Optimizing Ranked Retrieval over Categorical Attributes

As the entry and archival of medical data are being digitized, more and more medical data are becoming accessible. This paper studies how to enable an effective retrieval of medical data by ranked retrieval of only the most relevant highly-ranked data. While ranked retrieval has been actively studied lately, existing works have focused mainly on supporting ranking over numerical or text data. However, many existing medical data contain a large amount of categorical attributes, e.g., gender, race profile, or pain type, which cannot be efficiently supported by either line of existing algorithms Unlike numerical attributes where a natural ordering is inherent, formulating and processing ranked retrieval over categorical attributes with no such ordering are challenging. This paper studies an efficient and effective support of ranking over categorical data, and also a uniform support with other types of attributes, e.g., numerical attributes