Flora Dilys Salim

Context-Aware ubiquitous data mining based agent model for intersection safety

In USA, 2002, approximately 3.2 million intersection-related crashes occurred, corresponding to 50 percent of all reported crashes. In Japan, more than 58 percent of all traffic crashes occur at intersections. With the advances in Intelligent Transportation Systems, such as off-the-shelf and in-vehicle sensor technology, wireless communication and ubiquitous computing research, safety of intersection environments can be improved. This research aims to investigate an integration of intelligent software agents and ubiquitous data stream mining, for a novel context-aware framework that is able to: (1) monitor an intersection to learn for patterns of collisions and factors leading to a collision; (2) learn to recognize potential hazards in intersections from information communicated by road infrastructures, approaching and passing vehicles, and external entities; (3) warn particular threatened vehicles that are approaching the intersection by communicating directly to the in-vehicle system.

UML documentation support for XML schema

With the proliferation of XML as the lingua franca of Internet information exchange, engineering XML documents and maintaining their databases becomes a major challenge. In this context, the use of XML schema to constrain and validate XML document content is becoming a standard part of web information production. However, adoption of XML Schema poses new problems; specifically, the difficulty in understanding even a relatively small schema due to its textual nature and XML syntax. This problem can be addressed by use of a graphical modelling language such as UML to document XML schema and conforming XML document structure. We discuss the issues involved in representing XML Schema using UML and present a transformation approach which is both comprehensive and thorough.

U&I Aware: A Framework Using Data Mining and Collision Detection to Increase Awareness for Intersection Users

An intersection safety system should adapt to the particular characteristics that identify an intersection, by mining traffic and collision data. Given the large amount of sensor data that are obtained for intersections and from sensor-equipped cars, analysis and learning of such data is essential. This paper presents a new method to improve safety at intersections using a combination of a mathematical based collision detection algorithm and data mining. A number of scenarios at a simulated intersection are explored with encouraging results from our data mining implementation. The results suggest that our approach can help improve situation awareness and automate understanding of intersections, which, in turn, can be used to increase safety at intersections.

Graphical Representation of XML Schema

XML is becoming the de-facto standard for exchanging information in distributed applications and services. This has resulted in the development of a large number of XML documents with their associated schemas, such as DTD and XML Schema. A major challenge in using XML Schema is the difficulty in reading and understanding even a relatively small XML Schema because of its textual nature and its XML syntax. In this paper, we present transformations from textual XML Schema to graphical UML to facilitate understanding of XML Schema. Our transformation approach is unique in that we focus on all thirteen building blocks of XML Schema and is based on existing UML notation without introducing new stereotypes that would require additional user training. Keywords: XML Schema, UML, transformation, graphical modelling, conceptual, reverse engineering.

Road Intersections as Pervasive Computing Environments: Towards a Multiagent Real-Time Collision Warning System

Embedded with sensors and appropriate computational entities, a road intersection can be viewed as a pervasive computing environment. The crash rate in road intersections demonstrates the need for a fast and accurate collision detection system. We suggest that an intersection collision detection system should be able to adapt to different types of intersections for faster collision detection. Moreover, a real-time application-level communication protocol to warn affected drivers is required. An intersection agent that takes vehicular status information from vehicle agents and learns, detects and warns collisions at a road intersection is proposed. The issues, challenges, and cost of a multiagent collision avoidance system are discussed. A communication protocol that is designed specifically with intersection safety in mind is presented here.