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Data Mining at work

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Data Mining at work. Krithi Ramamritham. Dynamics of Web Data. Ad Component. Headline Component. Headline Component. Navigation Component. Headline Component. Headline Component. Personalized Component. Dynamically created Web Pages -- using scripting languages.

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Data Mining at work

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  1. Data Miningat work KrithiRamamritham

  2. Dynamics of Web Data Ad Component Headline Component Headline Component Navigation Component Headline Component Headline Component Personalized Component Dynamically created Web Pages -- using scripting languages

  3. 1. What to deliver? Page content may be based on • queries on dynamically changing data • e.g., sports scores, stock prices, environment • type of access device • time and location of access/user Existing sites may contain new information New sites (URLs) may come into being

  4. 2. How to deliver? wiredhost sensors Network Network servers Proxies /caches mobile host Data sources End-hosts

  5. Update Mumbai temperature every 2 degrees The proxy obtains data from the source(s) Maintains | U(t) - S(t) | <= 2 Keep Data Up-to-date Source S(t) Proxy / DB P(t) User U(t)

  6. After a specific interval When to poll the source? Server Proxy User Pull Basedon temporal data mining – time series analysis – and prediction of when change will exceed 2 degrees

  7. Where to do the work? • Diverse client devices • Differ in hardware, software, network connectivity, form factor • Web content needs to be tailored for each client type • Each response depends • not only on the requested URL • but also on the capabilities of the client

  8. Transcoding Conversion of one data version to another • Decreasing Image Quality (JPEG quality level) and size - “convert” utility in Linux • Summarizing text • transcode => Info extraction/ retrieval/ classification

  9. Who should transcode? • Download desired version from server • Transcode higher version locally • Factors influencing decision • Transcoding Complexity • Proxy-server network connection • Load on proxy (Multiple Linear) Regression Predict based on a (linear) model of overheads

  10. What is new on the Web? How is the monsoon progressing? Time series analysis: Change prediction, pattern mining

  11. ‘Bhav Puchiye’ www.broadmoor.com Interface for Bhav Puchiye

  12. Inverted Pyramid Interfaces Conclusion Discussions Background & related Information Findings Findings Background & related Information Discussions Conclusion Inverted pyramid approach

  13. Bhav Poochiye Pricing Module developed for selected commodities for selected markets for selected areas DEMO

  14. Building Usage Profiles Estimate access probabilities based on: • Current user/community navigational patterns over site contents (in the form of click streams) • Historical user/community access patterns over site contents (in the form of association rules) Cluster needs based on location, income/age of user, time-of-day

  15. Data Mining From data to information to knowledge to money!

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