Maximizing the Relevant Diversity of Social Swarming Information

Peter Terlecky, Yurong Jiang, Xing Xu, Amotz Bar-Noy, Ramesh Govindan

2014

Abstract

In social swarming applications, users are equipped with smartphones and generate data on specific tasks in the form pictures, video, audio, text. A central commander would like to gain access to data relevant to a particular query. Which data wirelessly uploaded to the commander maximizes the amount of diverse information received subject to a bandwidth constraint? We model such a problem in two distinct ways. It is first modeled as a maximum coverage with group budget constraints problem and then as a variant of the maximum edge-weighted clique problem. It is shown that the algorithm for the maximum coverage model outperforms a heuristic for the clique-based model theoretically and practically, with both performing very well experimentally compared to an upper bound benchmark.

References

  1. Alidaee, B., Glover, F., Kochenberger, G., and Wang, H. (2007). Solving the maximum edge weight clique problem via unconstrained quadratic programming. European journal of operational research, 181(2):592-597.
  2. Chekuri, C. and Kumar, A. (2004). Maximum coverage problem with group budget constraints and applications. Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, pages 72-83.
  3. Chen, B., HONG, J., and WANG, Y. (1997). The problem of finding optimal subset of features. CHINESE JOURNAL OF COMPUTERS-CHINESE EDITION-, 20:133-138.
  4. Dijkhuizen, G. and Faigle, U. (1993). A cutting-plane approach to the edge-weighted maximal clique problem. European Journal of Operational Research, 69(1):121-130.
  5. Hunting, M., Faigle, U., and Kern, W. (2001). A lagrangian relaxation approach to the edge-weighted clique problem. European Journal of Operational Research, 131(1):119-131.
  6. Jiang, Y., Xu, X., Terlecky, P., Abdelzaher, T. F., Bar-Noy, A., and Govindan, R. (2013). Mediascope: selective on-demand media retrieval from mobile devices. In IPSN, pages 289-300.
  7. Lappas, T., Crovella, M., and Terzi, E. (2012). Selecting a characteristic set of reviews. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 832- 840. ACM.
  8. Liu, B., Terlecky, P., Bar-Noy, A., Govindan, R., Neely, M. J., and Rawitz, D. (2012a). Optimizing information credibility in social swarming applications. IEEE Transactions on Parallel and Distributed Systems, 23(6):1147-1158.
  9. Liu, B., Terlecky, P., Xu, X., Bar-Noy, A., Govindan, R., and Rawitz, D. (2012b). Timely report delivery in social swarming applications. In DCOSS, pages 75-82.
  10. Macambira, E. and De Souza, C. (2000). The edgeweighted clique problem: valid inequalities, facets and polyhedral computations. European Journal of Operational Research, 123(2):346-371.
  11. Margules, C., Nicholls, A., and Pressey, R. (1988). Selecting networks of reserves to maximise biological diversity. Biological conservation, 43(1):63-76.
  12. Park, K., Lee, K., and Park, S. (1996). An extended formulation approach to the edge-weighted maximal clique problem. European Journal of Operational Research, 95(3):671-682.
  13. Tsaparas, P., Ntoulas, A., and Terzi, E. (2011). Selecting a comprehensive set of reviews. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 168- 176. ACM.
  14. Yu, W., Zhang, R., He, X., and Sha, C. (2013). Selecting a diversified set of reviews. In Web Technologies and Applications, volume 7808 of Lecture Notes in Computer Science, pages 721-733.
  15. Zhuang, L., Jing, F., and Zhu, X.-Y. (2006). Movie review mining and summarization. In Proceedings of the 15th ACM international conference on Information and knowledge management, CIKM 7806, pages 43-50.
Download


Paper Citation


in Harvard Style

Terlecky P., Jiang Y., Xu X., Bar-Noy A. and Govindan R. (2014). Maximizing the Relevant Diversity of Social Swarming Information . In Proceedings of the 3rd International Conference on Sensor Networks - Volume 1: SENSORNETS, ISBN 978-989-758-001-7, pages 365-372. DOI: 10.5220/0004717503650372


in Bibtex Style

@conference{sensornets14,
author={Peter Terlecky and Yurong Jiang and Xing Xu and Amotz Bar-Noy and Ramesh Govindan},
title={Maximizing the Relevant Diversity of Social Swarming Information},
booktitle={Proceedings of the 3rd International Conference on Sensor Networks - Volume 1: SENSORNETS,},
year={2014},
pages={365-372},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004717503650372},
isbn={978-989-758-001-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Sensor Networks - Volume 1: SENSORNETS,
TI - Maximizing the Relevant Diversity of Social Swarming Information
SN - 978-989-758-001-7
AU - Terlecky P.
AU - Jiang Y.
AU - Xu X.
AU - Bar-Noy A.
AU - Govindan R.
PY - 2014
SP - 365
EP - 372
DO - 10.5220/0004717503650372