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The Encoding Complexity of Two Dimensional Range Minimum Data Structures

Assumption m ≤ n. Cost Space (bits) Query time Preprocessing time Models Indexing (input accessible ) Encoding (input not accessible ). The Encoding Complexity of Two Dimensional Range Minimum Data Structures. i 1. i 2. j 1. j 2. RMQ( i 1 , i 2 , j 1 , j 2 ) = (2,3)

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The Encoding Complexity of Two Dimensional Range Minimum Data Structures

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  1. Assumption m ≤ n Cost • Space (bits) • Query time • Preprocessing time Models • Indexing (input accessible) • Encoding (input not accessible) The Encoding Complexity of Two Dimensional Range Minimum Data Structures i1 i2 j1 j2 RMQ(i1, i2, j1, j2) = (2,3) = positionof min European Symposium on Algorithms, Inria, Sophia Antipolis, France, September 3, 2013

  2. Some (Trivial) Results

  3. Encoding m = 1 (Cartesiantree) RMQ(j1, j2) = NCA(j1,j2) To support RMQ queriesweneed... • treestructure (111101001100110000100100) • mappingbetween nodes and cells (inorder) 1 2 8 3 9 4 10 6 14 5 11 7 n j1 j2 1 min ?

  4. Some (Less Trivial) Results

  5. New Results • O(nm(log m+loglogn)) bits • treerepresentation • component decomposition • O(nmlogmlog* n)) bits • bootstrapping • O(nmlogm) bits • relative positions of roots • refined component construction

  6. TreeRepresentation Requirements • Cells leafs • Query Answer = rightmostleaf 12 10 8 6 7 2 1 12 10 8 11 9 6 7 5 4 3 2 Trivial solution • Sort leafs • Ω(mnlogn) bits 12 11 10 9 8 7 6 5 4 3 2 1

  7. Components  = 3 Construction • Consider elements in decreasingorder • Find connected components with size≥  • L-adjacency  |C1|≤ 4-3, |Ci|≤ 2m Representation O(mn + mn/log n + mnlogm + mnlog(m))  =log n  O(mn(log m+loglogn)) L 1 L-adjacency 12 10 8 11 9 6 7 5 4 3 2 C2 C3 C1 1 Spanningtreestructures Spanningtreeedges Local leafranks in components Component rootpositions 12 10 8 11 9 6 7 5 4 3 2

  8. Results better upper or lower bound ? Thank you

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