Segmentifier : interactively refining clickstream data into actionable segments
department or school: Science, Faculty of
department or school: Computer Science, Department of
data provider: DSpace
degree campus: UBCV
creator: Dextras-Romagnino, Kimberly
issued: 2018-04-19T18:50:01Z
issued: 2018
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Clickstream data has the potential to provide actionable insights into e-commerce consumer behavior, but previous techniques fall short of handling the scale and complexity of real-world datasets. We present Segmentifier, a novel visual analytics interface that supports an iterative process of refining collections of action sequences into meaningful segments that are suitable for downstream analysis with techniques that require relatively small and clean input. We also present task and data abstractions for the application domain of clickstream data analysis, leading to a framework that abstracts the segment analysis process in terms of six functions: view, refine, record, export, abandon, and conclude. The Segmentifier interface is simple to use for analysts operating under tight time constraints. It supports filtering and partitioning through visual queries for both quantitative attributes and custom sequences of events, which are aggregated according to a three-level hierarchy. It features a rich set of views that show the underlying raw sequence details and the derived data of segment attributes, and a detailed glyph-based visual history of the automatically recorded analysis process showing the provenance of each segment in terms of an analysis path of attribute constraints. We demonstrate the effectiveness of our approach through a usage scenario with real-world data and a case study documenting the insights gained by an e-commerce analyst.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/65515
note: Segmentifier: Interactively Refining Clickstream Data intoActionable SegmentsbyKimberly Dextras-RomagninoB. Sc., Concordia University, 2015A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFMaster of ScienceinTHE FACULTY OF GRADUATE AND POSTDOCTORALSTUDIES(Computer Science)The University of British Columbia(Vancouver)April 2018c© Kimberly Dextras-Romagnino, 2018AbstractClickstream data has the potential to provide actionable insights into e-commerceconsumer behavior, but previous techniques fall short of handling the scale andcomplexity of real-world datasets. We present Segmentifier, a novel visual an-alytics interface that supports an iterative process of refining collections of actionsequences into meaningful segments that are suitable for downstream analysis withtechniques that require relatively small and clean input. We also present task anddata abstractions for the application domain of clickstream data analysis, leading toa framework that abstracts the segment analysis process in terms of six functions:view, refine, record, export, abandon, and conclude. The Segmentifier interface issimple to use for analysts operating under tight time constraints. It supports fil-tering and partitioning through visual queries for both quantitative attributes andcustom sequences of events, which are aggregated according to a three-level hier-archy. It features a rich set of views that show the underlying raw sequence detailsand the derived data of segment attributes, and a detailed glyph-based visual his-tory of the automatically recorded analysis process showing the provenance of eachsegment in terms of an analysis path of attribute constraints. We demonstrate theeffectiveness of our approach through a usage scenario with real-world data and acase study documenting the insights gained by an e-commerce analyst.iiLay SummaryCompanies engaged in e-commerce are recording every action consumers take onwebsites, resulting in rich clickstream datasets th…
has type: Thesis/Dissertation
date issued: 2018-05
is shown at: 10.14288/1.0365820
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/65515