We propose a system, called EVolution of Long-term Composed Services (Ev-LCS), to address the change management issues in long-term composed services (LCSs). An LCS is a dynamic collaboration between autonomous web services that collectively provide a value-added service. It has a long-term commitment to its users. We first present a formal model, which provides the grounding semantics to support the automation of change management. We present a set of change operators that allow to specify a change in a precise and formal manner. We then propose a change enactment strategy that actually implements the changes. We develop a prototype system for the proposed Ev-LCS to demonstrate its effectiveness. We also conduct an experimental study to assess the performance of the change management approach.

XML has become the universal data format for a wide variety of information systems. The large number of XML documents existing on the web and in other information storage systems makes classification an important task. As a typical type of semistructured data, XML documents have both structures and contents. Traditional text learning techniques are not very suitable for XML document classification as structures are not considered. This paper presents a novel complete framework for XML document classification. We first present a knowledge representation method for XML documents which is based on a typed higher order logic formalism. With this representation method, an XML document is represented as a higher order logic term where both its contents and structures are captured. We then present a decision-tree learning algorithm driven by precision/recall breakeven point (PRDT) for the XML classification problem which can produce comprehensible theories. Finally, a semi-supervised learning algorithm is given which is based on the PRDT algorithm and the cotraining framework. Experimental results demonstrate that our framework is able to achieve good performance in both supervised and semi-supervised learning with the bonus of producing comprehensible learning theories.

In order for the Bureau of Meteorology (BOM), Australia, to build and maintain an integrated national water information system, over 240 organisations are required to provide their data to BOM. These organisations use a wide range of systems and data formats. To ensure robust and reliable data delivery, BOM has established Water Data Transfer Format (WDTF) as a standard format for data transfer. Meanwhile, the Water Regulations 2008 were enacted to specify the water information required from organisations. This paper analyses semantic gaps between data from organisations, WDTF, and the Regulations requirements, and proposes a knowledge-driven approach in which these gaps are captured in a way that facilitates data translation and validation. Throughout the paper, real data examples are used to illustrate the details of the approach and its feasibility.

We propose a service-oriented approach to generate and manage mashups. The proposed approach is realized using the Mashup Services System (MSS), a novel platform to support users to create, use, and manage mashups with little or no programming effort. The proposed approach relieves users from programming-intensive, error-prone, and largely nonreusable output process for creating and maintaining mashups. We describe the overall design of MSS and discuss and evaluate its main enabling technologies.