SenseML : a platform for constructing IOT data pipelines
Name
1076272548-MIT.pdf
Description
Full printable version
Size
449.57 KB
Format
Adobe PDF
Checksum (MD5)
8eb0f7a23c1293d973c3f997a98dc55f
Author(s)
Choi, Donghyun Michael
Advisor(s)
Kalyan Veeramachaneni.
Alternative Title
Platform for constructing Internet of Things data pipelines
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
In this thesis, we present SenseML. SenseML is a general-purpose platform that enables users to transform sensor data from the IOT domain into a machine learning-ready format - what we call an attribute time series. It is a cloud-based platform that can process signals using user-specified functions. It offers users immense flexibility in integrating functions for transforming the data, while also providing parallel execution as a service. In addition, we enable users to contribute to the framework by submitting domain-specific signal processing functions. Such contributions are integrated into the platform and are then part of the library, available for others to use. We used the platform to generate 19 attribute time series for 9655 urban sound signals. To generate these time series, the platform did 32 million computations in approximately 140 minutes.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 67-68).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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