Big Data Small Footprint for Detecting Transportation Modes
As the “biggest big data,” multimedia big data introduces many technological challenges, including compression, storage, transmission, analysis, and recognition. Each year, the BigMM conference organization committee organizes an algorithmic competition to address one grand challenge in the field of multimedia big data, which is open to all tool vendors, academics and corporations. The major aim is to recognize the most innovative, efficient and methodologically advanced BigMM tools.
The BigMM 2016 Challenge is to address the problem of Big Data Small Footprint for Detecting Transportation Modes. Detecting transportation modes of a user is a critical subroutine of many mobile applications. The detected mode can be used to infer the user’s state to perform context-aware computing. However, sensors on mobile devices bring forth a couple of new challenges to big data research. First, the power consumption for analyzing and classifying sensor data must be low, since most wearables and portable devices are power-strapped. Second, the velocity of analyzing big data on these devices must be high; otherwise the limited local storage may overflow. Tackling these challenging problems has both scientific and practical importance.
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