RFID and RFID Data Processing.ppt

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  • 1. Handling RFID Data Zhang Yelei May 28, 2010
  • 2. Presentation Outline
    • RFID Introduction
    • Data Processing
    • Data Integration
    • Questions
  • 3. Presentation Outline
    • RFID Introduction
      • What is RFID
      • Standards
      • System architecture
      • Applications
      • Challenges
    • Data Processing
    • Data Integration
    • Questions
  • 4. What is RFID (1)
    • RFID: Radio Frequency Identification
      • A new method/technology of remotely storing and retrieving data
      • Need to use RFID readers and tags
    • A little historical information
      • First applied in World War II by UK
      • First paper exploring RFID “Communication by Means of Reflected Power” by Harry Stockman published in 1948
      • Not until 1990s it was widely deployed
  • 5. What is RFID (2) Roy Want, RFID: a Key to Automate Everything
  • 6. What is RFID (3)
    • RFID Tags (transponder)
      • Low cost, not application-specific
      • Operate on frequencies ranging from 100KHz to beyond 2.5 GHz
      • Type: passive, semi-passive, active
      • Majority are write-once/read-only, others offer r/w capability
      • Readability influenced by factors like frequency, environment, tag position, antenna direction etc. *
        • * Christian Floerkemeier and Matthias Lampe’s cards playing experiment illustrates some sources of errors in tag reading.
  • 7. What is RFID (4)
    • RFID Readers
      • Portable or fixed
      • Use serial port (RS232) or network interface/protocol (wired/ wireless connection) to communicate with computers
      • Radio could be software defined
  • 8. Related Standards
    • Standards about frequencies and communication
      • Identification cards and related areas: ISO/IEC 10536, ISO/IEC 14443 …
      • Automatic identification and data capture technologies: ISO/IEC 15961, ISO/IEC 15962…
      • Conformance: ISO/IEC 18046, ISO/IEC 18047
      • ETSI, ERO
    • Standards about the data format on tags
      • EPCGlobal: focuses on the standardization of the data format
      • EPC, electronic product code (64,96,256 bits long), is now the internationally accepted item-level code.
  • 9. System Architecture Savant: mapping low-level data stream from readers to a more manageable form, cleaning data, supporting simple queries and installed standard queries; Central IS: provide high-level services that are easier for application to use. tag tag tag tag tag tag tag Reader Reader Reader Savant Savant Central Information Server
  • 10. Applications (1)
    • Business applications
      • Transport and logistics
      • Supply chain management
      • Agriculture
    • Government applications
      • Defense and security
      • Library systems
    • Consumer applications
      • Personal welfare and safety
      • Sports and leisure
      • Shopping and dining out
      • Smart homes
    • ……
  • 11. Applications (2)
    • EPCGlobal Network
      • A method of using RFID to share information in the global supply chain
    • 5 components
      • EPC (Electronic Product Code), ID system, EPC middleware, Discovery services, EPC information services (EPC IS)
  • 12. Applications (3) Source: Sun and RFID
  • 13. Challenges
    • Reducing tag costs
    • Global standards
      • Frequency of tags and readers
      • Other specifications…
    • IT infrastructure
      • Data processing: handling large amount of stream data online, effecient use of storage, network bandwidth, and so on.
      • Integration: with databases, data warehouses and enterprise applications
    • Security issues
    • ……
  • 14. Presentation Outline
    • RFID Introduction
    • Data Processing
      • Challenges of Handling Data Stream
      • DSMS vs DBMS
      • Projects
      • Problems
      • Solutions
    • Data Integration
    • Questions
  • 15. Challenges of Handling Data Stream
    • What is the data stream
      • A potentially unbounded sequence of tuples (transactional data stream and measurement data stream*)
    • Data is continuous, infinite
      • Most operations should be done online without interrupting data stream.
      • Data recovery could also be a serious problem
    • Computational resources are limited
    • Real-time data stream requires efficient data handling
      • Complex queries need to be performed nearly real-time
    *AT&T Labs-research, Data Stream Query Processing
  • 16. DSMS vs. DBMS
    • DSMS
      • DAHP model
      • Deals with tuple sequences
      • Complex queries executed real-time and online
      • Database updated frequently
      • Query persistent, plan adaptive, answer approximate
    • DBMS
      • HADP model
      • Deals with tuple sets
      • Complex queries usually executed offline
      • Database relatively stable
      • Query transient, plan fixed, answer exact
  • 17. Research Projects
    • Aurora (supports cq, ad-hoc query, and materialized view)
      • Aims to better support monitoring applications
    • Borealis (distributed SPE, QoS based techniques)
      • A distributed stream processing engine based on Aurora and Medusa
    • TelegraphCQ (focuses on hybrid, ad-hoc query)
      • Intends to handle large streams of continuous queries over high-volume, hightly-variable data streams
    • PSoup (focuses on both ad-hoc and continuous query)
      • A query processor that supports both streaming data and streaming query
    • STREAM
      • A general purpose DSMS prototype
    • GigaScope, Hancock, Nile, TinyDB, COUGAR ……
  • 18. Problems
    • Data models
    • Window operations
    • Query languages
    • Query processing
    • System optimization
  • 19. Solutions (1): Data Models
    • Relation-based Models
      • Aurora: stream type ( TS, A1,…, An)
      • PSoup
      • STREAM: a stream S is an unbounded bag of pairs <s,т>, a relation R is a time-varying bag of tuples
    • Object-based Models
      • COUGAR and Tribeca: data types are associated with methods
  • 20. Solutions (2): Window Operations
    • Why?
      • Time/ordering is a very important aspect of streaming data;
      • Data processing is still based on a finite data set.
    • How to define?
      • Window can be time-based or tuple-based, or partitioned sliding window.
    • Types
      • Fixed, snapshot
      • Landmark
      • Sliding
  • 21. Solutions (3): Query Languages
    • Relation-based Languages
      • CQL used by STREAM: select * from S1 [Rows 1000], S2 [Range 2 Minutes] where S1.A=S2.A and S1.A>10
    • Object-based Languages *
      • COUGAR: select R.s.getTemperature() from R where R.floor=3 and $every(60)
    • Others (Procedural Languages ??) *
      • Aurora: 7 new operators like “map”, “resample” are defined
    * Golab and Ozsu, Data Stream Management Issues ----- A Survey
  • 22. Solutions (4): Query Processing
    • Use connection points to cach streaming data (Aurora)
      • TelegraphCQ use OSCAR for the trade-off of quality and size of the data (from the disk)
    • Attach data queues with operators
      • In Aurora, queue is managed by successors’ pointers.
    • Shared modules among different queries
      • In STREAM, synopses is replaced by stub and store to reduce redundancy.
  • 23. Solutions (5): System Optimization
    • Data gathering
      • Run-time statistics are gathered
    • Inserting, combining, reordering operators
    • Train scheduling, superbox scheduling (like batch operation)
    • Load shedding
      • Static analysis and delay-based dynamic analysis for overload detection
      • By dropping tuples, or by value-based tuple filtering
  • 24. Presentation Outline
    • RFID Introduction
    • Data Processing
    • Data Integration
      • Research gap
      • Design considerations
    • Questions
  • 25. Research Gap
    • Academic research
      • Focuses on issues like processing ability, efficient deployment, antenna design, and so on.
      • Lack of the emphasis on the effective interaction with data warehouses and high-level applications.
    • Enterprise IS and data warehouse
      • Emerged in 1980s, intend to deal with discrete, aggregated data, not continuous, real-time, single-item data.
  • 26. Design considerations
    • Manage data storage
      • Which data should be saved and where
      • Eliminate redundant data
      • Handle historical data
    • Query data
      • Study business scenarios
      • Identify typical on-site queries and data warehouse queries
    • Real-time processing
      • Design of triggers
      • Link real-time events with business processes. (for example, BPEL process and web service)
  • 27. Presentation Outline
    • RFID Introduction
    • Data Processing
    • Data Integration
    • Questions