METHOD FOR DATA COMPRESSION AND QUALITY CHECKING

• US 20090094266A1
• Filed: 12/11/2008
• Published: 04/09/2009
• Est. Priority Date: 12/10/2004
• Status: Abandoned Application

• Current Assignee: ECPG Net Incorporated
• Original Assignee: ECPG Net Incorporated
• Inventors: SLONE, Joseph C.
• Application Number:
  US12/332,694
• Publication Number:
  US 20090094266A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions

Abstract

Pricing data from a manufacturer is condensed to remove duplicate data and filtered to detect discrepant data. The non-discrepant data is delivered to a retailer and the discrepant data is delivered to the manufacturer for correction. The condensing is done by hashing the data using a key consisting of the data'"'"'s price information and a value consisting of the retailer store associated with the data. The filtering is done by using the condensed data and hashing with a key consisting of the stores and a value consisting of the pricing information.

Claims

1. A method of compressing and filtering data transmitted from a source to a receiving process in a data processing system, the method comprising:

condensing data;

filtering the data to detect discrepancies;

detecting discrepancies in the data;

creating a document identifying discrepant data; and

creating a document identifying non-discrepant data.

2. The method according to claim 1, wherein condensing the data further comprising:

establishing a condensed hashtable to condense the data,

wherein the key of said condensed hashtable represents the part of data that can be condensed and the value of said condensed hashtable represents the part of the data that cannot be condensed.

3. The method according to claim 2, wherein the filtering the data to detect discrepancies further comprising:

establishing a filter hashtable to filter and detect discrepancies in the data,

wherein a key of said filter hashtable representing the value part of the data from said condensed hashtable and a value of said filter hashtable representing the key part of the data from said condensed hashtable.

4. The method according to claim 3, wherein detecting discrepancies includes determining the number of keys in the filter hashtable.

5. The method according to claim 1, wherein condensing the data includes condensing the data via grouping and sorting the data and storing it as a set of related objects.

6. The method according to claim 5, wherein the filtering and detecting discrepancies the data includes iterating over the data and comparing the lists of a predetermined characteristic.

7. The method according to claim 1, further comprising forwarding the document identifying discrepant data to the source for correction.

8. The method according to claim 1, further comprising forwarding the document identifying non-discrepant data to a third party.

18. The method of claim 2, wherein the key of said condensed data hashtable is created by concatenating the string representation of the condensable parts of the data.

19. The method of claim 18, wherein the components of the key string are separated by a separator character not naturally occurring in the components.

20. The method of claim 3, wherein the key of said filter hashtable is created by concatenating the string representation of the value(s) associated with a key in said condensed price data hashtable, in sorted order.

21. The method of claim 20, wherein the components of the key string are separated by a separator character not naturally occurring in the components.

9. A method of handling item pricing information transmitted from a manufacturer to a receiving process in a data processing system, the method comprising:

condensing data related to pricing information;

filtering the data to detect discrepancies;

detecting discrepancies in the data;

creating a document identifying discrepant data; and

creating a document identifying non-discrepant data.

10. The method according to claim 9, wherein condensing the data further comprising:

establishing a condensed hashtable to condense the data,

wherein the key of said condensed hashtable represents the part of data that can be condensed and the value of said condensed hashtable represents the part of the data that cannot be condensed, where the data represents information associated with a plurality of stores, the information being in a X-to-Y relationship, where X (condensable) is distributed identically across Y (non-condensable).

11. The method according to claim 10, wherein the filtering the data to detect discrepancies further comprising:

establishing a filter hashtable to filter and detect discrepancies in the data,

wherein a key of said filter hashtable representing the value part of the data from said condensed hashtable and a value of said filter hashtable representing the key part of the data from said condensed hashtable.

12. The method according to claim 11, wherein detecting discrepancies includes determining the number of keys in the filter hashtable.

13. The method according to claim 9, wherein condensing the data includes condensing the data via grouping and sorting the data and storing it as a set of related objects.

14. The method according to claim 13, wherein the filtering and detecting discrepancies the data includes iterating over the data and comparing the lists of a predetermined characteristic.

15. The method according to claim 14, wherein the predetermined characteristic is a store ID.

16. The method according to claim 9, further comprising forwarding the document identifying discrepant data to the manufacturer for correction.

17. The method according to claim 9, further comprising forwarding the document identifying non-discrepant data to a third party.

22. A method of compressing data transmitted from a source to a receiving process in a data processing system, the method comprising:

condensing the data by establishing a condensed hashtable to condense the data,

wherein the key of said condensed hashtable represents the part of data that can be condensed and the value of said condensed hashtable represents the part of the data that cannot be condensed.

23. The method according to claim 22, wherein condensing the data includes condensing the data via grouping and sorting the data and storing the data as a set of related objects.