Similarity and ranking of databases based on database metadata

• US 10,303,793 B2
• Filed: 11/12/2014
• Issued: 05/28/2019
• Est. Priority Date: 03/19/2014
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Bhagavan, Srini, Kiernan, Gerald G.
• Primary Examiner(s): Hershley, Mark E
• Application Number:
  US14/538,882
• Publication Number:
  US 20150269161A1
• Time in Patent Office: 1,658 Days
• Field of Search: 707750
• CPC Class Codes: G06F 16/152 using file content signatures, e.g. hash values
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9538 Presentation of query results

Abstract

A processor selects a first database and a second database from a plurality of databases. The processor determines one or more terms found in the first and second database, wherein each term of the one or more terms includes metadata of a database of the plurality of databases. The processor identifies one or more common terms between the first database and the second database and determines the one or more common terms found in each of a plurality of groups of databases of the plurality of databases, wherein each group of databases corresponds to a number of databases which constitute the group of databases. The processor determines a similarity score between the first database and the second database of the plurality of databases based on the one or more common terms found in each group of databases of the plurality of databases.

Claims

1. A method for determining similarity of databases, the method comprising:

(a) one or more processors selecting a first database and a second database from a plurality of databases that includes one or more additional databases;

(b) one or more processors identifying one or more terms found in the first database and found in the second database of the plurality of databases as one or more common terms, wherein each term of the one or more terms is comprised of metadata of a structure of a database of the plurality of databases that defines the objects in the database;

(c) one or more processors determining for a common term of the one or more common terms, a quantity of databases of the plurality of databases in which the common term is found, wherein the quantity of databases in which the common term of the one or more common terms is found constitutes a group, and wherein a range of groups includes each quantity of databases, from a group of two databases to a group of a quantity of databases equal to the plurality of databases; and

(d) one or more processors determining a similarity score between the first database and the second database of the plurality of databases based on a tuple formed from the quantity of the one or more common terms found in each group of the range of groups.

2. The method of claim 1, further comprising:

performing steps (a) through (d) for each pairing of the first database with each database of the plurality of databases other than the second database.

3. The method of claim 2, further comprising:

ranking the similarity scores for each pairing of the first database with each database of the plurality of databases.

4. The method of claim 1, further comprising:

performing steps (a) through (d) on all pairings of databases of the plurality of databases, other than pairings with the first database; and

ranking the similarity scores of all the pairings of the plurality of databases, other than pairings with the first database.

5. The method of claim 1, further comprising:

creating a histogram based on the quantity of the one or more common terms found in each group of databases of the plurality of groups of databases, wherein the histogram is associated with a similarity of the second database to the first database.

6. The method of claim 1, wherein each of the one or more common terms includes a triplet comprised of a database table name, a database table column name, and a database table column type.

7. The method of claim 1, wherein at least one term of the one or more terms is a hash derived from the metadata of the database of the plurality of databases.

8. The method of claim 1, wherein determining the one or more common terms includes determining a partial match of the one or more common terms between the first database and the second database.

9. The method of claim 1, wherein determining the similarity score between the first database and the second database of the plurality of databases includes generating a tuple in which each element of the tuple corresponds to the quantity of the one or more common terms found in a group of a particular quantity of databases, and applying weighting factors to each element of the tuple.

10. A method for determining a similarity of databases to search criteria, the method comprising:

(a) one or more processors receiving search criteria, wherein the search criteria includes one or more terms, wherein the one or more terms are comprised of metadata of a structure of a database of the plurality of databases that defines the objects in the database, and wherein the one or more terms are selected from a list presented to a user;

(b) one or more processors determining the one or more terms found in both the search criteria and a first database of a plurality of databases, wherein the one or more terms found in both the search criteria and a first database are one or more common terms;

(c) one or more processors determining, a quantity of the one or more common terms found in each of a plurality of groups of databases of the plurality of databases, wherein a group of databases of the plurality of groups of databases includes a quantity of databases in which a common term of the one or more common terms is found, and wherein a range of the plurality of groups of databases extends from two databases to the quantity of the plurality of databases; and

(d) one or more processors determining a similarity score of the first database of the plurality of databases to the search criteria, based on a tuple formed from the quantity of the one or more common terms found in each group of the range of groups of databases, wherein the similarity of the first database to the search criteria is based on the similarity score.

11. The method of claim 10, wherein determining a similarity score of the first database, further comprises:

performing steps (a) through (d) for each pairing of the search criteria and each database of the plurality of databases other than the first database.

12. The method of claim 11, further comprising:

ranking the similarity of each database of the plurality of databases to the search criteria, based on the similarity score of each database of the plurality of databases.

13. The method of claim 10, wherein the one or more terms of the search criteria includes a triplet comprised of metadata of the structure of the first database the plurality of databases, the metadata containing at least one of:

a database table name, a database table column name, and a database table column type.

14. The method of claim 10, wherein the search criteria is a hash derived from the one or more terms of the search criteria.

15. The method of claim 10, wherein determining the one or more common terms includes determining a partial match of the one or more common terms between the search criteria and the first database.

16. The method of claim 10, wherein determining the similarity score between the first database and the second database of the plurality of databases includes generating a tuple in which each element of the tuple corresponds to the quantity of the one or more common terms found in a group of a particular quantity of databases, and applying weighting factors to each element of the tuple.