History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"skewnesses"
Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
374 | 2025-07-08 14:56:34 | skewnesses | 4 | 161450 | 484 | 51.676 | 3124.3 |
373 | 2025-07-07 22:30:23 | skewnesses | 1 | 67641 | 2 | 2.983 | 22675.5 |
372 | 2025-06-27 12:26:13 | skewnesses | 2 | 108824 | 6 | 17.986 | 6050.5 |
371 | 2025-06-26 16:10:04 | skewnesses | 1 | 67641 | 2 | 8.140 | 8309.7 |
370 | 2025-06-25 20:30:35 | skewnesses | 1 | 67641 | 2 | 5.860 | 11542.8 |
369 | 2025-06-21 08:06:52 | skewnesses | 1 | 67641 | 2 | 5.860 | 11542.8 |
368 | 2025-06-17 03:48:53 | skewnesses | 3 | 140603 | 72 | 22.143 | 6349.8 |
367 | 2025-06-15 14:50:09 | skewnesses | 2 | 108824 | 6 | 2.966 | 36690.5 |
366 | 2025-06-14 14:26:37 | skewnesses | 3 | 140603 | 72 | 19.890 | 7069.0 |
365 | 2025-06-13 05:54:22 | skewnesses | 3 | 140603 | 72 | 6.610 | 21271.3 |
364 | 2025-06-11 12:31:42 | skewnesses | 2 | 108824 | 6 | 15.703 | 6930.1 |
363 | 2025-06-11 12:14:15 | skewnesses | 3 | 140603 | 72 | 30.720 | 4576.9 |
362 | 2025-06-11 02:37:56 | skewnesses | 4 | 161450 | 484 | 64.220 | 2514.0 |
361 | 2025-06-08 11:59:18 | skewnesses | 1 | 67641 | 2 | 2.703 | 25024.4 |
360 | 2025-06-07 09:32:00 | skewnesses | 1 | 67641 | 2 | 2.750 | 24596.7 |
359 | 2025-06-04 07:25:26 | skewnesses | 1 | 67641 | 2 | 5.390 | 12549.4 |
358 | 2025-05-28 04:30:51 | skewnesses | 1 | 67641 | 2 | 1.203 | 56226.9 |
357 | 2025-05-26 19:42:28 | skewnesses | 1 | 67641 | 2 | 7.986 | 8469.9 |
356 | 2025-05-18 08:20:03 | skewnesses | 1 | 67641 | 2 | 5.956 | 11356.8 |
355 | 2025-05-16 17:16:42 | skewnesses | 1 | 67641 | 2 | 8.200 | 8248.9 |
354 | 2025-05-08 06:31:34 | skewnesses | 4 | 161450 | 484 | 46.216 | 3493.4 |
353 | 2025-05-07 00:25:18 | skewnesses | 4 | 161450 | 484 | 10.876 | 14844.6 |
352 | 2025-05-06 18:54:06 | skewnesses | 4 | 161450 | 484 | 67.223 | 2401.7 |
351 | 2025-05-04 19:29:45 | skewnesses | 2 | 108824 | 6 | 4.890 | 22254.4 |
350 | 2025-05-04 19:08:40 | skewnesses | 3 | 140603 | 72 | 7.233 | 19439.1 |
349 | 2025-05-04 10:04:00 | skewnesses | 1 | 67641 | 2 | 8.160 | 8289.3 |
348 | 2025-05-03 00:09:07 | skewnesses | 1 | 67641 | 2 | 8.110 | 8340.4 |
347 | 2025-04-18 07:11:27 | skewnesses | 1 | 67641 | 2 | 2.673 | 25305.3 |
346 | 2025-04-13 16:17:42 | skewnesses | 4 | 161450 | 484 | 11.436 | 14117.7 |
345 | 2025-04-13 07:45:21 | skewnesses | 3 | 140603 | 72 | 6.423 | 21890.5 |
344 | 2025-04-06 16:29:39 | skewnesses | 4 | 161450 | 484 | 58.946 | 2738.9 |
343 | 2025-04-06 15:00:42 | skewnesses | 1 | 67641 | 2 | 5.440 | 12434.0 |
342 | 2025-04-05 13:56:19 | skewnesses | 2 | 108824 | 6 | 7.580 | 14356.7 |
341 | 2025-04-05 11:28:00 | skewnesses | 3 | 140603 | 72 | 8.013 | 17546.9 |
340 | 2025-04-05 11:21:53 | skewnesses | 2 | 108824 | 6 | 3.450 | 31543.2 |
339 | 2025-04-05 02:37:25 | skewnesses | 4 | 161450 | 484 | 30.910 | 5223.2 |
338 | 2025-04-03 05:44:23 | skewnesses | 1 | 67641 | 2 | 1.126 | 60071.9 |
337 | 2025-03-17 13:20:59 | skewnesses | 1 | 67641 | 2 | 8.016 | 8438.2 |
336 | 2025-03-17 05:47:00 | skewnesses | 1 | 67641 | 2 | 8.580 | 7883.6 |
335 | 2025-03-04 04:57:38 | skewnesses | 1 | 67641 | 2 | 4.076 | 16594.9 |
334 | 2025-02-22 03:20:30 | skewnesses | 4 | 161450 | 484 | 53.750 | 3003.7 |
333 | 2025-02-21 22:37:14 | skewnesses | 4 | 161450 | 484 | 48.343 | 3339.7 |
332 | 2025-02-21 11:32:20 | skewnesses | 4 | 161450 | 484 | 35.223 | 4583.7 |
331 | 2025-02-21 11:32:20 | skewnesses | 2 | 108824 | 6 | 22.533 | 4829.5 |
330 | 2025-02-21 11:27:50 | skewnesses | 1 | 67641 | 2 | 2.826 | 23935.2 |
329 | 2025-02-14 08:04:51 | skewnesses | 3 | 140603 | 72 | 26.423 | 5321.2 |
328 | 2025-02-12 23:57:29 | skewnesses | 3 | 140603 | 72 | 30.720 | 4576.9 |
327 | 2025-01-24 15:53:20 | skewnesses | 3 | 140603 | 72 | 40.660 | 3458.0 |
326 | 2025-01-24 12:57:26 | skewnesses | 3 | 140603 | 72 | 30.140 | 4665.0 |
325 | 2025-01-23 11:41:10 | skewnesses | 3 | 140603 | 72 | 27.453 | 5121.6 |
324 | 2025-01-23 11:41:12 | skewnesses | 2 | 108824 | 6 | 22.093 | 4925.7 |
323 | 2025-01-23 11:40:19 | skewnesses | 1 | 67641 | 2 | 3.970 | 17038.0 |
322 | 2025-01-19 18:15:10 | skewnesses | 1 | 67641 | 2 | 5.436 | 12443.2 |
321 | 2025-01-13 15:46:43 | skewnesses | 1 | 67641 | 2 | 5.300 | 12762.5 |
320 | 2025-01-12 00:30:45 | skewnesses | 1 | 67641 | 2 | 3.860 | 17523.6 |
319 | 2024-12-19 10:23:40 | skewnesses | 2 | 108824 | 6 | 12.813 | 8493.2 |
318 | 2024-12-19 10:23:06 | skewnesses | 1 | 67641 | 2 | 1.263 | 53555.8 |
317 | 2024-12-12 20:34:54 | skewnesses | 3 | 140603 | 72 | 61.206 | 2297.2 |
316 | 2024-12-12 14:12:24 | skewnesses | 4 | 161450 | 484 | 59.486 | 2714.1 |
315 | 2024-12-12 12:25:46 | skewnesses | 3 | 140603 | 72 | 28.673 | 4903.7 |
314 | 2024-12-12 12:25:46 | skewnesses | 2 | 108824 | 6 | 15.470 | 7034.5 |
313 | 2024-12-12 12:22:19 | skewnesses | 1 | 67641 | 2 | 1.156 | 58513.0 |
312 | 2024-12-12 10:58:11 | skewnesses | 3 | 140603 | 72 | 33.363 | 4214.3 |
311 | 2024-12-12 09:20:26 | skewnesses | 4 | 161450 | 484 | 45.596 | 3540.9 |
310 | 2024-12-12 08:11:10 | skewnesses | 4 | 161450 | 484 | 56.520 | 2856.5 |
309 | 2024-12-12 06:44:29 | skewnesses | 4 | 161450 | 484 | 51.973 | 3106.4 |
308 | 2024-12-12 06:44:45 | skewnesses | 2 | 108824 | 6 | 18.813 | 5784.5 |
307 | 2024-12-12 06:44:36 | skewnesses | 3 | 140603 | 72 | 24.750 | 5680.9 |
306 | 2024-12-12 06:43:27 | skewnesses | 1 | 67641 | 2 | 2.563 | 26391.3 |
305 | 2024-12-04 11:51:42 | skewnesses | 1 | 67641 | 2 | 7.453 | 9075.7 |
304 | 2024-11-10 17:11:19 | skewnesses | 3 | 140603 | 72 | 25.676 | 5476.0 |
303 | 2024-11-10 17:11:21 | skewnesses | 2 | 108824 | 6 | 10.733 | 10139.2 |
302 | 2024-11-10 09:24:00 | skewnesses | 1 | 67641 | 2 | 1.153 | 58665.2 |
301 | 2024-11-07 08:35:44 | skewnesses | 3 | 140603 | 72 | 32.410 | 4338.3 |
300 | 2024-11-06 15:04:31 | skewnesses | 3 | 140603 | 72 | 31.030 | 4531.2 |
299 | 2024-11-05 10:12:22 | skewnesses | 3 | 140603 | 72 | 44.363 | 3169.4 |
298 | 2024-11-04 19:32:43 | skewnesses | 3 | 140603 | 72 | 34.566 | 4067.7 |
297 | 2024-11-04 19:32:46 | skewnesses | 2 | 108824 | 6 | 20.593 | 5284.5 |
296 | 2024-11-04 18:31:34 | skewnesses | 1 | 67641 | 2 | 1.106 | 61158.2 |
295 | 2024-10-13 13:25:02 | skewnesses | 1 | 67641 | 2 | 8.060 | 8392.2 |
294 | 2024-10-10 01:35:02 | skewnesses | 2 | 108824 | 6 | 15.593 | 6979.0 |
293 | 2024-10-10 01:32:08 | skewnesses | 1 | 67641 | 2 | 6.406 | 10559.0 |
292 | 2024-10-02 05:10:42 | skewnesses | 1 | 67641 | 2 | 8.000 | 8455.1 |
291 | 2024-09-07 17:51:47 | skewnesses | 3 | 140603 | 72 | 70.113 | 2005.4 |
290 | 2024-09-07 17:51:45 | skewnesses | 2 | 108824 | 6 | 34.423 | 3161.4 |
289 | 2024-09-07 17:50:08 | skewnesses | 1 | 67641 | 2 | 9.393 | 7201.2 |
288 | 2024-09-06 06:11:14 | skewnesses | 3 | 140603 | 72 | 68.073 | 2065.5 |
287 | 2024-09-05 07:43:04 | skewnesses | 3 | 140603 | 72 | 70.830 | 1985.1 |
286 | 2024-09-04 07:52:52 | skewnesses | 3 | 140603 | 72 | 69.486 | 2023.5 |
285 | 2024-09-02 02:07:04 | skewnesses | 1 | 67641 | 2 | 4.500 | 15031.3 |
284 | 2024-08-31 21:30:28 | skewnesses | 4 | 161450 | 484 | 115.943 | 1392.5 |
283 | 2024-08-31 21:30:28 | skewnesses | 3 | 140603 | 72 | 44.156 | 3184.2 |
282 | 2024-08-31 21:30:28 | skewnesses | 2 | 108824 | 6 | 11.953 | 9104.3 |
281 | 2024-08-31 21:26:17 | skewnesses | 1 | 67641 | 2 | 6.110 | 11070.5 |
280 | 2024-08-06 03:36:45 | skewnesses | 3 | 140603 | 72 | 22.750 | 6180.4 |
279 | 2024-08-04 01:09:50 | skewnesses | 4 | 161450 | 484 | 57.940 | 2786.5 |
278 | 2024-08-04 00:40:22 | skewnesses | 4 | 161450 | 484 | 49.000 | 3294.9 |
277 | 2024-08-04 00:40:18 | skewnesses | 2 | 108824 | 6 | 11.250 | 9673.2 |
276 | 2024-08-03 19:51:43 | skewnesses | 4 | 161450 | 484 | 26.630 | 6062.7 |
275 | 2024-08-03 17:17:52 | skewnesses | 4 | 161450 | 484 | 52.550 | 3072.3 |