Maria Indrawan

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.

Coalition formation protocol for e-commerce

Coalitions are considered beneficial in a market place. In this paper, we present a negotiation framework in e-market place that support the formation of coalitions. Unlike some of the existing models, the proposed framework supports multi-attribute coalition negotiation, The proposed framework is also designed with flexibility in mind in order to allow the choice of proposal generation and evaluation to be decided at the implementation level.

Countering Security Vulnerabilities in Agent Execution Using a Self Executing Security Examination

The paper describes the evolution and implementation of a self-executing security examination (SENSE) for agents executing in non-trusted domains. It outlines the shortcomings of some of the existing agent security schemas. To meet these shortcomings, the paper proposes the implementation of SENSE as a reliable method for detecting a malicious action attempted on an executing agent at run-time. The incorporation of SENSE into its normal processing operation will make the agent self-reliant with respect to its security function and allow it to confidently execute in an alien environment, without having to depend on an external support for security. To build a strong case for its adoption, the paper elaborates the ease of operation, simplicity and efficiency of the schema.

Background knowledge driven ontology discovery

We have previously proposed a GSOM-based hybrid model for automatic discovery of ontology, the first step towards semi-automation of ontology construction. One of the shortcomings of this previous model is the use of a threshold for selecting abstraction levels. The threshold might introduce an inappropriate concept and cause information loss. In this paper, we introduce a new parameter called context ratio (cr) to overcome this drawback. The cr is used as stopping criteria for traversing hypernyms and identifying appropriate abstraction levels. It allows us to extend the previously proposed framework to integrate methodology for multiple inheritance validation in the discovered ontology.

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.

A general inference network based architecture for multimedia information retrieval

Bayesian inference networks have found application in probabilistic information retrieval in the context of textual documents. The paper outlines an architecture whereby an inference network retrieval engine can be applied to multimedia retrieval. Initially, the system was developed to support the content based retrieval of images, however extensions are described so that retrieval of more general multimedia data may be performed.

Using mobile agents to support unreliable database retrieval operations

One major drawback in using a client server system in the mobile environment is the redundancy of data transmission in the event of connection failure. In this paper, we present a novel mobile agent architecture which allows minimum redundancy of data transmission in a client server database connection in a frequent disconnections environment. The system supports disconnection events caused by network failure or client power failure. Unlike current approaches, our model uses agent messaging to transfer the data. The messaging reduces the amount of migrations that the mobile agents performed. The architecture was implemented and tested in the Aglets platform.

Device Ecology: A Micro Digital Ecosystem

A digital ecosystem usually refers to a collection of small and medium enterprise businesses that interacts closely as a system. In this paper, we present a different type of digital ecosystems. We introduce the idea of creating an ecosystem from a number of smart devices. This ecosystem is categorised as a micro ecosystem rather than macro ecosystem. The proposed model of this digital ecosystem is called device ecology.

AgentUDM: a mobile agent based support infrastructure for ubiquitous data mining

We present AgentUDM a mobile agent based support infrastructure for ubiquitous data mining (UDM). The focus of AgentUDM is to address the issues of resource-constraints and disconnections with respect to UDM.

Optimising Bayesian belief networks: a case study of information retrieval systems

Bayesian belief networks have been used widely to solve many decision problem that involve uncertainty. One major advantage of this approach compared with other reasoning tools is its semantic richness in describing the decision process. Some inference algorithms for carrying out the reasoning process exist, but they are known to be computationally expensive. Hence, they require optimisation to make them practical. This paper proposes two optimisation techniques for Bayesian belief networks. These optimisation techniques were investigated for information retrieval applications, but can also be applied to different applications outside the information retrieval area.