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STATISTICS GRAPHICALLY
Technical Examples
- Methods for monitoring an application running on a server are described. Method steps include maintaining counters of statistics related to operation of the application, collecting first operational statistics based on counters from one or more application components, collecting second operational statistics based on counters from one or more application runtime environment components, updating aggregation statistics based on the collected statistics, and storing the statistics for access by a presentation agent which can interface with external monitoring tools. The nature and level of the collected statistics provide valuable insight into the operation of the application of interest.
- In one embodiment, a method for scheduling events in a Boolean satisfiability (SAT) solver includes collecting one or more first-order statistics on a search for a valid solution to an SAT problem, deriving one or more second-order statistics on the search from the one or more first-order statistics, and scheduling events in the search according to one or more of the second-order statistics.
- A statistics data collection mechanism for distributed, high-speed data processing environments is described. According to one embodiment, an update message containing statistics data related to a data packet carried along a virtual connection is assembled and the update message is then transmitted to a statistics collection engine for further processing. According to another embodiment, the update message is received from one or more processing devices, and multiple counters are then updated to store the statistics data.
- A Statistics Module (STS) is disclosed for collecting essential statistics about an image content for the purpose of applying various image enhancement operations such as page background removal and automatic neutral detection to determine if the page is gray or colored. The Statistics Module uses the blurred signal BLR from a De-Screen Module in order to eliminate some of the scanner noise. The output is a special 3D color histogram array.
- Noise-superimposed speech data is grouped according to acoustic similarity, and sufficient statistics are prepared using the speech data in each of the groups. A group acoustically similar to voice data of a user of the speech recognition is selected, and sufficient statistics acoustically similar to the user's voice data are selected from the sufficient statistics in the selected group. Using the selected sufficient statistics, an acoustic model is prepared.
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