1. Efficiently Maintaining Stock Portfolios Up-To-Date On The Web Prashant Shenoy Manish Bhide Krithi Ramamritham 2002 IEEE E-Commerce System Proceedings of the 12th Workshop on Research Issue in Data Engineering

2. Outline Introduction Introduction The Cr Balancing Algorithm The Cr Balancing Algorithm Experimental Evaluation Experimental Evaluation Experimental Result Experimental Result Conclusion Conclusion

3. Introduction Subject: Subject: Efficiently Maintaining Stock Portfolios Up-To-Date On The Web Derive the coherency requirement of each of the stocks constituting a portfolio, given the coherency requirement for the portfolio. Derive the coherency requirement of each of the stocks constituting a portfolio, given the coherency requirement for the portfolio. Dynamic data: Dynamic data: monitoring of patients, network traffic, experiment, traffic condition, sensor data monitoring of patients, network traffic, experiment, traffic condition, sensor data Cr: Coherency requirement Cr: Coherency requirement

4. Introduction (Cont.) Maintaining Cr (Coherency requirement) Maintaining Cr (Coherency requirement) S(t): the data value at the source (server) at time t U(t): the data value at the user (client) at time t U(t): the data value at the user (client) at time t C(t): the data value at the cache (proxy) at time t C(t): the data value at the cache (proxy) at time t the system must guarantee c: c: user specify a temporal coherency requirements for each data item of interest it can be specified in units of time or value (3 minutes or 1 dollar) it associated with user tolerance ex: a user may desire to have stronger coherency requirements for data items such as stock prices than news information. Adaptive TTR Algorithm (from paper ” Maintaining Temporal Coherency of Virtual Data Warehouses ” ) Adaptive TTR Algorithm (from paper ” Maintaining Temporal Coherency of Virtual Data Warehouses ” )

5. The Cr Balancing Algorithm Initial allocation of crs to stocks Initial allocation of crs to stocks

6. The Cr Balancing Algorithm (cont.) Example Example 0.72 * 100 + 0.09 * 200 = 90 Company B has a greater effect (20*200 > 10*100) on the portfolio and hence the cr of Company B stock is smaller. Company B stock will be polled more often than Company A stock. If Company B changes by $0.09 then the cr of the portfolio reduces to $72 (90-0.09*200=72), the crs of all stocks are re-evaluated.

7. The Cr Balancing Algorithm (cont.) Dynamic adjustment of crs of stocks The change crossed threshold within δ time interval: The change did not cross threshold within δ time interval: The change did not cross threshold for a long time, cr will reach cr max, in order to prevent that some data changes suddenly: In order to prevent cr is neither too large nor too small:

8. Experimental Evaluation fidelity: experiment: adjustable parameter:

9. Experimental Result

10. Conclusion This approach has several adjustable parameters which can be used to tune to algorithm to get the desired fidelity and network overhead characteristics. This approach has several adjustable parameters which can be used to tune to algorithm to get the desired fidelity and network overhead characteristics. The network overhead is around 40% less than that The network overhead is around 40% less than that offered by the static cr approach. offered by the static cr approach. This approach considers the stocks in a portfolio as a semantic unit, in the sense that it reduces the polling frequency of all the stock of the portfolio if the stock prices are changing such that there is very little chance of the portfolio being satisfied. This approach considers the stocks in a portfolio as a semantic unit, in the sense that it reduces the polling frequency of all the stock of the portfolio if the stock prices are changing such that there is very little chance of the portfolio being satisfied.