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Performance Evaluation of Embedded Time Series Indexes Using Bitmaps, Partitioning, and Trees

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Sensor Networks (SENSORNETS 2021, SENSORNETS 2020)

Abstract

Sensor devices collecting, storing, and processing data use index structures to improve query performance. Indexing approaches based on trees, hash and range partitioning, and space efficient summaries such as bitmap indexes have been proposed. The most efficient technique to use for a particular use case is often unknown, and there has been limited research comparing the different approaches to determine situations where each is the most effective. This work presents an experimental evaluation of the sequential binary index for time series (SBITS) versus tree and partition index structures. SBITS uses space-efficient bitmap indexes and sequential writes that results in significantly higher insert and query performance. It also has the ability to adapt the index structure to both the query requirements and data distribution.

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Notes

  1. 1.

    https://www.microchip.com/en-us/product/ATmega2560.

  2. 2.

    https://www.dialog-semiconductor.com/products/memory/dataflash-spi-memory.

  3. 3.

    https://www.dialog-semiconductor.com/products/memory/dataflash-spi-memory.

  4. 4.

    https://www-k12.atmos.washington.edu/k12/grayskies/.

References

  1. Global Microcontroller Market Share, Industry Size, Application (Automotive Industry, Consumer Devices, and Industrial Sector), by Type (8 bit, 32 bit and 16 bit), 2021 By Radiant Insights, Inc. (2021)

    Google Scholar 

  2. Bender, M.A., et al.: An introduction to B\(\epsilon \)-trees and write-optimization. Usenix Mag. 40(5), 22–28 (2015). https://www.usenix.org/publications/login/oct15/bender

  3. Bloom, B.H.: Space/time trade-offs in hash coding with allowable errors. Commun. ACM 13(7), 422–426 (1970)

    Article  Google Scholar 

  4. Fazackerley, S., Ould-Khessal, N., Lawrence, R.: Efficient flash indexing for time series data on memory-constrained embedded sensor devices. In: Proceedings of the 10th International Conference on Sensor Networks, SENSORNETS 2021, pp. 92–99. SCITEPRESS (2021). https://doi.org/10.5220/0010318800920099

  5. Feltham, A., Ould-Khessal, N., MacBeth, S., Fazackerley, S., Lawrence, R.: Linear hashing implementations for flash memory. In: Filipe, J., Śmiałek, M., Brodsky, A., Hammoudi, S. (eds.) ICEIS 2019. LNBIP, vol. 378, pp. 386–405. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-40783-4_18

    Chapter  Google Scholar 

  6. Ferragina, P., Vinciguerra, G.: The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds. Proc. VLDB Endow. 13(8), 1162–1175 (2020). https://doi.org/10.14778/3389133.3389135

  7. Fevgas, A., Akritidis, L., Bozanis, P., Manolopoulos, Y.: Indexing in flash storage devices: a survey on challenges, current approaches, and future trends. VLDB J. 29(1), 273–311 (2019). https://doi.org/10.1007/s00778-019-00559-8

    Article  Google Scholar 

  8. Hardock, S., Koch, A., Vinçon, T., Petrov, I.: IPA-IDX: in-place appends for B-tree indices. In: 15th International Workshop on Data Management, pp. 18:1–18:3. ACM (2019). https://doi.org/10.1145/3329785.3329929

  9. Kim, B., Lee, D.: LSB-tree: a log-structured B-Tree index structure for NAND flash SSDs. Des. Autom. Embed. Syst. 19(1-2), 77–100 (2015). https://doi.org/10.1007/s10617-014-9139-4

  10. Marcus, R., et al.: Benchmarking learned indexes. Proc. VLDB Endow. 14(1), 1–13 (2020). https://doi.org/10.14778/3421424.3421425

  11. Mathur, G., Desnoyers, P., Chukiu, P., Ganesan, D., Shenoy, P.: Ultra-low power data storage for sensor networks. ACM Trans. Sens. Netw. 5(4), 1–34 (2009)

    Article  Google Scholar 

  12. Na, G., Lee, S., Moon, B.: Dynamic in-page logging for B-tree index. IEEE Trans. Knowl. Data Eng. 24(7), 1231–1243 (2012). https://doi.org/10.1109/TKDE.2011.32

    Article  Google Scholar 

  13. O’Neil, P.E., Cheng, E., Gawlick, D., O’Neil, E.J.: The log-structured merge-tree (LSM-tree). Acta Informatica 33(4), 351–385 (1996). https://doi.org/10.1007/s002360050048

    Article  MATH  Google Scholar 

  14. Ould-Khessal, N., Fazackerley, S., Lawrence, R.: B-tree implementation for memory-constrained embedded systems. In: 19th International Conference on Embedded Systems, Cyber-Physical Systems, and Applications (ESCS). CSREA Press (2021)

    Google Scholar 

  15. Roh, H., Kim, S., Lee, D., Park, S.: AS B-tree: a study of an efficient B+-tree for SSDs. J. Inf. Sci. Eng. 30(1), 85–106 (2014)

    Google Scholar 

  16. Sezer, O.B., Dogdu, E., Ozbayoglu, A.M.: Context-aware computing, learning, and big data in internet of things: a survey. IEEE Internet Things J. 5(1), 1–27 (2018). https://doi.org/10.1109/JIOT.2017.2773600

    Article  Google Scholar 

  17. Sinha, R.R., Winslett, M., Wu, K., Stockinger, K., Shoshani, A.: Adaptive bitmap indexes for space-constrained systems. In: 2008 IEEE 24th International Conference on Data Engineering, pp. 1418–1420. IEEE (2008). https://doi.org/10.1109/ICDE.2008.4497575

  18. Tsiftes, N., Dunkels, A.: A database in every sensor. In: Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems, SenSys 2011, pp. 316–332. ACM (2011). https://doi.org/10.1145/2070942.2070974

  19. Wu, C., Kuo, T., Chang, L.: An efficient B-tree layer implementation for flash-memory storage systems. ACM Trans. Embed. Comput. Syst. 6(3), 19 (2007). https://doi.org/10.1145/1275986.1275991

    Article  Google Scholar 

  20. Wu, K., Otoo, E.J., Shoshani, A.: Optimizing bitmap indices with efficient compression. ACM Trans. Database Syst. 31(1), 1–38 (2006). https://doi.org/10.1145/1132863.1132864

    Article  Google Scholar 

  21. Wu, K., Shoshani, A., Stockinger, K.: Analyses of multi-level and multi-component compressed bitmap indexes. ACM Trans. Database Syst. 35(1), 1–52 (2008). https://doi.org/10.1145/1670243.1670245

    Article  Google Scholar 

  22. Yin, S., Pucheral, P.: PBFilter: a flash-based indexing scheme for embedded systems. Inf. Syst. 37(7), 634–653 (2012). https://doi.org/10.1016/j.is.2012.02.002

    Article  Google Scholar 

  23. Zeinalipour-Yazti, D., Lin, S., Kalogeraki, V., Gunopulos, D., Najjar, W.: MicroHash: an efficient index structure for flash-based sensor devices. In: Proceedings of the FAST 2005 Conference on File and Storage Technologies, pp. 31–43. USENIX Association (2005)

    Google Scholar 

  24. Zhang, H., et al.: Succinct range filters. ACM Trans. Database Syst. 45(2), 5:1–5:31 (2020). https://doi.org/10.1145/3375660

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Acknowledgment

The authors would like to thank NSERC for supporting this research.

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Correspondence to Ramon Lawrence .

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Ould-Khessal, N., Fazackerley, S., Lawrence, R. (2022). Performance Evaluation of Embedded Time Series Indexes Using Bitmaps, Partitioning, and Trees. In: Ahrens, A., Prasad, R.V., Benavente-Peces, C., Ansari, N. (eds) Sensor Networks. SENSORNETS SENSORNETS 2021 2020. Communications in Computer and Information Science, vol 1674. Springer, Cham. https://doi.org/10.1007/978-3-031-17718-7_7

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  • DOI: https://doi.org/10.1007/978-3-031-17718-7_7

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