Handling RFID Data
Zhang Yelei
April 9, 2023
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Presentation Outline
RFID Introduction Data Processing Data Integration Questions
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Presentation Outline
RFID Introduction What is RFID Standards System architecture Applications Challenges
Data Processing Data Integration Questions
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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
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What is RFID (2)Roy Want, RFID: a Key to Automate Everything
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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.
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What is RFID (4)
RFID ReadersPortable or fixedUse serial port (RS232) or network
interface/protocol (wired/ wireless connection) to communicate with computers
Radio could be software defined
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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.
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System Architecture
tagtag
tagtag
tagtag
tag
Reader Reader Reader
Savant Savant
Central Information Server
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.
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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
……
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Applications (2)
EPCGlobal NetworkA 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)
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Applications (3)
Source: Sun and RFID
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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 ……
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Presentation Outline
RFID Introduction Data Processing
Challenges of Handling Data Stream DSMS vs DBMS Projects Problems Solutions
Data Integration Questions
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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
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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
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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 ……
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Problems
Data models Window operations Query languages Query processing System optimization
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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
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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
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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
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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.
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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
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Presentation Outline
RFID Introduction Data Processing Data Integration
Research gapDesign considerations
Questions
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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.
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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)
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Presentation Outline
RFID Introduction Data Processing Data Integration Questions