JSM 2005 Online Program

Abstract #302866

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

To View the Program:
You may choose to view all activities of the program or just parts of it at any one time. All activities are arranged by date and time.

The views expressed here are those of the individual authors
and not necessarily those of the ASA or its board, officers, or staff.

The Program has labeled the meeting rooms with "letters" preceding the name of the room, designating in which facility the room is located:

Minneapolis Convention Center = “MCC” Hilton Minneapolis Hotel = “H” Hyatt Regency Minneapolis = “HY”

Back to main JSM 2005 Program page

Legend:

= Applied Session,

= Theme Session,

= Presenter

Activity Number:	177
Type:	Topic Contributed
Date/Time:	Monday, August 8, 2005 : 2:00 PM to 3:50 PM
Sponsor:	Business and Economics Statistics Section
Abstract - #302866
Title:	Incremental Quantiles for Monitoring Business Services
Author(s):	Scott Vander Wiel*+ and Diane Lambert and John M. Chambers and David A. James
Companies:	Bell Labs, Lucent Technologies and Bell Labs, Lucent Technologies and Bell Labs, Lucent Technologies and Bell Labs, Lucent Technologies
Address:	Rm 2C 273, Murray Hill, NJ, 07023, United States
Keywords:	agent ; data stream ; single pass
Abstract:	Business services can be large-scale operations managed for quality assurance and improvement. For example, telephone call centers need to utilize agents efficiently to handle large service volumes. Similarly, Application Service Providers (ASPs) need to monitor network-based business products to ensure they deliver high-quality eCommerce, database transactions, and network meeting services. Quality often is measured by software agents running at a large number of distributed service stations. We describe the Incremental Quantile (IQ) algorithm for calculating approximate quantiles on data collected at a large number of stations and over many successive time intervals. IQ uses a fixed amount of memory to update a set of quantile estimates as new data accumulate into small batches. The algorithm can be run at distributed stations and at a central monitoring location where quantiles are aggregated to report summaries at various time scales and over various groupings of the service stations. IQ has proven to be accurate when tested on real network service data and simulated data under various scenarios.

The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2005 program