Space Lower Bounds for Online Pattern Matching

Computer Science – Data Structures and Algorithms

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

10 pages, 7 figures, CPM 2011

Scientific paper

We present space lower bounds for online pattern matching under a number of different distance measures. Given a pattern of length m and a text that arrives one character at a time, the online pattern matching problem is to report the distance between the pattern and a sliding window of the text as soon as the new character arrives. We require that the correct answer is given at each position with constant probability. We give Omega(m) bit space lower bounds for L_1, L_2, L_\infty, Hamming, edit and swap distances as well as for any algorithm that computes the cross-correlation/convolution. We then show a dichotomy between distance functions that have wildcard-like properties and those that do not. In the former case which includes, as an example, pattern matching with character classes, we give Omega(m) bit space lower bounds. For other distance functions, we show that there exist space bounds of Omega(log m) and O(log^2 m) bits. Finally we discuss space lower bounds for non-binary inputs and show how in some cases they can be improved.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Space Lower Bounds for Online Pattern Matching does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Space Lower Bounds for Online Pattern Matching, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Space Lower Bounds for Online Pattern Matching will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-205734

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.