❆❜str❛❝t ✕ ❆ss❡ss✐♥❣ s♦❢t✇❛r❡ q✉❛❧✐t2 ❛ttr✐❜✉t❡s ✭s✉❝❤ ❛s ♣❡r❢♦r♠❛♥❝❡✱ r❡❧✐❛❜✐❧✐t2✱ ❛♥❞ s❡❝✉r✐t2✮ ❢r♦♠ s♦✉r❝❡ ❝♦❞❡ ✐s ♦❢ t❤❡ ✉t♠♦st ✐♠♣♦rt❛♥❝❡✳ ❚❤❡ ♣❡r❢♦r♠❛♥❝❡ ♦❢ ❛ s♦❢t✇❛r❡ s2st❡♠ ❝❛♥ ❜❡ ✐♠♣r♦✈❡❞ ❜2 ✐ts ♣❛r❛❧❧❡❧ ❛♥❞ ❞✐str✐❜✉t❡❞ ❡1❡❝✉t✐♦♥✳ ❚❤❡ ❛✐♠ ♦❢ t❤❡ ♣❛r❛❧❧❡❧ ❛♥❞ ❞✐str✐❜✉t❡❞ ❡1❡❝✉t✐♦♥ ✐s t♦ s♣❡❡❞ ✉♣ ❜2 ♣r♦✈✐❞✐♥❣ t❤❡ ♠❛1✐♠✉♠ ♣♦ss✐❜❧❡ ❝♦♥❝✉rr❡♥❝2 ✐♥ ❡1❡❝✉t✐♥❣ t❤❡ ❞✐str✐❜✉t❡❞ s❡❣♠❡♥ts✳ ■t ✐s ❛...

❆❜str❛❝t ✕ ❆ss❡ss✐♥❣ s♦❢t✇❛r❡ q✉❛❧✐t2 ❛ttr✐❜✉t❡s ✭s✉❝❤ ❛s ♣❡r❢♦r♠❛♥❝❡✱ r❡❧✐❛❜✐❧✐t2✱ ❛♥❞ s❡❝✉r✐t2✮ ❢r♦♠ s♦✉r❝❡ ❝♦❞❡ ✐s ♦❢ t❤❡ ✉t♠♦st ✐♠♣♦rt❛♥❝❡✳ ❚❤❡ ♣❡r❢♦r♠❛♥❝❡ ♦❢ ❛ s♦❢t✇❛r❡ s2st❡♠ ❝❛♥ ❜❡ ✐♠♣r♦✈❡❞ ❜2 ✐ts ♣❛r❛❧❧❡❧ ❛♥❞ ❞✐str✐❜✉t❡❞ ❡1❡❝✉t✐♦♥✳ ❚❤❡ ❛✐♠ ♦❢ t❤❡ ♣❛r❛❧❧❡❧ ❛♥❞ ❞✐str✐❜✉t❡❞ ❡1❡❝✉t✐♦♥ ✐s t♦ s♣❡❡❞ ✉♣ ❜2 ♣r♦✈✐❞✐♥❣ t❤❡ ♠❛1✐♠✉♠ ♣♦ss✐❜❧❡ ❝♦♥❝✉rr❡♥❝2 ✐♥ ❡1❡❝✉t✐♥❣ t❤❡ ❞✐str✐❜✉t❡❞ s❡❣♠❡♥ts✳ ■t ✐s ❛...

❆❜str❛❝t ✕ ❆ss❡ss✐♥❣ s♦❢t✇❛r❡ q✉❛❧✐t2 ❛ttr✐❜✉t❡s ✭s✉❝❤ ❛s ♣❡r❢♦r♠❛♥❝❡✱ r❡❧✐❛❜✐❧✐t2✱ ❛♥❞ s❡❝✉r✐t2✮ ❢r♦♠ s♦✉r❝❡ ❝♦❞❡ ✐s ♦❢ t❤❡ ✉t♠♦st ✐♠♣♦rt❛♥❝❡✳ ❚❤❡ ♣❡r❢♦r♠❛♥❝❡ ♦❢ ❛ s♦❢t✇❛r❡ s2st❡♠ ❝❛♥ ❜❡ ✐♠♣r♦✈❡❞ ❜2 ✐ts ♣❛r❛❧❧❡❧ ❛♥❞ ❞✐str✐❜✉t❡❞ ❡1❡❝✉t✐♦♥✳ ❚❤❡ ❛✐♠ ♦❢ t❤❡ ♣❛r❛❧❧❡❧ ❛♥❞ ❞✐str✐❜✉t❡❞ ❡1❡❝✉t✐♦♥ ✐s t♦ s♣❡❡❞ ✉♣ ❜2 ♣r♦✈✐❞✐♥❣ t❤❡ ♠❛1✐♠✉♠ ♣♦ss✐❜❧❡ ❝♦♥❝✉rr❡♥❝2 ✐♥ ❡1❡❝✉t✐♥❣ t❤❡ ❞✐str✐❜✉t❡❞ s❡❣♠❡♥ts✳ ■t ✐s ❛...

❆❜str❛❝t ✕ ❆ss❡ss✐♥❣ s♦❢t✇❛r❡ q✉❛❧✐t2 ❛ttr✐❜✉t❡s ✭s✉❝❤ ❛s ♣❡r❢♦r♠❛♥❝❡✱ r❡❧✐❛❜✐❧✐t2✱ ❛♥❞ s❡❝✉r✐t2✮ ❢r♦♠ s♦✉r❝❡ ❝♦❞❡ ✐s ♦❢ t❤❡ ✉t♠♦st ✐♠♣♦rt❛♥❝❡✳ ❚❤❡ ♣❡r❢♦r♠❛♥❝❡ ♦❢ ❛ s♦❢t✇❛r❡ s2st❡♠ ❝❛♥ ❜❡ ✐♠♣r♦✈❡❞ ❜2 ✐ts ♣❛r❛❧❧❡❧ ❛♥❞ ❞✐str✐❜✉t❡❞ ❡1❡❝✉t✐♦♥✳ ❚❤❡ ❛✐♠ ♦❢ t❤❡ ♣❛r❛❧❧❡❧ ❛♥❞ ❞✐str✐❜✉t❡❞ ❡1❡❝✉t✐♦♥ ✐s t♦ s♣❡❡❞ ✉♣ ❜2 ♣r♦✈✐❞✐♥❣ t❤❡ ♠❛1✐♠✉♠ ♣♦ss✐❜❧❡ ❝♦♥❝✉rr❡♥❝2 ✐♥ ❡1❡❝✉t✐♥❣ t❤❡ ❞✐str✐❜✉t❡❞ s❡❣♠❡♥ts✳ ■t ✐s ❛...

The performance of spatial queries depends mainly on the underlying index structure used to handle them. R-tree, a well-known spatial index structure, suffers largely from high overlap and high coverage resulting mainly from splitting the overflowed nodes. Assigning the remaining entries to the underflow node in order to meet the R-tree minimum fill constraint (Remaining Entries problem) may in...

R-trees can be used to store and query sets of point data in two or more dimensions. An easy way to construct and maintain R-trees for two-dimensional points, due to Kamel and Faloutsos, is to keep the points in the order in which they appear along the Hilbert curve. The R-tree will then store bounding boxes of points along contiguous sections of the curve, and the efficiency of the R-tree depe...

ÐPast R-tree studies have focused on the number of nodes visited as a metric of query performance. Since database systems usually include a buffering mechanism, we propose that the number of disk accesses is a more realistic measure of performance. We develop a buffer model to analyze the number of disk accesses required for spatial queries using R-trees. The model can be used to evaluate the q...

An important query for spatial database research is to find the closest pair of objects in a given space. Existing work assumes two objects of the closest pair come from two different data sets indexed by R-trees. The closest pair in the whole space will be found via an optimzed R-tree join technique. However, this technique doesn’t perform well when the two data sets are identical. And it does...

XML data integration tools are facing a variety of challenges for their efficient and effective operation. Among these is the requirement to handle a variety of inconsistencies or mistakes present in the data sets. In this paper we study the problem of integrating XML data sources through index assisted join operations, using notions of approximate match in the structure and content of XML docu...

Many emerging applications benefit from the extraction of geospatial data specified at different resolutions for viewing purposes. Data must also be topologically accurate and up-to-date as it often represents real-world changing phenomena. Current multiresolution schemes use complex opaque data types, which limit the capacity for in-database object manipulation. By using z-values and B+trees t...