INDEXING PROBLEMS IN SPATIOTEMPORAL DATABASES by George N. Kollios Advisor: Vassilis Tsotras Co-Advisor: Alex Delis Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy (Computer Science) June 2000 Spatiotemporal databases manage spatial objects that change positions and/or extents over time. Examples include traffic surveillance data, climate and land cover data, demographic data and multimedia applications (animated movies). Since these databases are large in size, it is important to design efficient indexing schemes that can access and explore them.

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This paper compares different indexing techniques proposed for supporting efficient access to temporal data. The comparison is based on a collection of important performance criteria, including the space consumed, update processing, and query time for representative queries. The comparison is based on worst-case analysis, hence no assumptions on data distribution or query frequencies are made. When a number of methods have the same asymptotic worst-case behavior, features in the methods that affect average case behavior are discussed. Additional criteria examined are the pagination of an index, the ability to cluster related data together, and the ability to efficiently separate old from current data (so that larger archival storage media such as write-once optical disks can be used). The purpose of the paper is to identify the difficult problems in accessing temporal data and describe how the different methods aim to solve them. A general lower bound for answering basic temporal queries is also introduced.