2026-09-19T14:27:19Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1195982026-06-06T00:54:22Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Kalyan Veeramachaneni.
Choi, Donghyun Michael
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2018-12-11T21:07:55Z
2018-12-11T21:07:55Z
2017
2017
http://hdl.handle.net/1721.1/119598
1076272548
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).
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.
by Donghyun Michael Choi
M.Eng.
68 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
SenseML : a platform for constructing IOT data pipelines
Platform for constructing Internet of Things data pipelines
Thesis
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<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.</Abstract>
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