History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"conciseness"
Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
392 | 2025-09-02 08:58:27 | conciseness | 1 | 49908 | 1 | 0.796 | 62698.5 |
391 | 2025-08-26 21:00:05 | conciseness | 1 | 49908 | 1 | 0.843 | 59202.8 |
390 | 2025-08-15 15:36:04 | conciseness | 1 | 49908 | 1 | 2.030 | 24585.2 |
389 | 2025-08-14 22:12:45 | conciseness | 1 | 49908 | 1 | 1.686 | 29601.4 |
388 | 2025-08-11 04:38:46 | conciseness | 3 | 121633 | 15 | 15.330 | 7934.3 |
387 | 2025-08-11 03:05:48 | conciseness | 2 | 86808 | 2 | 6.063 | 14317.7 |
386 | 2025-08-09 17:19:04 | conciseness | 1 | 49908 | 1 | 1.546 | 32282.0 |
385 | 2025-08-05 14:15:54 | conciseness | 2 | 86808 | 2 | 9.673 | 8974.3 |
384 | 2025-08-04 10:27:00 | conciseness | 1 | 49908 | 1 | 1.623 | 30750.5 |
383 | 2025-08-04 05:36:31 | conciseness | 1 | 49908 | 1 | 0.890 | 56076.4 |
382 | 2025-07-31 04:16:49 | conciseness | 4 | 148819 | 143 | 28.173 | 5282.3 |
381 | 2025-07-30 13:51:24 | conciseness | 4 | 148819 | 143 | 41.296 | 3603.7 |
380 | 2025-07-30 05:46:10 | conciseness | 4 | 148819 | 143 | 33.856 | 4395.6 |
379 | 2025-07-26 01:43:58 | conciseness | 4 | 148819 | 143 | 9.563 | 15562.0 |
378 | 2025-07-24 10:04:39 | conciseness | 3 | 121633 | 15 | 24.910 | 4882.9 |
377 | 2025-07-23 17:37:57 | conciseness | 4 | 148819 | 143 | 40.660 | 3660.1 |
376 | 2025-07-19 16:03:09 | conciseness | 4 | 148819 | 143 | 11.236 | 13244.8 |
375 | 2025-07-19 12:09:56 | conciseness | 1 | 49908 | 1 | 0.796 | 62698.5 |
374 | 2025-07-10 07:52:08 | conciseness | 1 | 49908 | 1 | 5.046 | 9890.6 |
373 | 2025-07-09 22:39:59 | conciseness | 1 | 49908 | 1 | 4.533 | 11009.9 |
372 | 2025-07-08 12:57:59 | conciseness | 1 | 49908 | 1 | 5.343 | 9340.8 |
371 | 2025-07-05 05:36:15 | conciseness | 1 | 49908 | 1 | 2.626 | 19005.3 |
370 | 2025-07-03 04:27:49 | conciseness | 1 | 49908 | 1 | 4.453 | 11207.7 |
369 | 2025-07-03 00:59:33 | conciseness | 1 | 49908 | 1 | 6.156 | 8107.2 |
368 | 2025-06-15 03:27:58 | conciseness | 1 | 49908 | 1 | 1.126 | 44323.3 |
367 | 2025-06-12 21:54:30 | conciseness | 1 | 49908 | 1 | 0.986 | 50616.6 |
366 | 2025-06-02 08:01:53 | conciseness | 3 | 121633 | 15 | 25.893 | 4697.5 |
365 | 2025-06-01 21:54:46 | conciseness | 1 | 49908 | 1 | 6.093 | 8191.0 |
364 | 2025-05-31 23:52:11 | conciseness | 1 | 49908 | 1 | 1.940 | 25725.8 |
363 | 2025-05-31 21:13:06 | conciseness | 1 | 49908 | 1 | 4.126 | 12096.0 |
362 | 2025-05-29 04:21:04 | conciseness | 1 | 49908 | 1 | 4.703 | 10611.9 |
361 | 2025-05-10 18:21:54 | conciseness | 3 | 121633 | 15 | 30.783 | 3951.3 |
360 | 2025-05-07 21:54:12 | conciseness | 1 | 49908 | 1 | 2.500 | 19963.2 |
359 | 2025-05-06 03:53:35 | conciseness | 3 | 121633 | 15 | 28.940 | 4202.9 |
358 | 2025-05-05 13:42:09 | conciseness | 1 | 49908 | 1 | 0.953 | 52369.4 |
357 | 2025-05-04 18:02:05 | conciseness | 3 | 121633 | 15 | 40.910 | 2973.2 |
356 | 2025-05-02 22:46:20 | conciseness | 1 | 49908 | 1 | 5.860 | 8516.7 |
355 | 2025-05-01 22:09:56 | conciseness | 1 | 49908 | 1 | 4.860 | 10269.1 |
354 | 2025-04-30 12:51:33 | conciseness | 2 | 86808 | 2 | 11.736 | 7396.7 |
353 | 2025-04-30 12:08:44 | conciseness | 3 | 121633 | 15 | 32.440 | 3749.5 |
352 | 2025-04-29 23:47:41 | conciseness | 1 | 49908 | 1 | 3.720 | 13416.1 |
351 | 2025-04-29 04:30:42 | conciseness | 1 | 49908 | 1 | 0.906 | 55086.1 |
350 | 2025-04-21 08:26:03 | conciseness | 4 | 148819 | 143 | 64.613 | 2303.2 |
349 | 2025-04-20 03:49:13 | conciseness | 1 | 49908 | 1 | 4.390 | 11368.6 |
348 | 2025-04-16 02:22:17 | conciseness | 4 | 148819 | 143 | 25.706 | 5789.3 |
347 | 2025-04-14 08:40:30 | conciseness | 2 | 86808 | 2 | 12.550 | 6917.0 |
346 | 2025-04-12 19:15:26 | conciseness | 4 | 148819 | 143 | 9.156 | 16253.7 |
345 | 2025-04-09 23:00:17 | conciseness | 4 | 148819 | 143 | 35.550 | 4186.2 |
344 | 2025-04-06 11:21:34 | conciseness | 3 | 121633 | 15 | 19.470 | 6247.2 |
343 | 2025-04-05 18:50:57 | conciseness | 3 | 121633 | 15 | 5.843 | 20816.9 |
342 | 2025-04-05 08:31:35 | conciseness | 2 | 86808 | 2 | 2.623 | 33094.9 |
341 | 2025-04-03 21:26:13 | conciseness | 1 | 49908 | 1 | 3.876 | 12876.2 |
340 | 2025-04-03 21:20:33 | conciseness | 1 | 49908 | 1 | 4.623 | 10795.6 |
339 | 2025-03-29 00:47:58 | conciseness | 1 | 49908 | 1 | 3.470 | 14382.7 |
338 | 2025-03-23 22:54:36 | conciseness | 1 | 49908 | 1 | 2.236 | 22320.2 |
337 | 2025-03-20 10:39:22 | conciseness | 1 | 49908 | 1 | 2.640 | 18904.5 |
336 | 2025-03-20 05:56:40 | conciseness | 1 | 49908 | 1 | 1.733 | 28798.6 |
335 | 2025-03-20 05:41:35 | conciseness | 1 | 49908 | 1 | 3.920 | 12731.6 |
334 | 2025-02-21 12:06:15 | conciseness | 1 | 49908 | 1 | 3.923 | 12721.9 |
333 | 2025-02-13 00:57:15 | conciseness | 4 | 148819 | 143 | 57.630 | 2582.3 |
332 | 2025-02-09 00:32:38 | conciseness | 4 | 148819 | 143 | 66.440 | 2239.9 |
331 | 2025-02-09 00:32:42 | conciseness | 2 | 86808 | 2 | 11.063 | 7846.7 |
330 | 2025-02-09 00:23:22 | conciseness | 1 | 49908 | 1 | 1.763 | 28308.6 |
329 | 2025-02-08 23:07:50 | conciseness | 3 | 121633 | 15 | 26.796 | 4539.2 |
328 | 2025-02-07 15:49:48 | conciseness | 3 | 121633 | 15 | 14.080 | 8638.7 |
327 | 2025-02-07 15:49:48 | conciseness | 2 | 86808 | 2 | 12.140 | 7150.6 |
326 | 2025-02-07 14:46:15 | conciseness | 1 | 49908 | 1 | 0.890 | 56076.4 |
325 | 2025-02-01 19:06:03 | conciseness | 1 | 49908 | 1 | 5.170 | 9653.4 |
324 | 2025-01-17 22:14:51 | conciseness | 1 | 49908 | 1 | 3.893 | 12819.9 |
323 | 2025-01-12 19:31:34 | conciseness | 3 | 121633 | 15 | 35.953 | 3383.1 |
322 | 2025-01-12 08:52:12 | conciseness | 3 | 121633 | 15 | 26.910 | 4520.0 |
321 | 2025-01-06 18:45:51 | conciseness | 3 | 121633 | 15 | 14.016 | 8678.2 |
320 | 2025-01-03 11:11:45 | conciseness | 4 | 148819 | 143 | 61.140 | 2434.1 |
319 | 2025-01-03 02:18:02 | conciseness | 4 | 148819 | 143 | 50.440 | 2950.4 |
318 | 2025-01-02 01:48:47 | conciseness | 4 | 148819 | 143 | 45.300 | 3285.2 |
317 | 2025-01-01 23:03:17 | conciseness | 4 | 148819 | 143 | 38.580 | 3857.4 |
316 | 2025-01-01 23:03:19 | conciseness | 3 | 121633 | 15 | 26.280 | 4628.3 |
315 | 2025-01-01 23:03:20 | conciseness | 2 | 86808 | 2 | 11.236 | 7725.9 |
314 | 2025-01-01 23:02:01 | conciseness | 1 | 49908 | 1 | 0.906 | 55086.1 |
313 | 2024-12-21 12:47:14 | conciseness | 1 | 49908 | 1 | 6.390 | 7810.3 |
312 | 2024-12-20 22:30:56 | conciseness | 1 | 49908 | 1 | 4.346 | 11483.7 |
311 | 2024-12-17 12:04:14 | conciseness | 1 | 49908 | 1 | 4.423 | 11283.7 |
310 | 2024-11-20 16:46:52 | conciseness | 3 | 121633 | 15 | 5.593 | 21747.4 |
309 | 2024-11-20 16:46:51 | conciseness | 2 | 86808 | 2 | 2.560 | 33909.4 |
308 | 2024-11-17 22:31:23 | conciseness | 3 | 121633 | 15 | 41.126 | 2957.6 |
307 | 2024-11-17 07:34:23 | conciseness | 2 | 86808 | 2 | 12.483 | 6954.1 |
306 | 2024-11-17 07:34:20 | conciseness | 3 | 121633 | 15 | 13.456 | 9039.3 |
305 | 2024-11-17 07:14:35 | conciseness | 1 | 49908 | 1 | 0.903 | 55269.1 |
304 | 2024-11-16 18:28:28 | conciseness | 1 | 49908 | 1 | 2.296 | 21736.9 |
303 | 2024-11-11 12:52:10 | conciseness | 1 | 49908 | 1 | 3.216 | 15518.7 |
302 | 2024-11-11 02:22:06 | conciseness | 1 | 49908 | 1 | 3.376 | 14783.2 |
301 | 2024-10-12 01:10:34 | conciseness | 2 | 86808 | 2 | 12.423 | 6987.7 |
300 | 2024-10-12 01:08:09 | conciseness | 1 | 49908 | 1 | 2.033 | 24548.9 |
299 | 2024-10-08 21:20:50 | conciseness | 3 | 121633 | 15 | 43.080 | 2823.4 |
298 | 2024-10-06 12:51:15 | conciseness | 1 | 49908 | 1 | 5.050 | 9882.8 |
297 | 2024-10-06 02:11:39 | conciseness | 1 | 49908 | 1 | 8.860 | 5633.0 |
296 | 2024-09-18 15:59:36 | conciseness | 2 | 86808 | 2 | 8.363 | 10380.0 |
295 | 2024-09-18 15:58:05 | conciseness | 1 | 49908 | 1 | 4.296 | 11617.3 |
294 | 2024-09-12 04:21:03 | conciseness | 3 | 121633 | 15 | 49.520 | 2456.2 |
293 | 2024-09-04 14:36:17 | conciseness | 3 | 121633 | 15 | 61.536 | 1976.6 |