Document Type

Article

Publication Date

2002

Abstract

Real-time production systems and other dynamic environments often generate tremendous (potentially infinite) amount of stream data; the volume of data is too huge to be stored on disks or scanned multiple times. Can we perform on-line, multi-dimensional analysis and data mining of such data to alert people about dramatic changes of situations and to initiate timely, high-quality responses? This is a challenging task.

In this paper, we investigate methods for online, multi-dimensional regression analysis of time-series stream data, with the following contributions: (1) our analysis shows that only a small number of compressed regression measures instead of the complete stream of data need to be registered for multi-dimensional linear regression analysis, (2) to facilitate on-line stream data analysis, a partially materialized data cube model, with regression as measure, and a tilt time frame as its time dimension, is proposed to minimize the amount of data to be retained in memory or stored on disks, and (3) an exception-guided drilling approach is developed for online, multi-dimensional exception-based regression analysis. Based on this design, algorithms are proposed for efficient analysis of time-series data streams. Our performance study compares the proposed algorithms and identifies the most memory- and time- efficient one for multi-dimensional stream data analysis.

Comments

Presented at the 28th International Conference on Very Large Data Bases, Hong Kong, China, August 20-23, 2002.

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