The 19thedition of the “Adaptive Computing (and Agents) for Enhanced Collaboration” (ACEC) track at WETICE 2021 targets the relevant topic of adaptation in relation to tools, methods and techniques for the enterprise. The aim of the track is to bring together researches and practitioners, working on agentbased software systems and/or adaptive computing, to present results and discuss innovative ideas. This report outlines the content of the papers accepted for presentation at the track.

Online social media platforms like Twitter, provide opinion rich repositories for conducting sentiment analysis. Users engage in open discussions on a variety of topics across a wide cross-section of problem domains. Commercial, government, educational, non-profit and other types of agencies are increasingly relying on extracting conversations on Twitter to determine the general sentiment of the public on particular topics, products, services and issues. Despite being readily available and in abundance, it is also laced with nuances which can disrupt, skew and potentially lead to inaccurate analysis if not handled properly. In this paper, we propose an SOA framework to enable the pre-processing of data origination on Twitter, and configurable components that allow data consumers to filter the data using useful social media signals.

The 17th edition of the "Adaptive Computing (and Agents) for Enhanced Collaboration" (ACEC) track at WETICE 2019 focuses mostly on the area of adaptive agent-based techniques for the enterprise. The aim of the track is to bring together researches and practitioners from the fields of software agents and adaptive computing to present results and discuss innovative ideas. This report outlines the content of papers accepted for presentation at the track.

The six papers in this special section focuses on services and software computing. Services computing provides a foundation to build software systems and applications over the Internet as well as emerging hybrid networked platforms motivated by it. Due to the open, dynamic, and evolving nature of the Internet, new features were born with these Internet-scale and service-based software systems. Such systems should be situation- aware, adaptable, and able to evolve to effectively deal with rapid changes of user requirements and runtime contexts. These emerging software systems enable and require novel methods in conducting software requirement, design, deployment, operation, and maintenance beyond existing services computing technologies. New programming and lifecycle paradigms accommodating such Internet- scale and service-based software systems, referred to as Internetware, are inevitable. The goal of this special section is to present the innovative solutions and challenging technical issues, so as to explore various potential pathways towards Internet-scale and service-based software systems.

The 16th edition of the "Adaptive Computing (and Agents) for Enhanced Collaboration" (ACEC) track at WETICE 2018 focuses mostly on the area of adaptive agent-based techniques for the enterprise. The objective of the track is to bring together researches from the fields of software agents and adaptive computing to present results and discuss in the context of adaptive techniques and enterprise collaboration. This report briefly reports on the content of accepted papers.

State-of-the-art practices have recognized the utility of leveraging human intervention as a crucial aspect of modern computing systems. The emerging crowdsourcing paradigm is based on harnessing human intelligence, effort and rational behaviors to augment computation and analysis. In addition to the crowdsourcing paradigm, new techniques have emerged that incorporate machine and human computational resources together forming a hybrid intelligence when addressing complex problems and tasks. This combined technique is particularly impactful if human and machine contributions can scale automatically in response to their respective efficiency and effectiveness when addressing subsets of a bigger problem - an approach that we have named mixed elastic systems. In this survey, we highlight state-of-the-art projects that investigate crowdsourcing, hybrid intelligence systems and mixed elastic systems. We also present a taxonomy and classification of the broader domain of human-enhanced computing systems as it assimilates crowdsourcing, hybrid intelligence, and mixed elastic systems.

The 15th edition of the "Adaptive Computing (and Agents) for Enhanced Collaboration" track at WETICE 2017 focuses on the areas of adaptive agent-based services and adaptive techniques for enterprises. The purpose of the track is to bring together researches in the fields of software agents and adaptive computing as they relate to the context of adaptive techniques and enterprise collaboration. This report briefly discusses the content of the papers accepted for presentation in the track.

Crowdsourcing labor market platforms consist of a variety of jobs spanning multiple problem domains and their respective specialized or diverse worker pools. Each platform currently operates independently and isolated from the potential benefits of sharing job and worker pool data across platforms. Previous work introduces infrastructure that optimizes the sharing of both job and worker data collectively, called the open push-pull model. In this paper, to support automated recommendation of workers, we introduce an interoperability standard and computational method that facilitates the aggregation of job data while supporting scalability in response to increasing volumes of data. (i.e. workers and jobs continuously entering the system).

Generally, in crowdsourcing, providers advertise their task offerings (i.e. the open call model) largely to crowdworkers who subscribe their interest in working (i.e. subscription model). The combined open call and subscription model represent significant bottlenecks for recruitment in the paradigm of crowdsourcing. Consequently, attracting and retaining a crowd are the major challenges to the success of a crowdsourcing platform and forming a labor market. To address this problem, we introduce a worker-job matching model for crowdsourcing supported by a service-oriented architecture. The service-oriented architecture implements a push-pull mechanism and an underlying algorithm based on collaborative filtering techniques. Preliminary studies show that the infrastructure can effectively infer the levels of expertise of potential crowdworkers based on their profile and past performance history.

This report describes the culmination of the 14th installment of the "Adaptive Computing (and Agents) for Enhanced Collaboration" (ACEC) track. ACEC focuses on collaborative computing that occurs dynamically among web-accessible software systems and devices. The articulation of these network artifacts must be controlled by adaptive sometimes intelligent control mechanisms, sometimes realizes as agents. ACEC have for over 14 years brought together researchers from multiple disciplines to address the challenges associated with effective, just-in-time collaboration. The track this year showcases perspective papers that address adaptive approaches for socio-technical systems, in different contexts such as smart spaces and crowd sourcing.

While the crowd sourcing paradigm facilitates the use of human-enacted resources from large groups of individuals, matching workers with jobs is limited by the need for these potential workers to proactively subscribe to various networks. This subscription phase is part of an "open call model" that reduces the ability for crowd sourcing platforms to scale or retain crowd-oriented workers. Leveraging collaborative filtering techniques, in this paper, we propose an alternative model that seeks to address the issue through a recommendation technique and system that exploits a push-pull model.

The burst of Web-based Restful services brings us a number of facilities in our life and work. We are used to take smartphones to access these Web services, like location-based services, weather search, mapping, social networking, et al. On smartphones, we have two options of service consumers, a.k.a, Native apps and Web apps. Despite the platform-independence, Web apps are claimed to provide the same features and comparable user experiences with native apps. However, one fact is that more and more people prefer native apps rather than Web apps. In this paper, we make an empirical study on characterizing the performance disparity of native apps and Web apps. Given the same functionalities provided by the same service providers, we explore the Restful Web services that are used by native apps and Web apps. With HTTP-level trace analysis, we demystify the workflows on how native apps and Web apps use Web services and summarize different service usage patterns from architectural style perspective. Then we characterize the performance differences between native apps and Web apps on realizing Restful Web services including GET, DELETE, PUT & POST, in terms of number of network connections, response time, and data drain, given the same functional features. Our observations reveal that Web apps do not always perform worse than native apps using Restful Web services under the same context. We further propose some implications to improve both native apps and Web apps on smartphones.

This report describes the culmination of the 13th installment of the “Adaptive Computing (and Agents) for Enhanced Collaboration” (ACEC) track. ACEC focuses on collaborative computing that occurs dynamically among Web-accessible software systems and devices. The articulation of these network artifacts must be controlled by adaptive, intelligent control mechanisms, sometimes realized as agents. ACEC has for over 13 years brought together researchers from multiple disciplines to address the challenges associated with effective, just-in-time collaboration. The track this year showcases papers that explore emerging models and design patterns for adaptive architectures for a varied set of domains such as robotic and crowdsourcing.

Increasing popularity in the use of crowd sourcing has led to many tasks that can be fulfilled by the wisdom of human actors. When natural disasters or criminal activities occur, then sometimes crowd sourced tasks must be generated in real-time and must be fulfilled in an on-demand fashion. Effective use of crowd sourcing techniques requires an array of services that fulfil many dimensions of the overall problem such as resource selection and allocation, solution selection, and compensation. Any architecture that can provide these services in real-time, on demand requires a dynamic configurable infrastructure. This paper describes an adaptive framework for on-demand crowd sourced tasks supported by a design pattern-inspired architecture.

Service computing promotes a large number of web-delivered services, including web services, APIs and data feeds. Composing data, functionalities and even UI from these web-delivered services into a single web application, usually called service mashup, becomes a popular web development paradigm. The web-delivered services can be modeled as mashup components, while the development of mashup actually yields a set of inter-connected mashup components. The growing popularity of mashup components enriches functionality and user experiences, while the possible connections among components are complex and difficult to mashup developers, who might be non-professional programmers or even end-users, as actions over one component may have potential impacts on another. This paper proposes a novel approach for recommending developers in terms of navigation and completion of mashup components with a large-scale components repository. From data-driven perspective, we model the relationships between mashup components by a generic layered-graph model. Developers are allowed to select some initial components as starting point, while a graph-based algorithm recommends how to navigate to potentially relevant mashup components and complete the relevant mashup application. We experimentally demonstrate the efficiency and effectiveness of our approach for rapid mashup construction.

Businesses and government agencies are continuously generating and collecting huge amounts of data and building related Big Data applications. Big Data applications involve the collaborative integration of APIs from different providers. A challenge in this domain is to guarantee the conformance of t

Crowd computing leverages human input in order to execute tasks that are computationally expensive, due to complexity and/or scale. Combined with automation, crowd computing can help solve problems efficiently and effectively. In this work, we introduce an elasticity framework that adaptively optimi

A major tenet of the service-oriented computing paradigm is the just-in-time approach to composing web services. While the majority of related research projects use nonfunctional evaluators to choose the best, cross-organizational composition scheme from many options, few projects leverage the real, domain-specific functional data. In this paper we describe a data-centric modelling approach that leverages the real operational data of any web service workflow. This model is a core aspect for the control of adaptive collaboration software that integrates web services that span distributed enterprises. Using Bayesian inference, we assess the message flow of long-running service workflows, and, in this paper, we introduce an operational model that represents the service workflow behavior. We believe that our operational model can be used for off-line or real-time adaptation of operational service workflows.

The "Adaptive Computing (and Agents) for Enhanced Collaboration" (ACEC) track currently enjoys its 12th year. ACEC focuses on the dynamic collaboration that occurs between networked systems as realized by agent-based or adaptive solutions or services. The purpose of this track is to bring together research in the fields of software agents and adaptive computing, more broadly, as they relate to the challenges associated with efficient, effective collaboration. This report briefly discusses the content of the papers presented in the track by respective authors and collective knowledge derived from the underlying state-of-the-art.

Cloud computing represents a viable approach for hosting scalable applications and services. As business processes and scientific jobs become more intricate, users may have unbalanced and evolving requirements that are associated to the specific underlying sub-job or service. In this paper, we investigate the problem of managing virtual resources for service workflows in a cloud environment and propose an agent-based framework that makes resource management decisions that are customized to optimize different levels of the application. To facilitate the management process, we introduce and evaluate an adaptive workflow configuration algorithm to assign virtual machines to services (composed within a workflow) while aggregating virtual machines on appropriately-resourced physical machines. We use simulations of real-world networked cluster traces to evaluate and demonstrate the effectiveness of the algorithm.

Web services allow organizations to capture their human and software-based capabilities as modular software components that are called remotely over a network. In such service-oriented settings, it is important to establish an agreement that sets the obligations of the service provider and the expectations of the service consumer. Since traditional approaches such as Service Level Agreements (SLAs) are loosely defined with respect to data integrity aspects, in previous work, we proposed a formal model for specifying data-centric Web services. The goal is to formally and unambiguously specify the service behavior in terms of its underlying data model and data interactions. In this paper, we present our model and our effort in specifying and verifying data-centric Web services using three state-of-the-art specification languages: JML, Dafny and RESOLVE. Our goal is to study the feasibility of our proposed model and also to pinpoint the challenges and limitations of current specification and verification tools.

Service-oriented computing enables organizations to package their offerings as modular, self-contained services. As these organizational capabilities are more commonly distributed, perhaps redundantly, across cloud environments, there is an opportunity to provision services in locations that maximize energy-efficiency. In this paper, we introduce a process, model, and power estimation techniques to facilitate a priori decision support with respect to provisioning software services in context of the server on which they reside. Favorable results from our experiments demonstrate the ability to estimate the power consumption of web services while differentiating energy-efficiency of redundant instances.

Research in transactions planning has recognized the evolvement of Web Services as an industry standard to implement transactional business processes. We proposed a data modeling and contracting framework for Web services and in this paper we are exploring how our framework can help formally verify data integrity properties in an ad-hoc transaction.

Service mashup is the act of integrating the resulting data of two complementary software services into a common picture. Such an approach is promising with respect to the discovery of new types of knowledge. However, before service mashup routines can be executed, it is necessary to predict which services (of an open repository) are viable candidates. Similar to Knowledge Discovery in Databases (KDD), we introduce the Knowledge Discovery in Services (KDS) process that identifies mashup candidates. In this work, the KDS process is specialized to address a repository of open services that do not contain semantic annotations. In these situations, specialized techniques are required to determine equivalences among open services with reasonable precision. This paper introduces a bottom-up process for KDS that adapts to the environment of services for which it operates. Detailed experiments are discussed that evaluate KDS techniques on an open repository of services from the Internet and on a repository of services created in a controlled environment.

Service-oriented architecture (SOA) promotes a paradigm where ad-hoc applications are built by dynamically linking service-based software capabilities. Service providers follow specification standards to advertise their services' capabilities and to enable loosely coupled integration between their services and other businesses over the Web. A major challenge in this domain is interpreting the data that must be marshaled between consumer and producer systems. We propose a framework to support formal modeling and contracts for data-centric Web services. We demonstrate how this framework can be used to verify correctness properties for composition of services.

Service-oriented computing (SOC) has emerged as the eminent market environment for sharing and reusing service-centric capabilities. The underpinning for an organization's use of SOC techniques is the ability to discover and compose Web services. Although industry approaches to composition have a strong notion of business processes, these approaches largely use syntactic descriptions. As such composition is limited since the true functionality of ambiguous service operations cannot be inferred. Alternatively, academia uses semantic approaches to disambiguate services, but, at the same time, most of these approaches neglect the process rigor needed for complex compositions. In this paper we present a generalized semantics-based technique for automatic service composition that combines the rigor of process-oriented composition with the descriptiveness of semantics. Our generalized approach extends the common practice of linearly linked services by introducing the use of a conditional directed acyclic graph (DAG) where complex interactions, containing control flow, information flow and pre/post conditions, are effectively represented. Furthermore, the composition can be represented semantically as OWL-S documents. Our contributions are applied for automatic workflow generation in context of the currently important bioinformatics domain.

Service-oriented computing (SOC) suggests that many open, network-accessible services will be available over the Internet for organizations to incorporate into their own processes. Developing new software systems by composing an organization's local services and externally-available web services is conceptually different from system development supported by traditional software engineering lifecycles. Consumer organizations typically have no control over the quality and/or consistency of the external services that they incorporate, thus top-down software development lifecycles are impractical. Software architects and designers will require agile, lightweight processes to evaluate tradeoffs in system design based on the "estimated" responsiveness of external services coupled with known performance of local services. We introduce a model-driven software engineering approach for designing systems (i.e. workflows of web services) under these circumstances and a corresponding simulation-based evaluation tool.

Service-oriented computing (SOC) enables organizations and individual users to discover openly-accessible capabilities realized as services over the Internet. However, service registries can potentially be very large preventing organizations from discovering services in real-time. In fact, consumers may not be aware of the services that can be of most benefit to them. In our work, we introduce a web service recommender system that proactively discovers and manages web services. This paper focuses on the underlying search and ranking algorithms that enable the recommendations. As an innovation, we have analyzed real, fully-operational web services currently available on the Internet and, as a result, have discovered insights into how real web service messages are defined. Using these general naming tendencies coupled with enhanced syntactical methods, we are able to aggregate services by their messages and accurately suggest candidate services to users as a part of daily routines.

Today, more and more software are augmented with service-oriented packaging. At the same time, more and more business and government services are provided and offered in the form of software. However, there are debates on whether and how much service engineering has in common with software engineering.

Social networks continue to become more and more feature rich. Using local and global structural properties and descriptive attributes are necessary for more sophisticated social network analysis and support for visual mining tasks. While a number of visualization tools for social network applications have been developed, most of them are limited to uni-modal graph representations. Some of the tools support a wide range of visualization options, including interactive views. Others have better support for calculating structural graph properties such as the density of the graph or deploying traditional statistical social network analysis. We present Invenio, a new tool for visual mining of socials. Invenio integrates a wide range of interactive visualization options from Prefuse, with graph mining algorithm support from JUNG. While the integration expands the breadth of functionality within the core engine of the tool, our goal is to interactively explore multi-modal, multi-relational social networks. Invenio also supports construction of views using both database operations and basic graph mining operations.

Each time an employee enters a new business endeavor, he/she is required to learn new tasks and processes. Learning in the workplace can be as simple as learning the task of logging onto your computer and as complex as learning a multi-week business process. Across an enterprise organization, many individuals will be required to learn the same or similar operational processes. Since human beings are unique in there learning styles, it is essential that training techniques be customized to enhance human learning performance across a diverse workforce. Intelligent agents represent an emerging paradigm where adaptive task selection is coupled with autonomous control. Agents represent a promising technology for delivering specialized training to their human counterparts by customizing instructions in multiple modalities. Insight into how an individual learns a specific business process can be used to enhance that business process for the best performance. This paper discusses the rationale for a personal learning assistant agent customized for business processes

Scaffolding is a well-established instructional approach that facilitates learning by incrementally removing training aids as the learner progresses. By combining multiple training aids (i.e. multimodal interfaces), a trainer, either human or virtual, must make real-time decisions about which aids to remove throughout the training scenario. A significant problem occurs in implementing scaffolding techniques since the speed and choice of removing training aids must be strongly correlated to the individual traits of a specific trainee. We detail an agent-based infrastructure that supports the customization of scaffolding routines per individual user. We describe the integration of this agent-based approach into a simulated augmented reality (AR) environment

Service-oriented computing (SOC) suggests that many open, network-accessible services will be available over the Internet for organizations to incorporate into their own processes. Developing new software systems by composing an organization's local services and externally-available Web services is conceptually different from system development supported by traditional software engineering lifecycles. Consumer organizations typically have no control over the quality and/or consistency of the external services that they incorporate, thus top-down software development lifecycles are impractical. Software architects and designers will require agile, lightweight processes to evaluate tradeoffs in system design based on the "estimated" responsiveness of external services coupled with known performance of local services. We introduce a model-driven software engineering approach for designing systems (i.e. workflows of Web services) under these circumstances and a corresponding simulation-based evaluation tool

Service-oriented computing (SOC) suggests that the Internet will be an open repository of many modular capabilities realized as Web services. Organizations may be able to leverage this SOC paradigm if their employees are able to ubiquitously incorporate such capabilities and their resulting information into their daily practices. It is impractical to assume that human users will be able to search vast distributed repositories at real-time. In addition, automated search tools may invasively present too much information. This paper presents an architecture, software agent-based groupware using e-services (SAGE), that incorporates the use of intelligent agents to integrate human users with Web services. SAGE provides background search and discovery approaches thus enabling human users to exploit service-based capabilities that were previously too time-consuming to locate and integrate. We present a multi-agent system where each agent learns the rule-based preferences of a human user and manages the incorporation of Web services

Service-oriented computing (SOC) enables organizations and individual users to discover openly-accessible capabilities realized as services over the Internet. Research in this area focuses on techniques for managing the messages that flow into and out of these services to ultimately compose higher-level functions. In our work, we investigate the nature of message definitions by analyzing real, fully-operational Web services currently available on the Internet (i.e., from the wild). By leveraging insights into how real Web service messages are defined, we develop enhanced syntactical methods to best aggregate these messages and ultimately the Web services

Service-oriented computing (SOC) suggests that the Internet will be an open repository of millions of modular capabilities realized as web services. Organizations may be able to leverage this SOC paradigm if their employees are able to ubiquitously incorporate such capabilities and their resulting information into their daily practices. This paper presents an architecture, Software Agent-Based Groupware using E-services (SAGE), that incorporates the use of intelligent agents to help integrate organizational processes with web services. Our first steps toward the development of SAGE consist of an operational concept and middleware prototype (i.e. groupware plug-in) to mediate service-oriented information.

Agent technologies represent a promising approach for the integration of interorganizational capabilities across distributed, networked environments. However, knowledge sharing interoperability problems can arise when agents incorporating differing ontologies try to synchronize their internal information. Moreover, in practice, agents may not have a common or global consensus ontology that will facilitate knowledge sharing and integration of functional capabilities. We propose a method to enable agents to develop a local consensus ontology during operation time as needed. By identifying similarities in the ontologies of their peer agents, a set of agents can discover new concepts/relations and integrate them into a local consensus ontology on demand. We evaluate this method, both syntactically and semantically, when forming local consensus ontologies with and without the use of a lexical database. We also report on the effects when several factors, such as the similarity measure, the relation search level depth, and the merge order, are varied. Finally, experimenting in the domain of agent-supported Web service composition, we demonstrate how our method allows us to successfully autonomously form service-oriented local consensus ontologies.

With the advancement of Web services technologies, online businesses have the ability to offer their capabilities to larger, lesser known communities of potential collaborators. Universal Description, Discovery, and Integration (UDDI) specification and supporting technologies support open frameworks for businesses to store, advertise and retrieve pertinent services. Many researchers investigate approaches that ubiquitously create higher-level processes by composing services discovered in and retrieved from UDDI registries. However, there are few studies that consider the impact of registry performance on future service automation. This work focuses on evaluating the performance of UDDI registries considering variability and concurrent load of publish and inquiry requests.

Abstract In some research analysis organizations, effort is duplicated when disseminating raw information to multiple research groups. Although the various groups work on different problems, many times, the initial raw information used is the same. A solution to this problem is creating a centralized process for storing and disseminating common information using database technologies. In this paper, the requirements that must be met to create this distributed database management architecture are described. To support these requirements, a specification language, Specialized Format Markup Language (SFML), and supporting service‐oriented architecture, Specialized Format Generation (SFG), are introduced. These implementations and technologies support the translation of data from relational database formats into user‐specified legacy formats. In addition, this implementation supports the presentation of the resulting data across a Web‐based, distributed medium. SFML and SFG incorporate current technologies such as the eXtensible Markup Language (XML), Java Servlets, the eXtensible Stylesheet Language (XSL), and other relational database approaches. Copyright © 2004 John Wiley & Sons, Ltd.

At the MITRE Corporation-Center for Advanced Aviation System Development (CAASD), software engineers work closely with both analyst and domain experts to develop software simulations in the air traffic management domain. In this environment, software simulations are applications that take large amounts of real-world operational information, and through calculations, derivations, and display extends the original information to produce some new insight into the domain. This new insight or knowledge typically comes in the form of a pertinent set of data. Based on this set of information other research groups can further extend this knowledge. The challenge in this environment is a distributed data management system that will allow a distributed set of researchers to share their extended knowledge. This paper presents the motivation and design of such an architecture to support this collaborative knowledge/data sharing environment. This run-time configurable architecture is implemented using web-based technologies such as the Extensible Markup Language (XML), Java Servlets, Extensible Stylesheets (XSL), and a relational database management system (RDBMS).

In order to develop students for successful careers in software engineering, specifically for software development, they must not only be immersed in the software development lifecycle and paradigms, but also in the workings of large project teams. Currently, most undergraduate software engineering courses are taught by presenting the concepts and methodologies and assigning fragmented 3-4 person group projects. In the Department of Computer Science at Georgetown University, a two-course approach to undergraduate software engineering education has been developed that incorporates the practical application of coursework in a large team setting. The first course presents a firm software design basis, while the second course demonstrates corporate-level software engineering concepts with a semester-long software development simulation where the entire class is the development team. This paper presents the experiences from offering this software engineering simulation approach.

The need for coordination among autonomous entities is common in dynamically changing domains. One such domain is the coordination of components in distributed component architectures. In these architectures, asynchronous communication is used to ensure autonomy. Management systems that control such architectures must respect this autonomy by decentralizing interaction policies and control. Middle agents (Decker et al., 1997) have been introduced as brokers or mediators in such dynamic settings. Rule-driven coordination (RDC) agents are middle agents that act as brokers to the individual components in component architectures. These RDC agents encapsulate the interaction policy definition (rules) and the aspects of communication, data management, and policy execution. This paper defines each of these aforementioned aspects of the RDC agents. Furthermore, there is the use of RDC agents to manage a workflow of Java-based components in a typical electronic commerce domain.

Over recent years, "Internet-able" applications and architectures have been used to support domains where there are multiple interconnected systems that are both decentralized and autonomous. One such domain is the support of data management at the enterprise level. Typically, in these domains, both the schema of the data repository and the individual query needs of the users evolve over time. These query needs can include both the type of data and the required format and structure. To handle this evolution, the resulting architecture must enforce the autonomy in systems that support the client needs and constraints, in addition to maintaining the autonomy in systems that support the actual data schema and extraction mechanisms. At the MITRE Corporation, this domain has been identified in the development of a composite data repository for the Center for Advanced Aviation System Development (CAASD). In the development of such a repository, the supporting architecture includes specialized mechanisms to disseminate the data to a diverse evolving set of researchers. This paper presents the motivation and design of such an architecture to support these autonomous data extraction environments. This run-time configurable architecture is implemented using Web-based technologies such as the Extensible Markup Language (XML), Java Servlets, Extensible Stylesheets (XSL), and a relational database management system.