| Summary: | 碩士 === 國立清華大學 === 資訊工程學系 === 94 === In some advanced applications, spatial data may have several location-independent attributes. For example, in a job finding database, each job opportunity (object) can be associated with both location-dependent attributes, e.g., the work location, and location-independent ones, e.g., the salary. A person who uses this database to find a job may prefer not only a shorter distance between his/her house and the work place but also a higher salary. Therefore, a query with both concepts of “shorter distance” and “higher salary” should be considered to meet the user’s needs. We call it the heterogeneous k-nearest neighbor (HkNN) query in distinction from the traditional k-nearest neighbor (kNN) query on spatial domain, which only concerns location-dependent attributes. To our knowledge, this thesis is the first work proposing a generic framework for solving the HkNN query in a dynamic environment in which the values of both the location-dependent attributes and the location-independent attributes of an object may change with time. Previous works on the traditional kNN problem cannot be applied to processing the HkNN query. In this thesis, we propose an efficient approach based on the bounding proprieties for the HkNN query evaluation. Furthermore, we provide an update mechanism for continuously monitoring the HkNN queries in a dynamic environment. Experimental results verify that the proposed framework is both efficient and scalable.
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