TRANSLATING TIME-STAMPED EVENTS TO PERFORMANCE INDICATORS

• US 20140096146A1
• Filed: 09/28/2012
• Published: 04/03/2014
• Est. Priority Date: 09/28/2012
• Status: Abandoned Application

• Current Assignee: Hewlett Packard Enterprise Development LP (Hewlett-Packard Enterprise Company)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: Maurer, Ron, Maor, Alina, Lavi, Michal
• Application Number:
  US13/629,930
• Publication Number:
  US 20140096146A1
• Time in Patent Office: Days
• US Class Current: 719/318
• CPC Class Codes: G06F 11/3419 by assessing time
• CPC Class Codes: G06F 11/3452 Performance evaluation by statistical analysis
• CPC Class Codes: G06F 11/3476 Data logging G06F11/14, G06F11/2205 take precedence
• CPC Class Codes: G06F 2201/835 Timestamp
• CPC Class Codes: G06F 2201/86 Event-based monitoring

Abstract

Systems and methods for translating time-stamped events to performance indicators are provided. A data component can receive sequences of time-stamped events associated with a device with a set of subsystem components. A conversion component can convert the sequences to time series based upon log transformations of event-intervals associated with the events. An estimation component can estimate local event-descriptor distribution characteristics at an event count associated with the time series. A translation component can translate the local event-descriptor distributions characteristics into performance indicators.

Claims

1. A system that translates time-stamped events to performance indicators, comprising:

a memory that stores computer executable components; and

a processor that executes the following computer executable components stored in the memory;

a data component that receives a sequence of time-stamped events and event-descriptors associated with a device that includes a set of subsystem components;

a conversion component that converts the sequence of event-descriptors to a generalized time series associated with an event count axis related to the events;

an estimation component that estimates a local event-descriptor distribution characteristic associated with the generalized time series; and

a translation component that translates the local event-rate distribution characteristic into a performance indicator.

2. The system of claim 1, wherein the event-descriptors are the intervals between consecutive similar events and the conversion component converts the sequence to a time series based upon a variance stabilization transformation of the event-descriptors associated with the event count axis and the estimation component estimates the local event-rate distribution characteristic at an event-interval count associated with the generalized time series.

3. The system of claim 2, wherein the variance stabilization transformation is a log transformation.

4. The system of claim 1, wherein time stamps associated with the events relate to calendar times, usage counts, or production counts.

5. The system of claim 1, wherein an event-descriptor distribution of the events is statistically challenging.

6. The system of claim 1, wherein the estimation component estimates the local event-descriptor distribution characteristic based upon a filter of a local section of the event-descriptor series.

7. The system of claim 6, wherein the filter is a weighted median filter and the translation component determines a first local event-descriptor distribution characteristic based upon a weighted median filter applied to the local section and a second local event-descriptor distribution characteristic based upon a median absolute deviation applied to the local section.

8. The system of claim 6, wherein the estimation component determines a global event-descriptor distribution characteristic based upon multiple local event-descriptor distributions.

9. The system of claim 8, further comprising a mode analysis component that determines significant peaks and troughs of the global event-descriptor distribution based upon a data-driven technique and partitions ranges of the global event-descriptor distribution into performance modes for the device, the performance modes bounded by two consecutive troughs.

10. The system of claim 9, further comprising a dashboard component that presents output associated with the local event-descriptor distribution or the global event-descriptor distribution and derived performance indicators.

11. A method for constructing performance indicators based upon time-stamped events, comprising:

employing a computer-based processor to execute computer executable components stored in a memory to perform the following;

analyzing a data log associated with a device with multiple subsystems for determining a stream of ordered events;

transform event-descriptors determined from the data log to a generalized time series associated with an event count axis relating to events included in the data log;

determining a local event-descriptor distribution characteristic in connection with the generalized time series; and

converting the local event-descriptor distribution characteristic to a performance indicator.

12. The method of claim 11, further comprising utilizing a log transformation of event-descriptors for transforming the stream of ordered events to the generalized time series.

13. The method of claim 11, further comprising filtering a local window about a particular event count for determining the local event-descriptor distribution characteristic.

14. The method of claim 13, further comprising applying multiple filters to the local window for determining multiple performance indicators and identifying a suitable distribution characteristic and aggregating multiple local event-descriptor distribution characteristics for determining a global event-descriptor distribution.

15. A method for identifying performance modes, comprising:

employing a computer-based processor to execute computer executable components stored in a memory to perform the following;

transforming event-descriptors of a stream of events identified in a data log associated with a set of device subsystems to a time series;

determining a performance indicator based upon a local event-descriptor distribution associated with a local event count of the time series;

determining a global event-descriptor distribution from multiple local event-descriptor distribution characteristics;

employing data-driven analysis for determining peaks and valleys of the global event-descriptor distribution; and

identifying performance modes over ranges of the global event-descriptor distribution bounded by two consecutive valleys of a sufficiently meaningful mode.