Bettina Fazzinga
Skip Abstract Section
Skip Supplemental Material SectionAbstract
RFID-based systems for object tracking and supply chain management have been emerging since the RFID technology proved effective in monitoring movements of objects. The monitoring activity typically results in huge numbers of readings, thus making the problem of efficiently retrieving aggregate information from the collected data a challenging issue. In fact, tackling this problem is of crucial importance, as fast answers to aggregate queries are often mandatory to support the decision making process. In this regard, a compression technique for RFID data is proposed, and used as the core of a system supporting the efficient estimation of aggregate queries. Specifically, this technique aims at constructing a lossy synopsis of the data over which aggregate queries can be estimated, without accessing the original data. Owing to the lossy nature of the compression, query estimates are approximate, and are returned along with intervals that are guaranteed to contain the exact query answers. The effectiveness of the proposed approach has been experimentally validated, showing a remarkable trade-off between the efficiency and the accuracy of the query estimation.
Supplemental Material
Available for Download
zip
fazzinga.zip (1 MB)
Supplemental movie, appendix, image and software files for, RFID-Data Compression for Supporting Aggregate Queries
- Acharya, S., Poosala, V., and Ramaswamy, S. 1999. Selectivity estimation in spatial databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data. 275--286. Google Scholar
Digital Library
- Aggarwal, C. and Yu, P. S. 2007. In Data Streams - Models and Algorithms: A Survey Of Synopsis Construction In Data Streams, C. Aggarwal, Ed., Advances in Database Systems Series, vol. 31, Springer, 169--207.Google ScholarCross Ref
- Bai, Y., Wang, F., and Liu, P. 2006. Efficiently filtering RFID data streams. In Proceedings of the CleanDB Workshop. 50--57.Google Scholar
- Bai, Y., Wang, F., Liu, P., Zaniolo, C., and Liu, S. 2007. RFID data processing with a data stream query language. In Proceedings of the International Conference on Data Engineering. 1184--1193.Google Scholar
- Bleco, D. and Kotidis, Y. 2009. RFID data aggregation. In Proceedings of the 3rd International Conference on GeoSensor Networks. 87--101. Google ScholarDigital Library
- Buccafurri, F., Furfaro, F., Mazzeo, G. M., and Saccà, D. 2011. A quad-tree based multiresolution approach for two-dimensional summary data. Inf. Syst. 36, 7, 1082--1103. Google ScholarDigital Library
- Buccafurri, F., Furfaro, F., and Saccà, D. 2012. A probabilistic framework for estimating the accuracy of aggregate range queries evaluated over histograms. Inf. Sci. 188, 121--150. Google ScholarDigital Library
- Cao, H., Wolfson, O., and Trajcevski, G. 2006. Spatio-temporal data reduction with deterministic error bounds. VLDB J. 15, 3, 211--228. Google ScholarDigital Library
- Cao, Z., Diao, Y., and Shenoy, P. J. 2009. Architectural considerations for distributed RFID tracking and monitoring. In Proceedings of the International NetDB Workshop.Google Scholar
- Chawathe, S. S., Krishnamurthy, V., Ramachandran, S., and Sarma, S. 2004. Managing RFID data. In Proceedings of the International Conference on Very Large Databases. 1189--1195. Google ScholarDigital Library
- Cocci, R., Diao, Y., and Shenoy, P. 2007. Spire: Scalable processing of RFID event streams. In Proceedings of the RFID Academic Convocation.Google Scholar
- Cocci, R., Tran, T. T. L., Diao, Y., and Shenoy, P. J. 2008. Efficient data interpretation and compression over RFID streams. In Proceedings of the International Conference on Data Engineering. 1445--1447. Google ScholarDigital Library
- Cormen, T. H., Leiserson, C. E., Rivest, R. L., and Stein, C. 2009. Introduction to Algorithms. MIT Press. Google ScholarDigital Library
- Cormode, G., Garofalakis, M. N., Haas, P. J., and Jermaine, C. 2012. Synopses for massive data: Samples, histograms, wavelets, sketches. Found. Trends Datab. 4, 1--3, 1--294. Google ScholarDigital Library
- Cuzzocrea, A., Furfaro, F., and Saccà, D. 2009. Enabling olap in mobile environments via intelligent data cube compression techniques. J. Intell. Inf. Syst. 33, 2, 95--143. Google ScholarDigital Library
- Eavis, T. and Lopez, A. 2007. Rk-hist: an r-tree based histogram for multi-dimensional selectivity estimation. In Proceedings of the International Conference on Information and Knowledge Management. ACM, New York, 475--484. Google ScholarDigital Library
- Fazzinga, B., Flesca, S., Furfaro, F., and Masciari, E. 2009. Efficient and effective RFID data warehousing. In Proceedings of the International Database Engineering & Applications Symposium. 251--258. Google ScholarDigital Library
- Floerkemeier, C. 2003. RFID Handbook: Fundamentals and Applications in Contactless Smart Cards and Identification. Wiley and Sons. Google ScholarDigital Library
- Frentzos, E., Gratsias, K., and Theodoridis, Y. 2007. Index-based most similar trajectory search. In Proceedings of the International Conference on Data Engineering. 816--825.Google Scholar
- Furfaro, F., Mazzeo, G. M., Saccà, D., and Sirangelo, C. 2008. Compressed hierarchical binary histograms for summarizing multi-dimensional data. Knowl. Inf. Syst. 15, 3, 335--380. Google ScholarDigital Library
- Giannotti, F., Nanni, M., Pinelli, F., and Pedreschi, D. 2007. Trajectory pattern mining. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 330--339. Google ScholarDigital Library
- Gonzalez, H., Han, J., and Li, X. 2006a. Flowcube: Constructing RFID flowcubes for multi-dimensional analysis of commodity flows. In Proceedings of the International Conference on Very Large Databases. 834--845. Google ScholarDigital Library
- Gonzalez, H., Han, J., and Li, X. 2006b. Mining compressed commodity workflows from massive RFID data sets. In Proceedings of the International Conference on Information and Knowledge Management. 162--171. Google ScholarDigital Library
- Gonzalez, H., Han, J., Li, X., and Klabjan, D. 2006c. Warehousing and Analyzing Massive RFID Data Sets. In Proceedings of the International Conference on Data Engineering. 83--88. Google ScholarDigital Library
- Gonzalez, H., Han, J., and Shen, X. 2007. Cost-conscious cleaning of massive RFID data sets. In Proceedings of the International Conference on Data Engineering. 1268--1272.Google Scholar
- Gudmundsson, J., Katajainen, J., Merrick, D., Ong, C., Gudmundsson, J., Katajainen, J., Merrick, D., Ong, C., and Wolle, T. 2009. Compressing spatiotemporal trajectories. Comput. Geom. Theory Appl. 42, 9, 825--841. Google ScholarDigital Library
- Guha, S., Shim, K., and Woo, J. 2004. Rehist: Relative error histogram construction algorithms. In Proceedings of the International Conference on Very Large Databases. 300--311. Google ScholarDigital Library
- Gunopulos, D., Kollios, G., Tsotras, V., and Domeniconi, C. 2005. Selectivity estimators for multidimensional range queries over real attributes. VLDB J. 14, 2, 137--154. Google ScholarDigital Library
- Hardgrave, B. and Miller, R. 2006. The myths and realities of RFID. Int. J. Global Logistics Supply Chain Management 1, 1, 1--16.Google Scholar
- Hu, Y., Sundara, S., Chorma, T., and Srinivasan, J. 2005. Supporting RFID-based item tracking applications in oracle dbms using a bitmap datatype. In Proceedings of the International Conference on Very Large Databases. 1140--1151. Google ScholarDigital Library
- Jeffery, S. R., Franklin, M. J., and Garofalakis, M. N. 2008. An adaptive RFID middleware for supporting metaphysical data independence. VLDB J. 17, 2, 265--289. Google ScholarDigital Library
- Kankonsae, S., Choeysuwan, P., and Choomchuay, S. 2010. A 2-stage compression for RFID tags data. In Proceedings of the International Workshop on Information Communication Technology.Google Scholar
- Kanne, C. C. and Moerkotte, G. 2010. Histograms reloaded: the merits of bucket diversity. In Proceedings of the ACM SIGMOD International Conference on Management of Data. 663--674. Google ScholarDigital Library
- Law, A. M. and Kelton, W. D. 2000. Simulation Modeling and Analysis. McGraw Hill. Google ScholarDigital Library
- Lee, C. H. and Chung, C. W. 2008. Efficient storage scheme and query processing for supply chain management using RFID. In Proceedings of the ACM SIGMOD International Conference on Management of Data. 291--302. Google ScholarDigital Library
- Liu, Y., Chen, L., Pei, J., Chen, Q., and Zhao, Y. 2007. Mining frequent trajectory patterns for activity monitoring using radio frequency tag arrays. In Proceedings of the International Conference on Computational Creativity. 37--46. Google ScholarDigital Library
- Megiddo, N. and Supowit, K. J. 1984. On the complexity of some common geometric location problems. SIAM J. Comput. 13, 1, 182--196.Google ScholarCross Ref
- Poosala, V. and Ioannidis, Y. 1997. Selectivity estimation without the attribute value independence assumption. In Proceedings of the International Conference on Very Large Databases. 486--495. Google ScholarDigital Library
- Roh, Y. J., Kim, J. H., Chung, Y. D., Son, J. H., and Kim, M. H. 2010. Hierarchically organized skew-tolerant histograms for geographic data objects. In Proceedings of the ACM SIGMOD International Conference on Management of Data. 627--638 Google ScholarDigital Library
- Tran, T. T. L., Sutton, C., Cocci, R., Nie, Y., Diao, Y., and Shenoy, P. J. 2009. Probabilistic inference over RFID streams in mobile environments. In Proceedings of the International Conference on Data Engineering. 1096--1107. Google ScholarDigital Library
- Turcu, C. 2009. Development and implementation of RFID technology. Intechweb.Google Scholar
- Virgilio, R. D., Sugamiele, P., and Torlone, R. 2009. Incremental aggregation of RFID data. In Proceedings of the International Database Engineering & Applications Symposium. 194--205. Google ScholarDigital Library
- Wang, F. and Liu, P. 2006. Temporal Management of RFID Data. In Proceedings of the International Conference on Very Large Databases. 1128--1139. Google ScholarDigital Library
- Wu, M. and Jermaine, C. 2009. Guessing the extreme values in a data set: A Bayesian method and its applications. VLDB J. 18, 2, 571--597. Google ScholarDigital Library
Index Terms
- RFID-data compression for supporting aggregate queries
Recommendations
Processing Aggregate Queries with Materialized Views in Data Warehouse Environment
Materialized views, which are derived from base relations and stored in the database, offer opportunities for significant performance gain in query evaluation by providing quick access to the pre-computed data. A materialized view can be utilized in ...
Efficient Execution of Range-Aggregate Queries in Data Warehouse Environments
ER '01: Proceedings of the 20th International Conference on Conceptual Modeling: Conceptual ModelingRange-aggregate queries on the data cube are powerful tools for analysis in data warehouse environments. Cubetree is a technique materializing a data cube through an R-tree. It provides efficient data accessibility, but involves some drawbacks to ...
Answering ad hoc aggregate queries from data streams using prefix aggregate trees
In some business applications such as trading management in financial institutions, it is required to accurately answer ad hoc aggregate queries over data streams. Materializing and incrementally maintaining a full data cube or even its compression or ...
Comments