By Jeff Erickson (auth.), Frank Dehne, Jörg-Rüdiger Sack, Norbert Zeh (eds.)
The papers during this quantity have been offered on the tenth Workshop on Algorithms and information constructions (WADS 2005). The workshop came about August 15 - 17, 2007, at Dalhousie college, Halifax, Canada. The workshop alternates with the Scandinavian Workshop on set of rules thought (SWAT), carrying on with the t- dition of SWAT and WADS beginning with SWAT 1988 and WADS 1989. From 142 submissions, this system Committee chosen fifty four papers for presentation on the workshop. moreover, invited lectures got by means of the next dist- guished researchers: Je? Erickson (University of Illinois at Urbana-Champaign) and Mike Langston (University of Tennessee). On behalf of this system Committee, we wish to specific our honest appreciation to the various people whose e?ort contributed to creating WADS 2007 successful. those comprise the invited audio system, individuals of the steerage and ProgramCommittees, the authorswho submitted papers, andthe manyreferees who assisted this system Committee. we're indebted to Gerardo Reynaga for fitting and editing the submission software program, retaining the submission server and interacting with authors in addition to for supporting with the training of the program.
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Extra resources for Algorithms and Data Structures: 10th International Workshop, WADS 2007, Halifax, Canada, August 15-17, 2007. Proceedings
Using a standard searching data structure, we identify the predecessor td of d and the successor tc of c among the leaves of T . Let q be the lowest common ancestor of tc and td . Then a range sum query can be answered by answering at most two queries to internal nodes of T on each level l, 1 ≤ l ≤ lq , where lq is the level of the node q. Hence, a range sum query can be answered in O(log n/ log log n) time. When a new element is inserted into Cij we insert the new leaf u into T . For the parent v of u we update Sv by inserting new components into Bv in O(1) time.
Nekrich Lemma 2. There exists a dynamic data structure for range counting queries on an O(log1/4 n) × O(n) grid with O(log n/ log log n) query time and O(log3/2 n/ log log n) update time that uses O(n log log n) bits. There exists a static data structure for range counting queries on an O(log1/4 n) × O(n) grid with O(1) query time that uses O(n log log n) bits. 4 Two-Dimensional Range Searching Let P be the set of points stored in the data structure; let Px and Py be the sets of x- and y-coordinates of points in P .
Finally we answer queries ([a, b] × (rl , d]) ∩ Rl and ([a, b] × [c, rf )) ∩ Rf . The search time is dominated by the search time of the one-dimensional query to Cab . When a new point p = (x, y) is inserted, we ﬁnd s = y/ log n . For every pair a, b such that 1 ≤ a ≤ x ≤ b ≤ W , we insert s into Cab if necessary. If the row Rs is empty, we construct the words Xs and Ds and insert them into the list L. Deletions are processed in a similar way. We can sum up the results of this section in the following: Lemma 1.
Algorithms and Data Structures: 10th International Workshop, WADS 2007, Halifax, Canada, August 15-17, 2007. Proceedings by Jeff Erickson (auth.), Frank Dehne, Jörg-Rüdiger Sack, Norbert Zeh (eds.)