挖掘概率频繁模式恢复不确定RFID数据流
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摘要
射频识别(RFID)技术是一种非接触自动识别技术,该技术凭借标签体积小、成本低、非接触识别、自动识别等特点,已广泛应用于多个领域。但是,由于易受外部环境的干扰和射频信号的不稳定,阅读器所产生的数据通常是不可靠、不完全和有噪声,其主要表现为漏读和交错读现象。如何正确恢复RFID数据流中的数据已成为RFID中间件急需解决的重要课题。
该文首先对RFID数据流的特征进行了详细分析,指出RFID数据具有简单性、关联性、时态性和空间性、不确定性(包括漏读和交错读),并对相关技术包括数据流窗口模型、数据流挖掘技术、RFID数据流恢复技术等方面的研究现状进行了详细介绍与分析。
为了更准确地恢复RFID不确定数据,该文提出了一种综合考虑多种因素的RFID不确定数据流恢复方法。该方法主要包括三个步骤：
第一,运用基于PFP-tree树的不确定数据流挖掘方法挖掘RFID数据流中的概率频繁模式。该方法通过设置数据项索引表和事务索引表,能够快速有效地进行频繁模式挖掘。该方法还运用了衰减窗口模型,通过区分“老”事务和“新”事务的贡献度,保证了该方法具有较高的模式召回率。
第二,基于所挖掘的概率频繁模式,通过计算标签对象之间的关联度,确定最大关联标签。而且,通过计算概率相似运动轨迹,确定最相似运动轨迹标签。基于这些信息,采用基于多元统计分析的不确定RFID数据流恢复方法——RR方法进行恢复。该方法综合考虑了标签的当前窗口信息、标签前一窗口信息、最大关联标签信息及最相似轨迹标签信息等四个因素,根据标签矢量的欧式距离判定标签的真实位置。
第三,由于RR方法可能存在误判现象,我们通过统计标签与阅读器的拒真率和交错读率,运用贝叶斯修正方法校正标签位于阅读器范围的概率,为后续窗口的准确恢复提供更为准确的后验概率信息。
最后,通过大量的实验分析和比较,证明基于PFP-tree树的概率模式挖掘方法比基于SUF-growth的方法具有更好的性能,以及所提出的RR恢复方法比SIS方法具有更高的恢复准确率。
RFID is a non-contact and automatic identification technology and has been widely used in many fields, owing to the properties of small size, low-cost, non-contact, automatic identification and so on. Because of the susceptible to the external environment and the instability of RF signals, the original data detected by RFID readers is always unreliable, incomplete and noise such as missing-reads and cross-reads. Therefore, how to recovery these data from RFID data steams has become an urgent and important topic in RFID middleware area.
Firstly, the paper analyzes the characteristics of RFID data streams in details, and indicates that RFID data is simple, relevance, temporal, spatial and uncertain. Then, it introduces and analyses related technologies including window models of data streams, mining technologies on data streams, recovery methods on RFID data streams, and so on.
In order to recover the uncertain RFID data more accurately, the paper proposes a new RFID data streams recovery method which takes several various factors into consideration comprehensively. The method mainly includes three steps:
The first step is to make use of an uncertain data streams mining method based on PFP-tree to mine probability frequent patterns. By setting a transaction-index table and an item-index table, the method can find the frequent patterns quickly and efficiently. The method uses a damping window model to distinguish the contributions of the data generated by new transactions and the data by historic transactions, to guarantees high recall rate of frequent patterns.
The second step is to determine the maximum relevance item using the mining results from PFP-tree, and the maximal similar trajectories item through computing similar trajectories for a specific item. Based on these information, a discriminate multivariate statistical analysis method—RR method is used to recovery RFID data, using a vector including the item's location information in current window and the information in previous window, the location information of the maximal relevance item, and the information of the maximal similar trajectories item. On the basis of these factors proposed, the true place of the tagged item can be found by computing the Euclidean distance of the vector.
The third step is to use a Bayesian-based correction method, using the ratios of missing-reads and cross-reads, to correct the probability of items in the reading ranges of readers, to provide more accurate posterior probability for next windows.
Finally, by experiment analysis and comparison, it has been proved that the performance of PFP-tree is better than that of SUF-growth, and RR recovery method can get higher recovery accurate rate than SIS method.
该文首先对RFID数据流的特征进行了详细分析,指出RFID数据具有简单性、关联性、时态性和空间性、不确定性(包括漏读和交错读),并对相关技术包括数据流窗口模型、数据流挖掘技术、RFID数据流恢复技术等方面的研究现状进行了详细介绍与分析。
为了更准确地恢复RFID不确定数据,该文提出了一种综合考虑多种因素的RFID不确定数据流恢复方法。该方法主要包括三个步骤：
第一,运用基于PFP-tree树的不确定数据流挖掘方法挖掘RFID数据流中的概率频繁模式。该方法通过设置数据项索引表和事务索引表,能够快速有效地进行频繁模式挖掘。该方法还运用了衰减窗口模型,通过区分“老”事务和“新”事务的贡献度,保证了该方法具有较高的模式召回率。
第二,基于所挖掘的概率频繁模式,通过计算标签对象之间的关联度,确定最大关联标签。而且,通过计算概率相似运动轨迹,确定最相似运动轨迹标签。基于这些信息,采用基于多元统计分析的不确定RFID数据流恢复方法——RR方法进行恢复。该方法综合考虑了标签的当前窗口信息、标签前一窗口信息、最大关联标签信息及最相似轨迹标签信息等四个因素,根据标签矢量的欧式距离判定标签的真实位置。
第三,由于RR方法可能存在误判现象,我们通过统计标签与阅读器的拒真率和交错读率,运用贝叶斯修正方法校正标签位于阅读器范围的概率,为后续窗口的准确恢复提供更为准确的后验概率信息。
最后,通过大量的实验分析和比较,证明基于PFP-tree树的概率模式挖掘方法比基于SUF-growth的方法具有更好的性能,以及所提出的RR恢复方法比SIS方法具有更高的恢复准确率。
RFID is a non-contact and automatic identification technology and has been widely used in many fields, owing to the properties of small size, low-cost, non-contact, automatic identification and so on. Because of the susceptible to the external environment and the instability of RF signals, the original data detected by RFID readers is always unreliable, incomplete and noise such as missing-reads and cross-reads. Therefore, how to recovery these data from RFID data steams has become an urgent and important topic in RFID middleware area.
Firstly, the paper analyzes the characteristics of RFID data streams in details, and indicates that RFID data is simple, relevance, temporal, spatial and uncertain. Then, it introduces and analyses related technologies including window models of data streams, mining technologies on data streams, recovery methods on RFID data streams, and so on.
In order to recover the uncertain RFID data more accurately, the paper proposes a new RFID data streams recovery method which takes several various factors into consideration comprehensively. The method mainly includes three steps:
The first step is to make use of an uncertain data streams mining method based on PFP-tree to mine probability frequent patterns. By setting a transaction-index table and an item-index table, the method can find the frequent patterns quickly and efficiently. The method uses a damping window model to distinguish the contributions of the data generated by new transactions and the data by historic transactions, to guarantees high recall rate of frequent patterns.
The second step is to determine the maximum relevance item using the mining results from PFP-tree, and the maximal similar trajectories item through computing similar trajectories for a specific item. Based on these information, a discriminate multivariate statistical analysis method—RR method is used to recovery RFID data, using a vector including the item's location information in current window and the information in previous window, the location information of the maximal relevance item, and the information of the maximal similar trajectories item. On the basis of these factors proposed, the true place of the tagged item can be found by computing the Euclidean distance of the vector.
The third step is to use a Bayesian-based correction method, using the ratios of missing-reads and cross-reads, to correct the probability of items in the reading ranges of readers, to provide more accurate posterior probability for next windows.
Finally, by experiment analysis and comparison, it has been proved that the performance of PFP-tree is better than that of SUF-growth, and RR recovery method can get higher recovery accurate rate than SIS method.
引文
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[3]孙剑,陈琪明.RFID中间件在世界及中国的发展现状.中国物流产品网.2007,2：98-101
[4]B. Babcock, S. Babu, M. Datar, et al. Models and Issues in Data Streams. in:Proceedings of the Twenty-First ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems. Madison, Wisconsin.2002. New York, NY, USA:ACM,2002.1-16.
[5]C. Floerkemeier, M. Lampe. Issues with RFID Usage in Ubiquitous Computing Applications. Lecture Notes in Computer Science,2004,3001/2004:188-193.
[6]S. Jeffery, M. Garofalakis, M. Franklin. Adaptive cleaning for RFID data streams. in:Proceedings of the 32nd International Conference on Very Large Data Bases. Seoul, Korea.2006. USA:VLDB Endowment,2006,163-174.
[7]Kostas Patroumpas and Timos Sellis, Window Update Patterns in Stream Operators. http://www.springerlink.com/content/51 u71 hg4g60133u6/
[8]Metwually, D. Agrawal, and A. El Abbadi. Efficient computation of frequent and top-k elements in data streams. in:Proceedings of 10th International Conference on Database Theory. DATABASE THEORY-ICDT 2005,398-412.
[9]Gurmeet Singh Manku, Rajeev Motwani. Approximate Frequency Counts over Data Streams. Proceedings of the 28th International Conference on Very Large Data Bases (VLDB), Hong Kong SAR, China,2002,346-357.
[10]E. D. Demaine, A. L'opez-Ortiz, and J. I Munro. Frequency estimation of Internet packet LNCS streams with limited space. In Proc.10th European Sympos. Algorithms,2461,2002,348-360.
[11]GS.Manku, R.Motwani. Approximate Frequency Counts over Data Streams. In:Proceedings of the 28th International Conference on Tlery Large Data Bases. Hong Kong, Morgan Kaufmann,2002, 346-357.
[12]JH.Chang, WS.Lee. estWin:Adaptively Monitoring the Recent Change of Frequent Itemsets over Online Data Streams. In:Proceedings of the twelfth International Conference on Information and Knowledge Management, New Orleans, USA:ACM,2003,536-539.
[13]H.Li, S.Lee, M.Shan. An Efficient Algorithm for Mining Frequent Itemsets over the Entire History of Data Streams. In:Proceedings of the first International Workshop on Knowledge Discovery in Data Streams. Pisa, Italy,2004,20-24.
[14]H.Li, S.Lee, M.Shan. Online Mining (Recently) Maximal Frequent Itemsets over Data Streams. In: Proceedings of the fifteenth International Workshops on Research Issues in Data Engineering: Stream Data Mining and Applications, Tokyo, Japan:IEEE,2005,11-18.
[15]C.Jin, W.Qian, C.Sha, JX.Yu, A.Zhou. Dynamically Maintaining Frequent Items over a Data Stream. In:Proceedings of the 2003 ACM CIKM International'Conference on Information and Knowledge Management. New Orleans:ACM,2003,287-294.
[16]JH.Chang, WS.Lee. Finding Recent Frequent Itemsets Adaptively over Online Data Streams. In: Proceedings of the ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, USA:ACM.2003,487-492.
[17]C.Giannella, J.Han, J.Pei, X.Yan, PS.Yu.Mining Frequent Patterns in Data Streams at Multiple Time Granularities.Data Mining:Next Generation Challenges and Future Directions,2003, 191-212.
[18]JH.Chang, WS.Lee.estWin. Adaptively Monitoring the Recent Change of Frequent Itemsets over Online Data Streams. In:Proceedings of the twelfth International Conference on Information and Knowledge Management, New Orleans, USA:ACM,2003,536-539.
[19]Y.Chi, H.Wang, PS.Yu, RR.Muntz. Moment:Maintaining Closed Frequent Itemsets over a Stream Sliding Window. In:Proceedings of the fourth International Conference on Data Mining, Brighton, UK:IEEE,2004,59-66.
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