Yes, Christian, that is the case. It never occurred to me that it could be a 
problem; I’ve always had it turned on and it used to work (I think). Perhaps I 
was temporarily turning it off previously.

Many thanks.

==Tamer

> On May 16, 2023, at 10:13 AM, Christiaan Hofman <[email protected]> wrote:
> 
> Do you have automatic cite key generation turned on (and is your cite key 
> format %a1:%Y%u2)?
> 
> Christiaan
> 
>> On 16 May 2023, at 16:03, M. Tamer Özsu via Bibdesk-users 
>> <[email protected]> wrote:
>> 
>> I have run into an interesting issue with Bibdesk (1.8.16) that I had not 
>> encountered before. I have a .bib file that has entries like the following. 
>> The relevant part of this is the @inproceedings{icde22_Wang:2022aa line. 
>> When I copy them into my master Bibdesk library, it loses the “icde22_” and 
>> shows the Cite Key as "Wang:2022aa”. If I manually go in and update the Cite 
>> key, it shows the “icde22_” but not when I copy/paste it the bibtex record. 
>> Any ideas as to why this might be happening would be appreciated. In the 
>> meantime, I’ll try on another machine to see if it something with my setup.
>> 
>> @inproceedings{icde22_Wang:2022aa,
>>      author = {Wang, Haibo and Ma, Chaoyi and Chen, Shigang and Wang, 
>> Yuanda},
>>      booktitle = ICDE22,
>>      date-added = {2023-05-16 09:11:03 -0400},
>>      date-modified = {2023-05-16 09:11:04 -0400},
>>      doi = {10.1109/ICDE53745.2022.00005},
>>      issn = {2375-026X},
>>      keywords = {ICDE22,Measurement;Estimation error;Art;Social networking 
>> (online);Throughput;Data engineering;Servers;Cardinality 
>> Estimationi;Online;Self morphing;Bitmap},
>>      pages = {1--13},
>>      title = {Online Cardinality Estimation by Self-morphing Bitmaps},
>>      year = {2022},
>>      abstract = {Estimating the cardinality of a data stream is a 
>> fundamental problem underlying numerous applications such as traffic 
>> monitoring in a network or a datacenter, popularity tracking on social 
>> media, and cache optimization in proxy servers. Existing solutions suffer 
>> from high processing/query overhead or memory in-efficiency, which prevents 
>> them from operating online for data streams with very high arrival rates. 
>> This paper takes a new solution path different from the prior art and 
>> proposes a self-morphing bitmap, which combines operational simplicity with 
>> structural dynamics, allowing the bitmap to be morphed in a series of steps 
>> with an evolving sampling probability that automatically adapts to different 
>> stream sizes. We evaluate the self-morphing bitmap theoretically and 
>> experimentally. The results demonstrate that it significantly outperforms 
>> the prior art.},
>>      bdsk-url-1 = {https://doi.org/10.1109/ICDE53745.2022.00005}}
>> 
>> 
>> ==Tamer
>> --
>> M. Tamer Özsu
>> University of Waterloo
>> Cheriton School of Computer Science
>> https://cs.uwaterloo.ca/~tozsu 
> 
> 
> 
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