出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：This work presents a novel ranking scheme for structured data. We show how to apply thenotion of typicality analysis from cognitive science and how to use this notion to formulate theproblem of ranking data with categorical attributes. First, we formalize the typicality querymodel for relational databases. We adopt Pearson correlation coefficient to quantify the extentof the typicality of an object. The correlation coefficient estimates the extent of statisticalrelationships between two variables based on the patterns of occurrences and absences of theirvalues. Second, we develop a top-k query processing method for efficient computation. TPFilterprunes unpromising objects based on tight upper bounds and selectively joins tuples of highesttypicality score. Our methods efficiently prune unpromising objects based on upper bounds.Experimental results show our approach is promising for real data.
