In industrial settings, machines such as trucks, hydraulic pumps, etc. are widely distributed at different geographic locations where sensors on machines produce large volumes of data. The data produced is stored locally in autonomous databases called log databases. The collection of log databases is dynamically changing when new sites are dynamically added or removed from the federation. In this application context, an efficient way to search and analyze passed behavior of products in use is desired. To enable scalable queries over collections of distributed and autonomous log databases we developed the FLOQ (Fused LOg database Query processor) system, which provides a global view of the working status of all machines on the sites through a meta-database integrating the dynamic log database collection. A particular challenge in this scenario is a scalable way to process numerical queries that identify anomalies by joining data from the meta-database with data selected from the collection of distributed and autonomous log databases. The Thesis describes the architecture of FLOQ. In particular different strategies to execute numerical queries over log database collections are investigated. FLOQ allows both the meta-database and the log databases to be stored in multiple formats using different kinds of data managers. FLOQ provides general and extensible mechanisms for efficient processing of queries over different kinds of distributed data sources.
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