Temporal Management - Advanced Database System - Lecture Slides, Slides of Database Management Systems (DBMS)

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RFID Topics

Temporal Management of RFID Data

OUTLINE

• RFID Background

• DRER Model

• Overview of Syntax

• Data Acquisition

• Tool for efficiency

• Siemens Work

Cont’d

• Integration & Information - What we need

to consider:

• Time
• Location
  • Being in the physical world
• Aggregation

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

cont’d

• Static entity tables

OBJECT (epc, name, description) SENSOR (sensor_epc, name, description)

LOCATION (location_id, name, owner)

TRANSACTION (transaction_id, transaction_type)

cont’d

• Dynamic relationship tables

OBSERVATION (sensor_epc, value, timestamp)

SENSORLOCATION (sensor_epc, location_id, position, tstart, tend)

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'

Cont’d

• Temporal aggregation of RFID data: find how many items loaded into the store “L003” on the day of 11/09/ 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' AND tend >= 'T'

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”)

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)?

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

Siemens's Product

• Middleware

• Automatic acquisition and filtering
• Have built a working prototype