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However, due to the preprocessing stage, we find that it can even perform better than other simple topic-aware heuristic algorithms that have short online response time.
Table 8b shows the average online response time of various algorithms in finding 50 seeds (topic-oblivious algorithms always use the same seeds and thus are not reported).
In addition, TA-PMIA on Flixster is much slower than on Arnetminer, because both the network size and the computed MIA tree size are much larger, indicating that PMIA is not suitable in providing stable online response time.
Our empirical results on data obtained in a couple of existing studies demonstrate that one of our algorithms stands out as a strong candidate providing microsecond online response time and competitive influence spread, with reasonable preprocessing effort.
In terms of online response time (Table 8b), the result is consistent with the result for Arnetminer: only MIS and BTS can achieve microsecond level online response, and all other topic-aware algorithms need at least milliseconds since they at least need a ranking computation among all nodes in the graph.
Field-tested subjects were included in the quantitative analyses if they had responded to 50% or more of the items, did not have repetitive strings of 10 or more identical responses, and their average online response time was greater than 1 second per item.
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Table 2 Online evaluation metrics Response time User-perceived service response time.
Since this test is performed using the real online services, the response time varies depending on the network conditions.
By creating its own network of people using the same hardware, Sega.com hopes to quell the common complaints of online gamers, mainly slow response time due to Internet traffic and hardware incompatibility.
The study advances a novel perspective on the role of response time in online service failure and recovery, and also accounts for how customers construe recovery efforts.
They have high cost in the query understanding stage; thus, it is most likely to result in slow response time in online RDF Q/A processing.
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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