MANAGING DATA WITHIN A TEMPORAL RELATIONAL DATABASE MANAGEMENT SYSTEM

• US 20170075975A1
• Filed: 09/12/2015
• Published: 03/16/2017
• Est. Priority Date: 09/12/2015
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Charron, David S., Gilbertson, Kyle E., Konik, Rafal P., Lawrence, Nicholas T.
• Granted Patent: US 9,996,605 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/211 Schema design and management
• CPC Class Codes: G06F 16/2282 Tablespace storage structures; Management thereof
• CPC Class Codes: G06F 16/23 Updating
• CPC Class Codes: G06F 16/2477 Temporal data queries
• CPC Class Codes: G06F 16/285 Clustering or classification

Abstract

Disclosed aspects include a temporal relational database management system initiating a set of operations. A set of columns in a transaction time temporal table is identified for change categorization. A change categorization model is constructed. The change categorization model is based on a set of changes with respect to the set of columns in the transaction time temporal table. The change categorization model uses a cluster analysis technique. Based on the change categorization model, a group of change categories for a set of rows coupled with the set of columns in the transaction time temporal table is determined. Based on the change categorization model, a first change category of the group of change categories is established in a first row of the set of rows.

Claims

1. A computer-implemented method for managing data within a temporal relational database management system, the method comprising:

identifying, by the temporal relational database management system, a set of columns in a transaction time temporal table for change categorization;

constructing, by the temporal relational database management system, a change categorization model, wherein the change categorization model is based on a set of changes with respect to the set of columns in the transaction time temporal table and uses a cluster analysis technique;

determining, by the temporal relational database management system based on the change categorization model, a group of change categories for a set of rows coupled with the set of columns in the transaction time temporal table; and

establishing, by the temporal relational database management system based on the change categorization model, a first change category of the group of change categories in a first row of the set of rows.

2. The method of claim 1, further comprising:

receiving, by the temporal relational database management system, a change request for a second row of the set of rows; and

establishing, by the temporal relational database management system based on the change categorization model, a second change category of the group of change categories in the second row of the set of rows.

3. The method of claim 1, further comprising:

receiving, by the temporal relational database management system, a request for a new row which is coupled with the set of columns in the transaction time temporal table; and

establishing, by the temporal relational database management system based on the change categorization model, a second change category of the group of change categories in the new row.

4. The method of claim 1, wherein the set of columns includes a selection from a group consisting of at least one of:

a set of numerical values, a set of free text, or a set of unstructured data.

5. The method of claim 1, further comprising:

computing a set of delta images based on the set of changes with respect to the set of columns in the transaction time temporal table, wherein the set of delta images includes a magnitude of change for the set of columns.

6. The method of claim 5, further comprising:

calculating a set of differences based on the set of changes with respect to the set of columns in the transaction time temporal table, wherein the set of differences include at least one of;

a mathematical difference or a statistical difference.

7. The method of claim 5, further comprising:

transforming the set of columns into a term vector of term frequency-inverse document frequency (TF-IDF) weights; and

using the term vector of term frequency-inverse document frequency (TF-IDF) weights to compute the set of delta images.

8. The method of claim 5, further comprising:

normalizing the set of delta images using a statistical analysis technique.

9. The method of claim 5, further comprising:

caching the set of delta images in the temporal relational database management system.

10. The method of claim 1, wherein the cluster analysis technique groups the set of changes by similarity using a selection from a group consisting of at least one of:

a k-means technique, a singular value decomposition (SVD) technique, a clustering using representatives (CURE) technique, a Bradley-Fayyad-Reina (BFR) technique, or a restricted Boltzmann machine (RBM) neural network technique.

11. The method of claim 1, further comprising:

generating, using the cluster analysis technique, a set of clusters based on the set of changes with respect to the set of columns in the transaction time temporal table; and

mapping, based on the set of clusters, the set of rows to the group of change categories.

12. The method of claim 1, further comprising:

providing to a user, by the temporal relational database management system, the first change category of the group of change categories in the first row of the set of rows.

13. The method of claim 1, further comprising:

predicting a change category of the group of change categories for a row of the set of rows; and

receiving a change request which requests changes to a plurality of the set of columns.

14. The method of claim 1, wherein the transaction time temporal table includes both a first table and a second table, and wherein establishing the first change category of the group of change categories in the first row of the set of rows includes:

establishing the first change category of the group of change categories in a first row of a set of rows of the first table, and

establishing a second change category of the group of change categories in a first row of a set of rows of the second table.

15. The method of claim 1, further comprising:

computing a set of delta images based on the set of changes with respect to the set of columns in the transaction time temporal table, wherein the set of delta images includes a magnitude of change for the set of columns;

normalizing the set of delta images using a statistical analysis technique; and

caching the set of delta images in the temporal relational database management system.

16. The method of claim 1, further comprising:

generating, using the cluster analysis technique, a set of clusters based on the set of changes with respect to the set of columns in the transaction time temporal table;

mapping, based on the set of clusters, the set of rows to the set of change categories;

predicting a change category of the group of change categories for a row of the set of rows;

receiving a change request which requests changes to a plurality of the set of columns; and

providing to a user, by the temporal relational database management system, the first change category of the group of change categories in the first row of the set of rows.

17-20. -20. (canceled)