History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"pretrained"
Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
550 | 2025-07-12 22:28:51 | pretrained | 1 | 67641 | 2 | 1.266 | 53428.9 |
549 | 2025-07-10 09:32:40 | pretrained | 3 | 140603 | 58 | 36.986 | 3801.5 |
548 | 2025-07-09 09:25:46 | pretrained | 1 | 67641 | 2 | 1.076 | 62863.4 |
547 | 2025-07-09 01:16:00 | pretrained | 1 | 67641 | 2 | 2.703 | 25024.4 |
546 | 2025-07-09 00:32:24 | pretrained | 3 | 140603 | 58 | 35.610 | 3948.4 |
545 | 2025-07-08 11:23:44 | pretrained | 3 | 140603 | 58 | 42.453 | 3312.0 |
544 | 2025-07-08 07:36:07 | pretrained | 4 | 161450 | 381 | 60.660 | 2661.6 |
543 | 2025-07-08 05:25:38 | pretrained | 2 | 108824 | 11 | 20.550 | 5295.6 |
542 | 2025-07-07 04:12:48 | pretrained | 1 | 67641 | 2 | 8.203 | 8245.9 |
541 | 2025-07-06 07:17:56 | pretrained | 4 | 161450 | 381 | 13.236 | 12197.8 |
540 | 2025-07-04 14:54:12 | pretrained | 4 | 161450 | 381 | 47.596 | 3392.1 |
539 | 2025-06-29 18:52:50 | pretrained | 1 | 67641 | 2 | 5.936 | 11395.0 |
538 | 2025-06-25 01:43:24 | pretrained | 1 | 67641 | 2 | 8.690 | 7783.8 |
537 | 2025-06-20 01:31:42 | pretrained | 3 | 140603 | 58 | 29.300 | 4798.7 |
536 | 2025-06-19 05:26:59 | pretrained | 3 | 140603 | 58 | 36.613 | 3840.2 |
535 | 2025-06-18 07:02:03 | pretrained | 1 | 67641 | 2 | 8.563 | 7899.2 |
534 | 2025-06-18 02:14:59 | pretrained | 1 | 67641 | 2 | 3.673 | 18415.7 |
533 | 2025-06-17 00:26:53 | pretrained | 3 | 140603 | 58 | 44.580 | 3153.9 |
532 | 2025-06-13 18:56:44 | pretrained | 3 | 140603 | 58 | 8.453 | 16633.5 |
531 | 2025-06-10 12:34:21 | pretrained | 1 | 67641 | 2 | 5.110 | 13237.0 |
530 | 2025-06-09 23:53:04 | pretrained | 1 | 67641 | 2 | 6.470 | 10454.6 |
529 | 2025-06-09 12:29:51 | pretrained | 3 | 140603 | 58 | 44.993 | 3125.0 |
528 | 2025-06-07 00:34:33 | pretrained | 4 | 161450 | 381 | 44.063 | 3664.1 |
527 | 2025-06-02 04:05:19 | pretrained | 4 | 161450 | 381 | 82.763 | 1950.8 |
526 | 2025-05-31 18:07:18 | pretrained | 4 | 161450 | 381 | 64.833 | 2490.2 |
525 | 2025-05-31 10:05:34 | pretrained | 1 | 67641 | 2 | 3.140 | 21541.7 |
524 | 2025-05-31 07:05:44 | pretrained | 4 | 161450 | 381 | 78.303 | 2061.9 |
523 | 2025-05-30 12:33:39 | pretrained | 1 | 67641 | 2 | 3.000 | 22547.0 |
522 | 2025-05-29 11:16:57 | pretrained | 4 | 161450 | 381 | 49.750 | 3245.2 |
521 | 2025-05-28 06:28:11 | pretrained | 1 | 67641 | 2 | 1.313 | 51516.4 |
520 | 2025-05-26 20:04:43 | pretrained | 2 | 108824 | 11 | 21.173 | 5139.8 |
519 | 2025-05-26 05:38:30 | pretrained | 1 | 67641 | 2 | 6.140 | 11016.4 |
518 | 2025-05-25 14:28:18 | pretrained | 1 | 67641 | 2 | 2.110 | 32057.3 |
517 | 2025-05-24 14:45:59 | pretrained | 2 | 108824 | 11 | 14.623 | 7442.0 |
516 | 2025-05-23 01:59:07 | pretrained | 1 | 67641 | 2 | 1.110 | 60937.8 |
515 | 2025-05-22 19:11:28 | pretrained | 1 | 67641 | 2 | 1.186 | 57032.9 |
514 | 2025-05-21 11:03:27 | pretrained | 1 | 67641 | 2 | 1.186 | 57032.9 |
513 | 2025-05-21 04:38:50 | pretrained | 1 | 67641 | 2 | 1.530 | 44209.8 |
512 | 2025-05-18 15:12:46 | pretrained | 1 | 67641 | 2 | 5.190 | 13032.9 |
511 | 2025-05-18 02:50:31 | pretrained | 1 | 67641 | 2 | 5.110 | 13237.0 |
510 | 2025-05-17 01:35:34 | pretrained | 3 | 140603 | 58 | 15.360 | 9153.8 |
509 | 2025-05-17 01:06:18 | pretrained | 1 | 67641 | 2 | 8.270 | 8179.1 |
508 | 2025-05-15 15:32:10 | pretrained | 3 | 140603 | 58 | 13.003 | 10813.1 |
507 | 2025-05-12 05:00:00 | pretrained | 2 | 108824 | 11 | 13.250 | 8213.1 |
506 | 2025-05-12 00:13:21 | pretrained | 3 | 140603 | 58 | 30.160 | 4661.9 |
505 | 2025-05-11 10:19:43 | pretrained | 1 | 67641 | 2 | 2.436 | 27767.2 |
504 | 2025-05-09 10:21:50 | pretrained | 4 | 161450 | 381 | 10.813 | 14931.1 |
503 | 2025-05-06 18:41:56 | pretrained | 1 | 67641 | 2 | 6.656 | 10162.4 |
502 | 2025-05-05 20:09:32 | pretrained | 1 | 67641 | 2 | 5.283 | 12803.5 |
501 | 2025-05-05 00:16:56 | pretrained | 4 | 161450 | 381 | 44.376 | 3638.2 |
500 | 2025-04-30 03:49:24 | pretrained | 1 | 67641 | 2 | 2.780 | 24331.3 |
499 | 2025-04-28 07:31:42 | pretrained | 4 | 161450 | 381 | 52.236 | 3090.8 |
498 | 2025-04-28 03:43:37 | pretrained | 4 | 161450 | 381 | 64.463 | 2504.5 |
497 | 2025-04-26 17:53:20 | pretrained | 4 | 161450 | 381 | 68.723 | 2349.3 |
496 | 2025-04-25 04:18:06 | pretrained | 1 | 67641 | 2 | 1.236 | 54725.7 |
495 | 2025-04-17 12:37:37 | pretrained | 2 | 108824 | 11 | 5.813 | 18720.8 |
494 | 2025-04-16 06:36:46 | pretrained | 2 | 108824 | 11 | 10.703 | 10167.6 |
493 | 2025-04-15 19:02:00 | pretrained | 1 | 67641 | 2 | 1.203 | 56226.9 |
492 | 2025-04-14 19:09:28 | pretrained | 3 | 140603 | 58 | 6.610 | 21271.3 |
491 | 2025-04-11 20:20:36 | pretrained | 3 | 140603 | 58 | 50.063 | 2808.5 |
490 | 2025-04-06 00:29:35 | pretrained | 1 | 67641 | 2 | 6.970 | 9704.6 |
489 | 2025-04-04 00:44:15 | pretrained | 4 | 161450 | 381 | 41.430 | 3896.9 |
488 | 2025-04-02 05:48:44 | pretrained | 4 | 161450 | 381 | 65.813 | 2453.2 |
487 | 2025-03-31 07:38:11 | pretrained | 4 | 161450 | 381 | 46.883 | 3443.7 |
486 | 2025-03-28 18:54:33 | pretrained | 3 | 140603 | 58 | 6.766 | 20780.8 |
485 | 2025-03-25 10:00:22 | pretrained | 4 | 161450 | 381 | 51.003 | 3165.5 |
484 | 2025-03-25 07:53:58 | pretrained | 1 | 67641 | 2 | 8.156 | 8293.4 |
483 | 2025-03-23 06:24:45 | pretrained | 1 | 67641 | 2 | 5.466 | 12374.9 |
482 | 2025-03-23 02:05:34 | pretrained | 4 | 161450 | 381 | 56.800 | 2842.4 |
481 | 2025-03-22 04:04:24 | pretrained | 4 | 161450 | 381 | 38.486 | 4195.0 |
480 | 2025-03-17 19:35:28 | pretrained | 2 | 108824 | 11 | 16.140 | 6742.5 |
479 | 2025-03-13 00:44:15 | pretrained | 4 | 161450 | 381 | 58.393 | 2764.9 |
478 | 2025-03-11 13:45:14 | pretrained | 1 | 67641 | 2 | 1.220 | 55443.4 |
477 | 2025-03-07 23:13:00 | pretrained | 1 | 67641 | 2 | 5.393 | 12542.4 |
476 | 2025-03-01 23:47:49 | pretrained | 3 | 140603 | 58 | 23.080 | 6092.0 |
475 | 2025-03-01 18:08:01 | pretrained | 3 | 140603 | 58 | 40.096 | 3506.7 |
474 | 2025-02-25 11:00:18 | pretrained | 1 | 67641 | 2 | 1.156 | 58513.0 |
473 | 2025-02-23 10:13:18 | pretrained | 3 | 140603 | 58 | 34.830 | 4036.8 |
472 | 2025-02-23 10:13:12 | pretrained | 2 | 108824 | 11 | 10.513 | 10351.4 |
471 | 2025-02-21 05:56:14 | pretrained | 3 | 140603 | 58 | 25.063 | 5610.0 |
470 | 2025-02-21 04:35:39 | pretrained | 3 | 140603 | 58 | 35.690 | 3939.6 |
469 | 2025-02-20 15:06:28 | pretrained | 1 | 67641 | 2 | 5.250 | 12884.0 |
468 | 2025-02-20 07:42:02 | pretrained | 3 | 140603 | 58 | 31.893 | 4408.6 |
467 | 2025-02-20 04:12:58 | pretrained | 4 | 161450 | 381 | 61.500 | 2625.2 |
466 | 2025-02-20 04:12:55 | pretrained | 3 | 140603 | 58 | 32.360 | 4345.0 |
465 | 2025-02-20 04:12:50 | pretrained | 2 | 108824 | 11 | 15.936 | 6828.8 |
464 | 2025-02-20 04:10:31 | pretrained | 1 | 67641 | 2 | 5.826 | 11610.2 |
463 | 2025-02-05 08:46:26 | pretrained | 1 | 67641 | 2 | 1.220 | 55443.4 |
462 | 2025-01-29 18:13:29 | pretrained | 1 | 67641 | 2 | 1.170 | 57812.8 |
461 | 2025-01-14 06:58:58 | pretrained | 1 | 67641 | 2 | 2.483 | 27241.6 |
460 | 2025-01-14 00:24:34 | pretrained | 3 | 140603 | 58 | 11.813 | 11902.4 |
459 | 2025-01-12 16:22:55 | pretrained | 2 | 108824 | 11 | 24.486 | 4444.3 |
458 | 2025-01-11 00:23:59 | pretrained | 2 | 108824 | 11 | 16.470 | 6607.4 |
457 | 2025-01-11 00:22:39 | pretrained | 1 | 67641 | 2 | 2.983 | 22675.5 |
456 | 2025-01-09 15:55:14 | pretrained | 1 | 67641 | 2 | 6.283 | 10765.7 |
455 | 2024-12-28 00:57:07 | pretrained | 3 | 140603 | 58 | 51.313 | 2740.1 |
454 | 2024-12-28 00:57:09 | pretrained | 2 | 108824 | 11 | 16.270 | 6688.6 |
453 | 2024-12-27 23:50:40 | pretrained | 1 | 67641 | 2 | 1.250 | 54112.8 |
452 | 2024-12-23 18:59:21 | pretrained | 3 | 140603 | 58 | 35.220 | 3992.1 |
451 | 2024-12-19 11:52:33 | pretrained | 3 | 140603 | 58 | 24.160 | 5819.7 |