A Collaborative Visualization Framework Using JINI™ Technology

It is difficult to achieve mutual understanding of complex information between individuals that are separated geographically. Two well-known techniques commonly used to deal with this difficultly are collaboration and information visualization. This thesis develops a generic flexible framework that supports both collaboration and information visualization. It introduces the Collaborative Visualization Environment (COVE) framework, which simplifies the development of real-time synchronous multi-user applications by decoupling the elements of collaboration from the application. This allows developers to focus on building applications and leave the difficulties of collaboration (i.e., concurrency controls, user awareness, session management, etc.) to the framework. The framework uses an object sharing approach to share information and views between participants in a collaborative session. This approach takes advantage of several Java technologies (i.e., JavaBeans™, Jini™, and JavaSpaces™). JavaBeans™ establish a well-known standard for applications to operate within the framework. Jini™ services provide framework stability and enable code sharing across the network. Objects are shared between remote clients through the JavaSpaces™ service

Engineering Automation for Reliable Software Interim Progress Report (10/01/2000 - 09/30/2001)

Prepared for: U.S. Army Research Office P.O. Box 12211 Research Triangle Park, NC 27709-2211The objective of our effort is to develop a scientific basis for producing reliable software that is also flexible and cost effective for the DoD distributed software domain. This objective addresses the long term goals of increasing the quality of service provided by complex systems while reducing development risks, costs, and time. Our work focuses on "wrap and glue" technology based on a domain specific distributed prototype model. The key to making the proposed approach reliable, flexible, and cost-effective is the automatic generation of glue and wrappers based on a designer's specification. The "wrap and glue" approach allows system designers to concentrate on the difficult interoperability problems and defines solutions in terms of deeper and more difficult interoperability issues, while freeing designers from implementation details. Specific research areas for the proposed effort include technology enabling rapid prototyping, inference for design checking, automatic program generation, distributed real-time scheduling, wrapper and glue technology, and reliability assessment and improvement. The proposed technology will be integrated with past research results to enable a quantum leap forward in the state of the art for rapid prototyping.U. S. Army Research Office P.O. Box 12211 Research Triangle Park, NC 27709-22110473-MA-SPApproved for public release; distribution is unlimited

Collaborative Ontology Development — Distributed Architecture and Visualization

In this paper we present the architecture of the browser-based community-driven ontology engineering platform Ontoverse. We will present the architectural needs and designs for an extensible collaborative ontology platform as well as the current implementation based on tuplespaces. In this context we briefly introduce the SQLSpaces and the Semantic Web Application Toolkit (SWAT). To provide interactive collaborative means for editing, merging, and discussing about ontologies adequate visualization techniques are needed to support the ontology designers and ontology users. Therefore we introduce a visualization method called SmartTree that implements focus and context techniques

Using policy to control data synchronisation in middleware for an ad-hoc mobile network

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

System engineering and evolution decision support, Final Progress Report (05/01/1998 - 09-30-2001)

The objective of our effort is to develop a scientific basis for system engineering automation and decision support. This objective addresses the long term goals of increasing the quality of service provided complex systems while reducing development risks, costs, and time. Our work focused on decision support for designing operations of complex modular systems that can include embedded software. Emphasis areas included engineering automation capabilities in the areas of design modifications, design records, reuse, and automatic generation of design representations such as real-time schedules and software.U.S. Army Research OfficeFunding number(s): DSAM 90387, DWAM 80013, DWAM 90215

Data Driven Adaptation of Heterogeneous Service-Oriented Processes

Η με βάση τα δεδομένα προσαρμογή διαδικασιών αποτελεί μια επέκταση της έννοιας των Δυναμικών και με βάση τα Δεδομένα Καθοδηγουμενων Συστήματων (DDDAS) όπως αυτά έχουν καθοριστεί από την Δαρεμά. Συγεκριμένα όπως και στα DDDAS συστήματα η προσέγγιση μας επιτρέπει την προσφορά προσαρμοζόμενων διαδικασιών χρησιμοποιώντας διαθέσιμες πληροφορίες και υπηρεσίες. H προσφορά προσαρμοζόμενων διαδικασιών περιλαμβάνει την αναγνώριση και χρήση πιθανών εναλλακτικών μονοπατιών εκτέλεσης (ή διαδρομών) για την επίτευξη των στόχων και υπό-στόχων της κάθε διαδικασίας. Τα εναλλακτικά μονοπάτια λαμβάνουν υπόψη και χρησιμοποιούν σχετικές πληροφορίες ή/και υπηρεσίες (ή συνθέσεις υπηρεσιών). Για την αναζήτηση των πιθανών εναλλακτικών χρησιμοποιούνται τεχνικές από το χώρο της Τεχνητής Νοημοσύνης Σχεδιασμού (AI Planning) και της υπολογιστικής Πλαισίου (Context-Aware computing) κατά τον χρόνο διάθεσης της διαδικασίας. Κατά τον υπολογισμό των πιθανών εναλλακτικών, στόχος της προσέγγισης μας είναι η μείωση των βημάτων εκτέλεσης, δλδ του πλήθους των εργασιών της διαδικασίας που έχουν οριστείIn principle the Data-Driven Process Adaptation (DDPA) approach is based on the concept of Dynamic Data Driven Application Systems (DDDAS) as this is stated by Darema in [8]. In accordance to the DDDAS notion such systems support the utilization of appropriate information at specific decision points so as to make real systems more efficient. In this regard, DDPA accommodates the provision of adaptable service processes by exploiting the use of information available to the process environment in addition to existing services. Adaptation in the context of our approach includes the identification and use of possible alternatives for the achievement of the goals and sub-goals defined in a process; alternatives include the utilization of available related information and/or services (or service chains). Data-Driven adaptation incorporates AI planning and Context-Aware Computing techniques to support the identification of possible alternatives at deployment time. When calculating the possible alternatives the goal of our approach is to reduce the number of steps, i.e. number of process tasks, defined in the original process

Coordinated collaboration for e-commerce based on the multiagent paradigm.

Lee Ting-on.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.Includes bibliographical references (leaves 116-121).Abstracts in English and Chinese.Acknowledgments --- p.iAbstract --- p.iiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Roadmap to the Thesis --- p.5Chapter 2 --- Software Agents and Agent Frameworks --- p.7Chapter 2.1 --- Software Agent --- p.7Chapter 2.1.1 --- Advantages of Agent --- p.10Chapter 2.1.2 --- Roles of Agent --- p.11Chapter 2.2 --- Agent Frameworks --- p.13Chapter 2.3 --- Communication Services and Concepts --- p.15Chapter 2.3.1 --- Message Channel --- p.15Chapter 2.3.2 --- Remote Procedure Call --- p.16Chapter 2.3.3 --- Event Channel --- p.17Chapter 2.4 --- Component --- p.18Chapter 3 --- Related Work --- p.20Chapter 3.1 --- Collaboration Behaviors --- p.20Chapter 3.2 --- Direct Coordination --- p.22Chapter 3.3 --- Meeting-oriented Coordination --- p.23Chapter 3.4 --- Blackboard-based Coordination --- p.24Chapter 3.5 --- Linda-like Coordination --- p.25Chapter 3.6 --- Reactive Tuple Spaces --- p.26Chapter 4 --- Background and Foundations --- p.27Chapter 4.1 --- Choice of Technologies --- p.27Chapter 4.2 --- Jini Technology --- p.28Chapter 4.2.1 --- The Lookup Service --- p.29Chapter 4.2.2 --- Proxy --- p.31Chapter 4.3 --- JavaSpaces --- p.32Chapter 4.4 --- Grasshopper Architecture --- p.33Chapter 5 --- The CoDAC Framework --- p.36Chapter 5.1 --- Requirements for Enabling Collaboration --- p.37Chapter 5.1.1 --- Consistent Group Membership --- p.37Chapter 5.1.2 --- Atomic Commitment --- p.39Chapter 5.1.3 --- Uniform Reliable Multicast --- p.40Chapter 5.1.4 --- Fault Tolerance --- p.40Chapter 5.2 --- System Components --- p.41Chapter 5.2.1 --- Distributed Agent Adapter --- p.42Chapter 5.2.2 --- CollaborationCore --- p.44Chapter 5.3 --- System Infrastructure --- p.45Chapter 5.3.1 --- Agent --- p.45Chapter 5.3.2 --- Distributed Agent Manager --- p.46Chapter 5.3.3 --- Collaboration Manager --- p.46Chapter 5.3.4 --- Kernel --- p.46Chapter 5.4 --- Collaboration --- p.47Chapter 5.5.1 --- Global Collaboration --- p.48Chapter 5.5.2 --- Local Collaboration --- p.48Chapter 6 --- Collaboration Life Cycle --- p.50Chapter 6.1 --- Initialization --- p.50Chapter 6.2 --- Resouces Gathering --- p.53Chapter 6.3 --- Results Delivery --- p.54Chapter 7 --- Protocol Suite --- p.55Chapter 7.1 --- The Group Membership Protocol --- p.56Chapter 7.1.1 --- Join Protocol --- p.56Chapter 7.1.2 --- Leave Protocol --- p.57Chapter 7.1.3 --- Recovery Protocol --- p.59Chapter 7.1.4 --- Proof --- p.61Chapter 7.2 --- Atomic Commitment Protocol --- p.62Chapter 7.3 --- Uniform Reliable Multicast --- p.63Chapter Chapter 8 --- Implementation --- p.66Chapter 8.1 --- Interfaces and Classes --- p.66Chapter 8.1.1 --- The CoDACAdapterInterface --- p.66Chapter 8.1.2 --- The CoDACEventListener --- p.69Chapter 8.1.3 --- The DAAdapter --- p.71Chapter 8.1.4 --- The DAManager --- p.75Chapter 8.1.5 --- The CoDACInternalEventListener --- p.77Chapter 8.1.6 --- The CollaborationManager --- p.77Chapter 8.1.7 --- The CollaborationCore --- p.78Chapter 8.2 --- Messaging Mechanism --- p.79Chapter 8.3 --- Nested Transaction --- p.84Chapter 8.4 --- Fault Detection --- p.85Chapter 8.5 --- Atomic Commitment Protocol --- p.88Chapter 8.5.1 --- Message Flow --- p.89Chapter 8.5.2 --- Timeout Actions --- p.91Chapter Chapter 9 --- Example --- p.93Chapter 9.1 --- System Model --- p.93Chapter 9.2 --- Auction Lifecycle --- p.94Chapter 9.2.1 --- Initialization --- p.94Chapter 9.2.2 --- Resource Gathering --- p.98Chapter 9.2.3 --- Results Delivery --- p.100Chapter Chapter 10 --- Discussions --- p.104Chapter 10.1 --- Compatibility --- p.104Chapter 10.2 --- Hierarchical Group Infrastructure --- p.106Chapter 10.3 --- Flexibility --- p.107Chapter 10.4 --- Atomicity --- p.108Chapter 10.5 --- Fault Tolerance --- p.109Chapter Chapter 11 --- Conclusion and Future Work --- p.111Chapter 11.1 --- Conclusion --- p.111Chapter 11.2 --- Future Work --- p.112Chapter 11.2.1 --- Electronic Commerce --- p.112Chapter 11.2.2 --- Workflow Management --- p.114Bibliography --- p.116Publication List --- p.12