SYSTEMS AND METHODS FOR DERIVING, STORING, AND VISUALIZING A NUMERIC BASELINE FOR TIME-SERIES NUMERIC DATA WHICH CONSIDERS THE TIME, COINCIDENTAL EVENTS, AND RELEVANCE OF THE DATA POINTS AS PART OF THE DERIVATION AND VISUALIZATION

• US 20150066966A1
• Filed: 09/04/2014
• Published: 03/05/2015
• Est. Priority Date: 09/04/2013
• Status: Abandoned Application

• Current Assignee: Know Normal, Inc.
• Original Assignee: Know Normal, Inc.
• Inventors: O'Donnell, Shane Michael, Cupitt, Maurice Bryant
• Application Number:
  US14/477,763
• Publication Number:
  US 20150066966A1
• Time in Patent Office: Days
• US Class Current: 707/756
• CPC Class Codes: G06F 16/258 Data format conversion from or to a database
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46

Abstract

Disclosed herein are methods and systems for deriving, storing, querying, retrieving and visualizing one or more numeric baselines for time-series numeric data which considers the time, coincidental events and relevance of the time-series data points as part of the baseline derivation and visualization. According to an aspect, a method includes receiving one of time-series numeric data and event data in one or more formats from one or more other computing devices. The method also includes standardizing the one of time-series numeric data and event data to a common format. The method also includes analyzing the standardized data in the common format.

Claims

1. A method comprising:

using a computing device comprising at least one processor and memory for;

receiving one of time-series numeric data and event data in one or more formats from one or more other computing devices;

standardizing the one of time-series numeric data and event data to a common format; and

analyzing the standardized data in the common format.

2. The method of claim 1, further comprising correlating the one of the time-series numeric data and event data to one of other time-series numeric data and other event data.

3. The method of claim 2, wherein correlating comprises correlating the one of the time-series numeric data and event data to the one of other time-series numeric data and other event data using any of a plurality of fields displayed in a common format.

4. The method of claim 1, wherein the computing devices are communicatively connected via the Internet.

5. The method of claim 1, further comprising presenting the analyzed data in the common format.

6. The method of claim 5, wherein presenting the analyzed data comprises presenting the analyzed data via a user interface.

7. The method of claim 5, wherein presenting the analyzed data comprises displaying the analyzed data via a display.

8. The method of claim 1, further comprising correlating the data using one of a Pearson product-moment correlation coefficient (PPMCC), Spearman'"'"'s rank correlation coefficient, and Kendall'"'"'s rank correlation coefficient.

9. The method of claim 1, further comprising correlating the data by:

analyzing the most significantly correlated and anti-correlated data making up a dynamically ascertained or manually-configured confidence interval for known-causal values; and

removing the known-causal values into a primary set, wherein the remaining members of the confidence interval are most closely correlated as members of a secondary set reflecting pure correlation and non-causal relationships.

10. The method of claim 1, further comprising determining the one of the time-series and event data within a predetermined time period, and

wherein standardizing and analyzing comprises standardizing and analyzing the data within the predetermined time period.

11. A system comprising:

a computing device comprising at least one processor and memory configured to;

receive one of time-series numeric data and event data in one or more formats from one or more other computing devices;

standardize the one of time-series numeric data and event data to a common format; and

analyze the standardized data in the common format.

12. The system of claim 11, wherein the computing device is configured to correlate the one of the time-series numeric data and event data to one of other time-series numeric data and other event data.

13. The system of claim 12, wherein the computing device is configured to correlate the one of the time-series numeric data and event data to the one of other time-series numeric data and other event data using any of a plurality of fields displayed in a common format.

14. The system of claim 11, wherein the computing devices are communicatively connected via the Internet.

15. The system of claim 11, wherein the computing device is configured to present the analyzed data in the common format.

16. The system of claim 11, further comprising a user interface configured to present the analyzed data.

17. The system of claim 15, further comprising a display configured to display the analyzed data.

18. The system of claim 11, wherein the computing device is configured to correlate the data using one of a Pearson product-moment correlation coefficient (PPMCC), Spearman'"'"'s rank correlation coefficient, and Kendall'"'"'s rank correlation coefficient.

19. The system of claim 11, wherein the computing device is configured to:

analyze the most significantly correlated and anti-correlated data making up a dynamically ascertained or manually-configured confidence interval for known-causal values; and

remove the known-causal values into a primary set, wherein the remaining members of the confidence interval are most closely correlated as members of a secondary set reflecting pure correlation and non-causal relationships.

20. The system of claim 21, wherein the computing device is configured to:

determine the one of the time-series and event data within a predetermined time period; and

standardize and analyze the data within the predetermined time period.