RFID and RFID Data Processing.ppt


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RFID and RFID Data Processing.ppt

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