The internet has become a more prominent part of people’s lives. Clickstream and other online data have enabled researchers to better understand consumers’ decision-making behavior in a variety of application areas. This dissertation focuses on using clickstream data in two application areas: the airline industry and the field of education. The first study investigates if airline passengers departing from or arriving to a multi-airport city actually consider itineraries at the airports not considered to be their preferred airport. It was found that customers do consider fares at multiple airports in multi-airport cities. However, other trip characteristics, typically linked to whether a customer is considered business or leisure, were found to have a larger impact on customer behavior than offered fares at competing airports. The second study evaluates airline customer search and purchase behavior near the advance purchase deadlines, which typically signify a price increase. Search and purchase demand models were constructed using instrumented two-stage least squares (2SLS) models with valid instruments to correct for endogeneity. Increased demand was found before each deadline, even though these deadlines are not well-known among the general public. It is hypothesized that customers are able to use two methods to unintentionally book right before these price increases: (1) altering their travel dates by one or two days using the flexible dates tools offered by an airline’s or online travel agency’s (OTA) website to receive a lower fare, (2) booking when the coefficient of variation across competitor fares is high, as the dynamics of one-way and roundtrip pricing differ near these deadlines. The third study uses clickstream data in the field of education to compare the success of the traditional, flipped, and micro-flipped classrooms as well as their impacts on classroom attitudes. Students’ quiz grades were not significantly different between the traditional and flipped classrooms. The flipped classroom reduced the impact of procrastination on success. In the end, it was found that micro-flipped was most preferred by students as it incorporated several benefits of the flipped classroom without the effects of a learning curve.

Given the increasing predominance of work teams within organisations, this study aimed to investigate the role that perceptions of person-team fit has in the recruitment process, in addition to other forms of person-environment fit. An experimental design was followed which manipulated the amount of team information made available to participants. It was hypothesised that participants who received more information would exhibit higher perceptions of person-team fit. Results supported this prediction with levels of person-team fit being successfully manipulated. Results also showed significant correlations between person-team fit and organisational attraction which is important in the early stages of recruitment. This study was conducted remotely over the internet with clickstream data associated with this medium being collected. It was hypothesised that viewing order and times may be related to dependent variables. No support for this prediction was found, however it did identify a group of participants that appeared not to engage in the task, which has implications for future research carried out online.

The aim of this thesis is to bring near to the readers the area of webmining and familiarize them with tools, which deal with data mining on the web. The main emphasis is placed on the analytical software program called Piwik. This analytical tool is compared with others nowadays available analytical tools. This thesis also aims to create a compact documentation of the software Piwik. The largest part of this documentation is devoted to the newly programmed plugin. The principle of information retrieval, based on user behavior on the web, is described from the common viewpoint and leads to more factual form of description of information retrieval using this new plugin.

Thesis introduces current research trends in clickstream analysis and proposes a new heuristic that could be used for dimensionality reduction of semantically enriched data in Web Usage Mining (WUM). Click-fraud and conversion fraud are identified as key prospective application areas for WUM. Thesis documents a conversion fraud vulnerability of Google Analytics and proposes defense - a new clickstream acquisition software, which collects data in sufficient granularity and structure to allow for data mining approaches to fraud detection. Three variants of K-means clustering algorithms and three association rule data mining systems are evaluated and compared on real-world web usage data.

<p>Det här arbetet har som syfte att genom en jämförelse av olika analysmetoder för clickstream-data kunna fungera som en vägledning när en metod ska implementeras. Metoden som använts vid jämförelsen är litteraturstudie i och med att de analyseringsmetoder som ska undersökas redan är framtagna och kunskap om dem fås genom att studera litteratur i vilka de förekommer. Ett antal kriterier används sedan vid själva jämförelsen, anledningen till detta är att metoderna ska jämföras utifrån en gemensam grund.</p><p>De metoder som uppfyllde kraven för de olika kriterierna bäst var page events fact model och subsession fact model. Subsession fact model kan dock upplevas som det bästa valet i alla lägen men samtidigt är den kanske lite överdriven om clickstream-datan bara ska användas till att se hur besökarna använder varje individuell sida för att användas i designsupport syfte. Det går alltså att påvisa att syftet styr vilken metod som är mest lämpad.</p>

Det här arbetet har som syfte att genom en jämförelse av olika analysmetoder för clickstream-data kunna fungera som en vägledning när en metod ska implementeras. Metoden som använts vid jämförelsen är litteraturstudie i och med att de analyseringsmetoder som ska undersökas redan är framtagna och kunskap om dem fås genom att studera litteratur i vilka de förekommer. Ett antal kriterier används sedan vid själva jämförelsen, anledningen till detta är att metoderna ska jämföras utifrån en gemensam grund. De metoder som uppfyllde kraven för de olika kriterierna bäst var page events fact model och subsession fact model. Subsession fact model kan dock upplevas som det bästa valet i alla lägen men samtidigt är den kanske lite överdriven om clickstream-datan bara ska användas till att se hur besökarna använder varje individuell sida för att användas i designsupport syfte. Det går alltså att påvisa att syftet styr vilken metod som är mest lämpad.

Processes are running everywhere. Understanding and analyzing business and software processes and their interactions is critical if we wish to improve them. There are many event logs generated from Information Systems and applications related to fraud detection, healthcare processes, e-commerce processes, and others. These event logs are the starting point for process mining. Process mining aims to discover, monitor, and improve real processes by extracting knowledge from event logs available in information systems. Process mining provides fact-based insight from real event logs that helps analyze and improve existing business processes by answering, for example performance or conformance questions. As the number of applications developed in a cloud infrastructure (often called Software as a Service – SaaS at the application level) is increasing, it becomes essential and useful to study and discover these processes. However, SaaS applications bring new challenges to the problem of process mining. Using the Design Science Research Methodology, this thesis introduces a new method to study, discover, and analyze cloud-based application processes using process mining techniques. It explores the applications and known challenges related to process mining in cloud applications through a systematic literature review (SLR). It then contributes a new Application Programming Interface (API), with an implementation in R, and a companion method called Cloud Pattern API – Process Mining (CPA-PM), for the preprocessing of event logs in a way that addresses many of the challenges identified in the SLR. A case study involving a SaaS company and real event logs related to the trial process of their online service is used to validate the proposed solution.

This Master's thesis project aims to investigate if Wikipedia's clickstream data can be used to improve the retrieval performance of information retrieval systems. The project is conducted under the assumption that a traversal between two article connects the two articles in regards to content. To extract useful terms out of the clickstream data, it needed to be structured so that it given a Wikipedia article it is possible to find all of the in-going or out-going article traversals.The project settled on using the clickstream data in an automatic query expansion approach.Two expansion methods were investigated, one based on expanding with full article title so that the context would be preserved, and the other expanded with individual terms from the article titles.The structure of the data and two proposed methods were evaluated using a set of queries and relevance judgments. The results of the evaluation shows that the method that expands with individual terms performed better than the full article title expansion method and that the individual term method managed to increase the MAP with 11.24%.  The expansion method was evaluated on two different query collections, and it was found that the proposed expansion method only improves the results where the average recall of the original queries are low.The thesis conclusion is that the clickstream can be used to improve retrieval performance for an information retrieval system. / Det här examensarbetets mål är att undersöka om Wikipedias klickströmsdata kan användas för att förbättra sökprestanda för informationsökningssystem. Arbetet har utförts under antagandet att en övergång mellan två artiklar på Wikipedia sammankopplar artiklarnas innehåll och är av intresse för användaren. För att kunna utnyttja klickströmsdatan krävs det att den struktureras på ett användbart sätt så att det givet en artikel går att se hur läsare har förflyttat sig ut eller in mot artikeln. Vi valde att utnyttja datamängden genom en automatisk sökfrågeexpansion. Två olika metoder togs fram, där den första expanderar sökfrågan med hela artikeltitlar medans den andra expanderar med enskilda ord ur en artikeltitel.Undersökningens resultat visar att den ordbaserade expansionsmetoden presterar bättre än metoden som expanderar med hela artikeltitlar. Den ordbaserade expansionsmetoden lyckades uppnå en förbättring för måttet MAP med 11.21%. Från arbetet kan man också se att expansionmetoden enbart förbättrar prestandan när täckningen för den ursprungliga sökfrågan är liten. Gällande strukturen på klickströmsdatan så presterade den utgående strukturen bättre än den ingående. Examensarbetets slutsats är att denna klickströmsdata lämpar sig bra för att förbättra sökprestanda för ett informationsökningssystem.

As more students interact with online learning platforms and eTextbooks, they generate massive amounts of data. For example, the OpenDSA eTextbook system collects clickstream data as users interact with prose, visualizations, and interactive auto-graded exercises. Ideally, instructors and system developers can harness this information to create better instructional experiences. But in its raw event-level form, it is difficult for developers or instructors to understand student behaviors, or to make testable hypotheses about relationships between behavior and performance. In this study, we describe our efforts to break raw event-level data first into sessions (a continuous series of work by a student) and then to meaningfully abstract the events into higher-level descriptions of that session. The goal of this abstraction is to help instructors and researchers gain insights into the students' learning behaviors. For example, we can distinguish when students read material and then attempt the associated exercise, versus going straight to the exercise and then hunting for the answers in the associated material. We first bundle events into related activities, such as the events associated with stepping through a given visualization, or with working a given exercise. Each such group of events defines a state. A state is a basic unit that characterizes the interaction log data, and there are multiple state types including reading prose, interacting with visual contents, and solving exercises. We harnessed the abstracted data to analyze studying behavior and compared it with course performance based on GPA. We analyzed data from the Fall 2020 and Spring 2021 sections of a senior-level Formal Languages course, and also from the Fall 2020 and Spring 2021 sections of a data structures course. / Master of Science / OpenDSA is an online learning platform used in multiple academic institutions including Virginia Tech's Computer Science courses. They use OpenDSA as the main instructional method and students in these courses generate massive amounts of clickstream data while interacting with the OpenDSA content. The system collects various events logs such as when students opened/closed a certain page, how long they stayed on the page, and how many times they clicked an interface element for visualizations and exercises. However, in its raw event-level form, it is difficult for instructors or developers to understand student behaviors, or to make testable hypotheses about relationships between behavior and performance. We describe our efforts to break raw event-level clickstreams into a session (continuous series of work by a student) and then to abstract the events into meaningful higher-level descriptions of students' behavior. We grouped raw events into related activities, such as the events associated with stepping through a given visualization, or working with a given exercise. We defined such a group of activities as a state, which is a basic unit that can characterize the interaction log data such as reading, slideshows, and exercises state. We harnessed the abstracted data to analyze students' studying behavior and compared it with their course performance based on their GPA. We analyzed data from two offerings of two CS courses at Virginia Tech to gain insights into students' learning behaviors.

This dissertation sought to explain goal achievement at limited traffic “long tail” Web sites using Information Foraging Theory (IFT). The central thesis of IFT is that individuals are driven by a metaphorical sense of smell that guides them through patches of information in their environment. An information patch is an area of the search environment with similar information. Information scent is the driving force behind why a person makes a navigational selection amongst a group of competing options. As foragers are assumed to be rational, scent is a mechanism by which to reduce search costs by increasing the accuracy on which option leads to the information of value. IFT was originally developed to be used in a “production rule” environment, where a user would perform an action when the conditions of a rule were met. However, the use of IFT in clickstream research required conceptualizing the ideas of information scent and patches in a non-production rule environment. To meet such an end this dissertation asked three research questions regarding (1) how to learn information patches, (2) how to learn trails of scent, and finally (3) how to combine both concepts to create a Clickstream Model of Information Foraging (CMIF). The learning of patches and trails were accomplished by using contrast sets, which distinguished between individuals who achieved a goal or not. A user- and site-centric version of the CMIF, which extended and operationalized IFT, presented and evaluated hypotheses. The user-centric version had four hypotheses and examined product purchasing behavior from panel data, whereas the site-centric version had nine hypotheses and predicted contact form submission using data from a Web hosting company. In general, the results show that patches and trails exist on several Web sites, and the majority of hypotheses were supported in each version of the CMIF. This dissertation contributed to the literature by providing a theoretically-grounded model which tested and extended IFT; introducing a methodology for learning patches and trails; detailing a methodology for preprocessing clickstream data for long tail Web sites; and focusing on traditionally under-studied long tail Web sites.