What meening this levels ?
I don't find any explanation in docs
Thanks
Impact levels take into account the number of total crashes and the number of users affected by a crash. We analyze these values and determine the impact based on a 5 point scale.
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Is it normal to get lesser ecommerce purchase data accuracy in GA4 (even lesser upto 20 to 30%) comparing the data base record? What is standard here? Also please mention the reason of the record and tracking discrepency.
To get the understanding
Well, the most obvious reason is adblockers. They would block your tracking.
The percentage depends on the likeliness of particular audience to block it. We generally expect it to be around 10%, but, as an example, it can reach even 50% when you look at the STEM student traffic.
Other common issue is poorly implemented tracking. Which, in the case of conversions, is more likely to double count the conversions rather than undercount them, but that is possible too.
Finally, GA4's interface is still not a very reliable tool to access your data, so you may take a glance at it in BQ if you're friendly with SQL just to make sure what you're seeing in a GA4 interface is really what's in data.
How to stabilize the RSSI (Received Signal Strength Indicator) of low energy Bluetooth beacons (BLE) for more accurate distance calculation?
We are trying to develop an indoor navigation system and came across this problem where the RSSI is fluctuating so much that, the distance estimation is nowhere near the correct value. We tried using an advance average calculator but to no use,
The device is constantly getting RSSI values, how to filter them, how to get the mean value, I am completely lost, please help.
Can anyone suggest any npm library or point in the right direction, I have been searching for many days but have not gotten anywhere.
FRONT END: ReactNative BACKEND: NODEJS
In addition to the answer of #davidgyoung, we would like to point out that any filtering method is a compromise between quality of noise level reduction and the time-lag introduced by this filtration (depending on the characteristic filtering time you use in your method). As was pointed by #davidgyoung, if you take characteristic filtering period T you will get an average time-lag of about T/2.
Thus, I think the best approach to solve your problem is not to try to find the best filtering method but to make changes on the transmitter’s end itself.
First you can increase the number of signals, transmitter per second (most of the modern beacon allow to do so by using manufacturer applications and API).
Secondly, you can increase beacon's power (which is also usually one of the beacon’s settings), which usually reduces signal-to-noise ratio.
Finally, you can compare beacons from different vendors. At Navigine company we experimented and tested lots of different beacons from multiple manufacturers, and it appears that signal-to-noise ratio can significantly vary among existing manufacturers. From our side, we recommend taking a look at kontakt.io beacons (https://kontakt.io/) as an one of the recognized leaders with 5+ years experience in the area.
It is unlikely that you will find a pre-built package that will do what you want as your needs are pretty specific. You will most likely have to wtite your own filtering code.
A key challenge is to decide the parameters of your filtering, as an indoor nav use case often is impacted by time lag. If you average RSSI over 30 seconds, for example, the output of your filter will effectively give you the RSSI of where a moving object was on average 15 seconds ago. This may be inappropriate for your use case if dealing with moving objects. Reducing the averaging interval to 5 seconds might help, but still introduces time lag while reducing smoothing of noise. A filter called an Auto-Regressive Moving Average Filter might be a good choice, but I only have an implementation in Java so you would need to translate to JavaScript.
Finally, do not expect a filter to solve all your problems. Even if you smooth out the noise on the RSSI you may find that the distance estimates are not accurate enough for your use case. Make sure you understand the limits of what is possible with this technology. I wrote a deep dive on this topic here.
I have a question. In an application close to 500 views have been designed based on different reportable granularities. My concern is will this high number impact performance of the application in any way. One more point , the views are interlinked. So all of the views are accessed at some point of time. Could you please provide insights on this.
Thanks And Regards
Aritra Bhattacharya
While checking the performance of Biztalk application using PAL,I observed that performance counter \Memory\Available MBytes has raised an alert.However I am not able to find out what is the actual process or program which is causing this.
I can see the below information along with the details of the performance counter,but there is no info on who or which process is causing it.
Alerts
An alert is generated if any of the above thresholds were broken during one of the time intervals analyzed. An alert condition of OK means that the counter instance was analyzed, but did not break any thresholds. The background of each of the values represents the highest priority threshold that the value broke. See the 'Thresholds Analyzed' section as the color key to determine which threshold was broken. A white background indicates that the value was not analyzed by any of the thresholds.
Time Condition Counter Min Avg Max Hourly Trend
PAL might be referring to the BizTalk memory thresholds used in throttling. If you run a perfmon against these counters, you can see how much memory each of your BTSNTSvc's is consuming, as well as the threshold levels which will cause throttling, and the current throttling states of your process, if applicable.
From firsthand experience, BizTalk throttling on memory conditions is a really bad thing
We're adding extra login information to an existing database record on the order of 3.85KB per login.
There are two concerns about this:
1) Is this too much on-the-wire data added per login?
2) Is this too much extra data we're storing in the database per login?
Given todays technology, are these valid concerns?
Background:
We don't have concrete usage figures, but we average about 5,000 logins per month. We hope to scale to larger customers, howerver, still in the 10's of 1000's per month, not 1000's per second.
In the US (our market) broadband has 60% market adoption.
Assuming you have ~80,000 logins per month, you would be adding ~ 3.75 GB per YEAR to your database table.
If you are using a decent RDBMS like MySQL, PostgreSQL, SQLServer, Oracle, etc... this is a laughable amount of data and traffic. After several years, you might want to start looking at archiving some of it. But by then, who knows what the application will look like?
It's always important to consider how you are going to be querying this data, so that you don't run into performance bottlenecks. Without those details, I cannot comment very usefully on that aspect.
But to answer your concern, do not be concerned. Just always keep thinking ahead.
How many users do you have? How often do they have to log in? Are they likely to be on fast connections, or damp pieces of string? Do you mean you're really adding 3.85K per time someone logs in, or per user account? How long do you have to store the data? What benefit does it give you? How does it compare with the amount of data you're already storing? (i.e. is most of your data going to be due to this new part, or will it be a drop in the ocean?)
In short - this is a very context-sensitive question :)
Given that storage and hardware are SOOO cheap these days (relatively speaking of course) this should not be a concern. Obviously if you need the data then you need the data! You can use replication to several locations so that the added data doesn't need to move over the wire as far (such as a server on the west coast and the east coast). You can manage your data by separating it by state to minimize the size of your tables (similar to what banks do, choose state as part of the login process so that they look to the right data store). You can use horizontal partitioning to minimize the number or records per table to keep your queries speedy. Lots of ways to keep large data optimized. Also check into Lucene if you plan to do lots of reads to this data.
In terms of today's average server technology it's not a problem. In terms of your server technology it could be a problem. You need to provide more info.
In terms of storage, this is peanuts, although you want to eventually archive or throw out old data.
In terms of network (?) traffic, this is not much on the server end, but it will affect the speed at which your website appears to load and function for a good portion of customers. Although many have broadband, someone somewhere will try it on edge or modem or while using bit torrent heavily, your site will appear slow or malfunction altogether and you'll get loud complaints all over the web. Does it matter? If your users really need your service, they can surely wait, if you are developing new twitter the page load time increase is hardly acceptable.