article

Spatially-decaying aggregation over a network

Authors:

Haim KaplanAuthors Info & Claims

Journal of Computer and System Sciences, Volume 73, Issue 3

Pages 265 - 288

https://doi.org/10.1016/j.jcss.2006.10.016

Published: 01 May 2007 Publication History

Abstract

Data items are often associated with a location in which they are present or collected, and their relevance or influence decays with their distance. Aggregate values over such data thus depend on the observing location, where the weight given to each item depends on its distance from that location. We term such aggregation spatially-decaying. Spatially-decaying aggregation has numerous applications: Individual sensor nodes collect readings of an environmental parameter such as contamination level or parking spot availability; the nodes then communicate to integrate their readings so that each location obtains contamination level or parking availability in its neighborhood. Nodes in a p2p network could use a summary of content and properties of nodes in their neighborhood in order to guide search. In graphical databases such as Web hyperlink structure, properties such as subject of pages that can reach or be reached from a page using link traversals provide information on the page. We formalize the notion of spatially-decaying aggregation and develop efficient algorithms for fundamental aggregation functions, including sums and averages, random sampling, heavy hitters, quantiles, and L"p norms.

References

[1]

Awerbuch, B., Distributed shortest paths algorithms. In: Proc. 21st Annual ACM Symposium on Theory of Computing, ACM. pp. 490-500.

[2]

Babcock, B., Babu, S., Datar, M., Motwani, R. and Widom, J., Models and issues in data stream systems. In: Proc. of the 2002 ACM Symp. on Principles of Database Systems, PODS 2002, ACM.

[3]

Bar-Yossef, Z., Jayram, T.S., Kumar, R., Sivakumar, D. and Trevisan, L., Counting distinct elements in a data stream. In: RANDOM, ACM.

[4]

Broder, A.Z., Charikar, M., Frieze, A.M. and Mitzenmacher, M., Min-wise independent permutations. J. Comput. System Sci. v60 i3. 630-659.

[5]

Terrestrial air temperature and precipitation: Monthly and annual climatologies.

[6]

Cohen, E., Size-estimation framework with applications to transitive closure and reachability. J. Comput. System Sci. v55. 441-453.

Digital Library

[7]

Cohen, E., Structure prediction and computation of sparse matrix products. J. Combin. Optim. v2. 307-332.

[8]

Cohen, E. and Kaplan, H., Efficient estimation algorithms for neighborhood variance and other moments. In: Proc. 15th ACM--SIAM Symposium on Discrete Algorithms, ACM--SIAM.

[9]

Maintaining time-decaying stream aggregates. In: Proc. of the 2003 ACM Symp. on Principles of Database Systems, PODS 2003, ACM.

[10]

.

[11]

A. Crespo, H. Garcia-Molina, Routing indices for peer-to-peer systems, in: ICDE, 2002

[12]

Datar, M., Gionis, A., Indyk, P. and Motwani, R., Maintaining stream statistics over sliding windows. SIAM J. Comput. v31 i6. 1794-1813.

[13]

A. Deshpande, P. Gibbons, S. Nath, S. Seshan, Cache-and-query for wide area sensor databases, in: Proceedings of the ACM SIGMOD, 2003

[14]

D. Estrin, L. Girod, G. Pottie, M. Srivastava, Instrumenting the world with wireless sensor networks. in: Proc. International Conference on Acoustics, Speech, and Signal Processing, ICASSP, 2001

[15]

Feigenbaum, J., Kannan, S., Strauss, M. and Viswanathan, M., An approximate L1-difference algorithm for massive data streams. In: Proc. 39th IEEE Annual Symposium on Foundations of Computer Science, IEEE. pp. 501-511.

[16]

Flajolet, P. and Martin, G.N., Probabilistic counting algorithms for data base applications. J. Comput. System Sci. v31. 182-209.

Digital Library

[17]

Floyd, S. and Jacobson, V., Random early detection gateways for congestion avoidance. IEEE/ACM Trans. Network. v1 i4.

[18]

Fredman, M. and Tarjan, R.E., Fibonacci heaps and their uses in improved network optimization algorithms. J. Assoc. Comput. Mach. v34 i3. 596-615.

[19]

S. Ganeriwal, C.C. Han, M.B. Srivastava, Spatial average of a continuous physical process in sensor networks (poster), in: Proceedings of the 1st ACM Conference on Embedded Networked Sensor Systems, Sensys, 2003

[20]

D. Ganesan, S. Ratnasamy, H. Wang, D. Estrin, Coping with irregular spatio-temporal sampling in sensor networks, in: Proceedings of the ACM SIGCOMM HOTNETS-II Workshop, 2003

[21]

Gibbons, P. and Tirthapura, S., Estimating simple functions on the union of data streams. In: Proceedings of the 13th Annual ACM Symposium on Parallel Algorithms and Architectures, ACM--SIGMOD.

[22]

Gibbons, P.B. and Tirthapura, S., Distributed streams algorithms for sliding windows. In: Proc. of the 14th Annual ACM Symposium on Parallel Algorithms and Architectures, ACM. pp. 63-72.

[23]

M. Greenwald, S. Khanna, Space-efficient online computation of quantile summaries, in: Proceedings of the ACM SIGMOD, 2001

[24]

Guibas, L.J., Knuth, D.E. and Sharir, M., Randomized incremental construction of Delaunay and Voronoi diagrams. Algorithmica. v7. 381-413.

[25]

Indyk, P., A small approximately min-wise independent family of hash functions. In: Proc. 10th ACM--SIAM Symposium on Discrete Algorithms, ACM--SIAM.

[26]

Indyk, P., Stable distributions, pseudorandom generators, embeddings and data stream computation. In: Proc. 41st IEEE Annual Symposium on Foundations of Computer Science, IEEE. pp. 189-197.

[27]

V. Jacobson, Congestion avoidance and control, in: Proceedings of the ACM SIGCOMM '88 Conference, August 1988

Digital Library

[28]

Kleinberg, J. and Kempe, D., Protocols and impossibility results for gossip-based communication mechanisms. In: Proc. 43rd IEEE Annual Symposium on Foundations of Computer Science, IEEE. pp. 471-480.

[29]

Kleinberg, J., Kempe, D. and Demers, A.J., Spatial gossip and resource location protocols. In: Proc. 33rd Annual ACM Symposium on Theory of Computing, ACM. pp. 163-172.

[30]

Korn, F. and Muthukrishnan, S., Influence sets based on reverse nearest neighbors. In: Proc. of the 2000 ACM SIGMOD Conference on Management of Data, ACM. pp. 201-212.

[31]

Korn, F., Muthukrishnan, S. and Srivastava, D., Reverse nearest neighbor aggregates over data streams. In: Proc. of the 28th International Conference on Very Large Databases, VLDB 2002, ACM.

[32]

Lin, X., Lu, H., Xu, J. and Yu, J.X., Continously maintaining quantile summaries of the most recent n elements over a data stream. In: Proceedings of the 20th International Conference on Data Engineering, ICDE, IEEE.

[33]

Madden, S., Franklin, M., Hellerstein, J. and Hong, W., Tag: A tiny aggregation service for ad-hoc sensor networks. In: Proceedings of the 5th USENIX Symposium on Operating Systems Design and Implementation, ODSI, USENIX Association.

[34]

A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler, J. Anderson, Wireless sensor networks for habitat monitoring, in: Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications, 2002

Digital Library

[35]

Manku, G.S. and Motwani, R., Approximate frequency counts over data streams. In: Proc. of the 28th International Conference on Very Large Databases, VLDB 2002, ACM. pp. 346-357.

[36]

Manku, G.S., Rajagopalan, S. and Lindsay, B.G., Approximate medians and other quantiles in one pass and with limited memory. In: Proc. of the 1998 ACM SIGMOD Conference on Management of Data, ACM. pp. 426-435.

[37]

M.R. Paterson, Progress in selection, Dept. of Computer Science, University of Warwick, Coventry, UK, 1997

[38]

J. Zhao, R. Govindan, D. Estrin, Computing aggregates for monitoring wireless sensor networks, in: Proc. of First IEEE International Workshop on Sensor Network Protocols and Applications, 2003

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Spatially-decaying aggregation over a network

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cover image Journal of Computer and System Sciences

Journal of Computer and System Sciences Volume 73, Issue 3

May, 2007

295 pages

ISSN:0022-0000

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Copyright © Elsevier Inc. © 2006.

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Academic Press, Inc.

United States

Publication History

Published: 01 May 2007

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Cited By

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https://dl.acm.org/doi/10.1145/3584372.3589935
Brown PDasu TKanza YSrivastava D(2019)From Rocks to PebblesACM Transactions on Spatial Algorithms and Systems10.1145/33296775:3(1-38)Online publication date: 12-Aug-2019
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