Exploiting Geospatial and Chronological Characteristics in Data Streams to Enable Efficient Storage and Retrievals
The authors describe the design of a high-throughput storage system, Galileo, for data streams generated in observational settings. To cope with data volumes, the shared nothing architecture in Galileo supports incremental assimilation of nodes, while accounting for heterogeneity in their capabilities. To achieve efficient storage and retrievals of data, Galileo accounts for the geospatial and chronological characteristics of such time-series observational data streams. Their benchmarks demonstrate that Galileo supports high-throughput storage and efficient retrievals of specific portions of large datasets while supporting different types of queries.