The purpose of this project is to work as an interdisciplinary team whose primary role is to mentor a team of WPI undergraduate students completing their Major Qualifying Project (MQP) in collaboration with Vestigo Ventures, LLC. (“Vestigo Ventures�) and Cogo Labs. We worked closely with the project sponsors at Vestigo Ventures and Cogo Labs to understand each sponsor’s goals and desires, and then translated those thoughts into actionable items and concrete deliverables to be completed by the undergraduate student team. As a graduate student team with a diverse set of educational backgrounds and a range of academic and professional experiences, we provided two primary functions throughout the duration of this project. The first function was to develop a roadmap for each individual project, with concrete steps, justification, goals and deliverables. The second function was to provide the undergraduate team with clarification and assistance throughout the implementation and completion of each project, as well as provide our opinions and thoughts on any proposed changes. The two teams worked together in lock-step in order to provide the project sponsors with a complete set of deliverables, with the undergraduate team primarily responsible for implementation and final delivery of each completed project.

Abstract The purpose of this study is to investigate how big data solutions in the Swedish healthcare system can generate a revenue. As technology continues to evolve, the use of big data is beginning to transform processes in many different industries, making them more efficient and effective. The opportunities presented by big data have been researched to a large extent in commercial fields, however, research in the use of big data in healthcare is scarce and this is particularly true in the case of Sweden. Furthermore, there is a lack in research that explores the interface between big data, healthcare and revenue models. The interface between these three fields of research is important as innovation and the integration of big data in healthcare could be affected by the ability of companies to generate a revenue from developing such innovations or solutions. Thus, this thesis aims to fill this gap in research and contribute to the limited body of knowledge that exists on this topic. The study conducted in this thesis was done via qualitative methods, in which a literature search was done and interviews were conducted with individuals who hold managerial positions at Region Västerbotten. The purpose of conducting these interviews was to establish a better understanding of the Swedish healthcare system and how its structure has influenced the use, or lack thereof, of big data in the healthcare delivery process, as well as, how this structure enables the generation of revenue through big data solutions. The data collected was analysed using the grounded theory approach which includes the coding and thematising of the empirical data in order to identify the key areas of discussion. The findings revealed that the current state of the Swedish healthcare system does not present an environment in which big data solutions that have been developed for the system can thrive and generate a revenue. However, if action is taken to make some changes to the current state of the system, then revenue generation may be possible in the future. The findings from the data also identified key barriers that need to be overcome in order to increase the integration of big data into the healthcare system. These barriers included the (i) lack of big data knowledge and expertise, (ii) data protection regulations, (iii) national budget allocation and the (iv) lack of structured data. Through collaborative work between actors in both the public and private sectors, these barriers can be overcome and Sweden could be on its way to transforming its healthcare system with the use of big data solutions, thus, improving the quality of care provided to its citizens. Key words: big data, healthcare, Swedish healthcare system, AI, revenue models, data-driven revenue models

Big data analysis and inbound marketing are interlinked and can play a significant role in the identification of target audience and in the production of communication content as per the needs of target audience for strategic communication campaigns. By introducing and bringing the marketing concepts of big data analysis and inbound marketing into the field of strategic communication this quantitative study attempts to fill the gap in the limited body of knowledge of strategic communication research and practice. This study has used marketing campaigns as case studies to introduce a new strategic communication model by introducing the big data analysis and inbound marketing strategy into the three staged model of strategic communication presented by Gulbrandsen, I. T., & Just, S. N. in 2016. Big data driven campaigns are used to explain the procedure of target audience selection, key concepts of big data analysis, future opportunities, practical applications of big data for strategic communication practitioners and researchers by identifying the need for more academic research and practical use of big data analysis and inbound marketing in the strategic communication area. The study shows that big data analysis has potential to contribute in the field of strategic and target oriented communication. Inbound marketing and big data analysis has been used and considered as marketing strategy but this study is an attempt to shift the attention towards its role in strategic communication so there is a need to study big data analysis and inbound marketing with an open mind without confining it with some particular fields.

This study discusses Online Behavioral Advertising (OBA), which fundamentally consists of collected data about a person’s online behavior. The phenomena are made possible via the use of cookies on webpages and social media, which the user needs to accept to access the platform. The saved information is then used by firms to present targeted advertising, so called Online Behavioral Advertising. The feeling of being watched over/controlled on the internet might seem intrusive, which studies show may have a negative effect on consumers purchase intentions. How the effect varies between different personalities has not been studied before. Previous studies have focused on studying correlation between personalities and purchase intention and correlation between OBA and purchase intention. Little light has been shed on the effects of OBA for different personalities, to complete previous studies this study aims to find out more by using an acknowledged personality model named Big Five. This study uses a quantitative research approach, a web survey has been constructed to collect data and enable the composed hypotheses to be tested. The collected data is being analyzed in SPSS into descriptive statistics and Spearman rank correlation between the personality dimensions of the Big Five and the effect of OBA on purchase intention. The results show that previous theories about the effect of OBA on purchase intention can be verified. The descriptive statistics also show a significant higher effect of OBA on men than women. Furthermore, Spearman rank correlation shows that the effect varies between the personality dimensions of Big Five. A positive correlation was found between the dimensions Openness and Extraversion and OBA's effect, which means that persons scoring higher in these dimensions also has a higher effect of OBA on purchase intention. A negative correlation was found between the dimension Conscientiousness and OBA's effect, which means that persons scoring higher in this dimension is not significantly affected by OBA.

Big data of type spatial is growing exponentially with the highest rate due to extensive growth in usage of sensors, IoT and mobile devices’ spatial data generation, therefore maintaining, processing and using such data efficiently, effectively with high performance has become one of the top priorities for Database management system providers, hence spatial database features and datatypes have become serious criteria in evaluating database management systems that are supposed to work as the back-end for spatial applications and services. With exponential growth of data and introducing of new types of data, “Big Data” has become strongly focused area that has gained the attention of different sectors e.g. academia, industries and governments to other organizations and studies. The rising trend in high resolution and large-scale geographical information systems have resulted in more companies providing location-based applications and services, therefore finding a proper database management system solution that can support spatial big data features, with multi-model big data support that is reliable and affordable has become a business need for many companies. Concerning the fact that choosing proper solution for any software project can be crucial due to the total cost and desired functionalities that any product could possibly bring into the solution. Migration is also a very complicated and costly procedure that many companies should avoid, which justifies the criticality of choosing the right solution based on the specific needs of any organization. Companies providing spatial applications and services are growing with the common concern of providing successful solutions and robust services. One of the most significant elements that ensures services’ and hence the providers’ reputation and positive depiction is services’ high availability. The possible future work for the thesis could be to develop the framework into a decision support solution for IT businesses with emphasize on spatial features. Another possibility for the future works would be to evaluate the framework by testing the evaluation framework on many other different DBMSs.

Big data concerns large-volume, complex, growing data sets, and it provides us opportunities as well as challenges. This thesis focuses on statistical methods for several specific large, complex data challenges - each involving representation of data with complex format, utilization of complicated information, and/or intensive computational cost. The first problem we work on is hypothesis testing for multilayer network data, motivated by an example in computational biology. We show how to represent the complex structure of a multilayer network as a single data point within the space of supra-Laplacians and then develop a central limit theorem and hypothesis testing theories for multilayer networks in that space. We develop both global and local testing strategies for mean comparison and investigate sample size requirements. The methods were applied to the motivating computational biology example and compared with the classic Gene Set Enrichment Analysis(GSEA). More biological insights are found in this comparison. The second problem is the source detection problem in epidemiology, which is one of the most important issues for control of epidemics. Ideally, we want to locate the sources based on all history data. However, this is often infeasible, because the history data is complex, high-dimensional and cannot be fully observed. Epidemiologists have recognized the crucial role of human mobility as an important proxy to a complete history, but little in the literature to date uses this information for source detection. We recast the source detection problem as identifying a relevant mixture component in a multivariate Gaussian mixture model. Human mobility within a stochastic PDE model is used to calibrate the parameters. The capability of our method is demonstrated in the context of the 2000-2002 cholera outbreak in the KwaZulu-Natal province. The third problem is about multivariate time series imputation, which is a classic problem in statistics. To address the common problem of low signal-to-noise ratio in high-dimensional multivariate time series, we propose models based on state-space models which provide more precise inference of missing values by clustering multivariate time series components in a nonparametric way. The models are suitable for large-scale time series due to their efficient parameter estimation. / 2019-05-15T00:00:00Z

The United States has been going through a road accident crisis for many years. The National Safety Council estimates 40,000 people were killed and 4.57 million injured on U.S. roads in 2017. Direct and indirect loss from tra c congestion only is more than $140 billion every year. Vehicular Ad-hoc Networks (VANETs) are envisioned as the future of Intelligent Transportation Systems (ITSs). They have a great potential to enable all kinds of applications that will enhance road safety and transportation efficiency. In this dissertation, we have aggregated seven years of real-life tra c and incidents data, obtained from the Florida Department of Transportation District 4. We have studied and investigated the causes of road incidents by applying machine learning approaches to this aggregated big dataset. A scalable, reliable, and automatic system for predicting road incidents is an integral part of any e ective ITS. For this purpose, we propose a cloud-based system for VANET that aims at preventing or at least decreasing tra c congestions as well as crashes in real-time. We have created, tested, and validated a VANET traffic dataset by applying the connected vehicle behavioral changes to our aggregated dataset. To achieve the scalability, speed, and fault-tolerance in our developed system, we built our system in a lambda architecture fashion using Apache Spark and Spark Streaming with Kafka. We used our system in creating optimal and safe trajectories for autonomous vehicles based on the user preferences. We extended the use of our developed system in predicting the clearance time on the highway in real-time, as an important component of the traffic incident management system. We implemented the time series analysis and forecasting in our real-time system as a component for predicting traffic flow. Our system can be applied to use dedicated short communication (DSRC), cellular, or hybrid communication schema to receive streaming data and send back the safety messages. The performance of the proposed system has been extensively tested on the FAUs High Performance Computing Cluster (HPCC), as well as on a single node virtual machine. Results and findings confirm the applicability of the proposed system in predicting traffic incidents with low processing latency. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

There is now more data being created than ever before and this data can be any form of data, textual, multimedia, spatial etc. To process this data, several big data processing platforms have been developed including Hadoop, based on the MapReduce model and LexisNexis’ HPCC systems. In this thesis we evaluate the HPCC Systems framework with a special interest in multimedia data analysis and propose a framework for multimedia data processing. It is important to note that multimedia data encompasses a wide variety of data including but not limited to image data, video data, audio data and even textual data. While developing a unified framework for such wide variety of data, we have to consider computational complexity in dealing with the data. Preliminary results show that HPCC can potentially reduce the computational complexity significantly. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

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.

The analysis of big data is now very popular. Big data may be very important for companies, societies or even human beings if we can take full advantage of them. Data scientists defined big data with four Vs: volume, velocity, variety and veracity. In a short, the data have large volume, grow with high velocity, represent with numerous varieties and must have high quality. Here we analyze data from many sources (varieties). In small area estimation, the term ``big data' refers to numerous areas. We want to analyze binary for a large number of small areas. Then standard Markov Chain Monte Carlo methods (MCMC) methods do not work because the time to do the computation is prohibitive. To solve this problem, we use numerical approximations. We set up four methods which are MCMC, method based on Beta-Binomial model, Integrated Nested Normal Approximation Model (INNA) and Empirical Logistic Transform (ELT) method. We compare the processing time and accuracies of these four methods in order to find the fastest and reasonable accurate one. Last but not the least, we combined the empirical logistic transform method, the fastest and accurate method, with time series to explore the sales data over time.