SMART: Stream Monitoring enterprise Activities by RFID Tags

Datastreams are potentially infinite data sources that flow continuously while monitoring a physical phenomenon, like temperature levels or other kind of human activities, such as clickstreams, telephone call records, and so on. RFID technology has lead in recent years the generation of huge streams of data. Moreover, RFID based systems allow the effective management of items tagged by RFID tags, especially for supply chain management or objects tracking. In this paper we introduce SMART (Stream Monitoring enterprise Activities by RFID Tags) a system based on an outlier template definition for detecting anomalies in RFID streams. We describe SMART features and its application on a real life scenario that shows the effectiveness of the proposed method for enterprise management. Moreover, we describe an outlier detection approach we defined and effectively exploited in SMART.

[1]  Prabhakar Raghavan,et al.  Computing on data streams , 1999, External Memory Algorithms.

[2]  Philip S. Yu,et al.  Outlier detection for high dimensional data , 2001, SIGMOD '01.

[3]  Johannes Gehrke,et al.  Query Processing in Sensor Networks , 2003, CIDR.

[4]  Ricardo J. G. B. Campello,et al.  On comparing two sequences of numbers and its applications to clustering analysis , 2009, Inf. Sci..

[5]  Kit Yan Chan,et al.  Modeling manufacturing processes using a genetic programming-based fuzzy regression with detection of outliers , 2010, Inf. Sci..

[6]  Lei Chen,et al.  Robust and fast similarity search for moving object trajectories , 2005, SIGMOD '05.

[7]  David West,et al.  Diversity of ability and cognitive style for group decision processes , 2009, Inf. Sci..

[8]  Alan V. Oppenheim,et al.  Discrete-Time Signal Pro-cessing , 1989 .

[9]  Samuel Madden,et al.  Distributing queries over low-power wireless sensor networks , 2002, SIGMOD '02.

[10]  Carlo Zaniolo,et al.  ESL: a Very Powerful SQL-Compliant Data Stream Language , 2005 .

[11]  Vicenç Torra,et al.  Towards the evaluation of time series protection methods , 2009, Inf. Sci..

[12]  Mong-Li Lee,et al.  ICICLES: Self-Tuning Samples for Approximate Query Answering , 2000, VLDB.

[13]  Philippe Bonnet,et al.  Querying the physical world , 2000, IEEE Wirel. Commun..

[14]  Prabhakar Raghavan,et al.  A Linear Method for Deviation Detection in Large Databases , 1996, KDD.

[15]  Christos Faloutsos,et al.  Efficient retrieval of similar time sequences under time warping , 1998, Proceedings 14th International Conference on Data Engineering.

[16]  Boaz Porat,et al.  A course in digital signal processing , 1996 .

[17]  Yunhao Liu,et al.  Mining Frequent Trajectory Patterns for Activity Monitoring Using Radio Frequency Tag Arrays , 2007, Fifth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom'07).

[18]  Christos Faloutsos,et al.  LOCI: fast outlier detection using the local correlation integral , 2003, Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405).

[19]  Joseph M. Hellerstein,et al.  Eddies: continuously adaptive query processing , 2000, SIGMOD 2000.

[20]  David E. Culler,et al.  Supporting aggregate queries over ad-hoc wireless sensor networks , 2002, Proceedings Fourth IEEE Workshop on Mobile Computing Systems and Applications.

[21]  Douglas M. Hawkins.  Identification of Outliers , 1980, Monographs on Applied Probability and Statistics.

[22]  Divyakant Agrawal,et al.  RHist: adaptive summarization over continuous data streams , 2002, CIKM '02.

[23]  Hans-Peter Kriegel,et al.  LOF: identifying density-based local outliers , 2000, SIGMOD 2000.

[24]  Pauli Miettinen,et al.  MDL4BMF: Minimum Description Length for Boolean Matrix Factorization , 2014, TKDD.

[25]  Elio Masciari,et al.  Fast detection of XML structural similarity , 2005, IEEE Transactions on Knowledge and Data Engineering.

[26]  Eamonn Keogh.  Exact Indexing of Dynamic Time Warping , 2002, VLDB.

[27]  Fabrizio Angiulli,et al.  DOLPHIN: An efficient algorithm for mining distance-based outliers in very large datasets , 2009, TKDD.

[28]  Deborah Estrin,et al.  Impact of network density on data aggregation in wireless sensor networks , 2002, Proceedings 22nd International Conference on Distributed Computing Systems.

[29]  Philippe Bonnet,et al.  Towards Sensor Database Systems , 2001, Mobile Data Management.

[30]  Samuel Madden,et al.  Fjording the stream: an architecture for queries over streaming sensor data , 2002, Proceedings 18th International Conference on Data Engineering.

[31]  Elio Masciari,et al.  Effectively Monitoring RFID Based Systems , 2010, ADBIS.

[32]  Vic Barnett,et al.  Outliers in Statistical Data , 1980 .

[33]  Ling Chen,et al.  A clustering algorithm for multiple data streams based on spectral component similarity , 2012, Inf. Sci..

[34]  Abraham B. Korol,et al.  Minimal-dot plot: "Old tale in new skin" about sequence comparison , 2011, Inf. Sci..

[35]  Raymond T. Ng,et al.  Distance-based outliers: algorithms and applications , 2000, The VLDB Journal.

[36]  Carlo Zaniolo,et al.  A data stream language and system designed for power and extensibility , 2006, CIKM '06.

[37]  Anne Rogers,et al.  Hancock: a language for extracting signatures from data streams , 2000, KDD '00.

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

[39]  Jennifer Widom,et al.  Models and issues in data stream systems , 2002, PODS.

[40]  Dimitrios Gunopulos,et al.  Temporal Aggregation over Data Streams Using Multiple Granularities , 2002, EDBT.