Weka: A machine learning workbench for data mining

The Weka workbench is an organized collection of state-of-the-art machine learning algorithms and data preprocessing tools. The basic way of interacting with these methods is by invoking them from the command line. However, convenient interactive graphical user interfaces are provided for data exploration, for setting up large-scale experiments on distributed computing platforms, and for designing configurations for streamed data processing. These interfaces constitute an advanced environment for experimental data mining. The system is written in Java and distributed under the terms of the GNU General Public License

A WebRTC Video Chat Implementation Within the Yioop Search Engine

Web real-time communication (abbreviated as WebRTC) is one of the latest Web application technologies that allows voice, video, and data to work collectively in a browser without a need for third-party plugins or proprietary software installation. When two browsers from different locations communicate with each other, they must know how to locate each other, bypass security and firewall protections, and transmit all multimedia communications in real time. This project not only illustrates how WebRTC technology works but also walks through a real example of video chat-style application. The application communicates between two remote users using WebSocket and the data encryption algorithm specified in WebRTC technology. This project concludes with a description of the WebRTC video chat application’s implementation in Yioop.com, a PHP-based internet search engine

Deriving query suggestions for site search

Modern search engines have been moving away from simplistic interfaces that aimed at satisfying a user's need with a single-shot query. Interactive features are now integral parts of web search engines. However, generating good query modification suggestions remains a challenging issue. Query log analysis is one of the major strands of work in this direction. Although much research has been performed on query logs collected on the web as a whole, query log analysis to enhance search on smaller and more focused collections has attracted less attention, despite its increasing practical importance. In this article, we report on a systematic study of different query modification methods applied to a substantial query log collected on a local website that already uses an interactive search engine. We conducted experiments in which we asked users to assess the relevance of potential query modification suggestions that have been constructed using a range of log analysis methods and different baseline approaches. The experimental results demonstrate the usefulness of log analysis to extract query modification suggestions. Furthermore, our experiments demonstrate that a more fine-grained approach than grouping search requests into sessions allows for extraction of better refinement terms from query log files. © 2013 ASIS&T