7236224
Classification of and experimentation on tool interfacing in software development environments
The effectiveness of tool integration in software development environments is ultimately measured by its ability in improving the productivity of the user. Traditionally, issues related to tool integration are often classified according to aspects of such mechanisms as user interface, control, and data integration which are purely from the viewpoint of the environment developer. This mechanism-based classification has worked well in analyzing the relevant issues of tool integration. However, its usefulness as a guide to achieving effective tool integration is limited. The authors introduce an alternative classification scheme aimed at gradual improvement to the effectiveness of tool integration. As such, this classification scheme provides a guide to tool integration along the line of improving the user's productivity. In terms of this classification, they compare and experiment in depth a variety of integration techniques using a uniform tool interfacing architecture.
3428244
An Organisational Perspective on Collaborative Business Processes
Business collaboration is about coordinating the flow of information among organisations and linking their business processes. It brings great challenge to keep participating organisations as autonomous entities in integrating business processes of these organisations seamlessly. To address this issue, we develop a new perspective on business collaborations with a novel concept called relative workflow, which defines what a participating organisation can perceive in collaboration. By incorporating a visibility control mechanism, relative workflows allow each organisation to define its own collaboration structure and behaviours. In this paper, we present a formal definition of relative workflows and related algorithms for generating relative workflows, along with a discussion on how to perform tracking over relative workflows.
1701823
Dynamic checking of temporal constraints for concurrent workflows
At present, workflow management systems have not sufficiently dealt with the issues of time, involving time modelling at build-time and time management at run-time. They are lack of the ability to support the checking of temporal constraints at run-time. Although some approaches have been devised to tackle this problem, they are limited to a single workflow and use only static techniques to verify temporal constraints. In reality, there are multiple workflows executing concurrently in a workflow management system. There may well exist resource constraints between these concurrent workflows, which affect significantly the verification of temporal constraints at run-time. This paper proposes a novel approach for dynamic verification of temporal constraints for concurrent workflows. We first investigate resource constraints in workflow management systems, and then define concurrent workflow executions. Based on these definitions, we propose a verification method by analysing the temporal relationship and resource constraints between activities among concurrent workflows.
4924842
Assigning Local Fixed-time Constraints in Grid Workflow Systems
Many complex grid workflow processes often have only one global fixed-time constraint at the last activity. Since grid workflow execution normally lasts a long time, local control is very important to ensure the overall temporal correctness. With only one global fixed-time constraint, we cannot control grid workflow execution locally in terms of time. As a result, for any temporal violations, we must handle them globally with the consideration of all activities. This would impact the overall cost-effectiveness of grid workflow execution. Therefore, in this paper, we investigate how to assign local fixed-time constraints within the global one so that we can control grid workflow execution locally. Corresponding assigning and verification methods are developed. With local fixed-time constraints, we can achieve better cost-effectiveness
4928164
A service workflow management framework based on peer-to-peer and agent technologies
Service workflow management which consolidates multiple services to achieve business goals has become a critical issue in service-oriented computing. Due to the unique feature of service workflows such as full distribution and high dynamicity, the contemporary workflow technology has exhibited inefficiency, vulnerability, inflexibility and lack of adaptability. Based upon the authors' previous work on peer-to-peer based workflow management and seamless integration of agents and Web services, this paper innovatively presents SwinDeW-A which is a decentralised service workflow management framework based on peer-to-peer and agent technologies. Based on this framework, this paper also discusses how autonomous service level agreement negotiation, decentralised process enactment, and autonomous process adaptation can be achieved. The proposed approach takes advantages of the peer-to-peer computing technology and the agent technology to provide more efficient, reliable, flexible and adaptive service workflow management.
14447070
Achieving convergence, causality preservation, and intention preservation in real-time cooperative editing systems
Real-time cooperative editing systems allow multiple users to view and edit the same text/graphic/image/multimedia document at the same time for multiple sites connected by communication networks. Consistency maintenance is one of the most significant challenges in designing and implementing real-time cooperative editing systems. In this article, a consistency model, with properties of convergence, causality preservation, and intention preservation, is proposed as a framework for consistency maintenance in real-time cooperative editing systems. Moreover, an integrated set of schemes and algorithms, which support the proposed consistency model, are devised and discussed in detail. In particular, we have contributed (1) a novel generic operation transformation control algorithm for achieving intention preservation in combination with schemes for achieving convergence and causality preservation and (2) a pair of reversible inclusion and exclusion transformation algorithms for stringwise operations for text editing. An Internet-based prototype system has been built to test the feasibility of the proposed schemes and algorithms
14899560
Dealing with Predictive-but-Unpredictable Attributes in Noisy Data Sources
Attribute noise can affect classification learning. Previous work in handling attribute noise has focused on those predictable attributes that can be predicted by the class and other attributes. However, attributes can often be predictive but unpredictable. Being predictive, they are essential to classification learning and it is important to handle their noise. Being unpredictable, they require strategies different from those of predictable attributes. This paper presents a study on identifying, cleansing and measuring noise for predictive-but-unpredictable attributes. New strategies are accordingly proposed. Both theoretical analysis and empirical evidence suggest that these strategies are more effective and more efficient than previous alternatives.
17059698
Towards composing information providing services
Composing Web services is an active field of research and development. Recently, many efforts are devoted to process integration on world changing services, e.g. business process composition. We present a solution to compose information providing services to achieve more powerful capabilities and free the user from trivial work. Our work serves as an important complement to the existing research on process integration.
295949
A Study of Approaches to Hypertext Categorization
Hypertext poses new research challenges for text classification. Hyperlinks, HTML tags, category labels distributed over linked documents, and meta data extracted from related Web sites all provide rich information for classifying hypertext documents. How to appropriately represent that information and automatically learn statistical patterns for solving hypertext classification problems is an open question. This paper seeks a principled approach to providing the answers. Specifically, we define five hypertext regularities which may (or may not) hold in a particular application domain, and whose presence (or absence) may significantly influence the optimal design of a classifier. Using three hypertext datasets and three well-known learning algorithms (Naive Bayes, Nearest Neighbor, and First Order Inductive Learner), we examine these regularities in different domains, and compare alternative ways to exploit them. Our results show that the identification of hypertext regularities in the data and the selection of appropriate representations for hypertext in particular domains are crucial, but seldom obvious, in real-world problems. We find that adding the words in the linked neighborhood to the page having those links (both inlinks and outlinks) were helpful for all our classifiers on one data set, but more harmful than helpful for two out of the three classifiers on the remaining datasets. We also observed that extracting meta data from related Web sites was extremely useful for improving classification accuracy in some of those domains. Finally, the relative performance of the classifiers being tested provided insights into their strengths and limitations for solving classification problems involving diverse and often noisy Web pages.
3895085
Network localization in partially localizable networks
Knowing the positions of the nodes in a network is essential to many next generation pervasive and sensor network functionalities. Although many network localization systems have recently been proposed and evaluated, there has been no systematic study of partially localizable networks, i.e., networks in which there exist nodes whose positions cannot be uniquely determined. There is no existing study which correctly identifies precisely which nodes in a network are uniquely localizable and which are not. This absence of a sufficient uniqueness condition permits the computation of erroneous positions that may in turn lead applications to produce flawed results. In this paper, in addition to demonstrating the relevance of networks that may not be fully localizable, we design the first framework for two dimensional network localization with an efficient component to correctly determine which nodes are localizable and which are not. Implementing this system, we conduct comprehensive evaluations of network localizability, providing guidelines for both network design and deployment. Furthermore, we study an integration of traditional geographic routing with geographic routing over virtual coordinates in the partially localizable network setting. We show that this novel cross-layer integration yields good performance, and argue that such optimizations will be likely be necessary to ensure acceptable application performance in partially localizable networks.
7913579
Robustness of adaptive filtering methods in a cross-benchmark evaluation
This paper reports a cross-benchmark evaluation of regularized logistic regression (LR) and incremental Rocchio for adaptive filtering. Using four corpora from the Topic Detection and Tracking (TDT) forum and the Text Retrieval Conferences (TREC) we evaluated these methods with non-stationary topics at various granularity levels, and measured performance with different utility settings. We found that LR performs strongly and robustly in optimizing T11SU (a TREC utility function) while Rocchio is better for optimizing Ctrk (the TDT tracking cost), a high-recall oriented objective function. Using systematic cross-corpus parameter optimization with both methods, we obtained the best results ever reported on TDT5, TREC10 and TREC11. Relevance feedback on a small portion (0.05~0.2%) of the TDT5 test documents yielded significant performance improvements, measuring up to a 54% reduction in Ctrk and a 20.9% increase in T11SU (with b=0.1), compared to the results of the top-performing system in TDT2004 without relevance feedback information.