This dissertation is a result of an effort over many years. There are so many people who helped me in various ways during this endeavor. Without their generous support and encouragement, this work would not have been possible. First of all, I am so grateful to my Ph.D. advisor, Prof. Beth Plale for her invaluable support, guidance, and encouragement throughout my Ph.D. Her research experience over many years across multiple areas of Computer Science helped me in many ways to solve hard research problems and to successfully present them as publications. In addition to that, she was so kind to me and my family during our hard times. I am truly honored to have worked with her throughout my Ph.D. studies. I would like to thank my research committee members Prof. David Leake, Prof. Ryan Newton and Prof. Judy Qiu for their guidance and advice on my qualifying exams, thesis proposal, and final dissertation. I should thank all professors at the School of Informatics, Computing, and Engineering from whom I took a number of courses which helped immensely to improve my knowledge and skills.
Open science is prompting wide efforts to make data from research available for broader use. However, sharing data is complicated by important protections on the data (e.g., protections of privacy and intellectual property). The spectrum of options existing between data needing to be fully open access and data that simply cannot be shared at all is quite limited. This paper puts forth a generalized remote secure enclave as a socio-technical framework consisting of policies, human processes, and technologies that work hand in hand to enable controlled access and use of restricted data. Based on experience in implementing the enclave for computational, analytical access to a massive collection of in-copyright texts, we discuss the synergies and trade-offs that exist between software components and policy and process components in striking the right balance between safety for the data, ease of use, and efficiency.
Modern scientific experiments generate vast volumes of data which are hard to keep track of. Consequently, scientists find it difficult to reuse and share these data sets. We address this problem by developing a schema-independent data cataloging framework for efficient management of scientific data. The proposed solution consists of an agent which automatically identifies new data products and extract metadata from them, as well as a server which indexes the metadata using a NoSQL database and provides a REST API for querying, sharing, and reusing the data sets. The novelty of our solution lies in the pluggable metadata extraction logic, extensible data product generation monitors, use of a NoSQL database, and the ability to dynamically add new metadata fields. The use of Apache Solr as the backend database enables the proposed solution to index and search data products much fatser than a solution based on relational databases. For example, our Apache Solr based implementation can resolve full text, sub-string, prefix, and suffix queries 91% -99% faster than a MySQL-based implementation.Index Terms-indexing; metadata catalog; scientific data management
In the landscape of exascale computing collaborative research campaigns are conducted as co-design activities of loosely coordinated experiments. But the higher level context and the knowledge of individual experimental activity is lost over time. We undertook a knowledge capture and representation aid called Campaign Knowledge Network(CKN), a codesign design and analysis tool. We demonstrate that CKN can satisfy the Hoarde abstraction and can distill campaign context from runtime information thereby creating a knowledge resource upon which analysis tools can run to provide more efficient experimentation.
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