1. RFID Topics Mo Liu Bart Shappee Temporal Management of RFID Data

2. Worcester Polytechnic Institute 2 OUTLINE RFID Background DRER Model Overview of Syntax Data Acquisition Tool for efficiency Siemens Work

3. Worcester Polytechnic Institute 3 RFID - Background Radio Frequency Identification Major Characteristics: –Streaming Data Temporal and Dynamic –Unreliable Data Mainly Missed Reads & Duplicates –Very Large Volume of Information –Integration RFID Data needs to be handled by existing applications

4. Worcester Polytechnic Institute 4 Cont’d Integration & Information - What we need to consider: –Time –Location Being in the physical world –Aggregation

5. Worcester Polytechnic Institute 5 Dynamic Relationship ER Model (DRER) RFID entities are static and are not altered in the business processes RFID relationships: dynamic and change all the time Dynamic Relationship ER Model – Simple extension of ER model Two types of dynamic relationships added: – Event-based dynamic relationship. A timestamp attribute added to represent the occurring timestamp of the event – State-based dynamic relationship. tstart and tend attributes added to represent the lifespan of a state

6. Worcester Polytechnic Institute 6 Dynamic Relationship ER Model (DRER) (cont’d)

7. Worcester Polytechnic Institute 7 cont’d Static entity tables OBJECT (epc, name, description) SENSOR (sensor_epc, name, description) LOCATION (location_id, name, owner) TRANSACTION (transaction_id, transaction_type)

8. Worcester Polytechnic Institute 8 cont’d Dynamic relationship tables OBSERVATION (sensor_epc, value, timestamp) SENSORLOCATION (sensor_epc, location_id, position, tstart, tend)

9. Worcester Polytechnic Institute 9 OBJECTLOCATION(epc, location id, tstart,tend) CONTAINMENT(epc, parent epc, tstart,tend) TRANSACTIONITEM (transaction_id, epc, timestamp)

10. Worcester Polytechnic Institute 10 Tracking and Monitoring RFID Data RFID object tracking: find the location history of object “EPC” SELECT * FROM OBJECTLOCATION WHERE epc='EPC‘ Missing RFID object detection: find when and where object “mepc” was lost SELECT location_id, tstart, tend FROM OBJECTLOCATION WHERE epc='mepc' and tstart =(SELECT MAX(o.tstart) FROM OBJECTLOCATION o WHERE o.epc=‘mepc') RFID object identification: a customer returns a product “XEPC”. Check if the product was sold from this store SELECT * FROM OBJECTLOCATION WHERE epc='XEPC' AND location_id='L003'

11. Worcester Polytechnic Institute 11 Cont’d Temporal aggregation of RFID data: find how many items loaded into the store “L003” on the day of 11/09/2004 SELECT count(epc)FROM OBJECTLOCATION WHERE location_id = 'L003' AND tstart <= '2004-11-09 00:00:00.000' AND tend >= '2004-11-09 00:00:00.000‘ RFID Data Monitoring—monitor the states of RFID objects RFID object snapshot query: find the direct container of object “EPC” at time T SELECT parent_epc FROM CONTAINMENT WHERE epc='EPC' AND tstart = 'T'

12. Worcester Polytechnic Institute 12 RFID - Data Acquisition Data is automatically generated from the physical world through Readers and Tags Modes if Acquisition –Full/Half Duplex –Sequential Mode This information includes EPCs and timestamps –Other stored values may also be transmitted PHYSICAL WORLD TAG 2 Antenna (interface) 2 Controller 2 Application

13. Worcester Polytechnic Institute 13 RFID - DATA Acquisition Part 2 Data is also pre-porocessed Data Filtering Local Transformation Data Aggregation How do we improve on this? OBSERVATION(Rx, e, Tx), OBSERVATION(Ry, e, Ty), Rx<>Ry, within(Tx, Ty, T) -> DROP:OBSERVATIONS(Rx, e, Tx) OBSERVATION(“R2”, e, t) -> UPDATE:OBJECTLOCATION(e, “L002”, t, “UC”) Seq(s,”r2”);OBSERVATION(“r2”. E. t) -> INSERT:CONTAINMENT(seg(s, “r2”, Tseq), e, t, “UC”)

14. Worcester Polytechnic Institute 14 RFID - DATA Acquisition Part 3 Data is also handled with rules some examples are: Sate Modification (i.e. time at toll) –Creation –Deletion Containment (1000 ipods in a case) –Change location of the 1000 ipods How do we improve on this (even more)?

15. Worcester Polytechnic Institute 15 A Tool to improve query efficiency

16. Worcester Polytechnic Institute 16 Data Partitioning Increase of data volumes slows down queries Data have a limited active cycle – Non-active objects can be periodically archived into history segments – Active segments with a high active object ratio is used for updates This partition technique assures efficient update and queries

17. Worcester Polytechnic Institute 17 Siemens's Product Middleware –Automatic acquisition and filtering –Have built a working prototype

18. Worcester Polytechnic Institute 18 Conclusion Laid a framework for the problems of RFID data acquisition and handling This paper introduced and pushed the DRER model