RFID-data compression for supporting aggregate queries

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.

[1]  Huiyun Li.  Development and Implementation of RFID Technology , 2009 .

[2]  FlescaSergio,et al.  RFID-data compression for supporting aggregate queries , 2013 .

[3]  Yannis Kotidis,et al.  RFID Data Aggregation , 2009, GSN.

[4]  Diego Klabjan,et al.  Warehousing and Analyzing Massive RFID Data Sets , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[5]  Francesco Buccafurri,et al.  A probabilistic framework for estimating the accuracy of aggregate range queries evaluated over histograms , 2012, Inf. Sci..

[6]  Fusheng Wang,et al.  Efficiently Filtering RFID Data Streams , 2006, CleanDB.

[7]  Chris Jermaine,et al.  Guessing the extreme values in a data set: a Bayesian method and its applications , 2009, The VLDB Journal.

[8]  Roberto De Virgilio,et al.  Incremental aggregation of RFID data , 2009, IDEAS '09.

[9]  Yannis E. Ioannidis,et al.  Selectivity Estimation Without the Attribute Value Independence Assumption , 1997, VLDB.

[10]  Philip S. Yu,et al.  A Survey of Synopsis Construction in Data Streams , 2007, Data Streams - Models and Algorithms.

[11]  Nimrod Megiddo,et al.  On the Complexity of Some Common Geometric Location Problems , 1984, SIAM J. Comput..

[12]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[13]  Xin-She Yang,et al.  Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.

[14]  Elio Masciari,et al.  Efficient and effective RFID data warehousing , 2009, IDEAS '09.

[15]  Francesco Buccafurri,et al.  A quad-tree based multiresolution approach for two-dimensional summary data , 2011, Inf. Syst..

[16]  Chun-Hee Lee,et al.  Efficient storage scheme and query processing for supply chain management using RFID , 2008, SIGMOD Conference.

[17]  Sudipto Guha,et al.  REHIST: Relative Error Histogram Construction Algorithms , 2004, VLDB.

[18]  Prashant J. Shenoy,et al.  Probabilistic Inference over RFID Streams in Mobile Environments , 2009, 2009 IEEE 25th International Conference on Data Engineering.

[19]  Prashant J. Shenoy,et al.  Efficient Data Interpretation and Compression over RFID Streams , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[20]  Jiawei Han,et al.  Cost-Conscious Cleaning of Massive RFID Data Sets , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[21]  Joachim Gudmundsson,et al.  Compressing spatio-temporal trajectories , 2009, Comput. Geom..

[22]  Ronald L. Rivest,et al.  Introduction to Algorithms, third edition , 2009 .

[23]  Yon Dohn Chung,et al.  Hierarchically organized skew-tolerant histograms for geographic data objects , 2010, SIGMOD Conference.

[24]  Minos N. Garofalakis,et al.  An adaptive RFID middleware for supporting metaphysical data independence , 2008, The VLDB Journal.

[25]  Yannis Theodoridis,et al.  Index-based Most Similar Trajectory Search , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[26]  Filippo Furfaro,et al.  Compressed hierarchical binary histograms for summarizing multi-dimensional data , 2007, Knowledge and Information Systems.

[27]  P. Shenoy,et al.  SPIRE : Scalable Processing of RFID Event Streams , 2007 .

[28]  Gerhard Weikum,et al.  ACM Transactions on Database Systems , 2005 .

[29]  Cristina Turcu.  Development and Implementation of RFID Technology , 2009 .

[30]  Fusheng Wang,et al.  Temporal Management of RFID Data , 2005, VLDB.

[31]  Sridhar Ramaswamy,et al.  Selectivity estimation in spatial databases , 1999, SIGMOD '99.

[32]  Dimitrios Gunopulos,et al.  Selectivity estimators for multidimensional range queries over real attributes , 2005, The VLDB Journal.

[33]  Ouri Wolfson,et al.  Spatio-temporal data reduction with deterministic error bounds , 2003, DIALM-POMC '03.

[34]  Jiawei Han,et al.  Flowcube: constructing RFID flowcubes for multi-dimensional analysis of commodity flows , 2006, VLDB.

[35]  S. Walton,et al.  To Appear in: , 2001 .

[36]  Guido Moerkotte,et al.  Histograms reloaded: the merits of bucket diversity , 2010, SIGMOD Conference.

[37]  Todd Eavis,et al.  Rk-hist: an r-tree based histogram for multi-dimensional selectivity estimation , 2007, CIKM '07.

[38]  Dino Pedreschi,et al.  Trajectory pattern mining , 2007, KDD '07.

[39]  Klaus Finkenzeller,et al.  Book Reviews: RFID Handbook: Fundamentals and Applications in Contactless Smart Cards and Identification, 2nd ed. , 2004, ACM Queue.

[40]  Ying Hu,et al.  Supporting RFID-based Item Tracking Applications in Oracle DBMS Using a Bitmap Datatype , 2005, VLDB.

[41]  Sudarshan S. Chawathe,et al.  Managing RFID Data , 2004, VLDB.

[42]  Francesco Buccafurri,et al.  A quad-tree based multiresolution approach for two-dimensional summary data , 2003, 15th International Conference on Scientific and Statistical Database Management, 2003..

[43]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[44]  Fusheng Wang,et al.  RFID Data Processing with a Data Stream Query Language , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[45]  Alfredo Cuzzocrea,et al.  Enabling OLAP in mobile environments via intelligent data cube compression techniques , 2008, Journal of Intelligent Information Systems.

[46]  Yunhao Liu,et al.  Mining Frequent Trajectory Patterns for Activity Monitoring Using Radio Frequency Tag Arrays , 2012, IEEE Transactions on Parallel and Distributed Systems.

[47]  Yanlei Diao,et al.  Architectural Considerations for Distributed RFID Tracking and Monitoring , 2009 .

[48]  Jiawei Han,et al.  Mining compressed commodity workflows from massive RFID data sets , 2006, CIKM '06.