14000419
Using an experimental study to develop group awareness support for real-time distributed collaborative writing
Supporting group awareness is vital for the success of real-time, distributed, collaborative writing systems. Many awareness mechanisms have been introduced, but highly effective solutions are few. The research presented in this paper focuses on the development of awareness mechanisms using an experimental study of synchronous distributed collaborative writing. Our study has made two major contributions to research on group awareness. First, the study compares the importance of different awareness elements in supporting group awareness for collaborative writing. The results of our Wilcoxon test on awareness elements identify the five most important elements, including ‘‘Being able to comment on what other users have done,’’ ‘‘Knowing what actions other users are currently taking,’’ ‘‘Providing a communication tool when audio is not available,’’ ‘‘Knowing other user’s working areas in the document,’’ and ‘‘Knowing other user’s tasks.’’ Second, the research proposes mechanisms corresponding to the above-mentioned five awareness elements. The mechanisms include Dynamic Task List (DTL), Modification Director (MD), Advanced Chat (AC) and Split Window View (SWV). These mechanisms provide support for various aspects of group awareness, and add many enhanced features to existing awareness mechanisms. For example, DTL presents high-level information about authors’ responsibilities and the correlation between their work allocations. MD notifies users instantaneously whenever their work is modified by other authors. AC enhances communication between users by allowing them to attach document objects such as text and diagrams to a conversation message. And, SWV provides the views of other authors’ working areas and viewing areas simultaneously. � 2005 Elsevier B.V. All rights reserved.
38554422
Coordination for Process Support is not Enough!
2913360
Dynamic verification of temporal constraints on-the-fly for workflow systems
Temporal verification is an important method to check the temporal correctness of workflow management systems (WfMSs). However, the current temporal verification is relatively independent of the workflow system environments. It does not pay sufficient attention to the interrelationship between the temporal verification at different stages, the mutual dependency between some temporal constraints, and the run-time checkpoint selection strategy, which hence hinders the incorporation and consistency between the temporal verification and the workflow system environments. In this paper, we effectively integrate the temporal verification at different stages and explore the dependency between some temporal constraints. In addition, we present a new effective runtime checkpoint selection strategy which dynamically selects appropriate checkpoints based on the activity completion duration. Furthermore, based on these analyses, we develop some new methods for more efficient temporal verification. These analyses and new methods help to eliminate the gap between the temporal verification and the workflow system environments.
16079123
Agent-Based Ontology Mapping Towards Ontology Interoperability
Globalisation of business environments on the Web has given rise to the advent of similar ontologies in which dynamic ontology mapping is called in. Ontology mapping is necessary for ontology interoperability. In this paper, a novel agent-based ontology mapping is presented for agents to operate ontology mapping flexibly in a dynamic environment regardless heterogeneous platforms and different ontology representations. The mapping mechanism is discussed by having a close look at both inherent inter-processes of mapping tasks of an agent and relevant interaction processes. The interrelated processes of agents also enable agent-based ontology mapping to take ontology changes into account whenever needed. A mapping prototype is built for verification.
17661227
RDF-based Knowledge Models for Network Management
SMIng (next generation structure of management information), an information model for network management, is a prospective structure of management information. When deploying the multi-agent systems to network management environments, we have established a lightweight self-contained knowledge model based on RDF (resource description framework) and its extensions. We also present an implementation prototype to support agent communication and coordination by RDF-based languages.
4914546
Multiple temporal consistency states for dynamical verification of upper bound constraints in grid workflow systems
Conventional upper bound constraint verification in grid workflow systems is based on the key assumption that an upper bound constraint only has two states: consistency and inconsistency. However, due to complexity of grid workflows and dynamic availability of participating grid services, this assumption is too restrictive as there may be some intermediate states. Therefore, in this paper, we introduce four states for an upper bound constraint. Namely, we treat conventional consistency as strong consistency and divide conventional inconsistency into weak consistency, weak inconsistency and strong inconsistency. Correspondingly, we develop their verification methods. For weak consistency, we present some algorithms on how to adjust it to strong consistency without triggering exception handling as in conventional work. For weak inconsistency, we analyse why it can rely on less costly exception handling than conventional work. The final evaluation demonstrates that our four-state approach can achieve better cost-effectiveness than the conventional two-state approach
241209
Transient behaviors of TCP-friendly congestion control protocols
We investigate the fairness, smoothness, responsiveness, and aggressiveness of TCP and three representative TCP-friendly congestion control protocols: GAIMD, TFRC, and TEAR. The properties are evaluated both analytically and via simulation by studying protocol responses to three network environment changes. The first environment change is the inherent fluctuations in a stationary network environment. Under this scenario, we consider three types of sending rate variations: smoothness, short-term fairness, and long-term fairness. For a stationary environment, we observe that smoothness and fairness are positively correlated. We derive an analytical expression for the sending rate coefficient of variation for each of the four protocols. These analytical results match well with experimental results. The other two environment changes we study are a step increase of network congestion and a step increase of available bandwidth. Protocol responses to these changes reflect their responsiveness and aggressiveness, respectively.
14162405
Dynamic classifier selection for effective mining from noisy data streams
Mining from data streams has become an important and challenging task for many real-world applications such as credit card fraud protection and sensor networking. One popular solution is to separate stream data into chunks, learn a base classifier from each chunk, and then integrate all base classifiers for effective classification. In this paper, we propose a dynamic classifier selection (DCS) mechanism to integrate base classifiers for effective mining from data streams. The proposed algorithm dynamically selects a single "best" classifier to classify each test instance at run time. Our scheme uses statistical information from attribute values, and uses each attribute to partition the evaluation set into disjoint subsets, followed by a procedure that evaluates the classification accuracy of each base classifier on these subsets. Given a test instance, its attribute values determine the subsets that the similar instances in the evaluation set have constructed, and the classifier with the highest classification accuracy on those subsets is selected to classify the test instance. Experimental results and comparative studies demonstrate the efficiency and efficacy of our method. Such a DCS scheme appears to be promising in mining data streams with dramatic concept drifting or with a significant amount of noise, where the base classifiers are likely conflictive or have low confidence.
13451873
The Enron Corpus: A New Dataset for Email Classification Research
Automated classification of email messages into user-specific folders and information extraction from chronologically ordered email streams have become interesting areas in text learning research. However, the lack of large benchmark collections has been an obstacle for studying the problems and evaluating the solutions. In this paper, we introduce the Enron corpus as a new test bed. We analyze its suitability with respect to email folder prediction, and provide the baseline results of a state-of-the-art classifier (Support Vector Machines) under various conditions, including the cases of using individual sections (From, To, Subject and body) alone as the input to the classifier, and using all the sections in combination with regression weights.
7877505
Three forbidden subgraphs for line graphs
We construct a set of three graphs with the property that a 3-connected graph with minimum degree at least 7 is a line graph if and only if it does not contain any of the three graphs as an induced subgraph. We also show that the number of excluded subgraphs cannot be reduced no matter what connectivity is specified.
7098682
High-performing feature selection for text classification
This paper reports a controlled study on a large number of filter feature selection methods for text classification. Over 100 variants of five major feature selection criteria were examined using four well-known classification algorithms: a Naive Bayesian (NB) approach, a Rocchio-style classifier, a k-nearest neighbor (kNN) method and a Support Vector Machine (SVM) system. Two benchmark collections were chosen as the testbeds: Reuters-21578 and small portion of Reuters Corpus Version 1 (RCV1), making the new results comparable to published results. We found that feature selection methods based on chi2 statistics consistently outperformed those based on other criteria (including information gain) for all four classifiers and both data collections, and that a further increase in performance was obtained by combining uncorrelated and high-performing feature selection methods.The results we obtained using only 3% of the available features are among the best reported, including results obtained with the full feature set.