Abstract
We present a new type of location-based queries, namely the Budget Range-based All Neighboring Object Group Query (BR-ANOGQ for short), to offer spatial object information while respecting distance and budget range constraints. This query type finds utility in numerous practical scenarios, such as assisting travelers in selecting fitting destinations for their journeys. To support the BR-ANOGQ, we develop data structures for efficient representation of road networks and employ two index structures, the \(R^{cC}\)-tree and the grid index, for managing spatial objects based on their locations and costs. We introduce two pruning criteria to filter out object sets that do not meet the specified distance d and budget range \([bgt_m, bgt_M]\) constraints. We also devise a road network traversal method that selectively accesses a small fraction of objects while generating the query result. The BR-ANOGQ algorithm effectively utilizes index structures and pruning criteria for query processing. Through a series of comprehensive experiments, we demonstrate its efficiency in terms of CPU time and index node accesses, providing valuable insights for location-based queries with constraints.
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References
Abeywickrama T, Cheema MA, Khan A (2020) K-spin: efficiently processing spatial keyword queries on road networks. TKDE 32(5):983–997
Benetis R, Jensen CS, Karciauskas G, Saltenis S (2006) Nearest neighbor and reverse nearest neighbor queries for moving objects. VLDB J 15(3):229–249
Bentley JL, Kung HT, Schkolnick M, Thompson CD (1978) On the average number of maxima in a set of vectors and applications. J ACM 25(4):536–543
Borzsonyi S, Kossmann D, Stocker K (2001) The skyline operator. In: 17th international conference on data engineering, pp 421–430
Brinkhoff T (2002) A framework for generating network-based moving objects. GeoInformatica 6(2):153–180
Brinkhoff T, Kriegel H-P, Seeger B (1993) Efficient processing of spatial joins using r-trees. In: international conference on ACM SIGMOD, pp 237–246
Cao X, Cong G, Guo T, Jensen CS, Ooi BC (2015) Efficient processing of spatial group keyword queries. ACM Trans Database Syst 40(2):1–48
Cao X, Cong G, Jensen CS (2010) Retrieving top-k prestige-based relevant spatial web objects. PVLDB 3(1):373–384
Cao X, Cong G, Jensen CS, Ooi BC (2011) Collective spatial keyword querying. In: ACM SIGMOD, pp 373–384
Chan HK, Liu S, Long C, Wong RC (2023) Cost-aware and distance-constrained collective spatial keyword query. TKDE 35(2):1324–1336
Chen J, Cheng R (2007) Efficient evaluation of imprecise location-dependent queries. In: ICDE, pp 586–595
Chen Y, Patel JM (2007) Efficient evaluation of all-nearest-neighbor queries. In: ICDE, pp 1056–1065
Chung BS, Lee W-C, Chen AL (2009) Processing probabilistic spatio-temporal range queries over moving objects with uncertainty. In: EDBT, pp 60–71
Deng K, Zhou X, Shen HT (2007) Multi-source skyline query processing in road networks. In: international conference on data engineering, pp 796–805
Dong Y, Xiao C, Chen H, Yu JX, Takeoka K, Oyamada M, Kitagawa H (2021) Continuous top-k spatial-keyword search on dynamic objects. VLDB J 2:30–51
Ekomie HB, Yao K, Li J, Li G, Li Y (2017) Group top-k spatial keyword query processing in road networks. In: DEXA, pp 395–408
Felipe D, Hristidis V, Rishe N (2008) Keyword search on spatial databases. In: international conference on data engineering, pp 656–665
Gao Y, Zhao J, Zheng B, Chen G (2016) Efficient collective spatial keyword query processing on road networks. IEEE Trans Intell Transp Syst 17(2):469–480
Guttman A (1984) R-trees: a dynamic index structure for spatial searching. In: ACM SIGMOD, pp 47–57
Hjaltason GR, Samet H (1998) Incremental distance join algorithms for spatial databases. In: international conference on ACM SIGMOD, pp 237–248
Hjaltason GR, Samet H (1999) Distance browsing in spatial databases. ACM Trans Database Syst 24(2):265–318
Huang Y-K (2017) Location-based aggregate queries for heterogeneous neighboring objects. IEEE Access 5:4887–4899
Huang Y-K (2020) Efficient processing of neighboring skyline queries with consideration of distance, quality, and cost. Computing 102(2):523–550
Huang Y-K (2020) Processing location-based aggregate queries in road networks. J Inf Sci Eng 36(4):921–935
Huang Y-K, Chang C-H, Lee C (2012) Continuous distance-based skyline queries in road networks. Inf Syst 37:611–633
Huang Y-K, Lin L-F (2011) Continuous within query in road networks. In: IWCMC, pp 1176–1181
Huang Y-K, Lin L-F (2014) Efficient processing of continuous min–max distance bounded query with updates in road networks. Inf Sci 278:187–205
Huang Y-K, Lin L-F (2023) Evaluating neighboring site group queries with constraint of budget range. In: ICCCM, pp 1–6
Iwerks G, Samet H, Smith K (2003) Continuous k-nearest neighbor queries for continuously moving points with updates. In: proceedings of the international conference on very large data bases, pp 512–523
Kalashnikov DV, Prabhakar S, Hambrusch S, Aref W (2002) Efficient evaluation of continuous range queries on moving objects. In: international conference on database and expert systems applications, pp 731–740
Li J, Xu M (2021) A parametric approximation algorithm for spatial group keyword queries. Intell Data Anal 25(2):305–319
Li Y, Li G, Li J, Yao K (2018) SKQAI: a novel air index for spatial keyword query processing in road networks. Inf Sci 430:17–38
Li Z, Chen L, Wang Y (2019) G*-tree: an efficient spatial index on road networks. In: ICDE, pp 268–279
Lin X, Xu J, Hu H (2013) Range-based skyline queries in mobile environments. IEEE Trans Knowl Data Eng 25(4):835–849
Mamoulis N, Papadias D (2001) Multiway spatial joins. ACM Trans Database Syst 26(4):424–475
Mokbel MF, Xiong X, Aref WG (2004) SINA: scalable incremental processing of continuous queries in spatio-temporal databases. In: proceedings of the ACM SIGMOD, pp 623–634
Papadias D, Shen Q, Tao Y, Mouratidis K (2004) Group nearest neighbor queries. In: ICDE, pp 301–312
Papadopoulos A, Manolopoulos Y (1998) Multiple range query optimization in spatial databases. In: ADBIS, pp 71–82
Roussopoulos N, Kelley S, Vincent F (1995) Nearest neighbor queries. In: proceedings of ACM SIGMOD, pp 71–79
Sharifzadeh M, Shahabi C (2006) The spatial skyline queries. In: international conference on very large data bases, pp 751–762
Sistla AP, Wolfson O, Chamberlain S, Dao S (1997) Modeling and querying moving objects. In: ICDE, pp 422–432
Su S, Zhao S, Cheng X, Rong B, Wang J (2017) Group-based collective keyword querying in road networks. Inf Process Lett 118:83–90
Tao Y, Papadias D (2002) Time-parameterized queries in spatio-temporal databases. In: ACM SIGMOD, pp 334–345
Yiu ML, Mamoulis N, Papadias D (2005) Aggregate nearest neighbor queries in road networks. IEEE Trans Knowl Data Eng 17(6):820–833
Zhang C, Li F, Jestes J (2012) Efficient parallel KNN joins for large data in MapReduce. In: EDBT
Zhang D, Chan C-Y, Tan K-L (2013) Nearest group queries. In: international conference on SSDBM
Zhang D, Chee YM, Mondal A, Tung AKH, Kitsuregawa M (2009) Keyword search in spatial databases: towards searching by document. In: IEEE international conference on data engineering, pp 688–699
Zhang Z, Jin P, Tian Y, Wan S, Yue L (2019) Efficient processing of spatial group preference queries. In: DASFAA, pp 642–659
Zhao S, Cheng X, Su S, Shuang K (2017) Popularity-aware collective keyword queries in road networks. GeoInformatica 21(3):1–34
Zhu L, Jing Y, Sun W, Mao D, Liu P (2010) Voronoi-based aggregate nearest neighbor query processing in road networks. In: SIGSPATIAL, pp 518–521
Acknowledgements
This work was supported by National Science and Technology Council (R.O.C.) under Grants NSTC 112-2221-E-992-063 and NSTC 110-2221-E-992 -049-MY2.
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Y-KH wrote the main manuscript text and prepared figures. C-PL reviewed and revised the manuscript.
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Huang, YK., Lee, CP. Efficient processing of all neighboring object group queries with budget range constraint in road networks. Computing (2024). https://doi.org/10.1007/s00607-024-01260-7
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DOI: https://doi.org/10.1007/s00607-024-01260-7
Keywords
- Location-based queries
- Budget Range-based All Neighboring Object Group Query
- Road networks
- \(R^{cC}\)-tree
- Grid index
- BR-ANOGQ algorithm