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System and method of behavior driven non-disruptive caching in web applications

IP.com Disclosure Number: IPCOM000247183D
Publication Date: 2016-Aug-15

Publishing Venue

The IP.com Prior Art Database

Abstract

This invention proposes a system and method of behavior driven non-disruptive caching mechanisms in web applications in a multi-user environment. Uses are identified and allocated into different groups based on their related attributes recognized. A set of rules and methods are conducted to perform preload/unload data as cache mechanism with a result of improved system's responsiveness and better user satisfaction.

This text was extracted from a PDF file.
This is the abbreviated version, containing approximately 52% of the total text.

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System and method of behavior driven non-disruptive caching in web applications

Abbreviated terms:


SQL: Structured Query Language
UID: user identifier
GID: group identifier
SID: SQL statement identifier
LRU: Least Recently Used
GC: Garbage Collection

We are proposing a system and method of a behavior driven non-disruptive caching mechanisms in web applications in a multi-user environment.

In a multi-user system, users can be classified into different groups. Based on a user's access pattern, we can classify him into one of these groups.

Different groups of users normally use different sets of data. Hence, once a user logs onto the system, we can preload that group's common used data into the cache.

Take below scenario as an example.

When user logs in,
Case 1: Data has overlapping

1


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• User 1 logs on, load A into cache,
• If User X of Group 2 is online, load B into cache as well
• If User Y of Group 3 is online, load C into cache, and so on.
• D won't be loaded, but accessed on demand.

Case 2: Data has no overlapping

2


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• User 1 logs on, but won't load A, except Group 1 is for VIP
• Load B, if User X just logs on and finds User Y of Group 3 is on as well.

When user logs out
Case 1: Data has overlapping

3


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• User 1 logs out and no user of Group 1 is online, unload D from cache,
• If no user of Group 2 is online, unload B as well
• If no user of Group 3 is online, unload C, and so on.

Case 2: Data has no overlapping

4


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User 1 logs out and no user of group 1 is online, unload A if it's not a VIP group.

The workflow of the method is:

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This is details of Log DB and Data Classifier.

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SQL: Structured Query Language

UID: user identifier
GID: group identifier
SID: SQL statement identifier

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This is the data loading workflow

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This is the details of cache loader.

SQL: Structured Query Language

UID: user identifier
GID: group identifier

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