Seasonality-based rules for data anomaly detection

• US 8,306,943 B2
• Filed: 03/04/2010
• Issued: 11/06/2012
• Est. Priority Date: 03/04/2010
• Status: Active Grant

• Current Assignee: NTELX, Inc. (Body & Mind, Inc.)
• Original Assignee: NTELX, Inc. (Body & Mind, Inc.)
• Inventors: Yepremyan, Lusine, Arora, Amrinder, Kakovitch, Christopher
• Primary Examiner(s): Singh, Gurkanwaljit
• Application Number:
  US12/717,460
• Publication Number:
  US 20110218836A1
• Time in Patent Office: 978 Days
• Field of Search: None
• US Class Current: 706/62
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

In one embodiment, we describe a method that generates seasonality rules for anomaly detection for a hierarchical/tree based data structure. A new algorithm for processing nodes in hierarchy, as well as business rules for nodes, is described. Variations and examples are given to describe different scopes and embodiments of the invention. Exclusion criteria and children nodes are used as some examples for the implementations, with flow charts to describe the methods of application, as examples.

Claims

1. A method for anomaly detection, using seasonality based rules, said method comprising:

a first central processing unit receiving a set of parameters for a manufactured or shipped product;

obtaining a set of criteria from a first storage unit;

with respect to a product class, said first central processing unit examining a first criteria to see if said first criteria is met;

if said first criteria is met, then said first central processing unit examining a second criteria to see if said second criteria is met; and

if said first criteria is not met, then said first central processing unit disabling children node generation wherein children node generation refers to generating an information value level for a classification hierarchy; and

if said second criteria is met, then said first central processing unit checking for exclusions, applying a first treatment for non-excluded items from a second storage unit, and creating rules and disabling children node generation;

otherwise, if said second criteria is not met, then said first central processing unit examining a third criteria to see if said third criteria is met; and

if said third criteria is not met, then said first central processing unit generating children nodes for the hierarchy, and returning said generated children nodes to said product class stored in a third storage unit; and

if said third criteria is met, then said first central processing unit checking for exclusions, applying a second treatment for non-excluded items from said second storage unit, and creating rules and generating children nodes, and returning said generated children nodes to said product class stored in said third storage unit.

2. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

examining for existence of strong seasonality or weak seasonality.

3. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

receiving a list of trade parties.

4. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

adding to a list of non-excluded trade parties.

5. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

examining if there are more trade parties.

6. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

terminating a process.

7. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

generating a tree or hierarchical structure.

8. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

generating a parent node.

9. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

assigning or choosing a class.

10. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

considering N different classes, wherein N is an integer bigger than 1.

11. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

calculating a first value for each attribute of a class.

12. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

determining a maximum value of a set of second values.

13. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

determining values beyond one or more thresholds.

14. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

determining anomalies.

15. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

determining odd shipments at a custom office.

16. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

reexamining odd shipments at a custom office.

17. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

generating seasonality rules.

18. The method for anomaly detection, using seasonality based rules, as recited in claim 1, further comprising:

aggregating reports for two or more trading parties.