History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"looseness"
| Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
| 444 | 2026-01-01 20:44:10 | looseness | 1 | 81242 | 2 | 1.280 | 63470.3 |
| 443 | 2025-12-29 11:41:14 | looseness | 1 | 81242 | 2 | 1.423 | 57092.1 |
| 442 | 2025-12-27 07:37:10 | looseness | 1 | 81242 | 2 | 1.436 | 56575.2 |
| 441 | 2025-12-07 22:30:02 | looseness | 1 | 81242 | 2 | 1.453 | 55913.3 |
| 440 | 2025-11-23 22:31:48 | looseness | 1 | 81242 | 2 | 1.373 | 59171.2 |
| 439 | 2025-10-31 08:03:09 | looseness | 1 | 81242 | 2 | 1.453 | 55913.3 |
| 438 | 2025-10-24 13:13:15 | looseness | 1 | 81242 | 2 | 1.360 | 59736.8 |
| 437 | 2025-10-18 16:47:06 | looseness | 1 | 81242 | 2 | 1.343 | 60492.9 |
| 436 | 2025-10-11 15:04:21 | looseness | 1 | 81242 | 2 | 1.233 | 65889.7 |
| 435 | 2025-09-13 21:00:49 | looseness | 3 | 148641 | 108 | 7.156 | 20771.5 |
| 434 | 2025-09-12 19:44:56 | looseness | 2 | 121436 | 14 | 3.423 | 35476.5 |
| 433 | 2025-09-10 17:37:52 | looseness | 3 | 148641 | 108 | 6.626 | 22433.0 |
| 432 | 2025-09-08 17:45:18 | looseness | 1 | 81242 | 2 | 1.436 | 56575.2 |
| 431 | 2025-08-30 01:28:04 | looseness | 1 | 81242 | 2 | 2.516 | 32290.1 |
| 430 | 2025-08-22 21:54:25 | looseness | 3 | 148641 | 108 | 18.376 | 8088.9 |
| 429 | 2025-08-22 16:14:37 | looseness | 1 | 81242 | 2 | 1.250 | 64993.6 |
| 428 | 2025-08-21 12:25:27 | looseness | 1 | 81242 | 2 | 3.346 | 24280.3 |
| 427 | 2025-08-18 12:31:13 | looseness | 2 | 121436 | 14 | 3.720 | 32644.1 |
| 426 | 2025-08-17 08:58:50 | looseness | 3 | 148641 | 108 | 30.796 | 4826.6 |
| 425 | 2025-08-16 17:35:34 | looseness | 3 | 148641 | 108 | 6.296 | 23608.8 |
| 424 | 2025-08-15 00:24:08 | looseness | 3 | 148641 | 108 | 18.423 | 8068.2 |
| 423 | 2025-08-11 21:23:03 | looseness | 3 | 148641 | 108 | 20.813 | 7141.7 |
| 422 | 2025-08-11 16:53:00 | looseness | 1 | 81242 | 2 | 3.123 | 26014.1 |
| 421 | 2025-08-10 08:20:00 | looseness | 1 | 81242 | 2 | 4.170 | 19482.5 |
| 420 | 2025-08-09 13:42:40 | looseness | 1 | 81242 | 2 | 7.706 | 10542.7 |
| 419 | 2025-08-02 03:58:15 | looseness | 1 | 81242 | 2 | 3.486 | 23305.2 |
| 418 | 2025-07-25 14:19:21 | looseness | 1 | 81242 | 2 | 5.906 | 13755.8 |
| 417 | 2025-07-24 20:09:19 | looseness | 1 | 81242 | 2 | 6.550 | 12403.4 |
| 416 | 2025-07-24 15:43:45 | looseness | 1 | 81242 | 2 | 4.143 | 19609.5 |
| 415 | 2025-07-18 22:50:44 | looseness | 1 | 81242 | 2 | 1.406 | 57782.4 |
| 414 | 2025-07-18 16:27:08 | looseness | 1 | 81242 | 2 | 3.063 | 26523.7 |
| 413 | 2025-07-15 20:05:30 | looseness | 1 | 81242 | 2 | 5.326 | 15253.8 |
| 412 | 2025-07-11 09:47:22 | looseness | 3 | 148641 | 108 | 25.376 | 5857.5 |
| 411 | 2025-07-10 20:29:14 | looseness | 1 | 81242 | 2 | 8.830 | 9200.7 |
| 410 | 2025-07-07 20:29:55 | looseness | 1 | 81242 | 2 | 5.016 | 16196.6 |
| 409 | 2025-07-05 08:00:02 | looseness | 1 | 81242 | 2 | 6.560 | 12384.5 |
| 408 | 2025-06-28 10:30:31 | looseness | 1 | 81242 | 2 | 10.093 | 8049.3 |
| 407 | 2025-06-24 08:01:39 | looseness | 1 | 81242 | 2 | 3.296 | 24648.7 |
| 406 | 2025-06-21 13:05:52 | looseness | 1 | 81242 | 2 | 6.550 | 12403.4 |
| 405 | 2025-06-16 12:24:14 | looseness | 1 | 81242 | 2 | 3.733 | 21763.2 |
| 404 | 2025-06-12 09:50:24 | looseness | 1 | 81242 | 2 | 6.546 | 12410.9 |
| 403 | 2025-06-11 00:22:33 | looseness | 1 | 81242 | 2 | 5.830 | 13935.2 |
| 402 | 2025-05-29 02:40:35 | looseness | 1 | 81242 | 2 | 6.060 | 13406.3 |
| 401 | 2025-05-23 07:39:06 | looseness | 1 | 81242 | 2 | 3.453 | 23527.9 |
| 400 | 2025-05-15 12:53:04 | looseness | 1 | 81242 | 2 | 5.563 | 14604.0 |
| 399 | 2025-05-09 11:45:35 | looseness | 1 | 81242 | 2 | 1.330 | 61084.2 |
| 398 | 2025-05-08 03:03:03 | looseness | 1 | 81242 | 2 | 7.453 | 10900.6 |
| 397 | 2025-05-07 22:55:41 | looseness | 3 | 148641 | 108 | 39.976 | 3718.3 |
| 396 | 2025-05-07 22:02:39 | looseness | 3 | 148641 | 108 | 33.970 | 4375.7 |
| 395 | 2025-05-06 03:25:11 | looseness | 3 | 148641 | 108 | 32.423 | 4584.4 |
| 394 | 2025-05-05 12:18:05 | looseness | 1 | 81242 | 2 | 3.300 | 24618.8 |
| 393 | 2025-05-03 02:45:09 | looseness | 1 | 81242 | 2 | 10.500 | 7737.3 |
| 392 | 2025-05-02 08:05:16 | looseness | 1 | 81242 | 2 | 4.623 | 17573.4 |
| 391 | 2025-04-23 07:19:50 | looseness | 1 | 81242 | 2 | 4.703 | 17274.5 |
| 390 | 2025-04-23 02:33:37 | looseness | 1 | 81242 | 2 | 3.296 | 24648.7 |
| 389 | 2025-04-22 13:24:26 | looseness | 3 | 148641 | 108 | 7.033 | 21134.8 |
| 388 | 2025-04-14 12:17:00 | looseness | 1 | 81242 | 2 | 4.486 | 18110.1 |
| 387 | 2025-04-10 12:11:26 | looseness | 3 | 148641 | 108 | 13.876 | 10712.1 |
| 386 | 2025-03-31 14:55:45 | looseness | 1 | 81242 | 2 | 7.140 | 11378.4 |
| 385 | 2025-03-27 15:50:14 | looseness | 2 | 121436 | 14 | 9.033 | 13443.6 |
| 384 | 2025-03-25 01:03:02 | looseness | 2 | 121436 | 14 | 8.343 | 14555.4 |
| 383 | 2025-03-22 03:36:03 | looseness | 3 | 148641 | 108 | 7.123 | 20867.8 |
| 382 | 2025-03-20 21:51:14 | looseness | 3 | 148641 | 108 | 32.723 | 4542.4 |
| 381 | 2025-03-20 09:49:35 | looseness | 1 | 81242 | 2 | 6.593 | 12322.5 |
| 380 | 2025-03-19 22:01:44 | looseness | 3 | 148641 | 108 | 33.206 | 4476.3 |
| 379 | 2025-03-19 01:03:47 | looseness | 3 | 148641 | 108 | 36.343 | 4089.9 |
| 378 | 2025-03-17 08:26:48 | looseness | 3 | 148641 | 108 | 51.896 | 2864.2 |
| 377 | 2025-03-17 02:11:36 | looseness | 3 | 148641 | 108 | 22.216 | 6690.7 |
| 376 | 2025-03-13 18:09:32 | looseness | 1 | 81242 | 2 | 6.423 | 12648.6 |
| 375 | 2025-03-10 21:27:40 | looseness | 2 | 121436 | 14 | 17.936 | 6770.5 |
| 374 | 2025-03-10 12:13:50 | looseness | 2 | 121436 | 14 | 8.140 | 14918.4 |
| 373 | 2025-03-08 19:20:20 | looseness | 2 | 121436 | 14 | 17.640 | 6884.1 |
| 372 | 2025-03-06 23:31:37 | looseness | 1 | 81242 | 2 | 6.626 | 12261.1 |
| 371 | 2025-02-24 21:38:27 | looseness | 1 | 81242 | 2 | 4.030 | 20159.3 |
| 370 | 2025-02-23 20:44:54 | looseness | 3 | 148641 | 108 | 32.140 | 4624.8 |
| 369 | 2025-02-17 15:55:44 | looseness | 1 | 81242 | 2 | 10.266 | 7913.7 |
| 368 | 2025-02-17 00:50:19 | looseness | 3 | 148641 | 108 | 33.983 | 4374.0 |
| 367 | 2025-02-12 17:17:03 | looseness | 1 | 81242 | 2 | 5.843 | 13904.2 |
| 366 | 2025-02-12 02:18:50 | looseness | 3 | 148641 | 108 | 36.173 | 4109.2 |
| 365 | 2025-02-11 15:01:36 | looseness | 3 | 148641 | 108 | 36.330 | 4091.4 |
| 364 | 2025-02-11 13:26:23 | looseness | 1 | 81242 | 2 | 2.953 | 27511.7 |
| 363 | 2025-02-11 06:36:02 | looseness | 3 | 148641 | 108 | 27.936 | 5320.8 |
| 362 | 2025-02-11 06:36:01 | looseness | 2 | 121436 | 14 | 18.153 | 6689.6 |
| 361 | 2025-02-11 06:33:59 | looseness | 1 | 81242 | 2 | 6.190 | 13124.7 |
| 360 | 2025-02-05 19:07:08 | looseness | 1 | 81242 | 2 | 9.296 | 8739.5 |
| 359 | 2025-01-20 23:45:37 | looseness | 2 | 121436 | 14 | 17.096 | 7103.2 |
| 358 | 2025-01-15 00:34:31 | looseness | 1 | 81242 | 2 | 7.690 | 10564.6 |
| 357 | 2025-01-09 18:01:02 | looseness | 1 | 81242 | 2 | 3.390 | 23965.2 |
| 356 | 2024-12-28 00:47:14 | looseness | 2 | 121436 | 14 | 19.233 | 6313.9 |
| 355 | 2024-12-26 21:51:30 | looseness | 1 | 81242 | 2 | 13.393 | 6066.0 |
| 354 | 2024-12-24 19:21:16 | looseness | 3 | 148641 | 108 | 36.056 | 4122.5 |
| 353 | 2024-12-23 13:56:02 | looseness | 3 | 148641 | 108 | 33.500 | 4437.0 |
| 352 | 2024-12-23 13:55:45 | looseness | 2 | 121436 | 14 | 11.516 | 10545.0 |
| 351 | 2024-12-23 13:55:04 | looseness | 1 | 81242 | 2 | 3.110 | 26122.8 |
| 350 | 2024-12-09 01:07:28 | looseness | 1 | 81242 | 2 | 4.356 | 18650.6 |
| 349 | 2024-12-08 16:23:18 | looseness | 1 | 81242 | 2 | 2.906 | 27956.6 |
| 348 | 2024-12-04 01:32:18 | looseness | 1 | 81242 | 2 | 5.640 | 14404.6 |
| 347 | 2024-11-24 10:39:56 | looseness | 3 | 148641 | 108 | 51.290 | 2898.1 |
| 346 | 2024-11-18 06:53:11 | looseness | 3 | 148641 | 108 | 31.970 | 4649.4 |
| 345 | 2024-11-15 05:22:33 | looseness | 3 | 148641 | 108 | 31.000 | 4794.9 |