History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"accuracy"
Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
490 | 2025-07-25 21:27:20 | accuracy | 1 | 82945 | 1 | 1.390 | 59672.7 |
489 | 2025-07-24 03:02:25 | accuracy | 1 | 82945 | 1 | 7.563 | 10967.2 |
488 | 2025-07-23 01:17:26 | accuracy | 1 | 82945 | 1 | 4.563 | 18177.7 |
487 | 2025-07-22 16:14:34 | accuracy | 1 | 82945 | 1 | 6.720 | 12343.0 |
486 | 2025-07-18 10:30:16 | accuracy | 1 | 82945 | 1 | 3.343 | 24811.5 |
485 | 2025-07-07 05:02:27 | accuracy | 1 | 82945 | 1 | 7.390 | 11224.0 |
484 | 2025-06-21 23:22:40 | accuracy | 1 | 82945 | 1 | 4.780 | 17352.5 |
483 | 2025-06-04 08:02:03 | accuracy | 3 | 146162 | 17 | 26.770 | 5459.9 |
482 | 2025-06-02 22:29:10 | accuracy | 3 | 146162 | 17 | 30.876 | 4733.8 |
481 | 2025-05-25 14:32:32 | accuracy | 2 | 121059 | 4 | 4.126 | 29340.5 |
480 | 2025-05-14 11:24:35 | accuracy | 1 | 82945 | 1 | 9.050 | 9165.2 |
479 | 2025-05-08 22:07:46 | accuracy | 1 | 82945 | 1 | 2.766 | 29987.3 |
478 | 2025-05-05 20:23:46 | accuracy | 3 | 146162 | 17 | 30.956 | 4721.6 |
477 | 2025-05-04 02:11:36 | accuracy | 1 | 82945 | 1 | 10.516 | 7887.5 |
476 | 2025-04-29 08:46:02 | accuracy | 1 | 82945 | 1 | 1.453 | 57085.3 |
475 | 2025-04-26 13:52:50 | accuracy | 3 | 146162 | 17 | 37.640 | 3883.2 |
474 | 2025-04-26 12:15:46 | accuracy | 4 | 161609 | 177 | 37.330 | 4329.2 |
473 | 2025-04-26 12:07:55 | accuracy | 4 | 161609 | 177 | 38.813 | 4163.8 |
472 | 2025-04-26 11:40:25 | accuracy | 4 | 161609 | 177 | 36.850 | 4385.6 |
471 | 2025-04-26 03:09:09 | accuracy | 2 | 121059 | 4 | 24.143 | 5014.2 |
470 | 2025-04-24 22:29:15 | accuracy | 1 | 82945 | 1 | 7.030 | 11798.7 |
469 | 2025-04-24 14:20:23 | accuracy | 4 | 161609 | 177 | 62.766 | 2574.8 |
468 | 2025-04-23 10:57:08 | accuracy | 4 | 161609 | 177 | 42.320 | 3818.7 |
467 | 2025-04-22 12:02:06 | accuracy | 2 | 121059 | 4 | 4.906 | 24675.7 |
466 | 2025-04-21 03:37:45 | accuracy | 4 | 161609 | 177 | 66.736 | 2421.6 |
465 | 2025-04-20 04:58:26 | accuracy | 1 | 82945 | 1 | 5.920 | 14011.0 |
464 | 2025-03-29 05:55:32 | accuracy | 1 | 82945 | 1 | 10.236 | 8103.3 |
463 | 2025-03-24 19:53:40 | accuracy | 3 | 146162 | 17 | 6.970 | 20970.2 |
462 | 2025-03-24 09:37:41 | accuracy | 2 | 121059 | 4 | 26.893 | 4501.5 |
461 | 2025-03-24 07:18:23 | accuracy | 3 | 146162 | 17 | 40.580 | 3601.8 |
460 | 2025-03-21 23:17:51 | accuracy | 1 | 82945 | 1 | 1.470 | 56425.2 |
459 | 2025-03-20 06:36:46 | accuracy | 1 | 82945 | 1 | 4.486 | 18489.7 |
458 | 2025-03-15 02:37:26 | accuracy | 3 | 146162 | 17 | 32.863 | 4447.6 |
457 | 2025-03-11 06:12:16 | accuracy | 3 | 146162 | 17 | 55.503 | 2633.4 |
456 | 2025-03-10 20:57:04 | accuracy | 3 | 146162 | 17 | 25.893 | 5644.8 |
455 | 2025-03-09 18:49:17 | accuracy | 3 | 146162 | 17 | 20.170 | 7246.5 |
454 | 2025-02-22 13:19:17 | accuracy | 1 | 82945 | 1 | 7.610 | 10899.5 |
453 | 2025-02-15 12:50:48 | accuracy | 2 | 121059 | 4 | 15.063 | 8036.8 |
452 | 2025-02-10 23:41:31 | accuracy | 1 | 82945 | 1 | 9.610 | 8631.1 |
451 | 2025-02-10 01:01:22 | accuracy | 4 | 161609 | 177 | 56.783 | 2846.1 |
450 | 2025-02-10 00:30:17 | accuracy | 4 | 161609 | 177 | 65.286 | 2475.4 |
449 | 2025-02-09 22:26:47 | accuracy | 4 | 161609 | 177 | 64.596 | 2501.8 |
448 | 2025-02-09 09:53:53 | accuracy | 4 | 161609 | 177 | 50.643 | 3191.1 |
447 | 2025-02-09 09:53:49 | accuracy | 2 | 121059 | 4 | 16.983 | 7128.2 |
446 | 2025-02-06 22:24:50 | accuracy | 3 | 146162 | 17 | 27.923 | 5234.5 |
445 | 2025-02-06 20:35:04 | accuracy | 3 | 146162 | 17 | 23.923 | 6109.7 |
444 | 2025-01-31 07:00:24 | accuracy | 1 | 82945 | 1 | 10.720 | 7737.4 |
443 | 2025-01-28 17:40:44 | accuracy | 3 | 146162 | 17 | 25.780 | 5669.6 |
442 | 2025-01-26 19:53:01 | accuracy | 3 | 146162 | 17 | 33.876 | 4314.6 |
441 | 2025-01-16 02:13:23 | accuracy | 2 | 121059 | 4 | 16.160 | 7491.3 |
440 | 2025-01-05 23:07:17 | accuracy | 1 | 82945 | 1 | 3.656 | 22687.4 |
439 | 2025-01-05 04:28:03 | accuracy | 1 | 82945 | 1 | 4.046 | 20500.5 |
438 | 2025-01-05 04:23:27 | accuracy | 2 | 121059 | 4 | 19.203 | 6304.2 |
437 | 2025-01-03 15:05:51 | accuracy | 3 | 146162 | 17 | 36.626 | 3990.7 |
436 | 2025-01-02 23:46:15 | accuracy | 3 | 146162 | 17 | 38.800 | 3767.1 |
435 | 2025-01-02 19:07:14 | accuracy | 3 | 146162 | 17 | 32.766 | 4460.8 |
434 | 2024-12-30 18:52:37 | accuracy | 3 | 146162 | 17 | 36.590 | 3994.6 |
433 | 2024-12-30 08:55:26 | accuracy | 4 | 161609 | 177 | 64.410 | 2509.1 |
432 | 2024-12-30 08:55:46 | accuracy | 2 | 121059 | 4 | 27.063 | 4473.2 |
431 | 2024-12-30 08:55:41 | accuracy | 3 | 146162 | 17 | 30.876 | 4733.8 |
430 | 2024-12-30 08:54:29 | accuracy | 1 | 82945 | 1 | 7.546 | 10991.9 |
429 | 2024-12-20 13:41:11 | accuracy | 3 | 146162 | 17 | 28.873 | 5062.2 |
428 | 2024-12-20 13:41:10 | accuracy | 2 | 121059 | 4 | 13.233 | 9148.3 |
427 | 2024-12-19 23:03:42 | accuracy | 3 | 146162 | 17 | 29.330 | 4983.4 |
426 | 2024-12-03 10:30:33 | accuracy | 3 | 146162 | 17 | 47.236 | 3094.3 |
425 | 2024-12-03 10:30:35 | accuracy | 2 | 121059 | 4 | 27.940 | 4332.8 |
424 | 2024-12-03 10:30:30 | accuracy | 3 | 146162 | 17 | 18.796 | 7776.2 |
423 | 2024-11-26 00:26:53 | accuracy | 1 | 82945 | 1 | 7.030 | 11798.7 |
422 | 2024-11-20 19:08:16 | accuracy | 1 | 82945 | 1 | 3.670 | 22600.8 |
421 | 2024-11-16 08:36:36 | accuracy | 2 | 121059 | 4 | 27.653 | 4377.8 |
420 | 2024-11-16 08:36:13 | accuracy | 1 | 82945 | 1 | 3.296 | 25165.4 |
419 | 2024-11-01 08:54:46 | accuracy | 3 | 146162 | 17 | 45.266 | 3229.0 |
418 | 2024-11-01 08:54:42 | accuracy | 2 | 121059 | 4 | 19.216 | 6299.9 |
417 | 2024-10-29 10:19:58 | accuracy | 3 | 146162 | 17 | 32.470 | 4501.4 |
416 | 2024-10-29 10:19:52 | accuracy | 3 | 146162 | 17 | 34.983 | 4178.1 |
415 | 2024-10-29 10:19:53 | accuracy | 2 | 121059 | 4 | 18.640 | 6494.6 |
414 | 2024-10-29 10:19:40 | accuracy | 1 | 82945 | 1 | 1.453 | 57085.3 |
413 | 2024-10-21 01:48:23 | accuracy | 3 | 146162 | 17 | 40.140 | 3641.3 |
412 | 2024-10-21 01:48:30 | accuracy | 3 | 146162 | 17 | 27.720 | 5272.8 |
411 | 2024-10-20 09:25:10 | accuracy | 3 | 146162 | 17 | 29.706 | 4920.3 |
410 | 2024-10-20 09:25:13 | accuracy | 2 | 121059 | 4 | 13.640 | 8875.3 |
409 | 2024-10-20 09:25:02 | accuracy | 1 | 82945 | 1 | 3.860 | 21488.3 |
408 | 2024-10-20 09:13:43 | accuracy | 4 | 161609 | 177 | 51.533 | 3136.0 |
407 | 2024-10-20 04:11:25 | accuracy | 4 | 161609 | 177 | 72.003 | 2244.5 |
406 | 2024-10-13 01:16:22 | accuracy | 1 | 82945 | 1 | 10.250 | 8092.2 |
405 | 2024-09-26 08:00:13 | accuracy | 4 | 161609 | 177 | 92.086 | 1755.0 |
404 | 2024-09-20 16:06:08 | accuracy | 1 | 82945 | 1 | 6.983 | 11878.1 |
403 | 2024-09-19 23:47:36 | accuracy | 4 | 161609 | 177 | 153.430 | 1053.3 |
402 | 2024-09-19 00:49:49 | accuracy | 4 | 161609 | 177 | 96.440 | 1675.7 |
401 | 2024-09-18 23:10:18 | accuracy | 4 | 161609 | 177 | 68.396 | 2362.8 |
400 | 2024-09-18 23:10:18 | accuracy | 2 | 121059 | 4 | 22.360 | 5414.1 |
399 | 2024-09-18 23:10:11 | accuracy | 1 | 82945 | 1 | 5.263 | 15760.0 |
398 | 2024-09-16 15:17:36 | accuracy | 1 | 82945 | 1 | 11.826 | 7013.8 |
397 | 2024-08-21 12:06:54 | accuracy | 1 | 82945 | 1 | 5.280 | 15709.3 |
396 | 2024-08-15 08:58:38 | accuracy | 1 | 82945 | 1 | 4.843 | 17126.8 |
395 | 2024-08-12 06:33:20 | accuracy | 1 | 82945 | 1 | 15.656 | 5298.0 |
394 | 2024-07-30 10:55:49 | accuracy | 3 | 146162 | 17 | 19.780 | 7389.4 |
393 | 2024-07-29 03:31:18 | accuracy | 3 | 146162 | 17 | 16.753 | 8724.5 |
392 | 2024-07-27 11:06:28 | accuracy | 3 | 146162 | 17 | 27.703 | 5276.0 |
391 | 2024-07-21 23:37:28 | accuracy | 3 | 146162 | 17 | 29.580 | 4941.2 |