{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T20:52:04Z","timestamp":1774385524936,"version":"3.50.1"},"reference-count":34,"publisher":"Association for Computing Machinery (ACM)","issue":"13","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2014,8]]},"abstract":"<jats:p>Sensors on mobile phones and wearables, and in general sensors on IoT (Internet of Things), bring forth a couple of new challenges to big data research. First, the power consumption for analyzing 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.<\/jats:p>\n          <jats:p>This paper presents our hardware-software co-design of a classifier for wearables to detect a person's transportation mode (i.e., still, walking, running, biking, and on a vehicle). We particularly focus on addressing the big-data small-footprint requirement by designing a classifier that is low in both computational complexity and memory requirement. Together with a sensor-hub configuration, we are able to drastically reduce power consumption by 99%, while maintaining competitive mode-detection accuracy. The data used in the paper is made publicly available for conducting research.<\/jats:p>","DOI":"10.14778\/2733004.2733015","type":"journal-article","created":{"date-parts":[[2015,5,12]],"date-time":"2015-05-12T15:37:52Z","timestamp":1431445072000},"page":"1429-1440","source":"Crossref","is-referenced-by-count":94,"title":["Big data small footprint"],"prefix":"10.14778","volume":"7","author":[{"given":"Meng-Chieh","family":"Yu","sequence":"first","affiliation":[{"name":"HTC, Taiwan"}]},{"given":"Tong","family":"Yu","sequence":"additional","affiliation":[{"name":"National Taiwan University"}]},{"given":"Shao-Chen","family":"Wang","sequence":"additional","affiliation":[{"name":"HTC, Taiwan"}]},{"given":"Chih-Jen","family":"Lin","sequence":"additional","affiliation":[{"name":"National Taiwan University"}]},{"given":"Edward Y.","family":"Chang","sequence":"additional","affiliation":[{"name":"HTC, Taiwan"}]}],"member":"320","published-online":{"date-parts":[[2014,8]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"adidas. miCoach SPEED CELL. http:\/\/www.adidas.com.  adidas. miCoach SPEED CELL. http:\/\/www.adidas.com."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/130385.130401"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.1994.576879"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-20429-6"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.5555\/1756006.1859899"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022627411411"},{"key":"e_1_2_1_9_1","unstructured":"Fitbit. 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