History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"cohort"
Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
695 | 2024-03-31 22:29:50 | cohort | 1 | 48755 | 3 | 2.763 | 17645.7 |
694 | 2024-03-03 20:46:23 | cohort | 2 | 82551 | 32 | 2.250 | 36689.3 |
693 | 2024-03-03 20:45:50 | cohort | 3 | 112716 | 391 | 5.766 | 19548.4 |
692 | 2024-02-28 11:00:22 | cohort | 3 | 112716 | 391 | 11.046 | 10204.2 |
691 | 2024-02-27 09:09:50 | cohort | 3 | 112716 | 391 | 6.546 | 17219.1 |
690 | 2024-02-27 09:09:47 | cohort | 2 | 82551 | 32 | 2.970 | 27794.9 |
689 | 2024-02-26 15:20:20 | cohort | 3 | 112716 | 391 | 5.843 | 19290.8 |
688 | 2024-02-26 15:20:21 | cohort | 2 | 82551 | 32 | 2.640 | 31269.3 |
687 | 2024-02-26 15:20:17 | cohort | 1 | 48755 | 3 | 0.810 | 60191.4 |
686 | 2024-02-20 20:26:53 | cohort | 1 | 48755 | 3 | 0.780 | 62506.4 |
685 | 2024-01-27 13:09:29 | cohort | 1 | 48755 | 3 | 0.813 | 59969.2 |
684 | 2024-01-18 02:29:40 | cohort | 1 | 48755 | 3 | 0.843 | 57835.1 |
683 | 2023-12-05 20:39:04 | cohort | 3 | 112716 | 391 | 5.390 | 20912.1 |
682 | 2023-12-05 20:39:01 | cohort | 2 | 82551 | 32 | 2.406 | 34310.5 |
681 | 2023-12-05 01:02:40 | cohort | 1 | 48755 | 3 | 0.780 | 62506.4 |
680 | 2023-11-21 16:53:26 | cohort | 2 | 82551 | 32 | 2.203 | 37472.1 |
679 | 2023-11-21 16:53:20 | cohort | 3 | 112716 | 391 | 4.876 | 23116.5 |
678 | 2023-11-19 04:33:15 | cohort | 1 | 48755 | 3 | 0.906 | 53813.5 |
677 | 2023-11-09 12:10:19 | cohort | 1 | 48755 | 3 | 0.860 | 56691.9 |
676 | 2023-11-01 23:35:20 | cohort | 3 | 112716 | 391 | 5.703 | 19764.3 |
675 | 2023-11-01 23:35:16 | cohort | 2 | 82551 | 32 | 4.483 | 18414.2 |
674 | 2023-10-28 03:10:04 | cohort | 1 | 48755 | 3 | 0.766 | 63648.8 |
673 | 2023-10-20 21:51:50 | cohort | 1 | 48755 | 3 | 0.766 | 63648.8 |
672 | 2023-10-18 01:58:41 | cohort | 1 | 48755 | 3 | 0.843 | 57835.1 |
671 | 2023-10-18 01:53:34 | cohort | 2 | 82551 | 32 | 2.890 | 28564.4 |
670 | 2023-10-17 06:57:56 | cohort | 3 | 112716 | 391 | 12.643 | 8915.3 |
669 | 2023-10-16 01:30:08 | cohort | 3 | 112716 | 391 | 5.000 | 22543.2 |
668 | 2023-10-16 01:30:07 | cohort | 2 | 82551 | 32 | 2.343 | 35233.0 |
667 | 2023-10-14 08:02:33 | cohort | 1 | 48755 | 3 | 0.873 | 55847.7 |
666 | 2023-10-10 23:32:33 | cohort | 1 | 48755 | 3 | 0.766 | 63648.8 |
665 | 2023-10-04 13:44:47 | cohort | 1 | 48755 | 3 | 0.826 | 59025.4 |
664 | 2023-09-25 05:21:43 | cohort | 3 | 112716 | 391 | 5.983 | 18839.4 |
663 | 2023-09-25 05:21:42 | cohort | 2 | 82551 | 32 | 3.670 | 22493.5 |
662 | 2023-09-19 14:53:58 | cohort | 1 | 48755 | 3 | 0.796 | 61250.0 |
661 | 2023-09-19 13:22:41 | cohort | 1 | 48755 | 3 | 0.763 | 63899.1 |
660 | 2023-09-12 07:02:29 | cohort | 1 | 48755 | 3 | 0.890 | 54780.9 |
659 | 2023-07-12 14:26:32 | cohort | 1 | 48755 | 3 | 0.766 | 63648.8 |
658 | 2023-07-08 13:39:43 | cohort | 1 | 48755 | 3 | 0.843 | 57835.1 |
657 | 2023-04-20 02:29:09 | cohort | 1 | 48755 | 3 | 0.763 | 63899.1 |
656 | 2023-04-04 19:11:26 | cohort | 1 | 48755 | 3 | 0.830 | 58741.0 |
655 | 2023-02-07 16:09:29 | cohort | 1 | 48755 | 3 | 0.750 | 65006.7 |
654 | 2023-01-17 08:50:35 | cohort | 1 | 48755 | 3 | 0.860 | 56691.9 |
653 | 2022-12-26 20:56:38 | cohort | 1 | 48755 | 3 | 0.733 | 66514.3 |
652 | 2022-12-06 00:35:14 | cohort | 1 | 48755 | 3 | 0.750 | 65006.7 |
651 | 2022-09-12 12:52:16 | cohort | 1 | 48755 | 3 | 0.783 | 62266.9 |
650 | 2022-08-02 05:47:40 | cohort | 1 | 48755 | 3 | 0.890 | 54780.9 |
649 | 2022-05-30 18:40:18 | cohort | 1 | 48755 | 3 | 0.796 | 61250.0 |
648 | 2022-03-25 10:16:58 | cohort | 1 | 48755 | 3 | 0.826 | 59025.4 |
647 | 2022-02-16 04:54:22 | cohort | 1 | 48755 | 3 | 0.750 | 65006.7 |
646 | 2021-12-31 07:30:00 | cohort | 1 | 48755 | 3 | 0.876 | 55656.4 |
645 | 2021-11-19 08:35:33 | cohort | 1 | 48755 | 3 | 1.310 | 37217.6 |
644 | 2021-11-14 21:01:15 | cohort | 1 | 48755 | 3 | 0.763 | 63899.1 |
643 | 2021-09-28 03:49:35 | cohort | 1 | 48755 | 3 | 0.813 | 59969.2 |
642 | 2021-09-23 15:19:19 | cohort | 1 | 48755 | 3 | 0.903 | 53992.2 |
641 | 2021-08-15 08:03:43 | cohort | 1 | 48755 | 3 | 0.826 | 59025.4 |
640 | 2021-06-23 13:51:25 | cohort | 1 | 48755 | 3 | 1.733 | 28133.3 |
639 | 2021-05-14 16:21:43 | cohort | 1 | 48755 | 3 | 0.873 | 55847.7 |
638 | 2021-05-08 10:27:03 | cohort | 1 | 48755 | 3 | 0.983 | 49598.2 |
637 | 2021-05-06 07:19:48 | cohort | 1 | 48755 | 3 | 1.753 | 27812.3 |
636 | 2021-03-18 03:18:48 | cohort | 3 | 112716 | 391 | 4.766 | 23650.0 |
635 | 2021-02-11 16:07:54 | cohort | 1 | 48755 | 3 | 0.903 | 53992.2 |
634 | 2021-02-06 18:21:00 | cohort | 1 | 48755 | 3 | 0.843 | 57835.1 |
633 | 2021-01-28 00:03:57 | cohort | 3 | 112716 | 391 | 5.470 | 20606.2 |
632 | 2021-01-19 19:39:29 | cohort | 1 | 48755 | 3 | 0.763 | 63899.1 |
631 | 2020-12-20 10:32:07 | cohort | 1 | 48755 | 3 | 0.953 | 51159.5 |
630 | 2020-11-15 05:33:28 | cohort | 1 | 48755 | 3 | 0.873 | 55847.7 |
629 | 2020-11-08 11:59:16 | cohort | 3 | 112716 | 391 | 5.283 | 21335.6 |
628 | 2020-11-04 18:00:42 | cohort | 1 | 48755 | 3 | 0.983 | 49598.2 |
627 | 2020-11-01 05:20:01 | cohort | 1 | 48755 | 3 | 0.860 | 56691.9 |
626 | 2020-10-20 15:32:17 | cohort | 1 | 48755 | 3 | 0.830 | 58741.0 |
625 | 2020-10-06 08:17:45 | cohort | 1 | 48755 | 3 | 0.860 | 56691.9 |
624 | 2020-10-04 20:51:17 | cohort | 1 | 48755 | 3 | 0.733 | 66514.3 |
623 | 2020-10-03 09:46:03 | cohort | 1 | 48755 | 3 | 0.766 | 63648.8 |
622 | 2020-09-13 20:32:25 | cohort | 1 | 48755 | 3 | 0.763 | 63899.1 |
621 | 2020-08-13 06:30:41 | cohort | 1 | 48755 | 3 | 0.783 | 62266.9 |
620 | 2020-08-01 01:52:02 | cohort | 1 | 48755 | 3 | 0.873 | 55847.7 |
619 | 2020-07-31 18:47:36 | cohort | 1 | 48755 | 3 | 0.826 | 59025.4 |
618 | 2020-07-28 20:18:06 | cohort | 1 | 48755 | 3 | 0.830 | 58741.0 |
617 | 2020-04-29 14:35:13 | cohort | 1 | 48755 | 3 | 0.830 | 58741.0 |
616 | 2020-03-26 08:02:58 | cohort | 1 | 48755 | 3 | 0.936 | 52088.7 |
615 | 2020-03-25 03:53:33 | cohort | 1 | 48755 | 3 | 0.906 | 53813.5 |
614 | 2020-03-24 05:09:26 | cohort | 1 | 48755 | 3 | 0.860 | 56691.9 |
613 | 2020-03-22 23:58:49 | cohort | 1 | 48755 | 3 | 0.750 | 65006.7 |
612 | 2020-03-22 04:49:51 | cohort | 1 | 48755 | 3 | 0.843 | 57835.1 |
611 | 2020-03-22 00:19:20 | cohort | 1 | 48755 | 3 | 0.890 | 54780.9 |
610 | 2020-03-18 17:43:09 | cohort | 1 | 48755 | 3 | 0.780 | 62506.4 |
609 | 2020-03-03 00:56:16 | cohort | 1 | 48755 | 3 | 0.876 | 55656.4 |
608 | 2020-03-02 22:55:27 | cohort | 1 | 48755 | 3 | 0.750 | 65006.7 |
607 | 2020-02-24 08:45:35 | cohort | 1 | 48755 | 3 | 1.830 | 26642.1 |
606 | 2020-02-23 01:30:21 | cohort | 1 | 48755 | 3 | 1.906 | 25579.7 |
605 | 2020-02-18 21:37:10 | cohort | 1 | 48755 | 3 | 2.343 | 20808.8 |
604 | 2020-02-11 20:41:13 | cohort | 1 | 48755 | 3 | 0.953 | 51159.5 |
603 | 2020-02-04 05:18:10 | cohort | 1 | 48755 | 3 | 0.983 | 49598.2 |
602 | 2020-01-31 15:03:33 | cohort | 1 | 48755 | 3 | 2.576 | 18926.6 |
601 | 2020-01-31 06:59:14 | cohort | 1 | 48755 | 3 | 0.890 | 54780.9 |
600 | 2020-01-31 00:00:34 | cohort | 1 | 48755 | 3 | 0.906 | 53813.5 |
599 | 2020-01-28 17:11:33 | cohort | 1 | 48755 | 3 | 0.750 | 65006.7 |
598 | 2020-01-26 11:17:53 | cohort | 1 | 48755 | 3 | 0.953 | 51159.5 |
597 | 2020-01-25 18:21:48 | cohort | 1 | 48755 | 3 | 0.983 | 49598.2 |
596 | 2020-01-25 15:28:06 | cohort | 1 | 48755 | 3 | 0.873 | 55847.7 |