History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"sparsities"
Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
382 | 2025-07-10 06:44:06 | sparsities | 3 | 140603 | 38 | 28.626 | 4911.7 |
381 | 2025-07-08 13:29:36 | sparsities | 3 | 140603 | 38 | 43.940 | 3199.9 |
380 | 2025-07-08 09:19:21 | sparsities | 3 | 140603 | 38 | 33.940 | 4142.7 |
379 | 2025-07-08 00:13:46 | sparsities | 2 | 108824 | 4 | 16.576 | 6565.2 |
378 | 2025-07-07 16:21:24 | sparsities | 1 | 67641 | 1 | 5.250 | 12884.0 |
377 | 2025-07-05 16:33:40 | sparsities | 1 | 67641 | 1 | 5.923 | 11420.1 |
376 | 2025-07-03 08:27:20 | sparsities | 1 | 67641 | 1 | 5.470 | 12365.8 |
375 | 2025-07-02 14:46:37 | sparsities | 1 | 67641 | 1 | 4.893 | 13824.0 |
374 | 2025-06-30 01:29:05 | sparsities | 1 | 67641 | 1 | 2.720 | 24868.0 |
373 | 2025-06-26 06:34:53 | sparsities | 1 | 67641 | 1 | 5.546 | 12196.4 |
372 | 2025-06-23 18:45:50 | sparsities | 1 | 67641 | 1 | 1.233 | 54858.9 |
371 | 2025-06-14 19:26:14 | sparsities | 1 | 67641 | 1 | 5.826 | 11610.2 |
370 | 2025-06-08 15:21:18 | sparsities | 1 | 67641 | 1 | 6.390 | 10585.4 |
369 | 2025-06-06 15:53:01 | sparsities | 1 | 67641 | 1 | 5.733 | 11798.5 |
368 | 2025-05-22 06:50:45 | sparsities | 1 | 67641 | 1 | 2.860 | 23650.7 |
367 | 2025-05-20 11:05:38 | sparsities | 3 | 140603 | 38 | 36.910 | 3809.3 |
366 | 2025-05-18 12:54:18 | sparsities | 2 | 108824 | 4 | 16.080 | 6767.7 |
365 | 2025-05-18 04:32:07 | sparsities | 3 | 140603 | 38 | 43.606 | 3224.4 |
364 | 2025-05-17 14:55:16 | sparsities | 1 | 67641 | 1 | 1.830 | 36962.3 |
363 | 2025-05-17 08:29:44 | sparsities | 1 | 67641 | 1 | 1.156 | 58513.0 |
362 | 2025-05-16 01:38:36 | sparsities | 1 | 67641 | 1 | 3.736 | 18105.2 |
361 | 2025-05-15 13:51:41 | sparsities | 3 | 140603 | 38 | 34.080 | 4125.7 |
360 | 2025-05-15 06:01:04 | sparsities | 1 | 67641 | 1 | 1.220 | 55443.4 |
359 | 2025-05-13 21:16:02 | sparsities | 1 | 67641 | 1 | 5.296 | 12772.1 |
358 | 2025-05-12 09:24:12 | sparsities | 3 | 140603 | 38 | 24.810 | 5667.2 |
357 | 2025-05-12 02:48:05 | sparsities | 3 | 140603 | 38 | 29.953 | 4694.1 |
356 | 2025-05-11 23:36:12 | sparsities | 3 | 140603 | 38 | 38.826 | 3621.4 |
355 | 2025-05-10 02:05:12 | sparsities | 2 | 108824 | 4 | 11.516 | 9449.8 |
354 | 2025-05-09 20:33:33 | sparsities | 3 | 140603 | 38 | 33.173 | 4238.5 |
353 | 2025-05-09 15:08:33 | sparsities | 1 | 67641 | 1 | 1.203 | 56226.9 |
352 | 2025-05-09 13:44:10 | sparsities | 1 | 67641 | 1 | 5.546 | 12196.4 |
351 | 2025-05-03 14:44:10 | sparsities | 1 | 67641 | 1 | 1.170 | 57812.8 |
350 | 2025-05-02 15:35:47 | sparsities | 1 | 67641 | 1 | 5.830 | 11602.2 |
349 | 2025-05-02 09:13:31 | sparsities | 4 | 161450 | 367 | 66.863 | 2414.6 |
348 | 2025-05-02 07:14:27 | sparsities | 4 | 161450 | 367 | 66.206 | 2438.6 |
347 | 2025-04-30 16:32:47 | sparsities | 4 | 161450 | 367 | 54.033 | 2988.0 |
346 | 2025-04-29 23:31:45 | sparsities | 4 | 161450 | 367 | 66.550 | 2426.0 |
345 | 2025-04-27 18:16:43 | sparsities | 4 | 161450 | 367 | 10.860 | 14866.5 |
344 | 2025-04-25 21:33:23 | sparsities | 3 | 140603 | 38 | 25.720 | 5466.7 |
343 | 2025-04-22 02:45:42 | sparsities | 1 | 67641 | 1 | 1.250 | 54112.8 |
342 | 2025-04-21 06:38:46 | sparsities | 1 | 67641 | 1 | 4.610 | 14672.7 |
341 | 2025-04-18 06:15:36 | sparsities | 1 | 67641 | 1 | 2.530 | 26735.6 |
340 | 2025-04-18 02:21:59 | sparsities | 3 | 140603 | 38 | 32.673 | 4303.3 |
339 | 2025-04-09 11:58:50 | sparsities | 3 | 140603 | 38 | 24.640 | 5706.3 |
338 | 2025-04-08 23:52:15 | sparsities | 2 | 108824 | 4 | 12.190 | 8927.3 |
337 | 2025-04-06 03:14:35 | sparsities | 4 | 161450 | 367 | 31.316 | 5155.5 |
336 | 2025-04-04 09:09:32 | sparsities | 4 | 161450 | 367 | 63.736 | 2533.1 |
335 | 2025-03-30 07:20:06 | sparsities | 3 | 140603 | 38 | 5.560 | 25288.3 |
334 | 2025-03-29 19:06:37 | sparsities | 4 | 161450 | 367 | 53.516 | 3016.9 |
333 | 2025-03-29 11:08:55 | sparsities | 4 | 161450 | 367 | 51.676 | 3124.3 |
332 | 2025-03-28 02:50:32 | sparsities | 2 | 108824 | 4 | 6.080 | 17898.7 |
331 | 2025-03-27 23:50:33 | sparsities | 4 | 161450 | 367 | 68.380 | 2361.1 |
330 | 2025-03-27 21:34:39 | sparsities | 3 | 140603 | 38 | 6.846 | 20538.0 |
329 | 2025-03-26 15:54:07 | sparsities | 1 | 67641 | 1 | 4.500 | 15031.3 |
328 | 2025-03-26 00:58:19 | sparsities | 1 | 67641 | 1 | 5.016 | 13485.0 |
327 | 2025-03-08 10:27:17 | sparsities | 1 | 67641 | 1 | 5.873 | 11517.3 |
326 | 2025-03-05 14:41:20 | sparsities | 3 | 140603 | 38 | 31.330 | 4487.8 |
325 | 2025-03-04 03:42:48 | sparsities | 3 | 140603 | 38 | 29.720 | 4730.9 |
324 | 2025-03-04 03:42:49 | sparsities | 3 | 140603 | 38 | 26.470 | 5311.8 |
323 | 2025-03-04 03:42:46 | sparsities | 2 | 108824 | 4 | 10.736 | 10136.4 |
322 | 2025-03-02 05:30:09 | sparsities | 3 | 140603 | 38 | 43.160 | 3257.7 |
321 | 2025-02-28 16:42:08 | sparsities | 3 | 140603 | 38 | 35.686 | 3940.0 |
320 | 2025-02-25 04:56:51 | sparsities | 3 | 140603 | 38 | 28.506 | 4932.4 |
319 | 2025-02-25 04:56:47 | sparsities | 2 | 108824 | 4 | 12.453 | 8738.8 |
318 | 2025-02-25 04:56:07 | sparsities | 3 | 140603 | 38 | 41.720 | 3370.2 |
317 | 2025-02-25 04:55:36 | sparsities | 1 | 67641 | 1 | 5.843 | 11576.4 |
316 | 2025-02-19 01:29:51 | sparsities | 1 | 67641 | 1 | 5.533 | 12225.0 |
315 | 2025-02-11 04:22:09 | sparsities | 1 | 67641 | 1 | 4.546 | 14879.2 |
314 | 2025-01-27 01:59:41 | sparsities | 1 | 67641 | 1 | 3.500 | 19326.0 |
313 | 2025-01-25 10:35:57 | sparsities | 1 | 67641 | 1 | 3.000 | 22547.0 |
312 | 2025-01-19 19:28:33 | sparsities | 3 | 140603 | 38 | 30.780 | 4568.0 |
311 | 2025-01-18 09:57:02 | sparsities | 3 | 140603 | 38 | 50.940 | 2760.2 |
310 | 2025-01-16 14:51:51 | sparsities | 3 | 140603 | 38 | 20.593 | 6827.7 |
309 | 2025-01-16 14:50:57 | sparsities | 3 | 140603 | 38 | 29.750 | 4726.2 |
308 | 2025-01-16 14:49:05 | sparsities | 1 | 67641 | 1 | 3.343 | 20233.6 |
307 | 2024-12-26 20:56:26 | sparsities | 3 | 140603 | 38 | 33.190 | 4236.3 |
306 | 2024-12-20 01:11:18 | sparsities | 3 | 140603 | 38 | 40.813 | 3445.1 |
305 | 2024-12-20 01:11:13 | sparsities | 3 | 140603 | 38 | 32.770 | 4290.6 |
304 | 2024-12-20 01:11:15 | sparsities | 2 | 108824 | 4 | 8.126 | 13392.1 |
303 | 2024-12-19 22:34:37 | sparsities | 1 | 67641 | 1 | 8.580 | 7883.6 |
302 | 2024-12-10 09:08:11 | sparsities | 1 | 67641 | 1 | 7.236 | 9347.8 |
301 | 2024-12-10 08:25:45 | sparsities | 1 | 67641 | 1 | 5.580 | 12122.0 |
300 | 2024-12-02 10:15:18 | sparsities | 3 | 140603 | 38 | 50.736 | 2771.3 |
299 | 2024-12-02 10:15:18 | sparsities | 2 | 108824 | 4 | 13.516 | 8051.5 |
298 | 2024-11-30 20:36:51 | sparsities | 3 | 140603 | 38 | 17.283 | 8135.3 |
297 | 2024-11-30 20:36:57 | sparsities | 2 | 108824 | 4 | 10.423 | 10440.8 |
296 | 2024-11-30 20:32:11 | sparsities | 1 | 67641 | 1 | 3.470 | 19493.1 |
295 | 2024-10-25 11:23:41 | sparsities | 1 | 67641 | 1 | 7.610 | 8888.4 |
294 | 2024-10-24 15:37:49 | sparsities | 1 | 67641 | 1 | 3.563 | 18984.3 |
293 | 2024-10-22 23:28:19 | sparsities | 1 | 67641 | 1 | 3.456 | 19572.0 |
292 | 2024-10-12 23:12:33 | sparsities | 1 | 67641 | 1 | 8.596 | 7868.9 |
291 | 2024-10-12 23:06:52 | sparsities | 3 | 140603 | 38 | 31.486 | 4465.6 |
290 | 2024-10-11 19:33:39 | sparsities | 4 | 161450 | 367 | 34.266 | 4711.7 |
289 | 2024-10-11 03:38:19 | sparsities | 4 | 161450 | 367 | 50.020 | 3227.7 |
288 | 2024-10-10 12:00:19 | sparsities | 3 | 140603 | 38 | 16.983 | 8279.0 |
287 | 2024-10-10 10:42:06 | sparsities | 4 | 161450 | 367 | 57.316 | 2816.8 |
286 | 2024-10-10 01:27:23 | sparsities | 4 | 161450 | 367 | 68.956 | 2341.3 |
285 | 2024-10-10 01:27:22 | sparsities | 3 | 140603 | 38 | 28.953 | 4856.2 |
284 | 2024-10-10 01:27:23 | sparsities | 2 | 108824 | 4 | 8.966 | 12137.4 |
283 | 2024-09-21 02:36:47 | sparsities | 1 | 67641 | 1 | 8.283 | 8166.2 |