TimelyMon: A Streaming Parallel First-Order Monitor
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 15191 LNCS, Page: 150-160
2025
- 4Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures4
- Readers4
Conference Paper Description
First-order monitors analyze data-carrying event streams. When event streams are generated by distributed systems, it may be difficult to ensure that events arrive at the monitor in the right order. We develop a new monitoring tool for metric first-order temporal logic, called TimelyMon, that can process out-of-order events. Using the stream processing framework Timely Dataflow, TimelyMon also supports parallelized monitoring. We demonstrate TimelyMon’s good performance and scalability on synthetic and real-world benchmarks.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85207657626&origin=inward; http://dx.doi.org/10.1007/978-3-031-74234-7_9; https://link.springer.com/10.1007/978-3-031-74234-7_9; https://dx.doi.org/10.1007/978-3-031-74234-7_9; https://link.springer.com/chapter/10.1007/978-3-031-74234-7_9
Springer Science and Business Media LLC
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