Temporal database support for cooperative creative work

Eaglestone, Barry; Desai, Bipin C.; Holton, R.; Gulatee, E.

doi:10.1109/ideas.1998.694386

Cited by 1 publication

(2 citation statements)

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“…The delete operations denoted by the horizontal edges are obtained by reading the identifier of the node above the target of each horizontal edge, while the inserted nodes are read from the left of the targets of vertical edges. Following this procedure yields del(A [4]), del(A [5]), del(A [6]), del(A [7]), ins(B [5]), ins(B [7]) as the edit script represented by the thinner path. We can verify that this edit script does indeed transform A to B.…”

Section: Edit Graphsmentioning

confidence: 99%

“…In order to merge these changes back into the main repository, while detecting potential conflicts, we need a method that examines different versions of the dataset and computes the differences. In addition to merging changes, such differencing also forms the basis of more sophisticated database techniques [7]. For example, the changes, once detected, may be used to trigger notifications or other actions.…”

Section: Introductionmentioning

confidence: 99%

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Differencing Data Streams

Chawathe

9th International Database Engineering &Amp;amp; Application Symposium (IDEAS'05)

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We present external-memory algorithms for differencing large hierarchical datasets. Our methods are especially suited to streaming data with bounded differences. For input sizes m and n and maximum output (difference) size e, the I/O, RAM, and CPU costs of our algorithm rdiff are, respectively, m + n, 4e + 8, and O(M N ). That is, given 4e + 8 blocks of RAM, our algorithm performs no I/O operations other than those required to read both inputs. We also present a variant of the algorithm that uses only four blocks of RAM, with I/O cost 8me + 18m + n + 6e + 5 and CPU cost O(M N ).

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