GS1/Oliot EPCIS and Next

Jun. 25, 2014
Auto-ID Labs, KAIST
Dept. of Computer Science, KAIST
GS1 / Oliot: EPC Information Service &
Big Data Analytics
Jaewook Byun
bjw0829@kaist.ac.kr, http://oliot.org, http://autoidlab.kaist.ac.kr, http://resl.kaist.ac.kr, http://autoidlabs.org, http://gs1.org

© Auto-ID Lab Korea / KAIST
Slide 2
 EPCIS and Next
– Introduction
– Four dimensions
– Event Types
– Services
 Oliot- Distributed Storage
 Oliot- Real-time Big-data Processing
 Conclusion
Contents

Slide 3
Introduction
EPCIS
RFID Reader
& Antenna
Event
Processing
Everyday
Object
EPCIS
Distributed Data Storage
RFID
Tag
+
+
+
+
EPCIS Event
Tag Event
 EPCIS in GS1 architecture
⁃ To share visible RFID event data
⁃ Pros.
 Supporting existing standardized
identifier
⁃ RFID TAG
⁃ Barcode
 Distributed database for SCM
⁃ Standard
⁃ Flexible

Slide 4
Introduction
EPCIS for IoT
RFID Reader
& Antenna
Everyday
Object
EPCIS for IoT
RFID
Tag
IoT Devices Support
Environmental
Sensor
Medical Device Healthcare Device Smart Appliance
Gateway Server Mobile Device
Event
Processing
EPCIS Event Sensor Event, Medicare Event,
…

Slide 5
Introduction
EPCIS Application
Visualization & Big Data Analysis
WholesaleShippingManufacturer
Supply Chain Management
Fine Dust Map Daily Medical Graph
EPCIS

Slide 6
Four dimensions of any EPCIS event

Slide 7
EPCIS Event Types
 EPCISEvent – Base event type
Object Event Transaction Event Transformation Event
Receiving time at Capturing Application
Receiving time at EPCIS repository
TimeZone, offset from UTC
Aggregation Event
Extends

Slide 8
EPCIS Event Types
Object Event
 Object Event
– Observation of object(s)
List of Observed objects
e.g.Created, Observed, Destroyed
c.F RED: new in EPCIS v1.1
(Optional)
Instance level master data: e.g. expiration date
(Optional)
(Optional)

Slide 9
EPCIS Event Types
Aggregation
 Aggregation Event
– Association between containing/contained object(s)
Aggregation Event
(e.g. box, case, pallet)
e.g. Box, case, pallet
e.g. Trade items in box
e.g. child added, observed, or deleted from parents
(Optional)

Slide 10
EPCIS Event Types
Transaction Event
 Transaction Event
– (Dis)Association of object(s) to business transaction(s)
(Optional)
e.g. Item (dis)associated to the BizTransaction
 Business Step
 Business process
 e.g. Loading, Packing, Shipping, Receiving
 Disposition
 Status of object
 Available for sale, in Storage
 Business Transaction
 Transaction information
 e.g. Purchase, Invoice
Transaction Event

Slide 11
EPCIS Event Types
Transformation Event
 Transformation Event
– Capture the relationship between the input (source) and the outputs (product)
 Many to one
 One to many
 Many to many
e.g. One to many
COW  Slides of Beef
Input Outputs
(Optional)
c.F RED: new in EPCIS v1.1

Slide 12
EPCIS Event Types
Extended Event for Oliot storage
 Extended Event for IoT in a case of Medical/Healthcare
– Complying EPCglobal Standard
– Supporting various sensor devices
EEG
Blood Pressure
ECG
BreathingGlucometerOxygen
Static/Medical Sensors
Accelerometer
Skin Response Temperature
Mobile/Healthcare Sensors
Wristband Headset
ScaleChestband
Oliot
Distributed Storage
Need!
Extended Event
with Extended Voc.

Slide 13
EPCIS Event Types
 Extended Event for IoT in a case of Medical/Healthcare (Cont.)
MedicalEvent
eventTime: Time
recordTime: Time
eventTimeZoneOffset: string
sensorEPC: EPC
patientEPC: EPC
bizLocation: BizLocationID
BizStep: Business Step ID
Disposition: DispositionID
sensorValueList: List<sensorValue>
ilmd: ILMD
• sensorEPC: Sensor Device ID
• e.g. EEG sensor
• patientEPC: Patient ID
• bizLocation: Location ID
• bizStep: Business Step ID
in operationMedicine Injection

Slide 14
EPCIS Event Types
 Extended Event for IoT in a case of Medical/Healthcare (Cont.)
• disposition: Patient’s status
• SensorValueList
• Example
<iot:SensorList>
<iot:Sensor type=“urn:oliot:sensor:bloodpressure”>117/87</iot:Sensor>
<iot:Sensor type=“urn:oliot:sensor:stepcount”>5700</iot:Sensor>
<iot:Sensor type=“urn:oliot:sensor:temperature”>36</iot:Sensor>
</iot:SensorList>
• ilmd: Master data for individual patient
DateOfBirth Name Gender
Height Weight Country
Extension point Vocabulary for healthcare

Slide 15
EPCIS Service

Slide 16
Oliot Distributed Storage
Previous Work
 Fosstrak – Open Source RFID platform
– Implements the GS1 EPCglobal Network specifications.
– Relational Database is implemented for EPCIS Repository
 Limitations:
– Centralized approach
– Focus on RFID data from supply chain management
– Not pay attention to tremendous amounts of IoT data generated at a rapid
pace.
FossTrak EPCIS

Slide 17
Cassandra
 One of the first and most widely used NoSQL solution
 Initially developed by Facebook
 Free, open-source under Apache license
 Features
– Decentralized
 No Single Point of Failure
– High Availability
– Tunable Consistency

Slide 18
Cassandra over EPCIS

Slide 19
Cassandra Data Model

Slide 20
Data Modelling Example
 ObjectEvent Column Family
 AggregationEvent Column Family
• Compound primary key (EPC|yyyymm : EventTime)
• EPC|yyyymm acts as a partition key for distributing row in the Column Family
among the various nodes that comprise the cluster.
• The EventTime acts as a clustering mechanism and ensures that columns in
one row are stored in sorted order (of EventTime) on disk.

Slide 21
Evaluation
 Method:
– Multiple Accessing Client for Multiple Reads
– Multiple Capturing Client for Multiple Writes
– Using nGrinder as a platform for stress tests
– Comparison between Cassandra 1 node and MySQL
– Intel Core i5 3.0GHz x 4 cores, 8GB RAM, 500GB HDD 7200rpm

Slide 22
Performance Evaluation Result
Capture Interface
Query Interface

Slide 23
Oliot Real-time Big-Data Processing
Motivation
Data Analyst
Company Director
Big Data
Doctor
 Question Example
– Q1: Stock Statistics for inventory control in last 1 hours?
– Q2: Contagious disease probabilistic in specific area?
 Storm vs. Hadoop
Oliot Platform
Q1
Q2

Slide 24
Storm vs. Hadoop
Storm Hadoop
Cluster Coordination Zookeeper Zookeeper
Master Node Daemon Nimbus Job Tracker
Worker Node Daemon Supervisor Task Tracker
Computation
Topologies.
Running forever
or until explicitly terminated
Map/Reduce Jobs.
Running until finish
Primary Usage Real-time processing Batch processing
Running functions Incremental functions Idempotent functions
Latency Very low High
 Big-Data on IoT
– Continuous incoming data needs real-time analysis
– On-demand analysis
 Storm!

Slide 25
Features on Storm
 An Apache open source project for distributed real-time data processing
 KEY properties:
Stream Processing Continuous Query Scalability

Slide 26
Storm Topology
 A tuple: An ordered list of key:value
pairs. For example, a tuple
{“word”:“KAIST”, “count”:10}
 A Stream: An unbounded sequence
of tuples.
 A Spout: A source of streams.
 A Bolt: A processing component to
transform streams. It consumes any
number of streams and possibly
emits new streams to other bolts.
 A Topology: The overall computation,
visually represented by a graph of
spouts and bolts. Users need to
program a topology and then submit
it to a Storm cluster.
Topology

Slide 27
Storm and EPC network
 A Storm cluster runs multiple topologies for different applications.
 Data sources from EPC network is published to a Pub/Sub System in different channels.
 Topologies may subscribe to these channels on demand.
 Output from Topologies may be consumed by Applications or persisted in Databases

Slide 28
 EPCIS
–Authoritative standard distributed storage for Supply Chain Management
–Oliot will broaden its SCOPE!
 Oliot distributed storage
–Cassandra-based approach
–Oliot shows improved response time, throughput, and flexibility
 Oliot event processing
–IoT needs real-time, on-demand event processing over continuous incoming
sensir big-data
–Storm-based approach
Conclusion

Slide 29
 EPC Information Services (EPCIS) Version 1.1 Specification
– http://www.gs1.org/gsmp/kc/epcglobal/epcis/epcis_1_1-standard-20140520.pdf
 The new EPCIS 1.1, GS1 Global Forum 17 Feb. 2014
 E-Health Sensor Platform V2.0
– http://www.cooking-hacks.com/documentation/tutorials/ehealth-biometric-sensor-platform-
arduino-raspberry-pi-medical
 Fitbit Flex- Make fitness a lifestyle with Flex
– http://www.fitbit.com/flex
 Neurosky ThinkGear EEG Hardware & Software
– http://neurosky.com/products-markets/eeg-biosensors/hardware/
 Withings Wireless Scale- Effortless weight tracking for everyone
– http://vitrine.withings.com/eu/ws-30.html
 H7 Heart Rate Sensor
– http://www.polar.com/en/products/accessories/H7_heart_rate_sensor
Reference

Slide 30
 FossTrak EPCIS Repository
– https://code.google.com/p/fosstrak/wiki/EpcisMain
 The Apache Cassandra
– http://cassandra.apache.org/
 Apache Hadoop
– http://hadoop.apache.org/
 Apache Storm- Distributed and fault-tolerant realtime computation
– http://storm.incubator.apache.org/
Reference

Slide 31
Thank you for listening
Q & A

GS1/Oliot EPCIS and Next

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