vkalavri@bu.edu CS 591 K1: Data Stream Processing and Analytics Spring 2020 2/06: Notions of time and progress Vasiliki Kalavri | Boston University 2020 Mobile game application • input stream: Vasiliki Kalavri | Boston University 2020 • Processing time • the time of the local clock where an event is being processed • a processing-time window wouldn’t account for game activity while the train Event time • the time when an event actually happened • an event-time window would give you the extra life • results are deterministic and independent of the processing speed Notions of time 5 Vasiliki

software for experimentation were also indicated to the Cure53 testers by Dapr. The project started on time and progressed efficiently. The communications between Cure53 and Dapr were done during this test Dapr was not using any form of authentication when sending requests to the application. At the same time, attackers from one pod could bypass the Dapr API and related authentication entirely and are, therefore RetryPolicy.Threshold; i++ { err := method(req) if err == nil { return nil } time.Sleep(duration) [...] } return fmt.Errorf("failed to set value after %d retries"

similar to the baseline, but does so in fewer epochs. We could ideally save an epoch’s worth of training time by terminating the training early, if we adopt this hypothetical sample efficient model training. effective utilization of the training data. Labeling data is often an expensive process both in terms of time consumption and fiscal expenditure because it involves human labelers looking at each example and the four classes, three of which are the keywords that the device will accept: hello, weather and time. The fourth class (none) indicates the absence of an acceptable keyword in the input signal. Figure

infrastructure. SUSE Rancher also is 100% open source with a large community backing. SUSE also invest heavily in the open source community with a large number of open source projects in development

cross-tabulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 13 Time Series / Date functionality 157 13.1 DateOffset objects . . . . . . . . . . . . . . . . . . . . ranges (DateRange) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 13.3 Time series-related instance methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . as in an SQL table or Excel spreadsheet • Ordered and unordered (not necessarily fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels

cross-tabulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 13 Time Series / Date functionality 157 13.1 DateOffset objects . . . . . . . . . . . . . . . . . . . . ranges (DateRange) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 13.3 Time series-related instance methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . as in an SQL table or Excel spreadsheet • Ordered and unordered (not necessarily fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels

cross-tabulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 13 Time Series / Date functionality 165 13.1 DateOffset objects . . . . . . . . . . . . . . . . . . . . ranges (DateRange) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 13.3 Time series-related instance methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . as in an SQL table or Excel spreadsheet • Ordered and unordered (not necessarily fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 5.9 Time Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 15 Time Series / Date functionality 287 15.1 Time Stamps vs. Time Spans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 15.6 Time series-related instance methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 5.9 Time Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374 16 Time Series / Date functionality 377 16.1 Time Stamps vs. Time Spans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 388 16.6 Time series-related instance methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 5.9 Time Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412 16 Time Series / Date functionality 413 16.1 Time Stamps vs. Time Spans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424 16.6 Time series-related instance methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .