Meeting Title: MatterMore | internal Standup Date: 2025-06-24 Meeting participants: Amber Lin, Luke Daque, Annie Yu
WEBVTT
1 00:01:34.930 ⇒ 00:01:35.930 Luke Daque: Hi amber.
2 00:01:37.290 ⇒ 00:01:39.280 Amber Lin: Hi! How are you?
3 00:01:40.110 ⇒ 00:01:41.200 Luke Daque: Doing well.
4 00:01:41.700 ⇒ 00:01:42.295 Amber Lin: Yeah.
5 00:01:44.340 ⇒ 00:01:51.399 Luke Daque: We we have a I’m not sure if a wish will be joining, because we also have a call with Meta plane. At the same time.
6 00:01:51.400 ⇒ 00:01:58.119 Amber Lin: Oh, I see, it’s okay. Yeah. I don’t think there’s much away she needs to do on
7 00:01:58.260 ⇒ 00:02:07.449 Amber Lin: this one, I think. Mostly I just want to check on the spike ticket that we had. Do we have any updates on that one.
8 00:02:09.619 ⇒ 00:02:16.649 Luke Daque: I did a little bit of research. Maybe we can try it, but still not what do you call this?
9 00:02:17.160 ⇒ 00:02:20.940 Luke Daque: It’s not going to be accurate or anything, but it’s like
10 00:02:21.950 ⇒ 00:02:26.910 Luke Daque: we might be able to get it from a different endpoint, which is the
11 00:02:28.030 ⇒ 00:02:33.600 Luke Daque: forgot what it was. But yeah, I think there were like 3 other endpoints where we can potentially use the
12 00:02:34.805 ⇒ 00:02:38.500 Luke Daque: signed in for onedrive. For example.
13 00:02:39.190 ⇒ 00:02:41.219 Luke Daque: And then we can use that, for.
14 00:02:42.140 ⇒ 00:02:46.780 Luke Daque: as you know, as as like the count of tool usage like sign in.
15 00:02:48.840 ⇒ 00:02:50.670 Luke Daque: But yeah, I would. I wouldn’t
16 00:02:50.800 ⇒ 00:02:56.030 Luke Daque: know if that’s going to be accurate, because sometimes we only sign in once, right? Like, if you.
17 00:02:56.610 ⇒ 00:02:57.489 Luke Daque: Think about signing.
18 00:02:57.490 ⇒ 00:03:01.559 Luke Daque: Yeah, like they sign out every day or something.
19 00:03:02.600 ⇒ 00:03:05.180 Luke Daque: You know, there’s definitely limitations to that.
20 00:03:05.710 ⇒ 00:03:06.350 Amber Lin: I see
21 00:03:08.210 ⇒ 00:03:08.840 Luke Daque: Yeah.
22 00:03:08.840 ⇒ 00:03:10.730 Amber Lin: Were you able to?
23 00:03:10.900 ⇒ 00:03:25.789 Amber Lin: I mean, my answers only came from Chat Gpt. So it it says, like the few options that says it’s it still requires us to do estimates. But you can. I guess we can
24 00:03:26.040 ⇒ 00:03:35.897 Amber Lin: infer it from the file level activities or infer it from the Microsoft 365 audit log, or
25 00:03:37.560 ⇒ 00:03:39.949 Luke Daque: Graph, the graph Api and plus some.
26 00:03:39.950 ⇒ 00:03:40.730 Amber Lin: Microfactor.
27 00:03:40.730 ⇒ 00:03:42.330 Amber Lin: And yeah.
28 00:03:42.330 ⇒ 00:03:51.679 Luke Daque: Yeah, it’s still in. Yeah, it’s it’s not going to be that accurate. But I guess we can use something like that. Oh, that’s also like what I
29 00:03:52.542 ⇒ 00:03:56.869 Luke Daque: what I saw from the audit log, onedrive audit logs, or like something.
30 00:03:59.130 ⇒ 00:04:02.909 Luke Daque: Yeah, it’s not really just for onedrive. It’s like audit logs for
31 00:04:04.123 ⇒ 00:04:10.309 Luke Daque: stuff. But like onedrive is like it looks like it’s 1 of the parameters there, or like
32 00:04:12.430 ⇒ 00:04:16.519 Luke Daque: events. I would say it’s it’s not really an event. But yeah, something like that.
33 00:04:16.529 ⇒ 00:04:17.349 Amber Lin: Hung.
34 00:04:18.209 ⇒ 00:04:21.699 Luke Daque: But yeah, we can try to infer stuff like
35 00:04:22.349 ⇒ 00:04:26.109 Luke Daque: file access or file, modified or download.
36 00:04:26.110 ⇒ 00:04:26.940 Amber Lin: Yeah.
37 00:04:27.800 ⇒ 00:04:33.120 Luke Daque: Yeah, we can. We can try to infer from those events.
38 00:04:34.910 ⇒ 00:04:36.150 Amber Lin: I see.
39 00:04:37.320 ⇒ 00:04:39.050 Amber Lin: Hmm! Hmm!
40 00:04:39.790 ⇒ 00:04:41.240 Luke Daque: But yeah, I can try to.
41 00:04:41.550 ⇒ 00:04:44.601 Luke Daque: I haven’t started yet, but I can try to
42 00:04:45.470 ⇒ 00:04:49.510 Luke Daque: create one and see how that how that works.
43 00:04:49.740 ⇒ 00:04:55.130 Amber Lin: Oh, sure! How do you plan to create that? What route are you planning on taking.
44 00:04:55.490 ⇒ 00:04:59.709 Luke Daque: Still going to be the the same. I’m just gonna create that table
45 00:05:00.432 ⇒ 00:05:05.090 Luke Daque: synthetic data based on the Api, the audit log api endpoint. And then.
46 00:05:05.090 ⇒ 00:05:05.770 Amber Lin: Okay.
47 00:05:06.790 ⇒ 00:05:10.710 Luke Daque: Yeah, like, we use the activity display name.
48 00:05:11.697 ⇒ 00:05:18.059 Luke Daque: like, file access or file, downloaded or file modified. Then that’s like we’ll be inferring us.
49 00:05:18.980 ⇒ 00:05:21.459 Luke Daque: Usage. And okay, and it’s.
50 00:05:21.460 ⇒ 00:05:30.585 Amber Lin: Yeah, I guess we could infer that as long as these activities occur within say like a 10 min timeframe, that they haven’t switched tools.
51 00:05:31.200 ⇒ 00:05:35.100 Amber Lin: I guess that’s that could be like a good inference.
52 00:05:35.100 ⇒ 00:05:38.470 Luke Daque: I guess I don’t know if we can do it by
53 00:05:39.350 ⇒ 00:05:47.060 Luke Daque: like there’s no start and end date like or time. We won’t be able to know, like they used it for 10 min or something. We just know that. Yeah.
54 00:05:47.060 ⇒ 00:05:48.240 Amber Lin: Oh, and yeah.
55 00:05:48.240 ⇒ 00:06:07.059 Amber Lin: yeah, yeah, I was thinking of. Say, we got 2. We got 3 logs right? We have fall access at 1030 fall modified at 1035. Then we know that between between those 2 events the user was probably still using onedrive. But say, between
56 00:06:07.430 ⇒ 00:06:18.999 Amber Lin: file file modified and say, file renamed. It was like 24 h. Then we can say, probably between those 24 h, you, the user probably wasn’t using the file anymore.
57 00:06:19.000 ⇒ 00:06:20.829 Luke Daque: I know. Yeah, I don’t know if that’s
58 00:06:21.670 ⇒ 00:06:25.499 Luke Daque: I. I mean, that works in theory. But I don’t know if that’s like
59 00:06:26.210 ⇒ 00:06:32.820 Luke Daque: works in actual like, like if someone access the file but did not modify anything, did not rename anything. It’s still you
60 00:06:32.820 ⇒ 00:06:34.860 Luke Daque: usage right? Because they still access the file.
61 00:06:35.325 ⇒ 00:06:36.255 Amber Lin: That’s correct
62 00:06:36.720 ⇒ 00:06:42.610 Luke Daque: And so do we really need a time spent for.
63 00:06:43.070 ⇒ 00:06:43.660 Luke Daque: This.
64 00:06:44.670 ⇒ 00:07:12.699 Amber Lin: I do think so because they want to see how how long people are spending in different times, right? And they want to look at focus time, and we need to come up with an inference with this. But I guess for for now let me go talk with Trevor and Matthew. Can you work on getting the audit log? Synthetic data set so that when I get the requirements from them, we’re ready to make that inference, and make it happen.
65 00:07:13.160 ⇒ 00:07:13.870 Luke Daque: Yeah, sure.
66 00:07:13.870 ⇒ 00:07:14.816 Amber Lin: Okay. Great.
67 00:07:15.290 ⇒ 00:07:20.880 Luke Daque: I can. I’ll do that. Yeah, yeah, awesome. We can. Okay.
68 00:07:20.880 ⇒ 00:07:21.709 Luke Daque: From there.
69 00:07:21.710 ⇒ 00:07:29.359 Amber Lin: Okay, let me go find a time with Ari or Trevor, and then I’ll also talk with a wish. And
70 00:07:29.500 ⇒ 00:07:34.270 Amber Lin: like, I think tomorrow. Hopefully, I’ll get a plan from them on how to move forward.
71 00:07:35.110 ⇒ 00:07:37.800 Amber Lin: Sounds good. Yeah, I’ll do that. Then I’ll
72 00:07:38.960 ⇒ 00:07:44.739 Luke Daque: Work on the creating the synthetic data for the audit logs, directory, audit logs, and then.
73 00:07:44.740 ⇒ 00:07:45.330 Amber Lin: Okay.
74 00:07:45.650 ⇒ 00:07:46.960 Luke Daque: We can go from there.
75 00:07:46.960 ⇒ 00:07:53.030 Amber Lin: Okay. Awesome any any anything that you’re stuck that you’re stuck on. I know you’re
76 00:07:53.140 ⇒ 00:07:55.679 Amber Lin: mostly done with the visualizations.
77 00:07:57.450 ⇒ 00:08:05.340 Annie Yu: Yeah, I I guess. Just let me know what to update. Regarding the last chart, the the date.
78 00:08:06.260 ⇒ 00:08:08.980 Annie Yu: the date field is that is that done?
79 00:08:09.490 ⇒ 00:08:10.650 Amber Lin: I believe so.
80 00:08:11.350 ⇒ 00:08:21.339 Luke Daque: Yeah, I already added the date. It should be like, Start date. And then field name is Start date. It’s basically just the start time, but without the time it’s just date.
81 00:08:21.790 ⇒ 00:08:23.968 Luke Daque: Got it? Got it, by the way,
82 00:08:24.740 ⇒ 00:08:26.869 Luke Daque: Yeah. Yeah. Sure. Go hand in hand.
83 00:08:26.870 ⇒ 00:08:32.940 Annie Yu: No, I’m just gonna say, I’ll swap the X-axis of that chart, using that.
84 00:08:32.940 ⇒ 00:08:35.550 Luke Daque: Cool, cool sounds good.
85 00:08:35.780 ⇒ 00:08:41.010 Luke Daque: And yeah, by the way, Amber, I also updated the Utc like.
86 00:08:41.010 ⇒ 00:08:41.669 Amber Lin: Yay!
87 00:08:41.679 ⇒ 00:08:43.319 Luke Daque: They define it. Yeah, it’s.
88 00:08:43.320 ⇒ 00:08:44.289 Amber Lin: Yeah, thank, you.
89 00:08:44.736 ⇒ 00:08:45.630 Luke Daque: DC, so.
90 00:08:45.630 ⇒ 00:08:52.469 Amber Lin: Awesome. Okay, I’ll grab a time away from Ari to get the roadmap set. But thank you all.
91 00:08:52.620 ⇒ 00:08:53.890 Amber Lin: I’ll talk to you guys tomorrow.
92 00:08:54.330 ⇒ 00:09:01.720 Annie Yu: Look sorry. One more thing. Would you be able to refresh the semantic model on your end?
93 00:09:02.420 ⇒ 00:09:08.179 Luke Daque: Oh, yeah, let me do that, is it not showing in the web yet?
94 00:09:09.580 ⇒ 00:09:16.809 Annie Yu: No, it’s not. It’s now not gonna be refreshed anytime. It’s not scheduled to refresh daily.
95 00:09:16.810 ⇒ 00:09:17.380 Luke Daque: I see.
96 00:09:17.380 ⇒ 00:09:21.859 Annie Yu: Because we don’t. We don’t have that service account.
97 00:09:22.350 ⇒ 00:09:23.510 Luke Daque: Gotcha.
98 00:09:24.103 ⇒ 00:09:28.970 Annie Yu: I think I can probably refresh using the web version.
99 00:09:28.970 ⇒ 00:09:29.620 Luke Daque: Image.
100 00:09:29.840 ⇒ 00:09:36.380 Luke Daque: Yeah, I’m trying to do that as well to see if we can do it through the web. If not, then I’ll just manually do it locally.
101 00:09:38.120 ⇒ 00:09:43.069 Annie Yu: Okay, yeah, and let me know if that’s doable with the web. So
102 00:09:43.180 ⇒ 00:09:47.899 Annie Yu: so so I know that I I can do it without needing your help.
103 00:09:48.700 ⇒ 00:09:53.400 Luke Daque: Sure sounds good.
104 00:09:54.560 ⇒ 00:09:55.240 Amber Lin: Okay.
105 00:09:55.970 ⇒ 00:09:57.180 Amber Lin: Thanks. Everyone.
106 00:09:57.710 ⇒ 00:09:58.240 Annie Yu: Thank you.
107 00:09:58.240 ⇒ 00:09:58.840 Luke Daque: Sounds. Good. Thank.
108 00:09:58.840 ⇒ 00:09:59.230 Amber Lin: Hi.
109 00:09:59.230 ⇒ 00:10:01.030 Luke Daque: Thanks, thanks, Andy, goodbye.