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The approach made extensive use of client-side rendering/interaction wherever practical, and focused on a server-side component that served data from web endpoints using the web-native JSON format where possible.
This is because Acompli, like another email app I've been using, (CloudMagic, a pivot from a universal search app), speeds up interactions by serving data from its own servers, instead from the email providers' own servers – like the Gmail servers, for example.
They're also working on a processing model for serving data from far away – it will help to move processing to the edge, similar to how CDNs work.
By serving data from memory instead of disks, RCSS can yield high random I/O performance with low latency and high throughput, and can achieve good availability and scalability as HDFS.
This has enabled the Pfam database (Finn et al., 2006) to provide access to their protein sequence alignments using ProServer to serve data directly from the underlying MySQL database (see http://das.sanger.ac.uk/das/pfamAlign).sanger.ac.uk/das/pfamAlign
( {mathbf{R}}_{mathbf{b}}^{mathbf{WLAN}} ): vector of serving data rates from WLANs.
( {mathbf{R}}_{mathbf{b}}^{mathbf{cell}} ): vector of serving data rates from cellular networks, ( {f}_{{mathbf{R}}_{mathbf{b}}}^{WLAN} ): objective function for WLANs.
( {mathbf{R}}_{mathbf{b}}^{mathbf{cell}} ): vector of serving data rates from cellular networks, N VNO: number of served VNOs by this VRRM, N srv: number of services for each VNO, ( {W}_{mathrm{ji}}^{mathrm{Srv}} ): weight of serving unit of data rate for service j of VNO i by VRRM, where ( {W}_{mathrm{ji}}^{mathrm{Srv}}in left[0,1right] ).
Though in practice there is some variation in population served, census data from the catchment area of the HPs were not available at the regional level.
Despite that memory usage is not major criteria to evaluate performance of algorithms, since these databases serve the data from memory, their memory usages are also experimented alongside the time taken to complete each operation in the paper to reveal which one uses the memory most efficiently.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com