Abstract
Recent interest in blockchain technology has spurred on a host of new applications in a variety of domains including spatio-temporal data management. The reliability and immutability of blockchain in addition to the decentralized trustless data processing offers promising solutions for modern enterprise systems. However, current blockchain proposals do not support spatio-temporal data processing. Further, a block-based sequential access data structure in the blockchain restricts efficient query processing. Therefore, a blockchain system is desirable that not only supports spatio-temporal data management but also provides efficient query processing. In this work, we propose efficient query processing for spatio-temporal blockchain data. We consider a spatio-temporal blockchain that records both time and location attributes for the transactions. The data storage and integrity is maintained by the introduction of a cryptographically signed tree data structure, a variant of Merkle KD-tree, which also supports fast spatial queries. For the temporal attribute, we consider Bitcoin like near uniform block generation and process temporal queries by a block-DAG data structure without the introduction of temporal indexes. For current position verification, we use Merkle-Patricia-Trie. We also propose a random graph model to generate a block-DAG topology for an abstract peer-to-peer network. A comprehensive evaluation demonstrates the applicability and the effectiveness of the proposed approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Nakamoto, S.: Bitcoin: A Peer-to-Peer Electronic Cash System (2008)
Wood, G.: Ethereum: a secure decentralised generalised transaction ledger. Ethereum Proj. Yellow Pap. 151, 1–32 (2014)
Nastrulin, B., Muzammal, M., Qu, Q.: ChainMOB: mobility analytics on blockchain. In: 19th IEEE International Conference on Mobile Data Management, MDM 2018, Aalborg, Denmark, IEEE Computer Society, pp. 556–557 (2018)
Fox, A.D., Eichelberger, C.N., Hughes, J.N., Lyon, S.: Spatio-temporal indexing in non-relational distributed databases. In: Proceedings of the 2013 IEEE International Conference on Big Data, Santa Clara, CA, USA, pp. 291–299 (2013)
Muzammal, M., Qu, Q., Nasrulin, B.: Renovating blockchain with distributed databases: an open source system. Futur. Gener. Comput. Syst. 90, 105–117 (2019)
Guttman, A.: R-trees: a dynamic index structure for spatial searching. In: Yormark, B., (ed.) SIGMOD 1984, pp. 47–57. ACM Press (1984)
Xu, X.: RT-Tree: an improved R-Tree index structure for spatiotemporal databases. In: Proceedings of the 4th International Symposium on Spatial Data Handling, 1999 (1990)
Theodoridis, Y., et al.: Spatio-temporal indexing for large multimedia applications. In: Proceedings of the IEEE ICMCS, pp. 441–448 (1996)
Bentley, J.L.: Multidimensional binary search trees used for associative searching. Commun. ACM 18(9), 509–517 (1975)
Mahapatra, R.P., Chakraborty, P.S.: Comparative analysis of nearest neighbor query processing techniques. Procedia Comput. Sci. 57, 1289–1298 (2015)
Papadias, D., Tao, Y., Fu, G., Seeger, B.: Progressive skyline computation in database systems. ACM Trans. Database Syst. 30(1), 41–82 (2005)
Li, F., et al.: Proof-infused streams: enabling authentication of sliding window queries on streams. In: Proceedings of the 33rd VLDB, pp. 147–158 (2007)
Mouratidis, K., et al.: Partially materialized digest scheme: an efficient verification method for outsourced databases. VLDB J. 18(1), 363–381 (2009)
Hu, L., Ku, W., Bakiras, S., Shahabi, C.: Spatial query integrity with voronoi neighbors. IEEE Trans. Knowl. Data Eng. 25(4), 863–876 (2013)
Komargodski, I., Naor, M., Yogev, E.: Collision resistant hashing for paranoids: dealing with multiple collisions. In: Nielsen, J.B., Rijmen, V. (eds.) EUROCRYPT 2018. LNCS, vol. 10821, pp. 162–194. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78375-8_6
Martel, C.U., et al.: A general model for authenticated data structures. Algorithmica 39(1), 21–41 (2004)
Becker, G.: Merkle signature schemes, merkle trees and their cryptanalysis. Ruhr-University Bochum, Technical report (2008)
Xu, J., Wei, L., Zhang, Y., Wang, A., Zhou, F., Gao, C.: Dynamic fully homomorphic encryption-based merkle tree for lightweight streaming authenticated data structures. J. Netw. Comput. Appl. 107, 113–124 (2018)
Lewenberg, Y., Sompolinsky, Y., Zohar, A.: Inclusive block chain protocols. In: Böhme, R., Okamoto, T. (eds.) FC 2015. LNCS, vol. 8975, pp. 528–547. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-47854-7_33
Mackey, G.E.: Efficient nearest neighbor searches in n-able tm. Technical report, Sandia National Laboratories (2010)
Acknowledgments
The work was partially supported by the CAS Pioneer Hundred Talents Program, China [grant number Y84402, 2017], and CAS President’s International Fellowship Initiative, China [grant number 2018VTB0005, 2018].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Nurgaliev, I., Muzammal, M., Qu, Q. (2018). Enabling Blockchain for Efficient Spatio-Temporal Query Processing. In: Hacid, H., Cellary, W., Wang, H., Paik, HY., Zhou, R. (eds) Web Information Systems Engineering – WISE 2018. WISE 2018. Lecture Notes in Computer Science(), vol 11233. Springer, Cham. https://doi.org/10.1007/978-3-030-02922-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-02922-7_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02921-0
Online ISBN: 978-3-030-02922-7
eBook Packages: Computer ScienceComputer Science (R0)