Fog Data: Enhancing Telehealth Big Data Through Fog Computing

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

Accelerating the Dynamic Time Warping Distance Measure using Logarithmetic Arithmetic

This paper describes an application-specific embedded processor with instruction set extensions (ISEs) for the Dynamic Time Warping (DTW) distance measure, which is widely used in time series similarity search. The ISEs in this paper are implemented using a form of logarithmic arithmetic that offers significant performance and power/energy advantages compared to more traditional floating-point operations

Similarity Search and Analysis Techniques for Uncertain Time Series Data

Emerging applications, such as wireless sensor networks and location-based services, require the ability to analyze large quantities of uncertain time series, where the exact value at each timestamp is unavailable or unknown. Traditional similarity search techniques used for standard time series are not always effective for uncertain time series data analysis. This motivates our work in this dissertation. We investigate new, efficient solution techniques for similarity search and analysis of both uncertain time series models, i.e., PDF-based uncertain time series (having probability density function) and multiset-based uncertain time series (having multiset of observed values) in general, as well as correlation queries in particular. In our research, we first formalize the notion of normalization. This notion is used to introduce the idea of correlation for uncertain time series data. We model uncertain correlation as a random variable that is a basis to develop techniques for similarity search and analysis of uncertain time series. We consider a class of probabilistic, threshold-based correlation queries over such data. Moreover, we propose a few query optimization and query quality improvement techniques. Finally, we demonstrate experimentally how the proposed techniques can improve similarity search in uncertain time series. We believe that our results provide a theoretical baseline for uncertain time series management and analysis tools that will be required to support many existing and emerging applications