This document proposes extensions to user behavior modeling for web application prefetching. It discusses using n-gram and n-gram+ techniques to predict the next actions users will take based on sequential patterns in their historical requests and responses. Relations between actions are defined to identify dependencies between tokens in requests. An algorithm is proposed to assign actions to endpoints, tokenize requests/responses, identify action relations through n-gram statistics, and predict/prefetch future actions by filling token values. This predictive modeling could help prefetch dependent resources to reduce latency.

1. User behaviour modeling for data
prefetching in web applications
Kacper Łukawski

2. The problem
Users tend to use the
application in a similar manner
over time
A majority of modern web
application uses multiple HTTP
calls to fully generate the
content
Each request increases total
latency perceived by a
particular user

3. Mobile applications
Network connection is not always good
Applications may become useless without an access to some
external resources
Would be great to have the data locally and synchronize whenever
possible

4. User behaviour modeling
Get only resources that the user requested for
Fetch everything what available
Try to predict what might be useful in the nearest
future

5. Content based methods
Statistical modeling
Predicting user's interests

6. Content based methods
Statistical modeling
Predicting user's interests

7. Content based methods
Statistical modeling
Predicting user's interests

8. NLP related technique
Performs statistical analysis of the language
As a base unit of modeling it uses fixed length
subsequences
Builds statistics of the collocations
N-gram statistical model

9. N-gram – an example
„The man who does not read has no advantage
over the man who cannot read”
The man 2
Has no
Man who 2
No advantage
Who does Advantage over
Does not Over the
Not read Who cannot
Read has Cannot read

10. Statistics are built in the similar manner like it is
done in standard n-gram architecture
But this time, n is a minimal length of a sequence,
not a fixed length
Such strategy usually leads to more accurate
predictions
N-gram+

11. N-gram+ – an example
„The man who does not read has no advantage
over the man who cannot read”
The man who does not
read has no advantage
over the man who cannot
read
The man Has no
The man who does not
read cannot
Man who No advantage
Man who does not read
has no advantage over
the man who cannot read
Who does Advantage over
... Does not Over the
... Not read Who cannot
... Read has Cannot read

12. A collection of Apache HTTP server logs
Delivers the following details of the requests:

Host

Timestamp

Request (URL and method)

HTTP reply code

Number of bytes in the response
NASA dataset

13. NASA dataset

14. NASA dataset - results
N-gram ~50% accuracy
N-gram+ ~60% accuracy

15. Typical web application are built on top of the HTTP
protocol
HTTP delivers 9 methods – each one has a different
meaning (GET, HEAD, PUT, POST, DELETE,
OPTIONS, TRACE, CONNECT, PATCH)
A proposal of extension

16. GET /order
GET /product/{product_id}
A proposal of extension – an example
GET /order [
{'id': 1, 'products: [{'id': 100}, {'id': 200}]},
{'id': 2, 'products: [{'id': 300}]}
]
GET /product/100 {
'id': 100,
'name': 'lorem'
}
GET /product/200 {
'id': 200,
'name': 'ipsum'
}
GET /product/300 {
'id': 300,
'name': 'dolor'
}

17. Typical API endpoint:
GET /resource/{id}/?foo={foo}&bar={bar}
Between two HTTP actions from one session:
➔
No relation at all
➔
Request or response tokens of second action are taken from
the first one
➔
Tokens are filled using some external knowledge
➔
Value of the token in the second action might be calculated
from the values of the first one
A proposal of extension - relations

18. Typical API endpoint:
GET /resource/{id}/?foo={foo}&bar={bar}
Between two HTTP actions from one session:
➔
No relation at all
➔
Request or response tokens of second action are taken from
the first one
➔
Tokens are filled using some external knowledge
➔
Value of the token in the second action might be calculated
from the values of the first one
A proposal of extension - relations

19. Let's treat each HTTP request and response as a
single action performed by the user
Try to find the relations between the actions that
often take place in the similar order
Use only the actions that do not change anything
(GET, HEAD)
A proposal of extension - contd

20. Assign the HTTP actions into the HTTP endpoints
that were used to process them
Tokenize each request and response
Using some n-gram-like architecture, try to find
the relations within the subsequences
(request/response request)→
Collect the statistics of token values in the requests
A proposal of extension – an algorithm

21. In the prediction phase try to find the most
probable endpoint that will be used in the next step
Having the endpoint, fill the tokens using relations
to previous actions
If not all the tokens can be filled, use the statistics
of the values
Perform the action(s) using predicted values of
tokens
Send aggregated responses at once
A proposal of extension – an algorithm

22. A. Georgakis, H. Li, “User behavior modeling and content
based speculative web page prefetching”, Data & Knowledge
Engineering 59 (2006) 770–788
M. Narvekar, S. S. Banu, “Predicting User’s Web Navigation
Behavior Using Hybrid Approach”, Procedia Computer
Science 45 ( 2015 ) 3 – 12
http://ita.ee.lbl.gov/html/contrib/NASA-HTTP.html
https://en.wikipedia.org/wiki/N-gram
References