Pathogens, such as HIV and influenza, evolve in response to the selective pressures of their host environments accumulating changes in their genomes that offer fitness benefits. This selective pressure is characterised by three properties: (1.) it is episodic, tracking changes in the adaptive immune response and drug therapy, (2.) it is directional in that only particular amino acid substitutions are favoured and (3.) it varies between genomic loci. Most previous models have ignored or inadequately addressed some of these phenomena. This work extends recent approaches to modelling episodic directional selection acting on protein-coding sequences. We use inference techniques within the topic model framework to identify loci evolving under natural selection. A notable example of such techniques are the variational Bayesian methods. We show that our approach performs well in terms of specificity and power, and demonstrate its utility by applying it to some real datasets of HIV sequences.

Approximate query processing (AQP) provides fewer representative samples to approximate large amounts of data. Processing these smaller data subsets enables visualization systems to provide end-users with real-time responses. However, challenges arise for real-world users in adopting AQP-based visualization systems, e.g., the absence of AQP modules in mainstream commercial databases, erroneous estimations caused by sampling bias, and end-user uncertainty when interpreting approximate query results. In this dissertation, we present an AQP-centered technique for enabling interactive visual analytics for large amounts of spatiotemporal data under the aforementioned challenges. First, we design, implement and evaluate a client-based visual analytics framework that progressively acquires spatiotemporal data from an AQP-absence server-side to client-based visualization systems so that interactive data exploration can be maintained on a client machine with modest computational power. Second, we design, implement, and evaluate an online sampling approach that selects samples of large spatiotemporal data in an unbiased manner and accordingly improves the accuracy of the associated estimates. Last, we design, implement and evaluate a difference assessment approach that compares approximate and exact spatial heatmap visualizations in terms of human perception. As such, information changes perceptible by users are well represented, and users can evaluate the reliability of approximate answers more easily. Our results show the superior performance of our proposed AQP-centered technique in terms of speed, accuracy, and user trust, as compared to a baseline of state-of-the-art solutions.

Indiana University-Purdue University Indianapolis (IUPUI) / This research took place as a cognitive exploration of sensemaking of learning analytics at Ivy Tech Community College of Indiana. For the courses with the largest online enrollment, quality standards in the course design are maintained by creating sections from a course design framework. This means all sections have the same starting content and the same framework for assessment. The course design framework is maintained by the curriculum committee composed of program chairs who oversee the program to which the course belongs. This research proposed to develop a learning analytics dashboard to elicit the best practices in instantiating a course design framework from the perspective of the program chair. The Instructional Design Implementation Dashboard, IDID, was designed to address the sensemaking needs of program chairs. The program chairs were asked to make sense of IDID built around the data collected from the course management system and the student information system. IDID leveraged metrics from the user activity and the learner performance from the learning management system, combined with data about the student demographics captured from the student information system. IDID was used to identify highly successful sections and examine the instructor behaviors that might be considered best practices. Data Frame Sensemaking theory was confirmed as an accurate description of the experience of program chairs when using IDID. A revised model of Data Frame Sensemaking theory was developed to explain the interaction of those using the IDID platform.

Analysts must filter through an ever-growing amount of data to obtain information relevant to their investigations. Looking at every piece of information individually is in many cases not feasible; there is hence a growing need for new filtering tools and techniques to improve the analyst process with large datasets. We present MineVis — an analytics system that integrates biclustering algorithms and visual analytics tools in one seamless environment. The combination of biclusters and visual data glyphs in a visual analytics spatial environment enables a novel type of filtering. This design allows for rapid exploration and navigation across connected documents. Through a user study we conclude that our system has the potential to help analysts filter data by allowing them to i) form hypotheses before reading documents and subsequently ii) validating them by reading a reduced and focused set of documents. / Master of Science

No / The advance of high throughput biotechnology enables the generation of large amount of biomedical data. The microarray is increasingly a popular approach for the detection of genome-wide gene expression. Microarray data have thus increased significantly in public accessible database repositories, which provide valuable big data for scientific research. To deal with the challenge of microarray big data collected in different research labs using different experimental set-ups and on different bio-samples, this paper presents a primary study to evaluate the impact of two important factors (the origin of bio-samples and the quality of microarray data) on the integrated analytics of multiple microarray data. The aim is to enable the extraction of reliable and robust gene biomarkers from microarray big data. Our work showed that in order to enhance biomarker discovery from microarray big data (i) it is necessary to treat the microarray data differently in terms of their quality, (ii) it is recommended to stratifying (i.e., sub-group) the data according to the origin of bio-samples in the analytics.

This research examines the healthcare services ecosystem and the impact and role service interventions made by providers and patients have on this ecosystem. Each area has an important role in contributing to the value and sustainability of the ecosystem. Healthcare, as a community service, requires a minimum of two counterparts: the providers and the customers, in this case the patients. Healthcare is a unique ecosystem because often the customers are not conscious of the interplay of the ecosystem but are reliant upon the system for their health and wellbeing. The first section of this dissertation examines the effects that occur in the healthcare ecosystem when part of the system experiences a disaster and the impact and role of other areas of the system in response to the disaster, particularly regarding the resilience. Similar to a biological ecosystem that is undergoing a flood, in the healthcare services ecosystem if too many patients present to the Emergency Department (ED) at the same time disaster level overcrowding will occur. We aim to measure the resilience of the healthcare ecosystem to this disaster level overcrowding. The second section of this dissertation examines how the components of the healthcare ecosystem maintain sustainability and usability. Healthcare professionals are assessed regarding their ability to maintain the healthcare ecosystem, with a specific focus on what occurs after patients are in the hospital system. To examine the ability of the healthcare professionals to maintain the ecosystem we analyze the usability and adaptability of the electronic health record and the professional's workflows to determine how they use this tool to sustain the healthcare ecosystem. The third section of this dissertation examines patient self-management and the influence this has on the healthcare ecosystem. Much of the management of health in patients, particularly those with chronic illnesses, occurs outside of the hospital, thus examining this aspect of self-care provides insight on the overall system. This research examines patients with a chronic illness and their use of online health communities, with a particular focus on their reciprocal behaviors and the impact this support system has on their overall health state. By examining these aspects of the healthcare services ecosystem, we can better improve our understanding of these phenomena. / Ph. D. / This dissertation examines healthcare as an ecosystem to discover how various aspects interact with each other. The first section looks at emergency department overcrowding to examine the resilience to determine the causative and mitigating factors. The second section examines the electronic medical record for usability and determines the most impactful factors for healthcare workers. The third section examines online health communities with consideration of reciprocal behaviors and their impact on users’ health. Consideration of the healthcare ecosystem and the broad applicability of this topic provides researchers with an overarching framework for future work.

The way organizations make decisions today is very often purely based on intuition or gut-feeling. It does not matter whether decisions are of high risk for the company’s future or not, managers golden-gut is the only thing that determines whether invest- ments should be made or not. Analytics is the opposite of this intuition-based decision making. If taken seriously, almost all decisions in organizations are made on facts that are analytically derived from massive amount of data from internal and external sources such as customer relationship systems to social networks. Business leaders are becoming more aware of analytically based decisions, and some use it more than others. Analytics is usually practiced in finance, customer relationships or marketing. There is, however, one area where analytics is practiced by a small number of companies, and that is on the organization’s workforce. The workforce is usually seen as one of the most complicated areas to practice analytics. An employee is, of course, more com- plicated than a product. Despite this fact, companies usually forget that conducting analytics on employees is very similar to conducting analytics on customers, which has been practiced for many decades. Some organizations are showing great success with applications of Workforce Analytics (WA). Most of these organizations are located in the US or outside of Sweden. This thesis has conducted research on to what extent Workforce Analytics is practiced in Sweden. Empirical findings show that some com- panies use WA in Sweden. The practice is not of highest sophistication of WA. Also, they show aspiration towards the idea of WA and some are locally conducting various of applications.

Syftet med denna studie är att utreda begreppen big data och analytics. Utifrån vetenskapliga teorier om begreppen undersöks hur konsultföretag uppfattar och använder sig av big data och analytics. För att skapa en nyanserad bild har även en organisation inom vården undersökts för att få kunskap om hur de kan dra nytta av big data och analytics. Ett antal viktiga svårigheter och framgångsfaktorer kopplade till båda begreppen presenteras. De svårigheterna kopplas sedan ihop med en framgångsfaktor som anses kunna bidra till att lösa det problemet. De mest relevanta framgångsfaktorer som identifierats är att högkvalitativ data finns tillgänglig men även kunskap och kompetens kring hur man hanterar data. Slutligen tydliggörs begreppens innebörd där man kan se att big data oftast beskrivs ur dimensionerna volym, variation och hastighet och att analytics i de flesta fall syftar till att deskriptiv och preventiv analys genomförs. / The purpose of this study is to investigate the concepts of big data and analytics. The concepts are explored based on scientific theories and interviews with consulting firms. A healthcare organization has also been interviewed to get a richer understanding of how big data and analytics can be used to gain insights and how an organisation can benefit from them. A number of important difficulties and sucess facors connected to the concepts are presented. These difficulties are then linked to a sucess factor that is considered to solve the problem. The most relevant success factors identified are the avaliability of high quality data and knowledge and expertise on how to handle the data. Finally the concepts are clarified and one can see that big data is usually described from the dimensions volume, variety and velocity and analytics is usually described as descriptive and preventive analysis.

The main aim of this work is describing and explaining the possibilities of web analytics as a tool for effective evaluation of the web and campaigns performance. The benefit appreciate everybody, who makes decision on further web development and its marketing based on measured data. The work is divided into three main parts. The first describes how set up Google Analytics for measuring the web, the second part shows how read and interpret the measured data. In the third practical part are both applied for analyzing prohifi.cz shop.

Denna uppsats undersöker problematiken runt att integrera datakällor då analytics används för flerkanalig digital marknadsföring i större organisationer. Ansatsen är kritiskt och går emot bilden av analytics som både enkelt att implementera och använda för flerkanalig digital marknadsföring inom större organisationer. Uppsatsen visar på hur strukturer i större organisationer gör övergripande analytics för fler digitala kanaler problematiskt. Uppsatsen bygger på resultat från en observationsstudie som gjordes inom en större multinationell organisation och fokuserar på problematik som återfanns i två huvudsakliga projekt som genomfördes under tiden för studien.