u niversity of m assachusetts, a mherst department of computer science spire: scalable processing of...

UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science

SPIRE: Scalable Processing of RFID

Event Streams

Yanlei Diao

University of Massachusetts, Amherst

Joint work with Richard Cocci and Prashant Shenoy

UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science 2

UMass RFID Research Center

Kevin FuRFID security & privacy

Yanlei DiaoRFID data management

Wayne BurlesonSecure RFID Hardware

Mark CornerRFID locationing and

mobile readers

Prashant ShenoyRFID software systems

UMass Center for Advanced RFID Research

http://rfid.cs.umass.edu

5 faculty + 9 students


Research Thrusts

Embedded devices & hardware thrust

Data management middleware thrust

Application thrust

Secu

rity

&

Pri

vacy

Rob

ust

ness

&

Sca

lab

ility

Contact-less payments

Contact-less payments

Precise locationing at home/office

Precise locationing at home/office

Healthcare processmanagement

Healthcare processmanagement

Trace-&-trace in supply chain

Trace-&-trace in supply chain

Monitoring &anomaly detection

Monitoring &anomaly detection


Real-time Visibility using Event ProcessingReal-time Visibility using Event Processing

Monitor, Alert, Correct, Control, Improve

Data management middleware thrust

Data Management Middleware

Tracking Individual Objects on a Large ScaleTracking Individual Objects on a Large Scale

Track-and-Trace


Challenges

Data-information mismatch RFID data <tag_id, reader_id, time> Meaningful, actionable information

Incomplete, insufficient, misleading data Missing data Overlapping read ranges Location unclear Containment unclear


Challenges

Scalability Large-scale deployment:

Dozens of locations Thousands of readers Millions of objects

Unprecedented volume of data Low-latency

Anomaly detection: up-to-the-second information

Online data warehousing: frequent updates


Historical Data

4. Event DBTrack & trace

Overview of SPIRE

RFID DevicesRFID Devices

Continuous queries Results SQL queries Results

Data/Queries

1. Data Cleaning1. Data Cleaning Incomplete data, misleading data

Live Data

3. Complex Event Processor

3. Complex Event Processor

Monitoring, anomaly detection

2. Data Interpretation, Compression2. Data Interpretation, Compression Insufficient data, redundant data


Data Cleaning

Raw RFID Stream

Anomaly Filtering

Temporal Smoothing

Time Conversion

Deduplication

Removes bad data

Handles missing data

Adds logical timestamp

Resolves misleading

data

For details see [CocciDS07, GyllstromWC+07, JefferyGM06].


Location-Containment View

A physical world Objects, locations, object containment at

time t RFID readings

Isolated, fragmented views of the physical world

An integrated location-containment view View evolves as new readings arrive View is used to interpret (give meanings to)

the readings


View Maintenance

View(time T, location A)

1, 6, 2, 4, 51, 6, 2, 4, 5

+ Stream(T+1, A)

7, 3, 87, 3, 8

+ Stream(T+2, B)

View(T+2, B)

?X

View(T+1, A)

?


Data Interpretation & Compression

Interpretation of low-quality, insufficient data Location unclear Containment unclear

Data Compression- Location compression- Containment compression- Compression vs. real-time anomaly detection

Archival to event database for track-and-trace Querying over both history and current state


Complex Event Processing

Real-time translation from data to actionable information Filtering Correlation Aggregation Transformation Predication

Proactive, adaptive systems Monitor, alert, correct, control,

improve


SASE Event Processor

Computation Complexity

Misplaced InventoryEVENT SEQ(Shelf_Reading x,

Shelf_Reading y, !(ANY(Register_Reading, Shelf_Reading) z) )

WHERE [TagId] AND x.AreaId != y.AreaId AND x.AreaId = z.AreaId

WITHIN 1 minuteRETURNx.TagId, x.ProdId, x.AreaId, y.AreaId,

retrieveHistOfMvmt(x.TagId)

Stock Market AnalysisEvent SEQ(Stock+ a[], Stock b)Where skip_till_next_match(a[]!,b) { [symbol] AND a[1].volume > 1000 AND

a[i].price > avg(a[…i-1].price)) AND b.volume < 80%*a[a.LEN].volume }

Within 1 hourReturn a[1].symbol, a[].(price,volume), b.

(price,volume)

Medical ComplianceEvent seq(MEDICINE-TAKEN x, MEDICINE-

TAKEN y) Where [name=‘John’] [medicine=‘Antibiotics’]

(x.amount + y.amount) > 1000Within 4 hours

Event Language

SS >: (A, B, D)

SC< : (A, B, D)

s: [attr1, attr2] a.attr4<d.attr4

WD: d.time - a.time < W

NG: !c (b.time<c.time<d.time b.attr1=c.attr1 b.attr2=c.attr2)

SSC

a1 c2 b3 a4 d5 b6 d7 c8 d9 ...Events

TF: sequence to composite event

0 31

*

2

*

A B D

Implementation Mechanisms


Monitoring & Anomaly Detection

Alert if a bottle of drug with an unknown manufacturer appeared in the supply chain.

Alert if a bottle of drug with an unknown manufacturer appeared in the supply chain.

Alert if a case of vaccines has not been seen at three consecutive points in the supply chain.

Alert if a case of vaccines has not been seen at three consecutive points in the supply chain.

Alert if a nominally peanut-free bottle was filled with food containing peanuts. Alert if a nominally peanut-free bottle was filled with food containing peanuts.

Alert if two credit cards with the same number have been found in different locations.

Alert if two credit cards with the same number have been found in different locations.

Alert if a patient has taken overdoses of medicine in past 12 hours. Alert if a patient has taken overdoses of medicine in past 12 hours. ……


Receiving AreaShelf 1

Shelf 2

Shipping Area

RFID Simulator: Supply RFID Simulator: Supply ChainChain


Questions

u niversity of m assachusetts, a mherst department of computer science spire: scalable processing of...

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