Fig. 1. The visual analysis interface of the VAET system. (a) The time-of-saliency (TOS) map overviews the saliency of each transaction computed with a probabilistic decision tree learner. (b) The KnotLines view shows the detailed information of transactions. The unfilled knots indicate fake transactions. (c) The legend of the sales category and the (d) bar chart shows the item volume of the selected transactions in TOS map. (e) Detailed transaction information and (f) statistical information

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... e shown in auxiliary views. 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.