PEEX : Extracting Probabilistic Events from RFID Data

Radio-Frequency Identification (RFID) technology is increasingly being used to support various industrial and ubiquitous computing applications. Although this technology holds the promise to facilitate many of our every day activities, the noisy and low-level data produced by RFID readers today is extremely difficult to use or comprehend in most but the simplest settings. In this paper, we present PEEX, a system that enables applications to easily define, extract, and manage meaningful probabilistic highlevel events from low-level RFID data. By using a declarative query language, the system simplifies definitions of new events. By using probabilities, the system copes with the noise and errors in the data and the inherent ambiguity in the event extraction. We have built PEEX as a layer on top of a traditional RDBMS, thus enabling applications not only to detect events but also manage them further as necessary. Through experiments with RFID traces collected on a real, building-wide RFID deployment, we demonstrate the performance and practicality of PEEX.

[1]  Andy Hopper,et al.  The active badge location system , 1992, TOIS.

[2]  Narain H. Gehani,et al.  Composite Event Specification in Active Databases: Model & Implementation , 1992, VLDB.

[3]  Hector Garcia-Molina,et al.  The Management of Probabilistic Data , 1992, IEEE Trans. Knowl. Data Eng..

[4]  Sharma Chakravarthy,et al.  Composite Events for Active Databases: Semantics, Contexts and Detection , 1994, VLDB.

[5]  Hari Balakrishnan,et al.  6th ACM/IEEE International Conference on on Mobile Computing and Networking (ACM MOBICOM ’00) The Cricket Location-Support System , 2022 .

[6]  Robert B. Ross,et al.  Probabilistic temporal databases, I: algebra , 2001, TODS.

[7]  Vincent M. Stanford,et al.  Pervasive Computing Goes the Last 100 Feet with RFID Systems , 2003, IEEE Pervasive Comput..

[8]  Robert E. Strom,et al.  Relational subscription middleware for Internet-scale publish-subscribe , 2003, DEBS '03.

[9]  Henry A. Kautz,et al.  Inferring High-Level Behavior from Low-Level Sensors , 2003, UbiComp.

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

[11]  Context-Aware Computing,et al.  Inferring Activities from Interactions with Objects , 2004 .

[12]  Gaetano Borriello,et al.  Reminding About Tagged Objects Using Passive RFIDs , 2004, UbiComp.

[13]  Roy Want,et al.  The Magic of RFID , 2004, ACM Queue.

[14]  Christian Floerkemeier,et al.  Issues with RFID Usage in Ubiquitous Computing Applications , 2004, Pervasive.

[15]  Michael J. Franklin,et al.  Events on the edge , 2005, SIGMOD '05.

[16]  Samuel Madden,et al.  Using Probabilistic Models for Data Management in Acquisitional Environments , 2005, CIDR.

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

[18]  Jennifer Widom,et al.  Trio: A System for Integrated Management of Data, Accuracy, and Lineage , 2004, CIDR.

[19]  Frederick Reiss,et al.  Design Considerations for High Fan-In Systems: The HiFi Approach , 2005, CIDR.

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

[21]  Wei Hong,et al.  Model-based approximate querying in sensor networks , 2005, The VLDB Journal.

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

[23]  Jun Rao,et al.  A deferred cleansing method for RFID data analytics , 2006, VLDB.

[24]  Dan Suciu,et al.  Towards correcting input data errors probabilistically using integrity constraints , 2006, MobiDE '06.

[25]  Christopher Ré,et al.  Query Evaluation on Probabilistic Databases , 2006, IEEE Data Eng. Bull..

[26]  Sharma Chakravarthy,et al.  SnoopIB: Interval-based event specification and detection for active databases , 2003, Data Knowl. Eng..

[27]  Daisy Zhe Wang,et al.  Probabilistic Data Management for Pervasive Computing: The Data Furnace Project , 2006, IEEE Data Eng. Bull..

[28]  Johannes Gehrke,et al.  Towards Expressive Publish/Subscribe Systems , 2006, EDBT.

[29]  Jennifer Widom,et al.  Working Models for Uncertain Data , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[30]  Yanlei Diao,et al.  High-performance complex event processing over streams , 2006, SIGMOD Conference.

[31]  Minos N. Garofalakis,et al.  Adaptive cleaning for RFID data streams , 2006, VLDB.

[32]  Dan Suciu,et al.  Efficient query evaluation on probabilistic databases , 2004, The VLDB Journal.

[33]  Val Tannen,et al.  Models for Incomplete and Probabilistic Information , 2006, IEEE Data Eng. Bull..

[34]  Gustavo Alonso,et al.  Declarative Support for Sensor Data Cleaning , 2006, Pervasive.

[35]  Magdalena Balazinska,et al.  Challenges for Pervasive RFID-Based Infrastructures , 2007, Fifth Annual IEEE International Conference on Pervasive Computing and Communications Workshops (PerComW'07).