Towards Semantic Integration of Heterogeneous Sensor Data with Indigenous Knowledge for Drought Forecasting

In the Internet of Things (IoT) domain, various heterogeneous ubiquitous devices would be able to connect and communicate with each other seamlessly, irrespective of the domain. Semantic representation of data through detailed standardized annotation has shown to improve the integration of the interconnected heterogeneous devices. However, the semantic representation of these heterogeneous data sources for environmental monitoring systems is not yet well supported. To achieve the maximum benefits of IoT for drought forecasting, a dedicated semantic middleware solution is required. This research proposes a middleware that semantically represents and integrates heterogeneous data sources with indigenous knowledge based on a unified ontology for an accurate IoT-based drought early warning system (DEWS).Comment: 5 pages, 3 figures, In Proceedings of the Doctoral Symposium of the 16th International Middleware Conference (Middleware Doct Symposium 2015), Ivan Beschastnikh and Wouter Joosen (Eds.). ACM, New York, NY, US

A Methodology for integration of heterogeneous databases

Reprint. Reprinted from IEEE transactions on knowledge and data engineering. Vol. 6, no. 6 (Dec. 1994) "December 1994."Includes bibliographical references (p. 932).Supported by the Productivity From Information Technology (PROFIT) Research Initiative at MIT.M.P. Reddy ... [et al.

Arcadia, a software development environment research project

The research objectives of the Arcadia project are two-fold: discovery and development of environment architecture principles and creation of novel software development tools, particularly powerful analysis tools, which will function within an environment built upon these architectural principles.Work in the architecture area is concerned with providing the framework to support integration while also supporting the often conflicting goal of extensibility. Thus, this area of research is directed toward achieving external integration by providing a consistent, uniform user interface, while still admitting customization and addition of new tools and interface functions. In an effort to also attain internal integration, research is aimed at developing mechanisms for structuring and managing the tools and data objects that populate a software development environment, while facilitating the insertion of new kinds of tools and new classes of objects.The unifying theme of work in the tools area is support for effective analysis at every stage of a software development project. Research is directed toward tools suitable for analyzing pre-implementation descriptions of software, software itself, and towards the production of testing and debugging tools. In many cases, these tools are specifically tailored for applicability to concurrent, distributed, or real-time software systems.The initial focus of Arcadia research is on creating a prototype environment, embodying the architectural principles, which supports Ada1 software development. This prototype environment is itself being developed in Ada.Arcadia is being developed by a consortium of researchers from the University of California at Irvine, the University of Colorado at Boulder, the University of Massachusetts at Amherst, TRW, Incremental Systems Corporation, and The Aerospace Corporation. This paper delineates the research objectives and describes the approaches being taken, the organization of the research endeavor, and current status of the work

Extracting, Transforming and Archiving Scientific Data

It is becoming common to archive research datasets that are not only large but also numerous. In addition, their corresponding metadata and the software required to analyse or display them need to be archived. Yet the manual curation of research data can be difficult and expensive, particularly in very large digital repositories, hence the importance of models and tools for automating digital curation tasks. The automation of these tasks faces three major challenges: (1) research data and data sources are highly heterogeneous, (2) future research needs are difficult to anticipate, (3) data is hard to index. To address these problems, we propose the Extract, Transform and Archive (ETA) model for managing and mechanizing the curation of research data. Specifically, we propose a scalable strategy for addressing the research-data problem, ranging from the extraction of legacy data to its long-term storage. We review some existing solutions and propose novel avenues of research.Comment: 8 pages, Fourth Workshop on Very Large Digital Libraries, 201