selltrend: inter-attribute visual analysis of temporal
TRANSCRIPT
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SellTrend: Inter-Attribute Visual Analysis of Temporal Transaction Data
Zhicheng Liu, John Stasko and Timothy Sullivan
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Looks Familiar?
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Travelport
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Sell Failure
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Attributes of Failed Transactions
Airline Flight No.
From To Date of Flight
…
United 455 SFO LAS 09/12/08
Delta 1934 ATL ORD 10/02/09 Delta 18 PVG ATL 05/23/09
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Attributes of Failed Transactions
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Airline Flight No.
From To Date of Flight
…
United 455 SFO LAS 09/12/08
Delta 1934 ATL ORD 10/02/09 Delta 18 PVG ATL 05/23/09
Travel Agent
Date of Transaction
Error Code
…
DFG 09/01/08 420
XLK 09/20/09 421 POS 03/25/09 200
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ANOMALY
Causes of Failure
• Inventory volatility: expected • A travel agent may deploy new software that performs
availability queries incorrectly • Agents use the Travelport system in an inefficient manner • Internal system errors
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Anomalous causes can consist of • A single attribute value, e.g. an airline, or • Any combination of any number of the attribute values
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Difficulty of Analyzing Current tools do not support in-depth analysis well • Lack of real-time situational awareness • Anomaly detection is not simply identifying top contributors
• Often requires historical trending analysis
Huge data and search space • Many millions of failed transactions stored for the latest 40
days • ~20 attributes, up to thousands of values within an attribute • Anomaly can be any combination of any number of attribute
values: impossible to enumerate all possibilities
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Analytic Tasks to Design for
• Enable Situational Awareness • What is the failure rate for the past hour? • How is today’s failure rate compared to yesterday and
one month ago? • Are any attribute values contributing irregularly high
failed transactions presently?
• Support Exploratory/Investigative Analysis • What are the root causes of a failure spike? • Can these root causes be explained by potentially
anomalous behaviors of relevant stakeholders?
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User-Centered Design
• Spreadsheet prototype to deployment over corporate database
• Formative Evaluation
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SellTrend
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Attribute Map View Table View
Timeline View
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Timeline View
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Day Panel Hour Panel
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Attribute Map View
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Travel agent
Sell Type
Departure City
Arrival City
AVL Type
Error Code
Flight Number
Flight Class
Airline
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Attribute Map View – A Closer Look
Analysis time frame: Oct 8th, No. of failed transactions: 10,000 on that day
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DYS (3600 : 36%)
Z7F (900 : 9%)
DYS (36%/42%)
Z7F (9%/6%)
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Table View
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Demo
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Related Work
• Time Series Data Visualization • Bar chart, color encoding of temporal change
• Categorical Data Visualization • Parallel Sets, InfoZoom, Pixel Bar Chart, EZChooser
• Faceted Browsing • Flamenco, FacetMap, FacetLens
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Design Considerations
• Event-based Coordination between the Views • Simple Visual Representation Consistent with Users’
Mental Model • Direct Manipulation Facilitates Cognitive Coupling • High Interactivity and Prompt Feedback
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Progress
SellTrend has been deployed since Dec 2008
“The data visualization tool provides [us] the ability to analyze potentially millions of rows of data at a customer and/or supplier level in a single ‘view’. By reducing this initial investigation time frame by greater than 90%, we can more quickly direct our resources to the underlying sell failure root cause. Addressing sell failure issues in a timelier manner improves our product quality, improves the customer experience, and provides a more efficient use of Travelport staff.” --- a company analyst
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Contributions
• Synthesizing temporal and categorical visualization techniques for a problem domain
• Approach can be generalized for similar datasets to identify interesting combinations and relationships between attribute values in user-defined time ranges
• A case study of successfully applying visualization techniques to a real world challenge on huge datasets with considerable complexity
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This research is supported in part by
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Thank You.
Questions?
IIS-0414667