QoS: Quality Driven Data Abstraction Generation For Large Databases

Data abstraction is the process of reducing a large dataset into one of moderate size, while maintaining dominant characteristics of the original dataset. Data abstraction quality refers to the degree to which the abstraction represents the original data. The quality of an abstraction directly affects the confidence an analyst can have in results derived from such abstracted views about the actual data. Some initial measures to quantify the quality of abstraction have been proposed; however, they currently can only be utilized as an after-thought. An analyst can be made aware of the quality of the data he works with, but he cannot control the quality he desires and the trade-off between the time required to generate the abstraction and its quality. While some analysts require at least a certain minimal level of quality, others must be able to work with certain abstraction quality due to time and resource limitations. To tackle these problems, we propose a new data abstraction generation model, called the QoS model, that presents the performance quality trade-off to the analyst. It then generates an abstraction based on the desired level of quality versus performance as indicated by the analyst. The framework has been integrated into XmdvTool, a freeware multi-variate data visualization tool developed at WPI. Our experimental results show that our approach provides better quality compared to existing abstraction techniques.