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International Conference on Enterprise Information Systems

ICEIS 2019: Enterprise Information Systems pp 386–405Cite as

Linear Hashing Implementations for Flash Memory

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 378))

Abstract

With flash memory having different performance characteristics, specifically fast random reads, there is an opportunity for indexing techniques based on hashing to have increased usage. Embedded devices have minimal memory and use flash memory for storage, which makes them interesting candidates for hash-based indexing. Linear hashing has constant time operations, and several implementation variants are evaluated as an index structure for embedded devices. Experimental results show that linear hash implementations are significantly affected by flash memory properties, and performance is hardware dependent.

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References

  1. Anciaux, N., Bouganim, L., Pucheral, P.: Memory requirements for query execution in highly constrained devices. In: VLDB 2003, pp. 694–705. VLDB Endowment (2003). http://dl.acm.org/citation.cfm?id=1315451.1315511

  2. Bonnet, P., Gehrke, J., Seshadri, P.: Towards sensor database systems. In: Tan, K.-L., Franklin, M.J., Lui, J.C.-S. (eds.) MDM 2001. LNCS, vol. 1987, pp. 3–14. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44498-X_1. http://dl.acm.org/citation.cfm?id=648058.746944

    Chapter  Google Scholar 

  3. Douglas, G., Lawrence, R.: LittleD: a SQL database for sensor nodes and embedded applications. In: Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC 2014, pp. 827–832. ACM, New York (2014). https://doi.org/10.1145/2554850.2554891

  4. Fazackerley, S., Huang, E., Douglas, G., Kudlac, R., Lawrence, R.: Key-value store implementations for Arduino microcontrollers. In: IEEE 28th Canadian Conference on Electrical and Computer Engineering, pp. 158–164, May 2015. https://doi.org/10.1109/CCECE.2015.7129178

  5. Feltham, A., MacBeth, S., Fazackerley, S., Lawrence, R.: Adapting linear hashing for flash memory resource-constrained embedded devices. In: Filipe, J., Smialek, M., Brodsky, A., Hammoudi, S. (eds.) Proceedings of the 21st International Conference on Enterprise Information Systems, ICEIS 2019, Heraklion, Crete, Greece, May 3–5, 2019, vol. 1, pp. 176–181. SciTePress (2019). https://doi.org/10.5220/0007709301760181

  6. Fritter, M., Ould-Khessal, N., Fazackerley, S., Lawrence, R.: Experimental evaluation of hash function performance on embedded devices. In: 2018 IEEE Canadian Conference on Electrical & Computer Engineering (CCECE). IEEE, May 2018. https://doi.org/10.1109/ccece.2018.8447870

  7. Gal, E., Toledo, S.: Algorithms and data sructures for flash memories. ACM Comput. Surv. 37(2), 138–163 (2005). https://doi.org/10.1145/1089733.1089735

    Article  Google Scholar 

  8. Larson, P.A.: Performance analysis of linear hashing with partial expansions. ACM Trans. Database Syst. 7(4), 566–587 (1982). https://doi.org/10.1145/319758.319763. http://doi.acm.org/10.1145/319758.319763

    Article  MATH  Google Scholar 

  9. Larson, P.: Linear hashing with overflow-handling by linear probing. ACM Trans. Database Syst. 10(1), 75–89 (1985). https://doi.org/10.1145/3148.3324. http://doi.acm.org/10.1145/3148.3324

    Article  Google Scholar 

  10. Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., Zhao, W.: A survey on internet of things: architecture, enabling technologies, security and privacy, and applications. IEEE Internet Things J. 4(5), 1125–1142 (2017). https://doi.org/10.1109/JIOT.2017.2683200

    Article  Google Scholar 

  11. Lin, S., Zeinalipour-Yazti, D., Kalogeraki, V., Gunopulos, D., Najjar, W.A.: Efficient indexing data structures for flash-based sensor devices. Trans. Storage 2(4), 468–503 (2006). https://doi.org/10.1145/1210596.1210601. http://doi.acm.org/10.1145/1210596.1210601

    Article  Google Scholar 

  12. Litwin, W.: Linear hashing: a new tool for file and table addressing. In: 6th International Conference on Very Large Data Bases, pp. 212–223. IEEE Computer Society (1980)

    Google Scholar 

  13. Madden, S.R., Franklin, M.J., Hellerstein, J.M., Hong, W.: TinyDB: an acquisitional query processing system for sensor networks. ACM Trans. Database Syst. 30(1), 122–173 (2005). https://doi.org/10.1145/1061318.1061322. http://doi.acm.org/10.1145/1061318.1061322

    Article  Google Scholar 

  14. Penson, W., Fazackerley, S., Lawrence, R.: TEFS: a flash file system for use on memory constrained devices. In: 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1–5, May 2016. https://doi.org/10.1109/CCECE.2016.7726822

  15. Severance, C.: Massimo banzi: building arduino. Computer 47(1), 11–12 (2014). https://doi.org/10.1109/MC.2014.19

    Article  Google Scholar 

  16. Tsiftes, N., Dunkels, A.: A database in every sensor. In: SenSys 2011, pp. 316–332. ACM, New York (2011) https://doi.org/10.1145/2070942.2070974, http://doi.acm.org/10.1145/2070942.2070974

  17. Yang, C., Jin, P., Yue, L., Zhang, D.: Self-adaptive linear hashing for solid state drives. In: ICDE, pp. 433–444, May 2016. https://doi.org/10.1109/ICDE.2016.7498260

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Authors and Affiliations

  1. University of British Columbia, Kelowna, BC, V1V 1V7, Canada

    Andrew Feltham, Nadir Ould-Khessal, Spencer MacBeth, Scott Fazackerley & Ramon Lawrence

Authors
  1. Andrew Feltham
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  2. Nadir Ould-Khessal
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  3. Spencer MacBeth
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  4. Scott Fazackerley
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  5. Ramon Lawrence
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Corresponding author

Correspondence to Ramon Lawrence .

Editor information

Editors and Affiliations

  1. INSTICC and Polytechnic Institute of Setúbal, Setúbal, Portugal

    Joaquim Filipe

  2. Warsaw University of Technology, Warsaw, Poland

    Michał Śmiałek

  3. George Mason University, Fairfax, VA, USA

    Alexander Brodsky

  4. MODESTE/ESEO, Angers, France

    Slimane Hammoudi

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Feltham, A., Ould-Khessal, N., MacBeth, S., Fazackerley, S., Lawrence, R. (2020). Linear Hashing Implementations for Flash Memory. In: Filipe, J., Śmiałek, M., Brodsky, A., Hammoudi, S. (eds) Enterprise Information Systems. ICEIS 2019. Lecture Notes in Business Information Processing, vol 378. Springer, Cham. https://doi.org/10.1007/978-3-030-40783-4_18

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  • DOI: https://doi.org/10.1007/978-3-030-40783-4_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-40782-7

  • Online ISBN: 978-3-030-40783-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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