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Development of foundation models for Internet of Things

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Abstract

With the advent of the Internet of Things (IoT) that offers capabilities to identify and connect worldwide physical objects into a unified system, the importance of modeling and processing IoT data has become significantly accentuated. IoT data is substantial in quantity, noisy, heterogeneous, inconsistent, and arrives at the system in a streaming fashion. Due to the unique characteristics of IoT data, the manipulation of IoT data for practical applications has encountered many fundamental challenging problems, such as data modeling and processing. This paper proposes the infrastructure for an IoT prototype system that aims to develop foundation models for IoT data. We illustrate major modules in the IoT prototype, as well as their functionalities, and provide our vision of the key techniques used for tacking the critical problems in each module.

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References

  1. Wong C Y. Integration of Auto-id Tagging System With Holonic Manufacturing Systems. White Article, Auto-id Labs, University of Cambridge, 2001

  2. Cooper J, James A. Challenges for Database Management in the Internet of Things. IETE Technical Review, 2009

  3. ITU. The Internet of Things. ITU Internet Reports, 2005

  4. Contactless Payment and the Retail Point of Sale: Applications, Technologies and Transaction Models. Smart Card Alliance White Paper, http://www.it.iitb.ac.in/~tijo/seminar/Contactless_Pmt_Report.pdf

  5. Landt J. The History of RFID. AIM, Inc.

  6. Internet Protocol, version 6 (IPv6) Specification, http://tools.ietf.org/html/rfc2460

  7. Buneman P, Khanna S, Tan W C. Why and Where: A Characterization of Data Provenance. ICDT, 2001

  8. Zogg J M. GPS Basics. U-Box. 2002

  9. Zigbee Alliance, 2009, http://www.zigbee.org/

  10. Buneman P, Khanna S, Tajima K, Tan W. Archiving scientific data. ACM Trans. Database Syst., 2004, 29(1): 2–42

    Article  Google Scholar 

  11. Cheng R, Kalashnikov D V, Prabhakar S. Evaluating probabilistic queries over imprecise data. SIGMOD, 2003, 551-562

  12. Jeffery S R, Alonso G, Franklin M J, Hong W, Widom J. Declarative support for sensor data cleaning. PerCom, 2006, 83–100

  13. Subramaniam S, Palpanas T, Papadopoulos D, Kalogeraki V, Gunopulos D. Online outlier detection in sensor data using non-parametric models. VLDB, 2006, 187–198

  14. Kriegel H-P, Kunath P, Pfeifle M, Renz M. Probabilistic similarity join on uncertain data. DASFAA, 2006, 295–309

  15. Lian X, Chen L. Probabilistic ranked queries in uncertain databases. EDBT, 2008, 261: 511–522

    Article  Google Scholar 

  16. Lian X, Chen L. Monochromatic and bichromatic reverse skyline search over uncertain databases. SIGMOD, 2008, 213–226

  17. Pei J, Jiang B, Lin X, Yuan Y. Probabilistic skylines on uncertain data. VLDB, 2007, 15–26

  18. Fan W. Dependencies revisited for improving data quality. PODS, 2008, 159–170

  19. Chomicki J, Marcinkowski J. Minimal-change integrity maintenance using tuple deletions. Info. Comput., 2005, 197(1–2): 90–121

    Article  MATH  MathSciNet  Google Scholar 

  20. Arenas M, Bertossi L, Chomicki J. Consistent query answers in inconsistent databases. PODS, 1999, 68–79

  21. Bohannon P, Fan W, Flaster M, Rastogi R. A cost-based model and effective heuristic for repairing constraints by value modification. SIGMOD, 2005, 143–154

  22. Cong G, Fan W F, Geerts F, Jia X B, Ma S. Improving data quality: Consistency and accuracy. VLDB, 2007, 315–326

  23. Wijsen J. Database repairing using updates. TODS, 2005, 30(3): 722–768

    Article  Google Scholar 

  24. Lian X, Chen L, Song S. Consistent query answers in inconsistent probabilistic databases. SIGMOD, 2010, 303–314

  25. Wu E, Diao Y, Rizvi S. High-performance complex event processing over streams. SIGMOD, 2006, 407–418

  26. Meng X L. Multiple-imputation inferences with uncongenial sources of input (with discussion). Statistical Science, 1995, 9: 538–558

    Google Scholar 

  27. Dong X, Halevy A. Indexing dataspaces. SIGMOD, 2007, 43–54

  28. Muralikrishna M, DeWitt D J. Equi-depth multidimensional histograms. SIGMOD Rec., 1988, 17(3): 28–36

    Article  Google Scholar 

  29. Olken F, Rotem D. Simple random sampling from relational databases. VLDB, 1986, 160–169

  30. Fuxman A, Fazli E, Miller R J. ConQuer: Efficient management of inconsistent databases. SIGMOD, 2005, 155–166

  31. Lian X, Chen L. Efficient join processing on uncertain data streams. CIKM, 2009, 857–866

  32. Letchner J, Ré C, Balazinska M, Philipose M. Access methods for Markovian streams. ICDE, 2009, 246–257

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Authors and Affiliations

  1. Department of Computer Science Engineering, Hong Kong University of Science and Technology, Hong Kong, China

    Lei Chen, Mitchell Tseng & Xiang Lian

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  1. Lei Chen
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  2. Mitchell Tseng
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  3. Xiang Lian
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Correspondence to Lei Chen.

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Chen, L., Tseng, M. & Lian, X. Development of foundation models for Internet of Things. Front. Comput. Sci. China 4, 376–385 (2010). https://doi.org/10.1007/s11704-010-0385-8

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  • DOI: https://doi.org/10.1007/s11704-010-0385-8

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