Analytic Queries over Geospatial Time-Series Data Using Distributed Hash Tables

As remote sensing equipment and networked observational devices continue to proliferate, their corresponding data volumes have surpassed the storage and processing capabilities of commodity computing hardware. This trend has led to the development of distributed storage frameworks that incrementally scale out by assimilating resources as necessary. While challenging in its own right, storing and managing voluminous datasets is only the precursor to a broader field of research: extracting insights, relationships, and models from the underlying datasets. The focus of this study is twofold: exploratory and predictive analytics over voluminous, multidimensional datasets in a distributed environment. Both of these types of analysis represent a higher-level abstraction over standard query semantics; rather than indexing every discrete value for subsequent retrieval, our framework autonomously learns the relationships and interactions between dimensions in the dataset and makes the information readily available to users. This functionality includes statistical synopses, correlation analysis, hypothesis testing, probabilistic structures, and predictive models that not only enable the discovery of nuanced relationships between dimensions, but also allow future events and trends to be predicted. The algorithms presented in this work were evaluated empirically on a real-world geospatial time-series dataset in a production environment, and are broadly applicable across other storage frameworks.