Population of context-based data gravity wells

• US 10,521,434 B2
• Filed: 04/01/2016
• Issued: 12/31/2019
• Est. Priority Date: 05/17/2013
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Friedlander, Robert R., Kraemer, James R., Ungar, David M.
• Primary Examiner(s): Gurmu, Muluemebet
• Application Number:
  US15/088,221
• Publication Number:
  US 20160217185A1
• Time in Patent Office: 1,369 Days
• Field of Search: 707752
• CPC Class Codes: G06F 16/24573 using data annotations, e.g. user-defined metadata
• CPC Class Codes: G06F 16/24575 using context
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 17/10 Complex mathematical operations function generation by table look-up G06F1/03; evaluation of elementary functions by calculation G06F7/544

Abstract

A computer system includes processors that execute instructions stored on storage media to sort data using a data gravity well membrane. When executed, the program instructions: convert raw data into a first logical address that describes metadata about a first payload data; compare the first logical address to a second logical address for a second payload data to derive a Hamming distance between the first and second logical addresses; create a data vector for the second payload data that includes the Hamming distance between the first and second logical addresses; sort data vectors into specific data gravity wells on a data gravity wells membrane according to the Hamming distance stored in the data vector; incorporate a context object and a non-contextual data object into the data vector for the second payload data; and sort the second payload data into specific data gravity wells on the data gravity wells membrane.

Claims

1. A computer system comprising one or more processors, one or more computer readable memories, and one or more computer readable storage mediums, and program instructions stored on at least one of the one or more storage mediums for execution by at least one of the one or more processors via at least one of the one or more memories, the stored program instructions comprising:

program instructions to convert raw data into a first logical address and first payload data, wherein the first logical address describes metadata about the first payload data;

program instructions to compare the first logical address to a second logical address to derive a Hamming distance between the first and second logical addresses, wherein the second logical address is for a second payload data;

program instructions to create a data vector for the second payload data, wherein the data vector comprises the Hamming distance between the first and second logical addresses;

program instructions to sort data vectors into specific data gravity wells on a data gravity wells membrane according to the Hamming distance stored in the data vector, wherein the data gravity wells membrane is a mathematical framework that

     1) performs to provide a virtual environment in which multiple context-based data gravity wells exist;

     2) populates the multiple context-based data gravity wells with synthetic context-based objects; and

     3) performs to display the multiple context-based data gravity wells on a display;

program instructions to apply a context object to a non-contextual data object, wherein the non-contextual data object is a component of the raw data, wherein the non-contextual data object ambiguously relates to multiple subject-matters, and wherein the context object provides a context that identifies a specific subject-matter, from the multiple subject-matters, of the non-contextual data object;

program instructions to incorporate the context object and the non-contextual data object into the data vector for the second payload data;

program instructions to sort the second payload data into specific data gravity wells on the data gravity wells membrane according to the context objects and the non-contextual data objects; and

program instructions to display the second payload data being sorted into the specific data gravity wells on the display.

2. The computer system of claim 1, wherein the first and second payload data quantitatively describe a commercial transaction.

3. The computer system of claim 1, wherein the first and second payload data qualitatively describe a commercial transaction.

4. The computer system of claim 1, wherein the first and second payload data qualitatively describe an entity.

5. The computer system of claim 1, wherein the hashing logic comprises one or more processors executing the instructions to convert the raw data into the first logical address and first payload data.

6. The computer system of claim 1, further comprising:

program instructions to calculate a virtual mass of a parsed synthetic context-based object, wherein the parsed synthetic context-based object comprises the context object, the non-contextual data object, and the Hamming distance, and wherein the virtual mass of the parsed synthetic context-based object is derived from a formula of;

(P(C)+P(S))×

Wt(S),

where P(C) is the probability that the non-contextual data object has been associated with the correct context object, wherein P(S) is the probability that the Hamming distance has been associated with the correct parsed synthetic context-based object, and where Wt(S) is the weighting factor of importance of the parsed synthetic context-based object.

7. The computer system of claim 1, further comprising:

program instructions to determine which logical addresses describe a same type of data according to a cutoff value for the Hamming distance based on historical experience, wherein the cutoff value is a maximum Hamming distance that describes a predetermined level of probability that two logical addresses describe a same type of data payload; and

program instructions to fine tune a probability that the two logical addresses describe the same type of payload data using a Bayesian probability formula, where A represents an event that two logical addresses both contain same significant bits that describe a same attribute of payload data stored at the two logical addresses, where B represents an event that the Hamming distance between two logical addresses is less than a predetermined number of bit differences, and wherein the Bayesian probability formula is;

8. A computer system comprising one or more processors, one or more computer readable memories, and one or more computer readable storage mediums, and program instructions stored on at least one of the one or more computer readable storage mediums for execution by at least one of the one or more processors via at least one of the one or more computer readable memories, the stored program instructions comprising:

program instructions to convert raw data into a first logical address and first payload data, wherein the first logical address describes metadata about the first payload data;

program instructions to compare the first logical address to a second logical address to derive a Hamming distance between the first and second logical addresses, wherein the second logical address is for a second payload data;

program instructions to create a data vector for the second payload data, wherein the data vector comprises the Hamming distance between the first and second logical addresses;

program instructions to sort data vectors into specific data gravity wells on a data gravity wells membrane according to the Hamming distance stored in the data vector, wherein the data gravity wells membrane is a mathematical framework that

     1) performs to provide a virtual environment in which multiple context-based data gravity wells exist;

     2) populates the multiple context-based data gravity wells with synthetic context-based objects; and

     3) performs to display the multiple context-based data gravity wells on a display;

program instructions to apply a context object to a non-contextual data object, wherein the non-contextual data object is a component of the raw data, wherein the non-contextual data object ambiguously relates to multiple subject-matters, and wherein the context object provides a context that identifies a specific subject-matter, from the multiple subject-matters, of the non-contextual data object;

program instructions to incorporate the context object and the non-contextual data object into the data vector for the second payload data; and

program instructions to sort the second payload data into specific data gravity wells on the data gravity wells membrane according to the context objects and the non-contextual data objects.