History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"precisions"
| Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
| 559 | 2026-04-11 19:40:28 | precisions | 2 | 108824 | 18 | 7.876 | 13817.2 |
| 558 | 2026-04-09 13:38:47 | precisions | 4 | 161450 | 403 | 11.796 | 13686.8 |
| 557 | 2026-04-09 13:10:16 | precisions | 3 | 140603 | 76 | 9.076 | 15491.7 |
| 556 | 2026-04-07 04:04:40 | precisions | 3 | 140603 | 76 | 21.876 | 6427.3 |
| 555 | 2026-04-01 15:45:00 | precisions | 1 | 67641 | 5 | 2.593 | 26086.0 |
| 554 | 2026-03-31 19:22:47 | precisions | 1 | 67641 | 5 | 1.233 | 54858.9 |
| 553 | 2026-03-29 21:40:56 | precisions | 1 | 67641 | 5 | 3.813 | 17739.6 |
| 552 | 2026-03-29 18:09:47 | precisions | 1 | 67641 | 5 | 3.890 | 17388.4 |
| 551 | 2026-03-29 18:05:34 | precisions | 1 | 67641 | 5 | 5.313 | 12731.2 |
| 550 | 2026-03-29 14:41:17 | precisions | 1 | 67641 | 5 | 4.233 | 15979.4 |
| 549 | 2026-03-29 14:32:21 | precisions | 1 | 67641 | 5 | 5.456 | 12397.5 |
| 548 | 2026-03-26 19:21:43 | precisions | 3 | 140603 | 76 | 61.176 | 2298.3 |
| 547 | 2026-03-23 03:51:14 | precisions | 3 | 140603 | 76 | 34.533 | 4071.6 |
| 546 | 2026-03-21 16:48:01 | precisions | 3 | 140603 | 76 | 14.673 | 9582.4 |
| 545 | 2026-03-20 02:31:19 | precisions | 3 | 140603 | 76 | 34.596 | 4064.1 |
| 544 | 2026-03-20 02:30:41 | precisions | 3 | 140603 | 76 | 35.156 | 3999.4 |
| 543 | 2026-03-20 02:30:49 | precisions | 3 | 140603 | 76 | 26.343 | 5337.4 |
| 542 | 2026-03-20 02:30:08 | precisions | 3 | 140603 | 76 | 31.796 | 4422.0 |
| 541 | 2026-03-18 17:26:28 | precisions | 1 | 67641 | 5 | 1.186 | 57032.9 |
| 540 | 2026-03-17 23:27:21 | precisions | 3 | 140603 | 76 | 34.533 | 4071.6 |
| 539 | 2026-03-16 16:21:48 | precisions | 1 | 67641 | 5 | 1.216 | 55625.8 |
| 538 | 2026-03-16 12:31:09 | precisions | 2 | 108824 | 18 | 3.450 | 31543.2 |
| 537 | 2026-03-15 01:47:50 | precisions | 3 | 140603 | 76 | 30.616 | 4592.5 |
| 536 | 2026-03-03 21:23:31 | precisions | 2 | 108824 | 18 | 3.263 | 33350.9 |
| 535 | 2026-02-27 14:32:40 | precisions | 3 | 140603 | 76 | 46.816 | 3003.3 |
| 534 | 2026-02-27 12:27:03 | precisions | 3 | 140603 | 76 | 49.993 | 2812.5 |
| 533 | 2026-02-25 04:07:06 | precisions | 4 | 161450 | 403 | 10.766 | 14996.3 |
| 532 | 2026-02-17 16:36:10 | precisions | 3 | 140603 | 76 | 6.733 | 20882.7 |
| 531 | 2026-02-13 06:11:12 | precisions | 4 | 161450 | 403 | 76.846 | 2101.0 |
| 530 | 2026-02-13 05:47:17 | precisions | 4 | 161450 | 403 | 60.040 | 2689.0 |
| 529 | 2026-02-13 05:47:00 | precisions | 4 | 161450 | 403 | 46.680 | 3458.7 |
| 528 | 2026-02-13 02:58:47 | precisions | 4 | 161450 | 403 | 45.126 | 3577.8 |
| 527 | 2026-02-11 18:22:34 | precisions | 1 | 67641 | 5 | 5.876 | 11511.4 |
| 526 | 2026-02-11 06:45:41 | precisions | 1 | 67641 | 5 | 1.593 | 42461.4 |
| 525 | 2026-02-07 06:05:38 | precisions | 2 | 108824 | 18 | 3.406 | 31950.7 |
| 524 | 2026-02-05 11:10:33 | precisions | 4 | 161450 | 403 | 10.563 | 15284.5 |
| 523 | 2026-01-31 06:57:32 | precisions | 4 | 161450 | 403 | 11.453 | 14096.7 |
| 522 | 2026-01-30 14:56:30 | precisions | 3 | 140603 | 76 | 6.030 | 23317.2 |
| 521 | 2026-01-27 03:39:00 | precisions | 1 | 67641 | 5 | 5.970 | 11330.2 |
| 520 | 2026-01-27 00:40:50 | precisions | 1 | 67641 | 5 | 1.470 | 46014.3 |
| 519 | 2026-01-18 16:16:53 | precisions | 3 | 140603 | 76 | 11.673 | 12045.1 |
| 518 | 2026-01-06 06:51:19 | precisions | 2 | 108824 | 18 | 3.393 | 32073.1 |
| 517 | 2025-12-28 23:48:26 | precisions | 1 | 67641 | 5 | 1.140 | 59334.2 |
| 516 | 2025-12-27 10:58:19 | precisions | 1 | 67641 | 5 | 1.110 | 60937.8 |
| 515 | 2025-12-21 19:57:02 | precisions | 3 | 140603 | 76 | 6.343 | 22166.6 |
| 514 | 2025-12-17 03:53:29 | precisions | 1 | 67641 | 5 | 1.343 | 50365.6 |
| 513 | 2025-12-15 21:08:26 | precisions | 1 | 67641 | 5 | 1.283 | 52721.0 |
| 512 | 2025-12-12 16:06:45 | precisions | 3 | 140603 | 76 | 35.566 | 3953.3 |
| 511 | 2025-12-12 03:39:16 | precisions | 2 | 108824 | 18 | 3.156 | 34481.6 |
| 510 | 2025-12-11 02:29:27 | precisions | 1 | 67641 | 5 | 1.313 | 51516.4 |
| 509 | 2025-11-29 19:40:23 | precisions | 1 | 67641 | 5 | 2.093 | 32317.7 |
| 508 | 2025-11-28 21:30:27 | precisions | 1 | 67641 | 5 | 1.406 | 48108.8 |
| 507 | 2025-11-28 18:41:32 | precisions | 1 | 67641 | 5 | 1.220 | 55443.4 |
| 506 | 2025-11-28 18:41:31 | precisions | 1 | 67641 | 5 | 1.170 | 57812.8 |
| 505 | 2025-11-28 14:37:36 | precisions | 3 | 140603 | 76 | 6.626 | 21219.9 |
| 504 | 2025-11-25 21:34:18 | precisions | 1 | 67641 | 5 | 1.216 | 55625.8 |
| 503 | 2025-11-25 09:49:45 | precisions | 1 | 67641 | 5 | 2.936 | 23038.5 |
| 502 | 2025-11-23 00:20:52 | precisions | 3 | 140603 | 76 | 23.406 | 6007.1 |
| 501 | 2025-11-19 02:36:07 | precisions | 1 | 67641 | 5 | 1.203 | 56226.9 |
| 500 | 2025-11-11 19:38:06 | precisions | 1 | 67641 | 5 | 1.156 | 58513.0 |
| 499 | 2025-11-10 08:23:06 | precisions | 1 | 67641 | 5 | 1.280 | 52844.5 |
| 498 | 2025-10-26 13:14:55 | precisions | 1 | 67641 | 5 | 1.186 | 57032.9 |
| 497 | 2025-10-26 02:48:31 | precisions | 1 | 67641 | 5 | 1.046 | 64666.3 |
| 496 | 2025-10-26 02:17:38 | precisions | 1 | 67641 | 5 | 1.263 | 53555.8 |
| 495 | 2025-10-22 10:49:41 | precisions | 1 | 67641 | 5 | 5.856 | 11550.7 |
| 494 | 2025-10-19 03:32:28 | precisions | 1 | 67641 | 5 | 1.233 | 54858.9 |
| 493 | 2025-09-30 10:20:44 | precisions | 1 | 67641 | 5 | 1.186 | 57032.9 |
| 492 | 2025-09-29 03:57:05 | precisions | 1 | 67641 | 5 | 1.326 | 51011.3 |
| 491 | 2025-09-28 03:16:38 | precisions | 1 | 67641 | 5 | 1.203 | 56226.9 |
| 490 | 2025-09-27 04:33:28 | precisions | 1 | 67641 | 5 | 1.220 | 55443.4 |
| 489 | 2025-09-26 03:07:21 | precisions | 1 | 67641 | 5 | 1.250 | 54112.8 |
| 488 | 2025-09-24 11:42:21 | precisions | 1 | 67641 | 5 | 1.063 | 63632.2 |
| 487 | 2025-09-23 22:45:49 | precisions | 1 | 67641 | 5 | 1.170 | 57812.8 |
| 486 | 2025-09-23 02:57:56 | precisions | 1 | 67641 | 5 | 1.170 | 57812.8 |
| 485 | 2025-09-22 00:53:02 | precisions | 1 | 67641 | 5 | 1.126 | 60071.9 |
| 484 | 2025-09-18 18:26:00 | precisions | 1 | 67641 | 5 | 1.203 | 56226.9 |
| 483 | 2025-09-16 03:21:15 | precisions | 1 | 67641 | 5 | 1.173 | 57665.0 |
| 482 | 2025-09-15 13:28:35 | precisions | 1 | 67641 | 5 | 1.220 | 55443.4 |
| 481 | 2025-09-09 08:18:35 | precisions | 1 | 67641 | 5 | 1.220 | 55443.4 |
| 480 | 2025-09-08 22:31:26 | precisions | 1 | 67641 | 5 | 1.093 | 61885.6 |
| 479 | 2025-09-05 15:38:29 | precisions | 1 | 67641 | 5 | 1.080 | 62630.6 |
| 478 | 2025-09-02 08:34:12 | precisions | 1 | 67641 | 5 | 1.173 | 57665.0 |
| 477 | 2025-08-30 00:48:33 | precisions | 1 | 67641 | 5 | 1.233 | 54858.9 |
| 476 | 2025-08-28 03:29:16 | precisions | 1 | 67641 | 5 | 1.266 | 53428.9 |
| 475 | 2025-08-23 08:48:20 | precisions | 4 | 161450 | 403 | 41.923 | 3851.1 |
| 474 | 2025-08-23 02:49:55 | precisions | 1 | 67641 | 5 | 8.486 | 7970.9 |
| 473 | 2025-08-20 01:15:08 | precisions | 1 | 67641 | 5 | 2.250 | 30062.7 |
| 472 | 2025-08-19 13:22:00 | precisions | 4 | 161450 | 403 | 27.733 | 5821.6 |
| 471 | 2025-08-19 12:26:46 | precisions | 1 | 67641 | 5 | 5.376 | 12582.0 |
| 470 | 2025-08-17 07:04:53 | precisions | 4 | 161450 | 403 | 27.140 | 5948.8 |
| 469 | 2025-08-17 04:40:57 | precisions | 4 | 161450 | 403 | 56.766 | 2844.1 |
| 468 | 2025-08-16 16:37:11 | precisions | 1 | 67641 | 5 | 1.140 | 59334.2 |
| 467 | 2025-08-15 07:23:35 | precisions | 4 | 161450 | 403 | 43.330 | 3726.1 |
| 466 | 2025-08-11 23:33:15 | precisions | 4 | 161450 | 403 | 28.110 | 5743.5 |
| 465 | 2025-08-09 13:31:45 | precisions | 1 | 67641 | 5 | 5.076 | 13325.7 |
| 464 | 2025-08-02 03:20:30 | precisions | 1 | 67641 | 5 | 2.736 | 24722.6 |
| 463 | 2025-07-29 02:30:24 | precisions | 1 | 67641 | 5 | 6.266 | 10794.9 |
| 462 | 2025-07-24 06:15:50 | precisions | 1 | 67641 | 5 | 2.923 | 23141.0 |
| 461 | 2025-07-23 16:35:53 | precisions | 1 | 67641 | 5 | 5.830 | 11602.2 |
| 460 | 2025-07-23 16:09:34 | precisions | 1 | 67641 | 5 | 3.593 | 18825.8 |