Implementing Online Viterbi Algorithm as Standard Relational Queries over Streaming Data

Rui Li; Lei Zhang

doi:10.4028/www.scientific.net/AMR.532-533.672

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 532-533Implementing Online Viterbi Algorithm as Standard...

Implementing Online Viterbi Algorithm as Standard Relational Queries over Streaming Data

Abstract:

In this paper, we discuss a method for implementing online Viterbi algorithm as standard relational queries over streaming data. Our primary contribution is an approach to storing and manipulating most probable state paths, the candidate solutions to the problem, within a relational data model. We evaluate our prototype implementation over real-world sensor data from the Intel Lab dataset.

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Periodical:

Advanced Materials Research (Volumes 532-533)

Pages:

672-676

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.532-533.672

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Online since:

June 2012

Authors:

Rui Li, Lei Zhang

Keywords:

Relational Database, Streaming Data, Viterbi Algorithm

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References

[1] Mert Akdere, Ugur Cetintemel, and Eli Upfal: Database-support for Continuous Prediction Queries over Streaming Data, Brown University. VLDB (2009).

DOI: 10.14778/1920841.1921000

Google Scholar

[2] S.K.M. Wong, C.J. Butz, and Y. Xiang: A method for implementing a probabilistic model as a relational database, UAI, 556-564, Montreal, (1995).

Google Scholar

[3] H. C. Bravo, and R. Ramakrishnan: Optimizing mpf queries: decision support and probabilistic inference, SIGMOD (2007).

DOI: 10.1145/1247480.1247558

Google Scholar

[4] B. Kanagal, and A. Deshpande: Online Filtering, Smoothing and Probabilistic Modeling of Streaming data, ICDE (2008).

DOI: 10.1109/icde.2008.4497525

Google Scholar

[5] Rastislav Sramek: The on-line Viterbi algorithm, Master's Thesis. Comenius University, Bratislava, (2007).

Google Scholar

[6] G. Shafer: An axiomatic study of computation in hypertrees, School of BusinessWorking Paper Series, (No. 232), University of Kansas, Lawrence, (1991).

Google Scholar

[7] L. R. Rabiner: A tutorial on hidden Markov models and selected applications in speech recognition, Proceedings of the IEEE, 77(2): 257–286, (1989).

DOI: 10.1109/5.18626

Google Scholar

[8] The Aurora Project. http: /www. cs. brown. edu/research/aurora.

Google Scholar

[9] Sam Madden, Intel lab data, 2004, http: /berkeley. intel-research. net/labdata.

Google Scholar

[10] H2 Database Engine. www. h2database. com.

Google Scholar