SCREEN

Abstract

Stream data are often dirty, for example, owing to unreliable sensor reading, or erroneous extraction of stock prices. Most stream data cleaning approaches employ a smoothing filter, which may seriously alter the data without preserving the original information. We argue that the cleaning should avoid changing those originally correct/clean data, a.k.a. the minimum change principle in data cleaning. To capture the knowledge about what is clean, we consider the (widely existing) constraints on the speed of data changes, such as fuel consumption per hour, or daily limit of stock prices.