History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"predictor"
| Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
| 431 | 2025-11-30 20:37:02 | predictor | 3 | 148641 | 55 | 6.953 | 21378.0 |
| 430 | 2025-11-28 10:35:54 | predictor | 1 | 81242 | 2 | 1.406 | 57782.4 |
| 429 | 2025-11-26 08:10:12 | predictor | 3 | 148641 | 55 | 7.923 | 18760.7 |
| 428 | 2025-11-24 00:00:58 | predictor | 1 | 81242 | 2 | 1.500 | 54161.3 |
| 427 | 2025-11-23 18:27:08 | predictor | 3 | 148641 | 55 | 7.283 | 20409.3 |
| 426 | 2025-11-23 05:56:22 | predictor | 3 | 148641 | 55 | 6.753 | 22011.1 |
| 425 | 2025-11-23 02:08:54 | predictor | 3 | 148641 | 55 | 6.466 | 22988.1 |
| 424 | 2025-11-22 14:31:34 | predictor | 1 | 81242 | 2 | 1.690 | 48072.2 |
| 423 | 2025-11-22 08:34:10 | predictor | 1 | 81242 | 2 | 1.593 | 50999.4 |
| 422 | 2025-11-21 00:00:56 | predictor | 3 | 148641 | 55 | 18.593 | 7994.5 |
| 421 | 2025-11-20 13:11:18 | predictor | 1 | 81242 | 2 | 3.343 | 24302.1 |
| 420 | 2025-11-12 12:31:21 | predictor | 1 | 81242 | 2 | 1.313 | 61875.1 |
| 419 | 2025-11-11 05:22:23 | predictor | 1 | 81242 | 2 | 1.423 | 57092.1 |
| 418 | 2025-11-01 11:07:34 | predictor | 4 | 165329 | 311 | 12.500 | 13226.3 |
| 417 | 2025-10-27 11:59:55 | predictor | 1 | 81242 | 2 | 1.346 | 60358.1 |
| 416 | 2025-10-24 23:09:06 | predictor | 1 | 81242 | 2 | 1.533 | 52995.4 |
| 415 | 2025-10-18 23:45:41 | predictor | 1 | 81242 | 2 | 1.360 | 59736.8 |
| 414 | 2025-10-18 17:21:05 | predictor | 1 | 81242 | 2 | 1.423 | 57092.1 |
| 413 | 2025-10-18 16:13:31 | predictor | 1 | 81242 | 2 | 6.346 | 12802.1 |
| 412 | 2025-10-16 22:56:07 | predictor | 1 | 81242 | 2 | 1.483 | 54782.2 |
| 411 | 2025-10-14 21:28:46 | predictor | 4 | 165329 | 311 | 11.923 | 13866.4 |
| 410 | 2025-10-11 06:04:40 | predictor | 1 | 81242 | 2 | 1.346 | 60358.1 |
| 409 | 2025-10-06 14:46:50 | predictor | 1 | 81242 | 2 | 1.423 | 57092.1 |
| 408 | 2025-09-29 13:28:16 | predictor | 1 | 81242 | 2 | 1.500 | 54161.3 |
| 407 | 2025-09-29 11:52:14 | predictor | 1 | 81242 | 2 | 1.313 | 61875.1 |
| 406 | 2025-09-27 23:23:52 | predictor | 1 | 81242 | 2 | 1.470 | 55266.7 |
| 405 | 2025-09-24 00:12:33 | predictor | 1 | 81242 | 2 | 1.420 | 57212.7 |
| 404 | 2025-09-22 09:41:16 | predictor | 4 | 165329 | 311 | 15.406 | 10731.5 |
| 403 | 2025-09-22 04:31:30 | predictor | 4 | 165329 | 311 | 13.283 | 12446.7 |
| 402 | 2025-09-18 02:25:34 | predictor | 4 | 165329 | 311 | 11.046 | 14967.3 |
| 401 | 2025-09-11 20:49:40 | predictor | 2 | 121436 | 12 | 4.470 | 27166.9 |
| 400 | 2025-09-11 11:50:45 | predictor | 4 | 165329 | 311 | 12.126 | 13634.3 |
| 399 | 2025-09-10 05:35:52 | predictor | 1 | 81242 | 2 | 1.483 | 54782.2 |
| 398 | 2025-09-03 23:25:08 | predictor | 2 | 121436 | 12 | 3.220 | 37713.0 |
| 397 | 2025-09-02 21:58:39 | predictor | 1 | 81242 | 2 | 1.313 | 61875.1 |
| 396 | 2025-09-02 07:26:28 | predictor | 1 | 81242 | 2 | 1.296 | 62686.7 |
| 395 | 2025-09-01 16:28:44 | predictor | 1 | 81242 | 2 | 1.843 | 44081.4 |
| 394 | 2025-09-01 14:16:58 | predictor | 4 | 165329 | 311 | 12.893 | 12823.2 |
| 393 | 2025-08-31 14:42:54 | predictor | 1 | 81242 | 2 | 1.343 | 60492.9 |
| 392 | 2025-08-29 14:44:55 | predictor | 1 | 81242 | 2 | 1.390 | 58447.5 |
| 391 | 2025-08-29 12:47:01 | predictor | 1 | 81242 | 2 | 1.500 | 54161.3 |
| 390 | 2025-08-29 05:00:22 | predictor | 1 | 81242 | 2 | 1.530 | 53099.3 |
| 389 | 2025-08-27 23:27:50 | predictor | 4 | 165329 | 311 | 11.813 | 13995.5 |
| 388 | 2025-08-27 23:09:04 | predictor | 1 | 81242 | 2 | 1.593 | 50999.4 |
| 387 | 2025-08-25 18:52:09 | predictor | 4 | 165329 | 311 | 12.016 | 13759.1 |
| 386 | 2025-08-25 05:31:58 | predictor | 4 | 165329 | 311 | 53.766 | 3075.0 |
| 385 | 2025-08-24 23:46:15 | predictor | 1 | 81242 | 2 | 7.503 | 10827.9 |
| 384 | 2025-08-24 13:22:49 | predictor | 4 | 165329 | 311 | 12.906 | 12810.2 |
| 383 | 2025-08-23 06:21:35 | predictor | 4 | 165329 | 311 | 34.550 | 4785.2 |
| 382 | 2025-08-21 00:22:56 | predictor | 3 | 148641 | 55 | 21.860 | 6799.7 |
| 381 | 2025-08-20 21:23:21 | predictor | 4 | 165329 | 311 | 60.576 | 2729.3 |
| 380 | 2025-08-20 12:16:52 | predictor | 3 | 148641 | 55 | 8.110 | 18328.1 |
| 379 | 2025-08-20 11:32:24 | predictor | 4 | 165329 | 311 | 13.093 | 12627.3 |
| 378 | 2025-08-20 03:47:37 | predictor | 1 | 81242 | 2 | 1.470 | 55266.7 |
| 377 | 2025-08-18 12:37:49 | predictor | 3 | 148641 | 55 | 6.500 | 22867.8 |
| 376 | 2025-08-11 13:34:51 | predictor | 1 | 81242 | 2 | 3.233 | 25129.0 |
| 375 | 2025-08-08 13:09:53 | predictor | 1 | 81242 | 2 | 2.890 | 28111.4 |
| 374 | 2025-08-04 18:40:37 | predictor | 3 | 148641 | 55 | 23.843 | 6234.2 |
| 373 | 2025-08-01 15:59:51 | predictor | 1 | 81242 | 2 | 2.890 | 28111.4 |
| 372 | 2025-07-27 02:41:30 | predictor | 3 | 148641 | 55 | 19.160 | 7757.9 |
| 371 | 2025-07-25 11:01:23 | predictor | 3 | 148641 | 55 | 40.750 | 3647.6 |
| 370 | 2025-07-25 08:17:37 | predictor | 3 | 148641 | 55 | 25.330 | 5868.2 |
| 369 | 2025-07-25 04:09:46 | predictor | 1 | 81242 | 2 | 7.410 | 10963.8 |
| 368 | 2025-07-25 00:28:07 | predictor | 1 | 81242 | 2 | 6.716 | 12096.8 |
| 367 | 2025-07-24 00:48:16 | predictor | 1 | 81242 | 2 | 7.720 | 10523.6 |
| 366 | 2025-07-22 23:11:35 | predictor | 1 | 81242 | 2 | 3.623 | 22424.0 |
| 365 | 2025-07-22 05:26:24 | predictor | 1 | 81242 | 2 | 1.326 | 61268.5 |
| 364 | 2025-07-20 02:25:57 | predictor | 3 | 148641 | 55 | 7.500 | 19818.8 |
| 363 | 2025-07-19 12:47:20 | predictor | 1 | 81242 | 2 | 1.466 | 55417.5 |
| 362 | 2025-07-13 12:43:34 | predictor | 1 | 81242 | 2 | 1.483 | 54782.2 |
| 361 | 2025-07-10 14:12:18 | predictor | 1 | 81242 | 2 | 3.686 | 22040.7 |
| 360 | 2025-07-09 07:38:49 | predictor | 1 | 81242 | 2 | 1.716 | 47343.8 |
| 359 | 2025-06-25 23:44:24 | predictor | 1 | 81242 | 2 | 4.096 | 19834.5 |
| 358 | 2025-06-18 02:34:58 | predictor | 3 | 148641 | 55 | 36.046 | 4123.6 |
| 357 | 2025-06-17 18:39:54 | predictor | 1 | 81242 | 2 | 1.516 | 53589.7 |
| 356 | 2025-06-16 01:07:59 | predictor | 2 | 121436 | 12 | 12.236 | 9924.5 |
| 355 | 2025-06-15 15:49:33 | predictor | 3 | 148641 | 55 | 6.780 | 21923.5 |
| 354 | 2025-06-14 14:41:00 | predictor | 1 | 81242 | 2 | 9.503 | 8549.1 |
| 353 | 2025-06-10 11:06:41 | predictor | 3 | 148641 | 55 | 34.706 | 4282.9 |
| 352 | 2025-06-09 22:03:30 | predictor | 2 | 121436 | 12 | 17.126 | 7090.7 |
| 351 | 2025-06-08 01:28:18 | predictor | 2 | 121436 | 12 | 15.046 | 8071.0 |
| 350 | 2025-06-07 22:42:27 | predictor | 3 | 148641 | 55 | 19.296 | 7703.2 |
| 349 | 2025-06-07 11:44:56 | predictor | 1 | 81242 | 2 | 8.843 | 9187.2 |
| 348 | 2025-06-01 05:39:25 | predictor | 3 | 148641 | 55 | 39.686 | 3745.4 |
| 347 | 2025-05-29 01:21:08 | predictor | 1 | 81242 | 2 | 6.953 | 11684.5 |
| 346 | 2025-05-26 00:09:21 | predictor | 1 | 81242 | 2 | 6.766 | 12007.4 |
| 345 | 2025-05-18 03:59:24 | predictor | 1 | 81242 | 2 | 3.610 | 22504.7 |
| 344 | 2025-05-15 17:42:33 | predictor | 1 | 81242 | 2 | 1.533 | 52995.4 |
| 343 | 2025-05-13 22:56:33 | predictor | 1 | 81242 | 2 | 10.360 | 7841.9 |
| 342 | 2025-05-12 20:34:10 | predictor | 3 | 148641 | 55 | 7.640 | 19455.6 |
| 341 | 2025-05-12 06:59:39 | predictor | 2 | 121436 | 12 | 18.486 | 6569.1 |
| 340 | 2025-05-04 11:36:10 | predictor | 1 | 81242 | 2 | 3.530 | 23014.7 |
| 339 | 2025-05-04 05:50:10 | predictor | 1 | 81242 | 2 | 6.623 | 12266.6 |
| 338 | 2025-04-30 13:52:13 | predictor | 1 | 81242 | 2 | 5.500 | 14771.3 |
| 337 | 2025-04-29 13:24:04 | predictor | 1 | 81242 | 2 | 5.500 | 14771.3 |
| 336 | 2025-04-26 01:50:34 | predictor | 3 | 148641 | 55 | 41.970 | 3541.6 |
| 335 | 2025-04-25 20:16:52 | predictor | 3 | 148641 | 55 | 7.763 | 19147.4 |
| 334 | 2025-04-23 08:06:36 | predictor | 3 | 148641 | 55 | 37.000 | 4017.3 |
| 333 | 2025-04-23 00:03:51 | predictor | 1 | 81242 | 2 | 6.453 | 12589.8 |
| 332 | 2025-04-21 00:31:23 | predictor | 3 | 148641 | 55 | 37.190 | 3996.8 |