Data-driven Decision-making: The Good, the Bad, and the Ugly
Thursday, December 5, 2002 - 2:10pm - 2:40pm
Rudolf Kulhavy (Honeywell)
The overwhelming amount of data stored in databases gives sometimes rise to exaggerated expectations. One of the popular myths is that a large amount of data carries necessarily a large amount of information. It is clearly not so^×data stored in databases is often redundant or showing just a couple of patterns from the multitude of all possible patterns of the process behavior. Very rarely the data collected is the result of a planned experiment, rather it is a series of snapshots of routine operation. What is so exciting then about the massive data sets available to us today? It is not that a huge amount of data can replace the domain knowledge and the art of modeling. It is that for the first time we have the whole process history at disposal to make decisions affecting the future behavior. This makes database-centric decision-making an exciting alternative to the current paradigms. The presentation discusses opportunities and challenges presented by the new paradigm. Special attention is paid to selection of a data cube capturing multi-dimensional data, definition of similar historical data points, and similarity search in high-dimensional spaces, while sharing experience from real-life applications of data-centric decision support systems.