An infrastructure for delivering geospatial data to field users

Federal agencies collect and analyze data to carry out their missions. A significant portion of these activities requires geospatial data collection in the field. Models for computer-assisted survey information collection are still largely based on the client-server paradigm with symbolic data representation. Little attention has been given to digital geospatial information resources, or emerging mobile computing environments. This paper discusses an infrastructure designs for delivering geospatial data users in a mobile field computing environment. Mobile field computing environments vary widely, and generally offer extremely limited computing resources, visual display, and bandwidth relative to the usual resources required for distributed geospatial data. Key to handling heterogeneity in the field is an infrastructure design that provides flexibility in the location of computing tasks and returns information in forms appropriate for the field computing environment. A view agent based infrastructure has been developed with several components. Wrappers are used for encapsulating not only the data sources, but the mobile field environment as well, localizing the details associated with heterogeneity in data sources and field environments. Within the boundaries of the wrappers, mediators and object-oriented views implemented as mobile agents work in a relatively homogeneous environment to generate query results. Mediators receive a request from the user application via the field wrapper, and generate a sequence of mobile view agents to search for, retrieve, and process data. The internal infrastructure environment is populated with computation servers to provide a location for processing, especially for combining data from multiple locations. Each computation server has a local object-oriented data warehouse equipped with a set of data warehouse tools for working with geospatial data. Since the prospect of query reuse is likely for a field worker, we store the final and intermediate results in the data warehouse, allowing the warehouse to act as an active cache. Even when field computing capacity is ample, the warehouse is used to process data so that network traffic can be minimized

Stepping Forwards by Looking Back: Underdetermination, Epistemic Scarcity & Legacy Data

Debate about the epistemic prowess of historical science has focused on local underdetermination problems generated by a lack of historical data; the prevalence of information loss over geological time, and the capacities of scientists to mitigate it. Drawing on Leonelli’s recent distinction between ‘phenomena-time’ and ‘data-time’ I argue that factors like data generation, curation and management significantly complexifies and undermines this: underdetermination is a bad way of framing the challenges historical scientists face. In doing so, I identify circumstances of ‘epistemic scarcity’ where underdetermination problems are particularly salient, and discuss cases where ‘legacy data’—data generated using differing technologies and systems of practice—are drawn upon to overcome underdetermination. This suggests that one source of overcoming underdetermination is our knowledge of science’s past. Further, data-time makes agnostic positions about the epistemic fortunes of scientists working under epistemic scarcity more plausible. But agnosticism seems to leave philosophers without much normative grip. So, I sketch an alternative approach: focusing on the strategies scientists adopt to maximize their epistemic power in light of the resources available to them

From unstructured HTML to structured XML: how XML supports financial knowledge management on internet.

by Yuen Lok-tin.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 88-95).Abstracts in English and Chinese.ABSTRACT --- p.I摘要 --- p.IIIACKNOWLEDGEMENT --- p.VTABLE OF CONTENTS --- p.VILIST OF FIGURES --- p.VIIILIST OF TABLES --- p.IXChapter 1 --- INTRODUCTION --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.2 --- Objectives --- p.2Chapter 1.3 --- Organization --- p.4Chapter 2 --- LITERATURE REVIEW & THEORETICAL FOUNDATION --- p.6Chapter 2.1 --- "Data, Information and Knowledge" --- p.6Chapter 2.2 --- Knowledge Management --- p.7Chapter 2.3 --- Information Transparency and Efficiency --- p.10Chapter 2.3.1 --- Transparency --- p.11Chapter 2.3.2 --- Efficiency --- p.13Chapter 2.4 --- extensible markup language (XML) --- p.14Chapter 3 --- DIGITAL FINANCIAL INFORMATION AND ISSUES --- p.16Chapter 3.1 --- Managing Financial Information on the Internet --- p.17Chapter 3.2 --- Existing Electronic Financial Filing Systems --- p.20Chapter 3.3 --- Financial Document Disclosure Model --- p.21Chapter 3.4 --- Interaction Between Information Producers and Consumers --- p.23Chapter 3.5 --- Gluing All Together --- p.26Chapter 4 --- IDEAL ELECTRONIC FINANCIAL DISCLOSURE SYSTEM --- p.27Chapter 4.1 --- Structure and Representation of Knowledge --- p.28Chapter 4.2 --- Content Creation --- p.33Chapter 5 --- PROPOSED APPROACH --- p.36Chapter 5.1 --- Preliminary XML Data Dictionary --- p.36Chapter 5.2 --- Creation of XML Tags --- p.40Chapter 5.2.1 --- Statistical Information Retrieval --- p.41Chapter 5.2.2 --- Accounting and Auditing Practice --- p.43Chapter 5.2.3 --- Investors´ةFeedback --- p.44Chapter 5.3 --- Value-Added Services --- p.45Chapter 6 --- DESIGN AND DEVELOPMENT OF ELFFS-XML --- p.49Chapter 6.1 --- Stages of ELFFS-XML --- p.49Chapter 6.1.1 --- Information Creation --- p.49Chapter 6.1.2 --- Information Collection/Storage --- p.50Chapter 6.1.3 --- Knowledge Generation --- p.51Chapter 6.1.4 --- Knowledge Dissemination/Presentation --- p.52Chapter 6.1.5 --- Feedback --- p.52Chapter 6.2 --- Components of ELFFS-XML --- p.53Chapter 6.2.1 --- Data Source Abstraction Layer --- p.55Chapter 6.2.2 --- Storage Abstraction Layer --- p.57Chapter 6.2.3 --- Logic Layer --- p.61Chapter 6.2.4 --- Presentation Layer --- p.63Chapter 7 --- EVALUATING ELFFS-XML --- p.66Chapter 7.1 --- Comparison with Other Financial Information Disclosure Systems --- p.66Chapter 7.2 --- Users' Evaluation --- p.70Chapter 7.3 --- Systems Efficiency --- p.71Chapter 7.4 --- XML Tag Generation Approach Performance Evaluation --- p.73Chapter 8 --- CONCLUSION AND FUTURE RESEARCH --- p.78APPENDIX I SURVEY ON INVESTMENT PATTERN --- p.80APPENDIX II CORE ELFFS-XML DTD --- p.84APPENDIX III PERFORMANCE RELATED XML TAGS --- p.86BIBLIOGRAPHY --- p.8

A Framework for the Organization and Discovery of Information Resources in a WWW Environment Using Association, Classification and Deduction

The Semantic Web is envisioned as a next-generation WWW environment in which information is given well-defined meaning. Although the standards for the Semantic Web are being established, it is as yet unclear how the Semantic Web will allow information resources to be effectively organized and discovered in an automated fashion. This dissertation research explores the organization and discovery of resources for the Semantic Web. It assumes that resources on the Semantic Web will be retrieved based on metadata and ontologies that will provide an effective basis for automated deduction. An integrated deduction system based on the Resource Description Framework (RDF), the DARPA Agent Markup Language (DAML) and description logic (DL) was built. A case study was conducted to study the system effectiveness in retrieving resources in a large Web resource collection. The results showed that deduction has an overall positive impact on the retrieval of the collection over the defined queries. The greatest positive impact occurred when precision was perfect with no decrease in recall. The sensitivity analysis was conducted over properties of resources, subject categories, query expressions and relevance judgment in observing their relationships with the retrieval performance. The results highlight both the potentials and various issues in applying deduction over metadata and ontologies. Further investigation will be required for additional improvement. The factors that can contribute to degraded performance were identified and addressed. Some guidelines were developed based on the lessons learned from the case study for the development of Semantic Web data and systems

Proceedings of the RESOLVE Workshop 2002

Proceedings of the RESOLVE Workshop 200