NDLI: Learning from Multiple Related Data Streams with Asynchronous Flowing Speeds

Content Provider	IEEE Xplore Digital Library
Author	Zhi Qiao Peng Zhang Jing He Jinghua Yan Li Guo
Copyright Year	2010
Abstract	Related data streams refer to data streams that can be joined together by matching their join attributes. Existing research on learning from related data streams is based on an assumption that all streams arrive at a central processing unit in a synchronous way, such that in an arbitrary sliding window, all tuples of the streams can be perfectly joined together. This assumption, however, does not hold when related data streams are generated or transferred at different speeds, and thus may arrive in the central processing unit in an asynchronous manner. In this paper, we argue that for asynchronous data streams, there exist a small portion of perfectly joined examples (i.e., complete examples) and a large portion of partially joined examples (i.e., incomplete examples). Accordingly, we present a new Learning from Complete and Fixed Examples (LCFE) framework that can fix incomplete examples to boost the learning. Experiments on both synthetic and real-world data streams demonstrate that LCFE is able to achieve a higher prediction accuracy for learning from related data streams than other simple solutions can offer.
Starting Page	272
Ending Page	277
File Size	224274
Page Count	6
File Format	PDF
ISBN	9781424492114
DOI	10.1109/ICMLA.2010.47
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Accuracy Predictive models Data structures Data models Central Processing Unit Lifting equipment
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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