  The size of multi-modal, heterogeneous data collected through various sensors
is growing exponentially. It demands intelligent data reduction, data mining
and analytics at edge devices. Data compression can reduce the network
bandwidth and transmission power consumed by edge devices. This paper proposes,
validates and evaluates Fog Data, a service-oriented architecture for Fog
computing. The center piece of the proposed architecture is a low power
embedded computer that carries out data mining and data analytics on raw data
collected from various wearable sensors used for telehealth applications. The
embedded computer collects the sensed data as time series, analyzes it, and
finds similar patterns present. Patterns are stored, and unique patterns are
transmited. Also, the embedded computer extracts clinically relevant
information that is sent to the cloud. A working prototype of the proposed
architecture was built and used to carry out case studies on telehealth big
data applications. Specifically, our case studies used the data from the
sensors worn by patients with either speech motor disorders or cardiovascular
problems. We implemented and evaluated both generic and application specific
data mining techniques to show orders of magnitude data reduction and hence
transmission power savings. Quantitative evaluations were conducted for
comparing various data mining techniques and standard data compression
techniques. The obtained results showed substantial improvement in system
efficiency using the Fog Data architecture.
