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36– 48, 2006. c Springer-Verlag Berlin Heidelberg 2006 KCAM: Concentrating on Structural Similarity for XML Fragments 37 cept methods in (i), most of them are sensitive to the labels or node positions, which makes them not able to retrieve the fragments with same structure but different labels. Even methods in (i) are not genuinely interested at the structural similarity, except MLP. Further they all do not consider the correlation between nodes. 2. xml with Dewey codes and XML fragment for keywords “Botnich Bibliography” (Circled).

Particularly, when the disk allocation unit is fixed in the operating system, if the index is large, more blocks must be read and the throughput consequently decreases. If the key size is small, a block can contain more keys and consequently fewer blocks are read. Fig. 1. LBS system architecture and spatial data management For spatial indexes, especially for R-tree managing two-dimensional data, MBR keys for each dimension account for approximately 80% [3] of the index. Therefore, if the keys are compressed, the number of entries stored in one node increases and search performance improves accordingly.

The probability that a node of height h will overlap a given query region is ( s + a ) . Let d be a dimension and s be the size of the query region, then the overlapping region of nodes with a height of h and the query region is M ( s + a ) ; this is represented as follows, where N is the total number of data and f is the average fanout of the leaf nodes: d d d h d h d d h d ⎛ ⎞ ⎜1 + d ⎡ N ⎤ ⋅ s ⎟ ⎢ h⎥ ⎟ ⎜ f ⎥ ⎢ ⎝ ⎠ . (4) The total number of node accesses from the root to the leaf nodes in R-tree consists of the summation of the nodes at each height, as represented by Equation (5).

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