to this platform using a suitable tool for this task. This system should also store and manage all metadata information about previous transfers. Theoretical part focuses on describing concepts of Big Data, brief introduction into their history and presents factors which led to need for this new approach. Next chapters describe main principles and attributes of these technologies and discuss benefits of their implementation within an enterprise. Thesis also describes technologies known as Business Intelligence, their typical use cases and their relation to Big Data. Minor chapter presents main components of Hadoop system and most popular related applications. Practical part of this work consists of implementation of a system to execute and manage transfers from traditional relation database, in this case representing a data warehouse, to cluster of a few computers running a Hadoop system. This part also includes a summary of most used applications to move data into Hadoop and a design of database metadata schema, which is used to manage these transfers and to store transfer metadata.

The research goal was to find out the current and possible level of procurement automation and its contribution to less routine and more creative jobs. The goal was accomplished by literature review and data collection via survey. The data collected evaluated enterprises in developing and developed countries. The research hypothesis of developing countries automating more than developed ones was not supported by the data tested via Mann-Whithey U test. The data collected was from 146 respondents from all around the world. Therefore, there are limitations of the conclusions. The thesis and its survey contributes to the knowledge about the level of procurement automation.

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.