A Web Application Path Analysis through Server Logs

Sevgi Koyuncu Tunç and Özgür Külcü

Department of Information Management, Hacettepe University, Beytepe, Ankara, Turkey

Keywords: Web Applications Usability, Server Log Analysis, Path Analysis, Human Computer Interaction.

Abstract: Web server logs contain certain information about accessing web pages. When this information is analysed

using data mining methods, information about users' web page visiting habit, frequency and behaviour can be

obtained. This allows us to improve service quality by measuring the usability and performance of web

applications. The most important advantage of the remote usability evaluation method with server logs is that

it is implemented without any communication with the user. While the user is using the system in a routine

and natural environment, it is possible to access real user activity information with recorded logs. Users visit

the web application one or more times. The web page calls during each visit bring up a session. User sessions

must be distinguished to determine the route that users follow when using a web application. For this purpose,

log records were processed through the following steps; Data Cleaning, Page Address Coding, Session

Detection and Specification of all URL sequences in this research. There are 3 methods in the literature to

distinguish the sessions from each other. The start of the sessions can be determined according to the total

duration of the session, the time spent on the page, and the page source (Spiliopoulou et al., 2003). This

research was based on total session duration, and the page calls made by the user in 10 minutes and 15 minutes

were considered as a session. Hacettepe University Electronic Document Management System’s (EDMS) 16

million access logs were pre - processed and analysed to obtain system usage data. The research revealed

weaknesses of the EDMS web pages in terms of usability via path frequency analysis.

1 INTRODUCTION

Usability is the degree to which a software can be

used by specified consumers to achieve quantified

objectives with effectiveness, efficiency and

satisfaction in a quantified context of use. Log

analysis, a relatively new method for testing the

usability of web applications is based on the analysis

of users’ server requests during web application

usage. In this method that is called "remote usability

evaluation", users and analyzers are separated by time

and space. This makes it possible to analyze user’s

behavior in their natural, everyday environment,

while removing the cost of bringing them to private

laboratories.

A certain amount of server logs can be used to

gather quantitative usage information. When logs are

processed and interpreted, technical information such

as usage intensity, server load, abnormal activity,

failed client calls can be obtained at specific time

intervals. However there are a number of studies that

show that web server logs can be derived from not

only statistical but also route-based inferences. By

analyzing recurring patterns, it is possible to evaluate

the usability and user experience, and for different

types of software, it is possible to identify the

interface problems by revealing the repetitive patterns

that users make while using the system. (Kim et al.,

2013) In this server path analysis study, it is aimed to

determine the route that users use most when using

Hacettepe University Electronic Document

Management System to unreveal the problematic user

interfaces.

2 LITERATURE REVIEW

Wang and Lee (2011) has proposed a compact

graphical structure to record the way users navigate

in the website. Munk and Kapusta (2010) has

recommended improving the web portal based on

hours of access to a web portal. Resul Das and

Turkoglu (2009) aimed to investigate URL

information related to accessing electronic resources

using route analysis technique by pre-processing

server log records.

Tunç, S. and Külcü, Ö.

A Web Application Path Analysis through Server Logs.

DOI: 10.5220/0007231804270430

In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - Volume 1: KDIR, pages 427-430

ISBN: 978-989-758-330-8

427

Poggi and Moren (2008) has developed benefit-

based web session management that can adapt itself

using machine learning and markov-chain

techniques. Liu and Keselj (2007) proposed a model

that automatically classifies user navigation and

predicts the future navigation of the user.

Even if the linking rules are not directly linked,

the visited pages are used for exploring and revealing

the common information of different groups with this

information. In addition, it is possible to improve the

web application by adding links between pages which

are not related to each other, but which are shared by

users (Chen et al., 1996, Punin et al., 2001, Batista et

al., 2002, Zaiane et al., 1998).

Finding Web pages accessed one after the other

makes it easier to develop users' activities, trends, and

predictions for the next page to visit. Tree-like

topology patterns and frequent route transitions have

been investigated by Chen et al., 1996; Lin et al.,

1999; Nanopoulos and Manolopoulos, 2000.

Borges and Levene (1999) use a probabilistic

grammar-based approach, a Ngram model to capture

user navigation behavioral patterns. The Ngram

model assumes that the last N page to be browsed

affects the probability of identifying the next page to

be visited.

Jin et al., (2004) used Probabilistic Hidden

Semantic Analysis (PLSA) to explore navigation

traces. PLSA can be used to identify hidden semantic

relationships between users and between Web pages

and users.

Jespersen et al., (2003) Markov assumptions are

used to investigate the structure of web application

use.

3 RESEARCH

In this study, the 29 day 16 million access logs

recorded in the Hacettepe University EDMS

application server have been analyzed. The total log

size is 12 GB.

For log records to be used for route analysis, they

must go through the following preliminary steps.

These are;

 Data Cleaning

 Page Address Coding

 Session Detection

3.1 Data Cleaning

When a web page call is made, the page's jpg, png,

css files are also called. Since these additional files

belong to the same page, they must be cleared before

the route analysis (Liu and Keselj, 2007).

Figure 1: Research steps.

Lines that contain image, JavaScript, and style file

invocations that do not point to any page in the access

log records have been deleted using SQL text

processing functions.

3.2 Page Address Coding

Individual page addresses are given letter codes to

increase readability of URL sequences in a separate

URL table.

3.3 Session Detection

Users visit the web application one or more times.

The web page calls during each visit bring up a

session. User sessions must be distinguished to

determine the path that users follow when using a web

application. There are 3 methods in the literature to

distinguish the sessions from each other. The start of

the sessions can be determined according to the total

duration of the session, the time spent on the page,

and the page source (Spiliopoulou et al., 2003). This

research was based on total session duration, and the

page calls made by the user in 10 minutes and 15

minutes were considered as a session. For example, if

the time of the first page call for the S1 session is t0

and the time for the second page call is t1, then t1- t0

<= 10 must be present for the two-page calls in S1

(Spiliopoulou et al., 2003).

A desktop program in c# language developed for

session detection. With this program, the ip-based

session list and the URL sequence that accessed

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during each session are specified for the desired time

interval (n-minute sessions). A data table named

Session is designed to save sessions. The program

follows these steps for each log record iteratively:

 If there is not a session record with the same IP

with the i.th record in the Session Table, within a

maximum of n minutes (n = defined session

duration: 10 minutes / 15 minutes); a new session

record is created. The page code is written in the

“URL sequence” field.

 If there is a session record with the same IP with

the i.th record in the Session Table, within a

maximum of n minutes; the accessed page code is

added to the URL sequence of the found session

record.

After pre-process of access log data, path analysis has

been implemented on session records as following.

3.4 Identification of URL Sequences

Once the sessions and URL sequences have been

defined, a program has been developed to determine

the paths that users use most frequently during their

sessions while using the EDMS. This program

performs the following operations:

3.4.1 Specify All Variety of URL Sequences

All different sequences of 4, 5, 6 .... 20 length URL

sequences in session table entries were extracted.

3.4.2 Determine the Frequency of URLs

Sequences

In all sessions, the total number of occurrences of

URL sequence strings was counted and the URL

sequence string and usage frequency data were

recorded in the PATH_COUNT table.

3.4.3 Identifying the Most Common URL

Sequences

On the Path_Count table, SQL queries that calculate

the usage frequency statistics of URL sequences have

been developed, and the most common routes have

been determined by running these SQL queries.

4 RESULTS

In both the 10-minute and 15-minute session lists, the

program's home page, the landing page "incoming

documents page" and the page used to view and

prepare correspondence appears to be the most visited

consecutive pages.

Figure 2: Most frequent paths of EDMS web application.

According to the path analysis the correspondence

page is often followed by a “document download”

call. The fact that the text of the correspondence

cannot be previewed without downloading can be

shown as the cause.

Another significant trend in most of the sessions

is that some certain page calls are always followed by

another specific page call. So, merging these two

pages in one page may increase the efficiency of the

system usage. For example, in folder merging

process, user is being directed to a second page to

choose folders for each folder and it means pages

posted and loaded extra 2 times for a file merge

process.

Documents Search screens appear to be not

popular. It has been found that users query the

correspondence list instead of the document search

pages and download the document. The lack of

frequent calls to Document Lookup and Detailed

Document Lookup screens may be because these

screens are not useful.

5 LIMITATIONS

Server log analysis gives some general information

about the system usage but cannot show specific

usability problems. Because of the caching

mechanism it is not possible to handle user path for

completing a task or we cannot know which page

elements are problematic to the user by using only

server logs.

Server log analysis may also show less users than

the site had because multiple users could potentially

use the same IP address. The data on entry and exit

points will be unreliable. Users will look like they've

left when they've simply gotten a new IP address.

Their next page view will make look like a new user.

The data on length of visit and time on each page will

also be unreliable.

A Web Application Path Analysis through Server Logs

429

Another use of server logs and route analysis work

is to capture tips on usability and improve the system

by obtaining information about whether a process

consisting of several steps is performed by following

the route expected by the users, the most time-

consuming process steps and the steps in which the

process is left most. In the Hacettepe EDMS program,

the process of "preparing correspondence" is a

process consisting of inputting general information,

writing text input, adding text input, and signing. But

all the steps are done by one-page URL. For this

reason, information such as the user's movement

between steps, the amount of time spent in each step,

and the step at which he left the process is not

obtained from the access logs.

6 CONCLUSION

To be effective in a knowledge and information-based

society, individuals need tools that allow them to

collect, manipulate and distribute the information

about their products. The problem for authors and

maintainers of such distributed resources is that to

measure the utility of information in such a process

and the question is: How do you analyse that the

information you have placed on the web is being

accessed in a significant way?

Organizations seeking to use server logs to

measure usability can reach more accurate data by

adding the following information to the log records:

1. Giving a unique number to each user will ensure

that sessions are distinguished precisely.

2. Specifying different URLs for each step of

processes like creating a legal document or

making a payment will enable analyzers to obtain

the detailed path of the users.

3. Saving details about the clicks made by the user

(scroll bar movements, time, etc.) will enable

effective analysis of usage data.

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