History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"specificity"
| Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
| 286 | 2025-09-10 11:24:28 | specificity | 1 | 49908 | 1 | 0.796 | 62698.5 |
| 285 | 2025-09-04 04:59:20 | specificity | 1 | 49908 | 1 | 1.750 | 28518.9 |
| 284 | 2025-08-31 12:00:46 | specificity | 4 | 148819 | 30 | 9.126 | 16307.1 |
| 283 | 2025-08-30 22:16:40 | specificity | 5 | 164607 | 133 | 16.110 | 10217.7 |
| 282 | 2025-08-28 08:04:59 | specificity | 5 | 164607 | 133 | 13.513 | 12181.4 |
| 281 | 2025-08-27 19:10:04 | specificity | 1 | 49908 | 1 | 0.890 | 56076.4 |
| 280 | 2025-08-26 08:04:49 | specificity | 5 | 164607 | 133 | 72.113 | 2282.6 |
| 279 | 2025-08-25 21:11:27 | specificity | 5 | 164607 | 133 | 46.440 | 3544.5 |
| 278 | 2025-08-22 14:40:27 | specificity | 5 | 164607 | 133 | 55.143 | 2985.1 |
| 277 | 2025-08-22 04:51:36 | specificity | 3 | 121633 | 7 | 8.783 | 13848.7 |
| 276 | 2025-08-22 02:23:35 | specificity | 2 | 86808 | 1 | 10.890 | 7971.3 |
| 275 | 2025-08-21 19:38:59 | specificity | 4 | 148819 | 30 | 10.096 | 14740.4 |
| 274 | 2025-08-09 16:14:46 | specificity | 1 | 49908 | 1 | 4.686 | 10650.4 |
| 273 | 2025-07-24 12:11:49 | specificity | 1 | 49908 | 1 | 2.986 | 16714.0 |
| 272 | 2025-07-23 10:31:33 | specificity | 1 | 49908 | 1 | 5.750 | 8679.7 |
| 271 | 2025-07-08 15:31:48 | specificity | 1 | 49908 | 1 | 4.060 | 12292.6 |
| 270 | 2025-05-26 15:41:27 | specificity | 1 | 49908 | 1 | 3.876 | 12876.2 |
| 269 | 2025-05-07 23:36:28 | specificity | 1 | 49908 | 1 | 4.703 | 10611.9 |
| 268 | 2025-03-26 20:32:55 | specificity | 1 | 49908 | 1 | 3.796 | 13147.5 |
| 267 | 2025-03-23 10:04:44 | specificity | 1 | 49908 | 1 | 3.873 | 12886.1 |
| 266 | 2025-03-14 03:28:29 | specificity | 1 | 49908 | 1 | 4.110 | 12143.1 |
| 265 | 2025-03-02 20:18:51 | specificity | 4 | 148819 | 30 | 48.580 | 3063.4 |
| 264 | 2025-02-22 10:41:53 | specificity | 4 | 148819 | 30 | 48.143 | 3091.2 |
| 263 | 2025-02-22 05:36:19 | specificity | 1 | 49908 | 1 | 4.280 | 11660.7 |
| 262 | 2025-02-20 05:31:39 | specificity | 4 | 148819 | 30 | 61.540 | 2418.2 |
| 261 | 2025-02-18 11:08:11 | specificity | 4 | 148819 | 30 | 58.260 | 2554.4 |
| 260 | 2025-02-18 11:07:54 | specificity | 4 | 148819 | 30 | 53.103 | 2802.5 |
| 259 | 2025-02-18 11:08:03 | specificity | 2 | 86808 | 1 | 10.550 | 8228.2 |
| 258 | 2025-02-18 11:05:17 | specificity | 1 | 49908 | 1 | 2.046 | 24393.0 |
| 257 | 2025-02-15 06:58:13 | specificity | 3 | 121633 | 7 | 25.563 | 4758.2 |
| 256 | 2025-02-10 02:48:22 | specificity | 3 | 121633 | 7 | 30.156 | 4033.5 |
| 255 | 2025-02-10 02:48:13 | specificity | 2 | 86808 | 1 | 14.766 | 5878.9 |
| 254 | 2025-02-10 02:36:50 | specificity | 4 | 148819 | 30 | 65.053 | 2287.7 |
| 253 | 2025-02-10 01:52:04 | specificity | 1 | 49908 | 1 | 0.753 | 66278.9 |
| 252 | 2025-01-22 21:29:08 | specificity | 4 | 148819 | 30 | 45.173 | 3294.4 |
| 251 | 2025-01-20 16:27:51 | specificity | 4 | 148819 | 30 | 45.270 | 3287.4 |
| 250 | 2025-01-20 16:28:03 | specificity | 2 | 86808 | 1 | 13.970 | 6213.9 |
| 249 | 2025-01-20 16:27:21 | specificity | 4 | 148819 | 30 | 51.066 | 2914.2 |
| 248 | 2025-01-20 16:24:32 | specificity | 1 | 49908 | 1 | 2.483 | 20099.9 |
| 247 | 2025-01-18 15:26:23 | specificity | 4 | 148819 | 30 | 70.643 | 2106.6 |
| 246 | 2025-01-18 03:48:57 | specificity | 4 | 148819 | 30 | 48.956 | 3039.9 |
| 245 | 2025-01-18 03:46:27 | specificity | 3 | 121633 | 7 | 23.376 | 5203.3 |
| 244 | 2025-01-18 03:46:18 | specificity | 2 | 86808 | 1 | 8.843 | 9816.6 |
| 243 | 2025-01-18 03:46:06 | specificity | 1 | 49908 | 1 | 2.393 | 20855.8 |
| 242 | 2025-01-16 21:59:25 | specificity | 2 | 86808 | 1 | 13.296 | 6528.9 |
| 241 | 2025-01-16 21:56:04 | specificity | 1 | 49908 | 1 | 1.826 | 27331.9 |
| 240 | 2025-01-12 04:57:53 | specificity | 3 | 121633 | 7 | 29.953 | 4060.8 |
| 239 | 2025-01-05 12:15:34 | specificity | 3 | 121633 | 7 | 17.860 | 6810.4 |
| 238 | 2024-12-31 13:57:06 | specificity | 3 | 121633 | 7 | 38.440 | 3164.2 |
| 237 | 2024-12-31 13:57:20 | specificity | 2 | 86808 | 1 | 14.080 | 6165.3 |
| 236 | 2024-12-31 13:55:06 | specificity | 1 | 49908 | 1 | 4.703 | 10611.9 |
| 235 | 2024-12-12 15:57:38 | specificity | 3 | 121633 | 7 | 39.003 | 3118.6 |
| 234 | 2024-12-12 15:57:40 | specificity | 3 | 121633 | 7 | 29.483 | 4125.5 |
| 233 | 2024-12-12 15:57:36 | specificity | 3 | 121633 | 7 | 26.406 | 4606.3 |
| 232 | 2024-12-12 15:57:42 | specificity | 2 | 86808 | 1 | 11.486 | 7557.7 |
| 231 | 2024-12-12 15:55:34 | specificity | 1 | 49908 | 1 | 2.190 | 22789.0 |
| 230 | 2024-12-07 01:52:57 | specificity | 1 | 49908 | 1 | 6.080 | 8208.6 |
| 229 | 2024-12-07 01:52:05 | specificity | 1 | 49908 | 1 | 2.873 | 17371.4 |
| 228 | 2024-11-17 14:47:21 | specificity | 3 | 121633 | 7 | 36.956 | 3291.3 |
| 227 | 2024-11-13 16:10:33 | specificity | 1 | 49908 | 1 | 0.856 | 58303.7 |
| 226 | 2024-11-11 15:08:18 | specificity | 3 | 121633 | 7 | 36.226 | 3357.6 |
| 225 | 2024-11-06 02:07:50 | specificity | 1 | 49908 | 1 | 0.750 | 66544.0 |
| 224 | 2024-11-02 14:20:15 | specificity | 3 | 121633 | 7 | 29.296 | 4151.9 |
| 223 | 2024-10-29 08:57:35 | specificity | 2 | 86808 | 1 | 10.876 | 7981.6 |
| 222 | 2024-10-29 08:55:30 | specificity | 1 | 49908 | 1 | 2.233 | 22350.2 |
| 221 | 2024-10-20 02:27:29 | specificity | 3 | 121633 | 7 | 23.986 | 5071.0 |
| 220 | 2024-10-11 00:09:56 | specificity | 3 | 121633 | 7 | 35.470 | 3429.2 |
| 219 | 2024-10-10 17:17:15 | specificity | 3 | 121633 | 7 | 18.030 | 6746.1 |
| 218 | 2024-10-03 02:48:05 | specificity | 3 | 121633 | 7 | 45.876 | 2651.3 |
| 217 | 2024-10-03 02:47:02 | specificity | 3 | 121633 | 7 | 42.330 | 2873.4 |
| 216 | 2024-09-27 19:35:49 | specificity | 4 | 148819 | 30 | 76.966 | 1933.6 |
| 215 | 2024-09-27 19:35:52 | specificity | 3 | 121633 | 7 | 40.303 | 3018.0 |
| 214 | 2024-09-27 19:35:15 | specificity | 4 | 148819 | 30 | 74.230 | 2004.8 |
| 213 | 2024-09-27 19:35:52 | specificity | 2 | 86808 | 1 | 28.346 | 3062.4 |
| 212 | 2024-09-27 19:33:24 | specificity | 1 | 49908 | 1 | 2.330 | 21419.7 |
| 211 | 2024-09-04 10:24:02 | specificity | 1 | 49908 | 1 | 7.563 | 6599.0 |
| 210 | 2024-08-06 00:07:31 | specificity | 1 | 49908 | 1 | 3.143 | 15879.1 |
| 209 | 2024-07-27 14:14:14 | specificity | 1 | 49908 | 1 | 4.233 | 11790.2 |
| 208 | 2024-07-22 08:57:26 | specificity | 4 | 148819 | 30 | 46.876 | 3174.7 |
| 207 | 2024-07-16 12:08:01 | specificity | 4 | 148819 | 30 | 41.836 | 3557.2 |
| 206 | 2024-07-15 05:36:45 | specificity | 4 | 148819 | 30 | 47.986 | 3101.3 |
| 205 | 2024-07-15 03:32:37 | specificity | 4 | 148819 | 30 | 48.096 | 3094.2 |
| 204 | 2024-07-15 03:32:38 | specificity | 3 | 121633 | 7 | 22.360 | 5439.8 |
| 203 | 2024-07-15 03:32:41 | specificity | 2 | 86808 | 1 | 13.596 | 6384.8 |
| 202 | 2024-07-15 03:30:30 | specificity | 1 | 49908 | 1 | 2.373 | 21031.6 |
| 201 | 2024-07-12 04:52:03 | specificity | 3 | 121633 | 7 | 25.846 | 4706.1 |
| 200 | 2024-07-12 04:52:09 | specificity | 2 | 86808 | 1 | 9.030 | 9613.3 |
| 199 | 2024-07-12 04:42:47 | specificity | 1 | 49908 | 1 | 2.826 | 17660.3 |
| 198 | 2024-07-12 04:39:51 | specificity | 4 | 148819 | 30 | 27.350 | 5441.3 |
| 197 | 2024-07-10 23:25:00 | specificity | 1 | 49908 | 1 | 3.830 | 13030.8 |
| 196 | 2024-07-10 23:24:48 | specificity | 1 | 49908 | 1 | 1.440 | 34658.3 |
| 195 | 2024-07-09 06:09:49 | specificity | 4 | 148819 | 30 | 29.313 | 5076.9 |
| 194 | 2024-07-08 07:51:32 | specificity | 1 | 49908 | 1 | 4.640 | 10756.0 |
| 193 | 2024-07-05 05:10:08 | specificity | 4 | 148819 | 30 | 31.440 | 4733.4 |
| 192 | 2024-07-05 01:56:44 | specificity | 3 | 121633 | 7 | 18.436 | 6597.6 |
| 191 | 2024-07-04 14:16:58 | specificity | 3 | 121633 | 7 | 30.923 | 3933.4 |
| 190 | 2024-07-04 14:16:58 | specificity | 2 | 86808 | 1 | 9.126 | 9512.2 |
| 189 | 2024-07-04 14:10:38 | specificity | 1 | 49908 | 1 | 1.296 | 38509.3 |
| 188 | 2024-07-04 03:31:49 | specificity | 4 | 148819 | 30 | 43.876 | 3391.8 |
| 187 | 2024-07-03 21:38:19 | specificity | 4 | 148819 | 30 | 58.533 | 2542.5 |