Sumo logic sum timeslice5/27/2023 The processor of claim 5, wherein results of the first path and the second path are concatenated into a stack of GRU layers.ħ. The processor of claim 1, wherein the one or more neural networks include a first path including a gated recurrent unit (GRU) layer and a parallel, second path including a sequence of convolution layers.Ħ. The processor of claim 1, wherein the one or more circuits are further to concatenate the one or more first frequencies with the one or more second frequencies to generate one or more output audio signals having a higher audio resolution than one or more input audio signals.ĥ. The processor of claim 2, wherein the one or more first frequencies are determined from the one or more input audio signals and quantized into a first plurality of frequency bins for input to the one or more neural networks, and wherein output from the one or more neural networks corresponds to a second plurality of frequency bins containing the one or more second frequencies.Ĥ. The processor of claim 1, wherein the one or more first frequencies are lower than the one or more second frequencies, and wherein the one or more first frequencies are represented in one or more input audio signals.ģ. A processor, comprising: one or more circuits to use one or more neural networks to determine one or more second frequencies of one or more audio signals based, at least in part, on only one or more first frequencies of the one or more audio signals.Ģ. The length of bars represents number of trading requests per minute, and the colored segments represent the distribution of response time.1. The “stacking” option allows you to draw bar charts with values from different columns stacked onto each other. This is especially useful when the data is visualized. Here we tell the query engine to rearrange the table using time slice values as row labels, and response time as column labels. Stocktrade | timeslice 1m | extract “(?d+$)” | toInt(ceil(response_time/100) * 100) as response_time | count by _timeslice, response_time | transpose row _timeslice column response_time Wouldn’t it be nice if we could rearrange the data into the following table? For example, in the table above, the first five rows give us the distribution of response time at 8:00, the next five rows at 8:01, etc. This gets the data we want, but it is not presented in a format that is easy to digest. Stocktrade | timeslice 1m | extract “(?d+$)” | toInt(ceil(response_time/100) * 100) as response_time | count by _timeslice, response_time That is easy with the timeslice operator: Now, it would also be interesting to see how the distribution changes over time. Here we start with a search for “stocktrade” to get only the lines we are interested in, extract the response time using a regular expression, round it up to the next 100 millisecond, and count the occurrence of each number. Stocktrade | extract “(?d+$)” | toInt(ceil(response_time/100) * 100) as response_time | count by response_time One way to do that is to build a histogram of the response time using the following query: We are interested in finding out the distribution of this number so as to know how quickly individual trades are processed. There is a wealth of information in this log line, but to keep it simple, let’s focus on the last number, in this case 449, which is the server response time in milliseconds. Let’s say you work for an online brokerage firm, and your trading server logs lines that look like the following, among other things: In this post I want to introduce you to a recent addition to the toolbox, the transpose operator. There are currently about two dozen operators available and we are constantly adding new ones. In addition to searching for individual log messages, you may extract, transform, filter and aggregate data from them using a sequence of operators. Sumo Logic lets you access your logs through a powerful query language.
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