StreamFitter: A Real Time Linear Regression Analysis System for Continuous Data Streams

Autor:	Fang Li, Jaehwan John Lee, Yuni Xia, Jiaqi Ge, Chandima Hewa Nadungodage
Rok vydání:	2011
Předmět:	Multivariate adaptive regression splines Proper linear model Data stream mining Computer science Linear regression Linear model Regression analysis Data mining computer.software_genre computer Data modeling Nonparametric regression
Zdroj:	Database Systems for Advanced Applications ISBN: 9783642201516 DASFAA (2)
DOI:	10.1007/978-3-642-20152-3_39
Popis:	In this demo, we present the StreamFitter system for real-time linear regression analysis on continuous data streams. In order to perform regression on data streams, it is necessary to continuously update the regression model while receiving new data. In this demo, we will present two approaches for on-line, multi-dimensional linear regression analysis of stream data, namely Incremental Mathematical Stream Regression (IMSR) and Approximate Stream Regression (ASR). These methods dynamically recompute the regression model, considering not only the data records of the current window, but also the synopsis of the previous data. Therefore, the refined parameters more accurately model the entire data stream. The demo will show that the proposed methods are not only efficient in time and space, but also generate better fitted regression functions compared to the traditional sliding window methods and well-adapted to data changes.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::6c00e051e6efaafe2548f48cf99f8db8 https://doi.org/10.1007/978-3-642-20152-3_39 Zobrazit plný text záznamu