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© Science and Education Publishing DOI:10.12691/dt-1-1-8 Pattern-based Data Sharing in Big Data Environments
, 2015
"... Abstract The staggering growth in Internet of Things (IoTs) technologies is the key driver for generation of massive raw data streams in big data environments. In addition, the collection of raw data streams in big data systems increases computational complexity and resource consumption in cloud-ena ..."
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Abstract The staggering growth in Internet of Things (IoTs) technologies is the key driver for generation of massive raw data streams in big data environments. In addition, the collection of raw data streams in big data systems increases computational complexity and resource consumption in cloud-enabled data mining systems. In this paper, we are introducing the concept of pattern-based data sharing in big data environments. The proposed methodology enables local data processing near the data sources and transforms the raw data streams into actionable knowledge patterns. These knowledge patterns have dual utility of availability of local knowledge patterns for immediate actions as well as for participatory data sharing in big data environments. The proposed concept has the wide potential to be applied in numerous application areas.
Article Energy-Efficient Integration of Continuous Context Sensing and Prediction into Smartwatches
, 2015
"... sensors ..."
Article Detection of Site-Specific Blood Flow Variation in Humans during Running by a Wearable Laser Doppler Flowmeter
, 2015
"... sensors ..."
Article One Small Step for a Man: Estimation of Gender, Age and Height from Recordings of One Step by a Single Inertial Sensor
, 2015
"... Abstract: A number of previous works have shown that information about a subject is encoded in sparse kinematic information, such as the one revealed by so-called point light walkers. With the work at hand, we extend these results to classifications of soft biometrics from inertial sensor recordings ..."
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Abstract: A number of previous works have shown that information about a subject is encoded in sparse kinematic information, such as the one revealed by so-called point light walkers. With the work at hand, we extend these results to classifications of soft biometrics from inertial sensor recordings at a single body location from a single step. We recorded accelerations and angular velocities of 26 subjects using integrated measurement units (IMUs) attached at four locations (chest, lower back, right wrist and left ankle) when performing standardized gait tasks. The collected data were segmented into individual walking steps. We trained random forest classifiers in order to estimate soft biometrics (gender, age and height). We applied two different validation methods to the process, 10-fold cross-validation and subject-wise cross-validation. For all three classification tasks, we achieve high accuracy values for all four sensor locations. From these results, we can conclude that the data of a single walking step (6D: accelerations and angular velocities) allow for a robust estimation of the gender, height and age of a person.
Article Distributed Global Function Model Finding for Wireless Sensor Network Data
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