Consumer behavior analysis on sales process model using process discovery algorithm for the omnichannel distribution system

Currently, Omnichannel distribution services are experiencing very rapid development around the world. In the Omnichannel distribution services, each existing sales channel will be connected to each other through integration capabilities so that no channels are left neglected. This is able to provide the best experience for consumers when shopping both online through mobile devices, laptops, and in physical stores. But on the other hand, this creates problems for business people who develop Omnichannel services. On the one hand, it facilitates the marketing process, but on the other hand, business people have difficultyreading the behavior of consumers who use Omnichannel distribution services. One way to analyze consumer behavior is to use the Process Discovery approach to obtain a process model. There are several Process Discovery Algorithms capable of describing and analyzing process models. In this paper, an experiment was carried out using the sales event log dataset generated from the Omnichannel distribution service system. Service channels used are Marketplace, Web Store, Social Media, Social Media Shop, and Media Messenger. Sales process modelling is generated using the Inductive Miner Algorithm, Heuristic Algorithm, Alpha Miner Algorithm and Fuzzy Miner Algorithm. Then the next step is to measure the process model obtained by Conformance Checking. The purpose of process modeling and measurement is to obtain a sales process model that can predict consumer behavior patterns well. The results of the analysis show that the process model generated by the Fuzzy Miner Algorithm is the best process model for describing consumer behavior in Omnichannel Distribution Services in this study. Based on the process model obtained with the Fuzzy Miner Algorithm, consumer behavior shows that the majority of consumers spend time on social media channels and then make purchases on the Marketplace channel. In addition, the results of the analysis show that consumers make more transactions on the Marketplace channel compared to the webstore channel or Social Media Shop channel

NEW ARTIFACTS FOR THE KNOWLEDGE DISCOVERY VIA DATA ANALYTICS (KDDA) PROCESS

Recently, the interest in the business application of analytics and data science has increased significantly. The popularity of data analytics and data science comes from the clear articulation of business problem solving as an end goal. To address limitations in existing literature, this dissertation provides four novel design artifacts for Knowledge Discovery via Data Analytics (KDDA). The first artifact is a Snail Shell KDDA process model that extends existing knowledge discovery process models, but addresses many existing limitations. At the top level, the KDDA Process model highlights the iterative nature of KDDA projects and adds two new phases, namely Problem Formulation and Maintenance. At the second level, generic tasks of the KDDA process model are presented in a comparative manner, highlighting the differences between the new KDDA process model and the traditional knowledge discovery process models. Two case studies are used to demonstrate how to use KDDA process model to guide real world KDDA projects. The second artifact, a methodology for theory building based on quantitative data is a novel application of KDDA process model. The methodology is evaluated using a theory building case from the public health domain. It is not only an instantiation of the Snail Shell KDDA process model, but also makes theoretical contributions to theory building. It demonstrates how analytical techniques can be used as quantitative gauges to assess important construct relationships during the formative phase of theory building. The third artifact is a data mining ontology, the DM3 ontology, to bridge the semantic gap between business users and KDDA expert and facilitate analytical model maintenance and reuse. The DM3 ontology is evaluated using both criteria-based approach and task-based approach. The fourth artifact is a decision support framework for MCDA software selection. The framework enables users choose relevant MCDA software based on a specific decision making situation (DMS). A DMS modeling framework is developed to structure the DMS based on the decision problem and the users\u27 decision preferences and. The framework is implemented into a decision support system and evaluated using application examples from the real-estate domain

Is "Better Data" Better than "Better Data Miners"? (On the Benefits of Tuning SMOTE for Defect Prediction)

We report and fix an important systematic error in prior studies that ranked classifiers for software analytics. Those studies did not (a) assess classifiers on multiple criteria and they did not (b) study how variations in the data affect the results. Hence, this paper applies (a) multi-criteria tests while (b) fixing the weaker regions of the training data (using SMOTUNED, which is a self-tuning version of SMOTE). This approach leads to dramatically large increases in software defect predictions. When applied in a 5*5 cross-validation study for 3,681 JAVA classes (containing over a million lines of code) from open source systems, SMOTUNED increased AUC and recall by 60% and 20% respectively. These improvements are independent of the classifier used to predict for quality. Same kind of pattern (improvement) was observed when a comparative analysis of SMOTE and SMOTUNED was done against the most recent class imbalance technique. In conclusion, for software analytic tasks like defect prediction, (1) data pre-processing can be more important than classifier choice, (2) ranking studies are incomplete without such pre-processing, and (3) SMOTUNED is a promising candidate for pre-processing.Comment: 10 pages + 2 references. Accepted to International Conference of Software Engineering (ICSE), 201

Analytics of self-regulated learning: a temporal and sequential approach

In educational settings, the increasingly sophisticated use of digital technology has provided students with greater agency over their learning. This has focused educational research on the metacognitive and cognitive activities with which students engage to manage their learning and the achievement of their learning goals. This field of research is articulated as self-regulated learning (SRL) and has seen the development of several key theoretical models. Despite key differences, these models are broadly defined by thematic variations of the same fundamental phases: i) a preparatory phase; ii) a performance phase, and; iii) an appraisal phase. Given the phasic nature of these models, the conceptualisation of SRL as a phenomenon that unfolds in temporal space has gained much traction. In acknowledging this dimension of SRL, researchers are bound to address the methodological demands of process, sequence, and temporality. Learning Analytics research, however, is largely characterised by the use of statistical models for data interrogation and analysis. Despite their value, several researchers posit that the use of statistical methods imposes ontological limitations with respect to the temporal and sequential nature of SRL. Another challenge is that while learner data are mostly collected at the micro level, (e.g., page access, video view, quiz attempt), SRL theory is defined at a macro level (e.g., planning, monitoring, evaluation), highlighting a need to bridge this gap in order to provide meaningful results. This thesis aims to explore the methodological opportunities and address the theoretical challenges presented in the area of temporally focused SRL learning analytics. First, the thesis explores the corpus of research in the area. As such, we present a systematic review of literature that analyses the findings of studies that explore SRL through the lenses of order and sequence, to provide insights into the temporal dynamics of SRL. Second, the thesis demonstrates the use of a novel process mining method to analyse how certain temporal activity traits relate to academic performance. We determined that more strategically minded activity, embodying aspects self-regulation, generally demonstrated to be more successful than less disciplined reactive behaviours. Third, the thesis presents a methodological framework designed to embed our analyses in a model of SRL. It comprises the use of: i) micro-level processing to transform raw trace data into SRL processes; and ii) first order Markov models to explore the temporal associations between SRL processes. We call this the “Trace-SRL” framework. Fourth, using the Trace-SRL framework, the thesis explores the deployment of multiple analytic methods and posits that richer insights can be gained through a combined methodological perspective. Fifth, building on this theme, the thesis presents a systematic analysis of four process mining algorithms, as deployed in the exploration of common SRL event data, concluding that the choice of algorithm and metric is of key importance in temporally-focused SRL research, and that combined metrics can provide deeper insights than those presented individually. Finally, the thesis concludes with a discussion of practical implications, the significance of the results, and future research directions