14,581 research outputs found
Multilingual log analysis: LogCLEF
The current lack of recent and long-term query logs makes
the verifiability and repeatability of log analysis experiments very limited. A first attempt in this direction has been made within the Cross-Language Evaluation Forum in 2009 in a track named LogCLEF which aims to stimulate research on user behaviour in multilingual environments and promote standard evaluation collections of log data. We report on similarities and differences of the most recent activities for LogCLEF
User centred evaluation of a recommendation based image browsing system
In this paper, we introduce a novel approach to recommend images by mining user interactions based on implicit feedback of user browsing. The underlying hypothesis is that the interaction implicitly indicates the interests of the users for meeting practical image retrieval tasks. The algorithm mines interaction data and also low-level content of the clicked images to choose diverse images by clustering heterogeneous features. A user-centred, task-oriented, comparative evaluation was undertaken to verify the validity of our approach where two versions of systems { one set up to enable diverse image recommendation { the other allowing browsing only { were compared. Use was made of the two systems by users in simulated work task situations and quantitative and qualitative data collected as indicators of recommendation results and the levels of user's satisfaction. The responses from the users indicate that they nd the more diverse recommendation highly useful
Measuring children's search behaviour on a large scale
Children often experience problems during information-seeking using traditional search interfaces and search technologies, that are designed for adults. This is because children engage with the world in fundamentally different ways than adults. To design search technologies that support children in effective and enjoyable information-seeking, more research is needed to examine childrenâs specific skills and needs concerning information-seeking. Therefore, we developed an application that can monitor childrenâs search behaviour on a large scale. In this paper, we present the steps taken to develop this application. The basis of the application is UsaProxy, an existing system that is used to monitor the userâs usage of websites. We have increased the accuracy of UsaProxy and have developed an application that is able to extract useful information from UsaProxyâs log files
A Secured Cloud System based on Log Analysis
Now-a-days, enterprisesâ acceptance over the Cloud is increasing but businesses are now finding issues related to security. Everyday, users store a large amount of data in the Cloud and user input may be malicious. Therefore, security has become the critical feature in the applications stored in the Cloud. Though there are many existing systems which provide us different encryption algorithms and security methods, there is still a possibility of attacks to applications and increasing data modifications. The idea behind this project is to find attacks and protect the applications stored in the Cloud using log analysis. The proposed solution detects the SQL injection attack, which is supposed to be the most critical security risk of vulnerable applications. The goal of this research is to detect the SQL injection attacks for an application stored in the Cloud by analyzing the logs. To achieve this, the proposed system automates the intrusion detection process for an application by performing log analysis. Log Analysis is performed by combining the implementation of two different methodologies called learn and detect methodology and pattern recognition system. The accuracy of SQL injections detected on log data is dependent on the order in which these two methodologies are applied. The outcome after applying these two methodologies results in information which helps a security analyst to understand and know the root cause of every attack that is detected on an application
Digital Library logging using XML
This project explores the various ways in which relevant statistics can be extracted from digital library logs collected in XML. A set of potential statistics that can be used for performing clickstream analysis are listed. Clickstream analysis deals with the path taken by the user when he/she is using the digital library site.
This project also involves visualization of the statistics collected. Visualizations are an intuitive way to represent raw data and they can help in gaining more insight into the statistics.
The target digital library was CITIDEL and the XML logs collected from this digital library were used in the project. We also designed and developed a prototype for collection of statistics and visualizing them. Implementation of the tools was done using Java and PHP. JpGraph was used for building visualizations in PHP
DOBBS: Towards a Comprehensive Dataset to Study the Browsing Behavior of Online Users
The investigation of the browsing behavior of users provides useful
information to optimize web site design, web browser design, search engines
offerings, and online advertisement. This has been a topic of active research
since the Web started and a large body of work exists. However, new online
services as well as advances in Web and mobile technologies clearly changed the
meaning behind "browsing the Web" and require a fresh look at the problem and
research, specifically in respect to whether the used models are still
appropriate. Platforms such as YouTube, Netflix or last.fm have started to
replace the traditional media channels (cinema, television, radio) and media
distribution formats (CD, DVD, Blu-ray). Social networks (e.g., Facebook) and
platforms for browser games attracted whole new, particularly less tech-savvy
audiences. Furthermore, advances in mobile technologies and devices made
browsing "on-the-move" the norm and changed the user behavior as in the mobile
case browsing is often being influenced by the user's location and context in
the physical world. Commonly used datasets, such as web server access logs or
search engines transaction logs, are inherently not capable of capturing the
browsing behavior of users in all these facets. DOBBS (DERI Online Behavior
Study) is an effort to create such a dataset in a non-intrusive, completely
anonymous and privacy-preserving way. To this end, DOBBS provides a browser
add-on that users can install, which keeps track of their browsing behavior
(e.g., how much time they spent on the Web, how long they stay on a website,
how often they visit a website, how they use their browser, etc.). In this
paper, we outline the motivation behind DOBBS, describe the add-on and captured
data in detail, and present some first results to highlight the strengths of
DOBBS
- âŠ