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Abstract Details
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Activity Number:
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358
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Quality and Productivity
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| Abstract - #302412 |
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Title:
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Data Quality for Online Experimentation
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Author(s):
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Ji Chen*+ and Roger Longbotham and Justin Wang and Shaojie Deng and Dave DeBarr
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Companies:
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Microsoft Corporation and Microsoft Corporation and Microsoft Corporation and Microsoft Corporation and Microsoft Corporation
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Address:
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One Microsoft Way, Redmond, WA, ,
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Keywords:
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online experimentation ;
data quality ;
web analytics ;
anomaly detection
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Abstract:
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Controlled experimentation has been proven to be an effective way to test ideas and evaluate changes in websites and web services. While the basic theoretical foundation for controlled experiments has been well established, in reality more often than not, we are faced with data quality issues that could easily bias the results of the experiments and confound the decision making process. This talk will discuss several data quality concerns specific to online experimentation and provide best practices to address them. We will cover challenges such as web robot detection, traffic anomaly alerts, user session identification, page instrumentation issues and web data cleansing. Most of the techniques discussed are also applicable to web analytics in general. Some research questions will be presented at the end.
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Authors who are presenting talks have a * after their name.
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