Data logs management in a multi-client architecture

• US 10,007,685 B2
• Filed: 09/17/2013
• Issued: 06/26/2018
• Est. Priority Date: 10/04/2012
• Status: Active Grant

• Current Assignee: Alcatel-Lucent SA (Nokia Corporation)
• Original Assignee: Alcatel-Lucent SA (Nokia Corporation)
• Inventors: Achar, Anitha, Unnikrishnan, Brijesh
• Primary Examiner(s): Rostami, Mohammad S
• Application Number:
  US14/432,595
• Publication Number:
  US 20150254285A1
• Time in Patent Office: 1,743 Days
• CPC Class Codes: G06F 16/215 Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
• CPC Class Codes: G06F 16/278 Data partitioning, e.g. horizontal or vertical partitioning

Abstract

Systems and methods for data logs management in a multi-client architecture are described. According to the present subject matter, the system(s) implement the described method(s) for efficient data logs management. The method includes identifying purging parameters associated with each entity of the plurality of entities, where the purging parameters signify a mechanism of purging data logs stored in partition corresponding the entity, and where the purging parameters comprises at least criticality point associated with data logs to relinquish storage space. Further, the method includes purging of data logs stored in the partition of the entity based on the purging parameters, wherein the purging relinquishes the storage space.

Claims

1. A method, performed by one or more processors, for data logs management, comprising:

partitioning a database into a plurality of partitions to store data logs, wherein the plurality of partitions are allocated among a plurality of entities, and wherein each entity from amongst the plurality of entities is allocated at least one partition from amongst the plurality of partitions; and

identifying purging parameters associated with an entity from amongst the plurality of entities, wherein the purging parameters comprise at least criticality point associated with the data logs, wherein the purging parameters signify criteria of purging data logs stored by the entity in the database, wherein important data logs are associated with a high criticality point when a threshold number is exceeded, and wherein normal data logs are associated with a low criticality point when the threshold number is not exceeded;

wherein the data logs are generated within a pre-defined time duration; and

the method further comprises;

purging the data logs based on the purging parameters;

identifying fragmentation parameters associated with each entity from amongst the plurality of entities, wherein the fragmentation parameters define a fragmentation criteria of data logs into different sizes; and

fragmenting the data logs generated by each of the entities from amongst the plurality of entities to different sizes based on the identified fragmentation parameters;

wherein;

the partitioning is based on partitioning parameters associated with each entity of the plurality of entities; and

the partitioning parameters define the size of partition allocated to each entity of the plurality of the entities.

2. The method as claimed in claim 1, wherein the different sizes of the data logs fragmented for each entity is based on storage size of the database allocated to the entity and number of the data logs generated by the entity.

3. The method as claimed in claim 1, wherein the purging parameters further comprise frequency of purging and time instances based on which the stored data logs are purged.

4. The method as claimed in claim 1, wherein the purging of the data logs is further based on a pre-defined time duration and a threshold number of data logs stored in the partition, wherein the pre-defined time duration and the threshold of data logs are indicative of the data logs that are stored with an impulse.

5. The method as claimed in claim 1, wherein the method further comprises:

identifying overwriting factors associated with each entity from amongst the plurality of entities, wherein the overwriting factors define overwriting criteria of data logs from one partition to another; and

overwriting the data logs of the entity onto an unused partition of another entity based on the overwriting factors.

6. The method as claimed in claim 5, wherein the overwriting factors associated with each entity comprise at least one of storage space available, criticality of the data logs, and sharing capability of entities'"'"' partition.

7. The method as claimed in claim 1, wherein the method further comprises dynamically partitioning the storage space among the plurality of entities based on changing requirement of storage space of the plurality of entities.

8. The method as claimed in claim 1, further comprising fragmenting the data logs generated by each of the entities from amongst the plurality of entities to different sizes based on the partition size allocated to an entity of the plurality of entities divided by a number of data logs generated by the entity.

9. The method as claimed in claim 1, wherein a partitioning parameter of the partitioning parameters is determined from a size of a data log generated by an entity of the plurality of entities.

10. The method as claimed in claim 1, wherein a partitioning parameter of the partitioning parameters is determined from a frequency that data logs are generated by an entity of the plurality of entities.

11. A system for data logs management comprising:

a processor;

a partitioning module configured to partition a database into a plurality of partitions to store data logs, wherein;

the plurality of partitions are allocated among the plurality of entities;

each entity from amongst the plurality of entities is allocated at least one partition from amongst the plurality of partitions;

the partitioning is based on partitioning parameters associated with each entity of the plurality of entities; and

the partitioning parameters define the size of partition allocated to each entity of the plurality of the entities; and

a purging module coupled to the processor, configured to;

identify purging parameters associated with an entity from amongst the plurality of entities, wherein the purging parameters comprise at least criticality point associated with the data logs, wherein the purging parameters signify criteria of purging data logs stored by the entity in the database, wherein important data logs are associated with a high criticality point if a threshold number is exceeded, wherein normal data logs are associated with a low criticality point if the threshold number is not exceeded, and wherein the data logs are generated within a pre-defined time duration; and

purge the data logs based on the purging parameters;

wherein the system further comprises a fragmentation module configured to;

identify fragmentation parameters associated with each entity from amongst the plurality of entities, wherein the fragmentation parameters define a fragmentation criteria of data logs into different sizes; and

fragment the data logs generated by each of the entity from amongst the plurality of entities to different sizes based on the identified fragmentation parameters.

12. The system as claimed in claim 11, wherein the system further comprises a overwriting module configured to:

identify overwriting factors associated with each entity from amongst the plurality of entities, wherein the overwriting factors define overwriting criteria of data logs from one partition to another; and

overwrite the data logs of the entity onto partition of another entity based on the overwriting factors, wherein the partition of another entity is unused.

13. The system as claimed in claim 11, wherein the partitioning module is further configured to dynamically partition the storage space among the plurality of entities based on changing requirement of storage space of the plurality of entities.

14. A non-transitory computer-readable medium having embodied thereon a computer readable program code for executing a method comprising:

partitioning a database into a plurality of partitions to store data logs, wherein the plurality of partitions are allocated among a plurality of entities, and wherein each entity from amongst the plurality of entities is allocated at least one partition from amongst the plurality of partitions;

identifying purging parameters associated with an entity from amongst the plurality of entities, wherein the purging parameters comprise at least criticality point associated with the data logs, wherein the purging parameters signify criteria of purging data logs stored by the entity in the database, wherein important data logs are associated with a high criticality point if a threshold number is exceeded, and wherein normal data logs are associated with a low criticality point if the threshold number is not exceeded;

wherein the data logs are generated within a pre-defined time duration; and

the method further comprises;

purging the data logs based on the purging parameters;

identifying fragmentation parameters associated with each entity from amongst the plurality of entities, wherein the fragmentation parameters define a fragmentation criteria of data logs into different sizes; and

fragmenting the data logs generated by each of the entity from amongst the plurality of entities to different sizes based on the identified fragmentation parameters;

wherein;

the partitioning is based on partitioning parameters associated with each of the plurality of entities; and

the partitioning parameters define the size of partition allocated to each of the plurality of the entities.