Skip to main content
SpringerLink
Log in

A hash-based index for processing frequent updates and continuous location-based range queries

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

Wide range of location-based services and sensors in GIS have to manage moving objects that change their position with respect to time. These applications generate voluminous amount of real-time data that demands an effective query processing mechanism to minimize the response time of a query. Spatial indexing emerged as an active area for research to accelerate the efficiency of a query processing engine by pruning the search space. Most of the existing indices in the literature for moving objects are based on tree structure and have poor update performance due to node splitting and other computationally expensive operations. Since the moving objects change their location continuously, the underlying index must be updated continuously to keep it up to date. An efficient index is needed that can perform well when there are excessive update operations. Recently, hash-based indices have been used to deal with the limitations of tree-based indices. However, most of them utilize excessive data structures in their operation and usually hold separate indices for queries and object updates. In this paper, we have designed a hash-based index that gives good performance for range search operation under high updates. Our proposed index relies only on one structure to serve both the updates and queries. We proposed a grid-based safe region method for processing continuous range queries under high updates. Experimental evaluation shows that the proposed index outperforms the hash-based indices present in the literature for continuous range search operations. For update operation, LHashMov performs 2.4 times better than u-Grid index and 43 times better than fixed grid index. For continuous-range search operation, LHashMov performs \(3.06 \times 10^{9}\) times better than u-Grid index and \(8.58 \times 10^{9}\) times better than fixed grid index.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27

Similar content being viewed by others

References

  1. Abbasifard MR, Naderi H, Alamdari OI (2017) Efficient indexing for past and current position of moving objects on road networks. IEEE Trans Intell Transp Syst 19(9):2789–2800

    Article  Google Scholar 

  2. Bamba B, Liu L, Iyengar A, Philip SY (2009) Distributed processing of spatial alarms: a safe region-based approach. In: 2009 29th IEEE International conference on distributed computing systems. IEEE, 207–214

  3. Ban Y, Wang R, Liu H, Yuan J, Luo H, Yang F, Yu L, Xu X (2018) A moving objects index method integrating GeoHash and quadtree. In: Proceedings of the international conference on computer modeling, simulation and algorithm (CMSA’18). Atlantis Press, Paris, France, vol 4

  4. Bentley JL, Friedman JH (1979) Data structures for range searching. ACM Comput Surveys (CSUR) 11(4):397–409

  5. Bok K, Park Y, Yoo J (2019) An efficient continuous k-nearest neighbor query processing scheme for multimedia data sharing and transmission in location based services. Multimedia Tools Appl 78(5):5403–5426

  6. Brinkhoff T (2002) A framework for generating network-based moving objects. Geoinformatica 6(2):153–180

    Article  MATH  Google Scholar 

  7. Chaudhry N, Yousaf MM (2022) Concurrency control for real-time and mobile transactions: historical view, challenges, and evolution of practices. Concurr Comput: Practice Exp 34(3):e6549

  8. Chaudhry N, Yousaf MM, Khan MT (2020) Indexing of real time geospatial data by IoT enabled devices: Opportunities, challenges and design considerations. J Ambient Intell Smart Environ 2020:1–32

  9. Chen KL, Li CW, Lu G, Li JQ, Zhang T (2021) An adaptive parallel method for indexing transportation moving objects. Complexity 2021:1–11

    Article  Google Scholar 

  10. Chen Z, Ding J, Zhang M, Tavanapong W, Wong JS (2003) Hierarchical clustering-merging for multidimensional index structures. In: Image and video retrieval: second international conference, CIVR 2003 Urbana-Champaign, IL, USA, July 24–25, 2003 Proceedings 2. Springer, 81–90

  11. Cormode G, Procopiuc C, Srivastava D, Shen E, Yu T (2012) Differentially private spatial decompositions. In: 2012 IEEE 28th international conference on data engineering. IEEE, 20–31

  12. Dong Tianyang, Yuan Lulu, Shang Yuehui, Ye Yang, Zhang Ling (2019) Direction-aware continuous moving k-nearest-neighbor query in road networks. ISPRS Int J Geo Inf 8(9):379

    Article  Google Scholar 

  13. Gao J, Agarwal PK, Yang J (2018) Durable top-k queries on temporal data. Proc VLDB Endow 11(13):2223–2235

  14. Guan X, Bo C, Li Z, Yu Y (2017) ST-hash: An efficient spatiotemporal index for massive trajectory data in a NoSQL database. In: 2017 25th international conference on geoinformatics. IEEE, 1–7

  15. AL-Khalidi H, David T, John B, Sultan A (2013) On finding safe regions for moving range queries. Math Comput Model 58(5–6):1449–1458

  16. Huang Y-W, Jing N, Rundensteiner EA (1997) Integrated query processing strategies for spatial path queries. In: Proceedings 13th international conference on data engineering. IEEE, 477–486

  17. Jiang W, Wei F, Li G, Bai M, Ren Y, An J (2021) Tree index nearest neighbor search of moving objects along a road network. Wireless Commun Mobile Comput 2021:1–8

    Google Scholar 

  18. Jin J, An N, Sivasubramaniam A (2000) Analyzing range queries on spatial data. In: Proceedings of 16th international conference on data engineering (Cat. No. 00CB37073). IEEE, 525–534

  19. Kwon D, Lee S, Choi W, Lee S (2006) An adaptive hashing technique for indexing moving objects. Data Knowl Eng 56(3):287–303

  20. Li C, Gu Y, Qi J, He J, Deng Q, Yu G (2018) A GPU accelerated update efficient index for kNN queries in road networks. In: 2018 IEEE 34th international conference on data engineering (ICDE). IEEE, 881–892

  21. Li H, Liu L, Zhang X, Wang S (2015) Hike: a high performance kNN query processing system for multimedia data. In: 2015 IEEE conference on collaboration and internet computing (CIC). IEEE, 296–303

  22. Litwin W (1980) Linear hashing: a new tool for file and table addressing. In VLDB 80:1–3

  23. MA Y, Chen X, Yu Z (2023) Range query algorithm for large scale moving objects in distributed environment. J Comput Appl 43(1):111

  24. Qi J, Zhang R, Jensen CS, Ramamohanarao K, He J (2018) Continuous spatial query processing: a survey of safe region based techniques. ACM Comput Surveys (CSUR) 51(3):64

  25. Rocha-Junior JB, Vlachou A, Doulkeridis C, Nørvåg K (2010) Efficient processing of top-k spatial preference queries. Proc VLDB Endow 4(2):93–104

    Article  Google Scholar 

  26. Shin J, Wang J, Aref WG (2021) The LSM RUM-tree: a log structured merge R-tree for update-intensive spatial workloads. In: 2021 IEEE 37th international conference on data engineering (ICDE)

  27. Šidlauskas D, Šaltenis S, Christiansen CW, Johansen JM, Šaulys Donatas (2009) Trees or grids?: Indexing moving objects in main memory. In: Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems. ACM, 236–245

  28. Šidlauskas D, Šaltenis S, Jensen CS (2012) Parallel main-memory indexing for moving-object query and update workloads. In: Proceedings of the 2012 ACM SIGMOD international conference on management of data. ACM, 37–48

  29. Wu W, Tan K-L (2007) ISEE: Efficient continuous k-nearest-neighbor monitoring over moving objects. In: 19th International conference on scientific and statistical database management (SSDBM 2007). IEEE, 36–36

  30. Xu X, Xiong L, Sunderam V (2016) D-grid: an in-memory dual space grid index for moving object databases. In: 2016 17th IEEE international conference on mobile data management (MDM)

  31. Zang A, Luo S, Chen X, Trajcevski G (2019) Real-time applications using high resolution 3D objects in high definition maps (systems paper). In: Proceedings of the 27th ACM SIGSPATIAL international conference on advances in geographic information systems. 229–238

  32. Zhang J, Mouratidis K, Pang H (2014) Direct neighbor search. Inf Syst 44(2014):73–92

  33. Zhang S, Mao X, Choo K-KR, Peng T, Wang G (2020) A trajectory privacy-preserving scheme based on a dual-K mechanism for continuous location-based services. Inf Sci 527(2020):406–419

  34. Zhu H, Yu Z (2023) Distributed processing of continuous range queries over moving objects. In: Intelligent networked things: 5th China conference, CINT 2022, Urumqi, China, August 7-8, 2022, Revised Selected Papers

Download references

Author information

Authors and Affiliations

  1. Department of Software Engineering, University of the Punjab, Lahore, Pakistan

    Natalia Chaudhry & Muhammad Murtaza Yousaf

Authors
  1. Natalia Chaudhry
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muhammad Murtaza Yousaf
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All the authors contributed equally in preparing the manuscript.

Corresponding author

Correspondence to Natalia Chaudhry.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chaudhry, N., Yousaf, M.M. A hash-based index for processing frequent updates and continuous location-based range queries. Knowl Inf Syst 65, 4233–4271 (2023). https://doi.org/10.1007/s10115-023-01884-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-023-01884-9

Keywords

Search

Navigation