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

[email protected], http://oliot.org, http://autoidlab.kaist.ac.kr, http://resl.kaist.ac.kr, http://autoidlabs.org, http://gs1.org

mailto:[email protected]

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

Wholesale Shipping Manufacturer

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


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 operation Medicine Injection


Slide 14


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

Oliot Distributed Storage 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

Oliot Distributed Storage Cassandra over EPCIS


Slide 19

Oliot Distributed Storage Cassandra Data Model


Slide 20

Oliot Distributed Storage 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

Oliot Distributed Storage 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

Oliot Distributed Storage 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

Oliot Real-time Big-Data Processing 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

Oliot Real-time Big-Data Processing Features on Storm

An Apache open source project for distributed real-time data processing

KEY properties:

Stream Processing Continuous Query Scalability


Slide 26

Oliot Real-time Big-Data Processing 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

Oliot Real-time Big-Data Processing 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

http://www.gs1.org/gsmp/kc/epcglobal/epcis/epcis_1_1-standard-20140520.pdf






http://www.cooking-hacks.com/documentation/tutorials/ehealth-biometric-sensor-platform-arduino-raspberry-pi-medical


















http://www.fitbit.com/flex

http://www.fitbit.com/flex

http://neurosky.com/products-markets/eeg-biosensors/hardware/






http://vitrine.withings.com/eu/ws-30.html




http://www.polar.com/en/products/accessories/H7_heart_rate_sensor

http://www.polar.com/en/products/accessories/H7_heart_rate_sensor


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

https://code.google.com/p/fosstrak/wiki/EpcisMain

https://code.google.com/p/fosstrak/wiki/EpcisMain

http://cassandra.apache.org/

http://cassandra.apache.org/

http://hadoop.apache.org/

http://hadoop.apache.org/

http://storm.incubator.apache.org/

http://storm.incubator.apache.org/


Slide 31

Thank you for listening

Q & A

gs1/oliot epcis and next

Software

epcis event tag event

autoid lab korea kaist

medicare event

oliot storage extended

invoice transaction

autoid labs

utc aggregation event

visible rfid event data