David P. Hansen

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OBJECTIVE To classify automatically lung tumor-node-metastases (TNM) cancer stages from free-text pathology reports using symbolic rule-based classification. DESIGN By exploiting report substructure and the symbolic manipulation of systematized nomenclature of medicine-clinical terms (SNOMED CT) concepts in reports, statements in free text can be(More)
OBJECTIVE To develop a system for the automatic classification of Cancer Registry notifications data from free-text pathology reports. METHOD The underlying technology used for the extraction of cancer notification items is based on the symbolic rule-based classification methodology, whereby formal semantics are used to reason with the systematised(More)
AbstrAct In this article we investigate how approximate query processing (AQP) can be used in medical multidatabase systems. We identify two areas where this estimation technique will be of use. First, approximate query processing can be used to preprocess medical record linking in the mul-tidatabase. Second, approximate answers can be given for aggregate(More)
In this paper, we study the maintenance of role-based access control (RBAC) models in database environments using transitive closure relations. In particular, the algorithms that express and remove redundancy from a component, a RBAC state, and from conflict constraints. The transitive closure relations on a RBAC state specify the reachability among user(More)
The constructions of Haar wavelet synopses for large data sets have proven to be useful tools for data approximation. Recently, research on constructing wavelet synopses with a guaranteed maximum error has gained attention. Two relevant problems have been proposed: One is the size bounded problem that requires the construction of a synopsis of a given size(More)
Constructing Haar wavelet synopses under a given approximation error has many real world applications. In this paper, we take a novel approach towards constructing unrestricted Haar wavelet synopses under an error bound on uniform norm (<i>L</i><sub>&#8734;</sub>). We provide two approximation algorithms which both have linear time complexity and a (log(More)
Constructing Haar wavelet synopses with guaranteed maximum error on data approximations has many real world applications. In this paper, we take a novel approach towards constructing unrestricted Haar wavelet synopses under maximum error metrics (L ∞). We first provide two linear time (logN)-approximation algorithms which have space complexities of O(logN)(More)
AbstrAct In this article we investigate how approximate query processing (AQP) can be used in medical multidatabase systems. We identify two areas where this estimation technique will be of use. First, approximate query processing can be used to preprocess medical record linking in the mul-tidatabase. Second, approximate answers can be given for aggregate(More)