New and faster filters for multiple approximate string matching

We present three new algorithms for on-line multiple string matching allowing errors. These are extensions of previous algorithms that search for a single pattern. The average running time achieved is in all cases linear in the text size for moderate error level, pattern length and number of patterns. They adapt (with higher costs) to the other cases. However, the algorithms diier in speed and thresholds of usefulness. We analyze theoretically when each algorithm should be used, and show experimentally their performance. The only previous solution for this problem allows only one error. Our algorithms are the rst to allow more errors, and are faster than previous work for a moderate number of patterns (e.g. less than 50-100 on English text, depending on the pattern length).