The heterogeneous, distributed and voluminous nature of many government and corporate data sources impose severe constraints on meeting the diverse requirements of users who analyze the data. Additionally, communication bandwidth limitations, time constraints, and multiple data formats impose further restrictions on users of these distributed data sources. In this paper, we present an Agent-based Complex QUerying and Information Retrieval Engine (ACQUIRE) for large, heterogeneous, and distributed data sources. ACQUIRE acts as a softbot or interface agent by presenting users with a view of a single, unified, homogenous data source, against which users can pose high-level declarative queries. ACQUIRE translates each such user query into a set of sub-queries by employing a combination of planning and traditional database query optimization techniques. ACQUIRE then spawns a set of mobile agents corresponding to these sub-queries, which in turn retrieve the data from various distributed data sources by dynamically optimizing the retrieval strategy as it is carried out. These mobile agents carry with them data-processing code that can be executed at the remote site, thus reducing the size of data returned by the agent. When all mobile agents have returned, ACQUIRE filters and merges the retrieved data and presents the results to the user. While the system is still very much a work in progress, current validation experiments on simulated NASA Distributed Active Archive Centers (DAACs) have demonstrated that complex queries can be effectively decomposed and retrieved by this approach.

This paper describes the development of techniques inGeographic Information Systems to support the visualization of "meta-information" -qualifiers of incoming data that significantly influence a user's perception of that data (e.g., uncertainty, recency, source). We present a brief literature review and discuss specific issues with the representation of metainformation.

Mobile agents have the potential to substantially improve the speed and efficiency with which distributed and heterogeneous data is retrieved. By moving the computation to the data, retrieval times can be reduced by the elimination of unnecessary data transfer. One way to improve a mobile agent system's retrieval efficiency is to incorporate various query optimization techniques (Das et. al., 2002). These methods involve re-writing of the query execution graph so each mobile agent retrieves its requested data in an optimized order, thus minimizing total data transfer size. While these query re-writing methods can be highly effective in reducing both retrieval times and data transfer sizes, they are generally "static", in that the mobile agents retrieve data in a particular order based on an itinerary that is fixed at the time the plan is generated. We have developed a system by which the advantages of mobile agents are leveraged to optimize data retrieval by dynamically optimizing the retrieval strategy as it is carried out. This strategy equips each spawned agent with the full query execution graph and necessary code to execute the retrieval plan at any data site in the network. The spawned agents communicate and collaborate with each other to dynamically decide where to migrate, send data, and perform necessary computations. These decisions depend on retrieval factors such as network speed, data size, and the computational capabilities of the data servers involved in the retrieval. The feasibility of the approach has been demonstrated within a local area network environment using Earth Science data and we present some experimental results in this context.

The heterogeneous, distributive and voluminous nature of many government and corporate data sources impose severe constraints on meeting the diverse requirements of users who analyze the data. Additionally, communication bandwidth limitations, time constraints, and multiplicity of data formats impose further restrictions on users of these distributed data sources. What is required is a reliable, robust, and efficient data retrieval technique that can access data from distributed data sources while maintaining the autonomy of individual sources. In this paper, we present an Agent-based Complex QUerying and Information Retrieval Engine (ACQUIRE) for large, heterogeneous, and distributed data sources. ACQUIRE acts as a softbot or interface agent by presenting users with the appearance of a single, unified, homogenous data source, against which users can pose high-level declarative queries. ACQUIRE translates each such user query into a set of sub-queries by employing a combination of planning and traditional database query optimization techniques. For each sub-query, ACQUIRE then spawns a corresponding mobile agent, which retrieves data from the appropriate data source. These mobile agents carry with them data-processing code that can be executed at the remote site, thus reducing the size of data returned by the agent. When all mobile agents have returned, ACQUIRE filters and merges the retrieved data and presents the results to the user. Validation experiments on simulated NASA Distributed Active Archive Centers (DAACs) have demonstrated that complex queries can be effectively decomposed and retrieved by this approach, resulting in the twin benefits of improved ease of use and significantly reduced query retrieval times.