A multicriteria data retrieval model: An application of multiattribute preference model to data retrieval.

This dissertation proposes a new data retrieval model as an alternative to exact matching. While exact matching is an effective data retrieval model, it is based on fairly strict assumptions and limits our capabilities in data retrieval. A new category of data retrieval, multi-criteria data retrieval, is defined to include many-valued queries, (which require partitioning of data entities into more than two, possibly infinite, subsets), and multi-derived data, (which are derived by non-homogeneous multiple rules). A metric-based preference model is proposed as a referential model for multi-criteria data retrieval. The model is based on the idea that we human beings prefer outcomes close to an ideal alternative (the positive anchor) and far removed from the worst imaginable alternative (the negative anchor). A relative distance metric is proposed to operationalize the concept of closeness in matching. Many-valued and multi-derived data retrieval queries are formalized within the framework of the metric-based preference model. Query interpretation is defined as measuring the relative distances of data entities from the (positive and the negative) anchors. The viability of the proposed data retrieval model is proved by analyzing its logical properties and by evaluating its performance against the current data retrieval models for both exact matching and non-exact matching. The multi-criteria data retrieval model is proved to satisfy the De Morgan logic and therefore has the same query interpretation values as the exact match data retrieval model for the conventional data retrieval queries. With regard to many-valued query interpretation, the proposed relative distance metric is proved to better represent a user's actual preferences for data entities than the current fuzzy metric or the Euclidian distance metric. With regard to retrieval of multi-derived data, the proposed model is proved to result in fewer errors than current exact matching. These findings show that, both at the logical level and at the performance level, the proposed multi-criteria data retrieval model retains all the desirable features for data retrieval.Ph.D.Applied SciencesComputer scienceManagementSocial SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/128644/2/9116229.pd