J. Joshua Thomas

A Visual Analytics Framework for the Examination Timetabling Problem

The research aspiration is to develop a visual framework for the examination timetabling problem. To perform the task we identified visual analytics as a multi-disciplinary field, directly supports the timetable designers, assist with visual metaphors, patterns to make decisions. Here we deemed three stages on the problem domain. It deals with pre-processing, during the processing and post-processing; in each of the processes are with transaction on data which transform into timetables. These processes are interrelated with one another for any heuristic based, evolutionary algorithm guided timetabling systems. However, the importance aspect is the significance of visualization among the three processes and how it contributes in each process. This paper discuss two aspects, it wraps the framework with literature and an implication on the pre-processing through visualizing data and knowledge with graph visualization.

A parallel coordinates visualization for the uncapaciated examination timetabling problem

The paper describes a new approach, based on visualization, to the uncapacitated examination timetabling problem. This approach involves with a naturally inspired evolutionary technique with parallel coordinates that facilitates exploration of data. By explicitly representing the visualization parameter space, the interface provides an overview of rendering options and enables user to easily explore different processes namely preprocessing, during the processing and post processing of examination schedules. The paper discusses the performance of this method on the Carter set of benchmark problems. This data set is comprised of real-world timetabling problems.

SpaceViz: Visualization Tool for the Computer Storage Big-Data

This paper presents SpaceViz, an information visualization tool designed for depicting hierarchies, such as directory structures. SpaceViz has scanned, map and manage PC, network and cloud storage of multimedia data. Classical, space-filling squarified techniques are used as methodology, but differ from Tree-map single view in that it employs multiple views in two-dimensional space, which is used to more clearly the structural relationships of data from colour code and pattern based hierarchy. The paper includes an empirical study comparing SpaceViz and Tree-map. The study shows the participants perform significantly better to interact with multiple file formats using SpaceViz.

Exploration of rough sets analysis in real-world examination timetabling problem instances

The examination timetabling problem is widely studied and a major activity for academic institutions. In real world cases, an increasing number of student enrolments, variety of courses throw in the growing challenge in the research with a wider range of constraints. Many optimization problems are concerned with the best feasible solution with minimum execution time of algorithms. The aim of this paper is to propose rough sets methods to investigate the Carter datasets. Two rough sets (RS) approaches are used for the data analysis. Firstly, the discretization process (DP) returns a partition of the value sets into intervals. Secondly the rough sets Boolean reasoning (RSBR) achieves the best decision table on the large data instances. The rough sets classified datasets are experimented with an examination scheduler. The improvements of the solutions on Car-s-91 and Car-f-91 datasets are reported.

Process visualization to solve the travelling salesman problem

The Traveling Salesman Problem (TSP) is one of the well studied combinatorial optimization problems. Multiple approximation algorithms are derived for solving the distance measure TSP that determines the shortest route through a given set of points or cities. In this paper, we visualize the process of genetic parameters and explain the solution converges. It deals with interactive animation to understand the progress view of the solutions obtained. A Java-based FX and interactive visualization (TSP-ViZ) is alo developed using Java FX and optimization results are discussed.

Information visualization Approach on the University Examination Timetabling Problem

The university examination timetabling is known to be a highly constrained combinatorial optimization problem. We classify the examination timetabling into preprocessing, during the processing, and post-processing stages. In each of the processes the data places a crucial role to engross into the stages to generate timetables. Major research work on timetabling is to tune the algorithms or focused on the optimal timetable generations and its performance. In this paper, we propose an interactive system based on visualization techniques to guide the optimization process with constraint satisfaction algorithms. Visualization assists the user to learn the data (pre-processing) with that to make a medium between the scheduler and the timetable designers to improve the obtained solutions. To show the usefulness of our ideas, we apply graph visualization (Prefuse) on the pre-processing data that could effectively support a human timetable designer in the analysis of complex timetable generations.

Visual Analytics Solution for Scheduling Processing Phases

University Examination Timetabling Problem (UETP) is a computationally complex scheduling problem. Visual Analytics (VA) is a modern visualization supported with automated processing method. The major impulse of the method lies in its ability to integrate the key component of scientific visualization and search based heuristics in the same optimization model. This paper presents a visual analytics process (VAP) adapted for UETP. The adaption involves the human context of visual analytics on timetabling data, which are typically processed computationally with local search algorithm and then visualized and interpreted by the user in order to perform problem solving with direct interactions between the primary data, processing and visualization. The three processing phases are invoked with user-driven and algorithmic-driven steering that analyses the combined effect with automatic tuning of algorithmic parameters based on constraints and the criticality of the application for the simulations is proposed. The optimal solution for the small datasets and best overall results for the medium and large datasets are experimented.

Travel Route Recommendation Based on Geotagged Photo Metadata

Travellers usually look for two kinds of information when they are planning a trip to a new destination: the points of interest (POI) and the interesting travel sequences given the POI in the destination. In recent years, due to the spread of the photo-taking gadgets with the global positioning system (GPS) functionality and the act of the travellers sharing and contributing photos on websites, such as Flickr and Panoramio, there are plenty of geotagged photos available on the Web. Through assembling diverse sets of geotagged photos shared by the travellers from the Web, the POI and the travel sequences given the POI in a destination can be mined if the travellers visit several POI in a day and take photos at each of the visited POI. In this paper, a web-based travel route recommendation system, namely Travel Route Recommendation System (TRRS), is presented. The purpose of this system is to generate and recommend travel route to the travellers who are visiting a destination for the first time and only for one day based on geotagged photo metadata.

DengueViz: A Knowledge-Based Expert System Integrated with Parallel Coordinates Visualization in the Dengue Diagnosis

The DengueViz is a knowledge-based expert system integrated with parallel coordinates as its visualization technique to diagnose dengue. The dengue diagnosis results includes the dengue classifications and their probability according to the interactions of users with the system. The knowledge base of this system consists of 140 rules for the classification of dengue. The integration of parallel coordinates visually presents the large amount of dengue information into a single visualization, where data interactions such as the selection of axes, filtering and highlighting reduces the clutter for it to be more comprehensible and enhances the correlation between the attributes of the information.

Integrated problem solving steering framework on clash reconciliation strategies for university examination timetabling problem

University Examination timetabling problem (UETP) is a hard combinatorial scheduling and it is fully automated. Visualization and steering of critical processing phases like examination clashes identification are essential for effective data analysis by examination timetabling research community. The visual analytics combined steering framework with interactive visualization techniques needs to take into account for both the inputs of the researcher (human timetabler) and the critical needs of the application including simulations and continuous visualization of significant events. In this work, we have developed an integrated problem solving environment (PSE) a computational steering mechanism for user-driven and automated steering interactions. The well sophisticated ExamViz user interface is for simulations, and analysis for critical conflict and reconciliation with visual cues. ExamViz provides the user steering control visualization over various parameters of the inputs, including the initial raw data solutions. The user are able to interact with conflicting data while the decision making algorithm in execution with visual cues to reconciliate the clashes and guide the user throughout the improvement of the solution. We have evaluated our approach for 13-real world examination timetabling problem (Carter et., al 1996) and the result are reported.