title: VAET: A Visual Analytics Approach for E-Transactions Time-Series.
abstract: Previous studies on E-transaction time-series have mainly focused on finding temporal trends of transaction behavior. Interesting transactions that are time-stamped and situation-relevant may easily be obscured in a large amount of information. This paper proposes a visual analytics system, Visual Analysis of E-transaction Time-Series (VAET), that allows the analysts to interactively explore large transaction datasets for insights about time-varying transactions. With a set of analyst-determined training samples, VAET automatically estimates the saliency of each transaction in a large time-series using a probabilistic decision tree learner. It provides an effective time-of-saliency (TOS) map where the analysts can explore a large number of transactions at different time granularities. Interesting transactions are further encoded with KnotLines, a compact visual representation that captures both the temporal variations and the contextual connection of transactions. The analysts can thus explore, select, and investigate knotlines of interest. A case study and user study with a real E-transactions dataset (26 million records) demonstrate the effectiveness of VAET.
authors.forename: Cong
authors.initials: C
authors.lastname: Xie
authors.forename: Wei
authors.initials: W
authors.lastname: Chen
authors.forename: Xinxin
authors.initials: X
authors.lastname: Huang
authors.forename: Yueqi
authors.initials: Y
authors.lastname: Hu
authors.forename: Scott
authors.initials: S
authors.lastname: Barlowe
authors.forename: Jing
authors.initials: J
authors.lastname: Yang