Chapter 29 - Multi-Dimensional Regression Analysis of Time-Series Data Streams

Abstract

This chapter illustrates methods for online, multidimensional regression analysis of time-series stream data. 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. With years of research and development of data warehouse and OLAP technology, a large number of data warehouses and data cubes have been successfully constructed and deployed in applications. Data cube has become an essential component in most data warehouse systems and in some extended relational database systems, and has been playing an increasingly important role in data analysis and intelligent decision support. The data warehouse and OLAP technology is based on the integration and consolidation of data in multidimensional space to facilitate powerful and fast online data analysis. Data are aggregated either completely or partially in multiple dimensions and multiple levels and are stored in the form of either relations or multidimensional arrays. The dimensions in a data cube are of categorical data, such as products, region, time, etc., and the measures are numerical data, representing various kinds of aggregates, such as sum, average, and variance of sales or profits, etc.