Abstract
Data sets that are used for answering a single query only once (or just a few times) before they are replaced by new data sets appear frequently in practical applications. The cost of buiding indexes to accelerate query processing would not be repaid for such data sets. We consider an extension of the popular (K) Nearest-Neighbor Query, called the (K) Group Nearest Neighbor Query (GNNQ). This query discovers the (K) nearest neighbor(s) to a group of query points (considering the sum of distances to all the members of the query group) and has been studied during recent years, considering data sets indexed by efficient spatial data structures. We study (K) GNNQs, considering non-indexed RAM-resident data sets and present an existing algorithm adapted to such data sets and two Plane-Sweep algorithms, that apply optimizations emerging from the geometric properties of the problem. By extensive experimentation, using real and synthetic data sets, we highlight the most efficient algorithm.
G. Roumelis, M. Vassilakopoulos, A. Corral and Y. Manolopoulos—Work funded by the GENCENG project (SYNERGASIA 2011 action, supported by the European Regional Development Fund and Greek National Funds); project number 11SYN_ 8_1213.
A. Corral—Supported by the MINECO research project [TIN2013-41576-R].
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
This paper is a post proceedings enhanced version of [6], where the last two algorithms of the current paper are presented and compared.
References
Rigaux, P., Scholl, M., Voisard, A.: Spatial Databases - with Applications to GIS. Elsevier, San Francisco (2002)
Preparata, F.P., Shamos, M.I.: Computational Geometry - An Introduction. Springer, New York (1985)
Hinrichs, K., Nievergelt, J., Schorn, P.: Plane-sweep solves the closest pair problem elegantly. Inf. Process. Lett. 26, 255–261 (1988)
Jacox, E.H., Samet, H.: Spatial join techniques. ACM Trans. Database Syst. 32, 7 (2007)
Roumelis, G., Vassilakopoulos, M., Corral, A., Manolopoulos, Y.: A new plane-sweep algorithm for the K-closest-pairs query. In: Geffert, V., Preneel, B., Rovan, B., Štuller, J., Tjoa, A.M. (eds.) SOFSEM 2014. LNCS, vol. 8327, pp. 478–490. Springer, Heidelberg (2014)
Roumelis, G., Vassilakopoulos, M., Corral, A., Manolopoulos, Y.: Plane-sweep algorithms for the k group nearest-neighbor query. In: GISTAM Conference, pp. 83–93. Scitepress (2015)
Papadias, D., Shen, Q., Tao, Y., Mouratidis, K.: Group nearest neighbor queries. In: ICDE Conference, pp. 301–312. IEEE (2004)
Papadias, D., Tao, Y., Mouratidis, K., Hui, C.K.: Aggregate nearest neighbor queries in spatial databases. ACM Trans. Database Syst. 30, 529–576 (2005)
Li, H., Lu, H., Huang, B., Huang, Z.: Two ellipse-based pruning methods for group nearest neighbor queries. In: ACM-GIS Conference, pp. 192–199. ACM (2005)
Luo, Y., Chen, H., Furuse, K., Ohbo, N.: Efficient methods in finding aggregate nearest neighbor by projection-based filtering. In: Gervasi, O., Gavrilova, M.L. (eds.) ICCSA 2007, Part III. LNCS, vol. 4707, pp. 821–833. Springer, Heidelberg (2007)
Namnandorj, S., Chen, H., Furuse, K., Ohbo, N.: Efficient bounds in finding aggregate nearest neighbors. In: Bhowmick, S.S., Küng, J., Wagner, R. (eds.) DEXA 2008. LNCS, vol. 5181, pp. 693–700. Springer, Heidelberg (2008)
Hashem, T., Kulik, L., Zhang, R.: Privacy preserving group nearest neighbor queries. In: EDBT Conference, pp. 489–500. ACM (2010)
Zhu, L., Jing, Y., Sun, W., Mao, D., Liu, P.: Voronoi-based aggregate nearest neighbor query processing in road networks. In: ACM-GIS Conference, pp. 518–521. ACM (2010)
Jiang, T., Gao, Y., Zhang, B., Liu, Q., Chen, L.: Reverse top-k group nearest neighbor search. In: Wang, J., Xiong, H., Ishikawa, Y., Xu, J., Zhou, J. (eds.) WAIM 2013. LNCS, vol. 7923, pp. 429–439. Springer, Heidelberg (2013)
Zhang, D., Chan, C., Tan, K.: Nearest group queries. In: SSDBM Conference, p. 7. ACM (2013)
Lian, X., Chen, L.: Probabilistic group nearest neighbor queries in uncertain databases. IEEE Trans. Knowl. Data Eng. 20, 809–824 (2008)
Li, J., Wang, B., Wang, G., Bi, X.: Efficient processing of probabilistic group nearest neighbor query on uncertain data. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds.) DASFAA 2014, Part I. LNCS, vol. 8421, pp. 436–450. Springer, Heidelberg (2014)
Ahn, H.-K., Bae, S.W., Son, W.: Group nearest neighbor queries in the L \(_\text{1 }\) plane. In: Chan, T.-H.H., Lau, L.C., Trevisan, L. (eds.) TAMC 2013. LNCS, vol. 7876, pp. 52–61. Springer, Heidelberg (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Appendix
Lemma: The sum of dx-distances between one given point \(p(x,y) \in P\) and all points of the query set Q (sumdx(p, Q)):
- A :
-
Is minimized at the median point q[m] (where q[m] is the array notation of \(q_m\)),
- B :
-
For all \(p.x \ge q[m].x\), sumdx is constant or increasing with the increment of x, and
- C :
-
For all \(p.x < q[m].x\), sumdx is increasing while x decreases.
Proof: Property A has been proved in [18]. To prove property B, for every point \(p \in P\) and \(q \in Q\), we use
If the point p has K query points on the left (\(p.x < q[K-1].x\)) and \(M-K\) query points on the right (Fig. 5), then:
For another point \(p' \in P\) with \(p'.x > p.x\) which has \(K'\) query points on the left (Fig. 5) and \(M-K'\) query points on the right, it is:
The difference between dx-distances of the points \(p'\) and p is: . If the set of the query points Q has cardinality M and this is an even number then there are two medians q[m1] and q[m2], while if M is odd then there is only one median point q[m].
B.1 M is even and \(q[m1].x \le p.x < p'.x\) then \(M \le 2K \le 2K'\) so \((2K-M)\ge 0\), \((p'.x-p.x) \ge 0\) and \((K'-K)p'.x - \displaystyle \sum _{i=K}^{K'-1} q[i].x \ge 0\) because \(p'.x \ge q[i].x\), whereas \(K \le i \le K'\)
B.2 All of the above apply to M if it is odd and it is only one median point \(q[m].x \le p.x < p'.x\). It is proven that for all points p on the right of the median query point the sum of dx-distances is increasing.
C For both types of cardinality of the query set Q and for the case \(p.x < p'.x < q[m].x\) it is: \( \varDelta sumdx = (2K-M)(p'.x-p.x) + 2(K'-K)p'.x \ - 2 \displaystyle \sum _{i=K}^{K'-1} q[i].x \ \le (2K-M)(p'.x-p.x) + 2(K'-K)p'.x \ - 2(K'-K)p.x \ = 2(K-M)(p'.x-p.x) \ + 2(K'-K)(p'.x-p.x) \ = (2K-M+2K'-2K)(p'.x-p.x) \ = (2K'-M)(p'.x-p.x) < 0 \). It is proven that for all points p on the left of the median query point the sum of dx-distances is strictly decreasing. \(\square \)
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Roumelis, G., Vassilakopoulos, M., Corral, A., Manolopoulos, Y. (2016). The K Group Nearest-Neighbor Query on Non-indexed RAM-Resident Data. In: Grueau, C., Gustavo Rocha, J. (eds) Geographical Information Systems Theory, Applications and Management. GISTAM 2015. Communications in Computer and Information Science, vol 582. Springer, Cham. https://doi.org/10.1007/978-3-319-29589-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-29589-3_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-29588-6
Online ISBN: 978-3-319-29589-3
eBook Packages: Computer ScienceComputer Science (R0)