Efficient Management of Large Metadata Catalogs in a Ubiquitous Computing Environment

Trends in experimental sciences, such as astrophysics, have led to many critically needed, non-normalized, and massive metadata catalogs that organize collections of recorded photographic and spectrographic observations of similar size. Observations of the night sky can best be presented using a data model that conveys the observations, analysis, objects contained with the observations, and results of analysis pertaining to those objects. Such a model is proposed, and it is referred to as the internet Flexible Image Transport System (iFITS). In addition, a set of mapping functions to transform instances of the Sloan Digital Sky Survey into instances of iFITS, a lightweight marshaling method to transfer data to and from server side instances to mobile instances. Furthermore, this dissertation explores four architectures such as content management, software/ infrastructure/ platform as a service, context rule engine-based request-response loop factory, and representational state transfer (REST)-based query engines to facilitate the mining of the metadata catalogs containing these observations.