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On Cross-Domain Data Access for Cyber-Physical-Social S ystems - - PowerPoint PPT Presentation

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On Cross-Domain Data Access for Cyber-Physical-Social S ystems Koji Zettsu zettsu@nict.go.jp Universal Communication Research Institute National Institute of Information and Communications Technology (NICT) International S ymposium on Social

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SLIDE 1

On Cross-Domain Data Access for Cyber-Physical-Social S ystems

Koji Zettsu

zettsu@nict.go.jp

Universal Communication Research Institute National Institute of Information and Communications Technology (NICT) International S ymposium on Social M ultimedia and M ultimedia Computing CASIA, China August 15-16, 2013

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SLIDE 2

Smarter society & Life innovation Environmental application science Disaster response

Analysis

Cyber-Physical-Social Information Ecosystem

Ubiquitous Network

2

2

Life events Disasters Phenomena Traffics

Sensor networks Science databases SNS Web

Actionable feedback Sensing

Natural environments Social activities

2 2013/ 8/ 15 (C) NICT

Service Gathering

Cyber Space Physical Space

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SLIDE 3

Collective Awareness of Cyber-Physical Social Event

2013/ 8/ 15 (C) NICT 3

Environment Sensing Resource Availability Analysis/ Prediction Action/Advice Individual Interaction Profile Gathering (E.g.) PM 2.5 air pollution

Sensitivities PM 2.5 treatments PM 2.5 distributions

  • Disaster response communities
  • Social healthcare

Recommendation Locations

Social Response

Individual Response

Courtesy: Event information management system (UC Irvine, NICT, 2013)

Disaster risk analysis

  • Climate influences
  • Health influences
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SLIDE 4

Cyber-Physical Social System: A Vision

2013/ 8/ 15 (C) NICT 4

Recognize evolving complex situations at particular location over period of time, then provide actionable feedback to device, people, and society M odel and manipulate cyber-physical-social events based on ‘correlations’ between individually- disseminated, heterogeneous sensor data

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SLIDE 5

Analyzing Spatial-Temporal-Thematic Correlations

2013/ 8/ 15 (C) NICT 5

High PM 2.5 (> 35 μg/ m3) High humidity (> 85%) Twitter (“face mask”) “Wearing face mask makes my face smaller in such a humid day” “I need to ware face mask, because PM 2.5 increases extraordinarily”

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SLIDE 6

STICKER Spatio-Temporal Information Clustering and Knowledge ExtRaction

2013/ 8/ 15 (C) NICT 6

Sensor data STT Cell STT Cell Composite Trajectory Composite Plotting Iso-sphere T ag cloud

集約 変換 変換

Filtering Filtering Aggregation Filtering Data M anipulation Visualization Data M odel

Visual presentation of movements and continuity

  • Discover

correlating dataset

  • Narrow STT

conditions

Infoglut!

Visual correlation mining of heterogeneous sensor data in spatial-temporal-thematic (STT) space Visual analysis of STT correlations

intersection, surrounding, synchronization, complement, etc.

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SLIDE 7

NICT K-L Grid: Cyber-Physical Social Sensing Platform

2013/ 8/ 15 (C) NICT 7

User Node

J

  • in

User Node

New Generation Network/JGN-X testbed

Kyoto Tokyo Fukuoka

F a c i l i t i e s H e a l t h c a r e E n v i r

  • n

m e n t P e

  • p

l e

Participatory Grid Network

Data warehouse Data aggregation Hokuriku Okayama

S c i e n c e

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SLIDE 8

On-demand creation of application-specific virtual sensor networks

Sensing Platform Issue

2013/ 8/ 15 (C) NICT 8

Application developer Network administrator

  • Suffer from unforeseen traffics
  • Hard to reconfigure existing

sensor networks Agile gathering, processing and archiving of heterogeneous sensor data Collective sensing application Physical networks

  • Sudden deluge of data from

anonymous sources

  • Ad-hoc sensing and trial-

error analysis

  • Quick response to ongoing

situation changes

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SLIDE 9

Cyber-Physical-Social Sensor Service (CPSenS)

(C) NICT

  • Sensor Service Collaboration Overlay

– Sensor virtualization: encapsulates data

sources as sensor services

– Vertical sensor integration: combines

heterogeneous sensor services on demand

– Horizontal sensor integration: complements

missing data with multiple sensors

  • Service Controlled Networking (SCN)

1. Declarative Service Networking (DSN): defines application-specific sensor service collaboration by declarative rule language 2. Network Control Protocol Stack (NCPS): Invoke programmable network commands for dynamic configuration; service node discovery, path setting, status monitoring 3. DSN/ NCPS Translator: Generate NCPS commands by interpreting DSN descriptions with multiple-overlay coordination

  • 1. Declarative Service Networking
  • 2. Network Control Protocol Stack
  • 3. DSN/ NCPS

Translator Programmable Networks NCPS for OpenFlow NCPS for IEEE 1888

SCN

IEEEE 1888 NW

IEEE 1888 Command/API OpenFlow Command/API

OpenFlow NW Sensor Service Collaboration

9 2013/ 8/ 15

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SLIDE 10

(C) NICT

CPSenS Example

Overlay GeoSocialApp // 1. Service registration R1 REGIST(GeWE) <~ IDENT(GeoSocialWeb, GeWE, “192.168.94.62”) // 2. Service search F1 FIND(RaQU) <~ REQUEST(GeWE, RaQU) F2 FIND(TwQU) <~ REQUEST(GeWE, TwQU) // 3. Path creation and message exchange S1 SEND(GeWE, RaQU, “50 <= Rain” & “1000 < SampleRate”) <~ FIND(RaQU) S2 SEND(GeWE, TwQU, “disaster” & “4000 < Record”) <~ FIND(TwQU)

DSN description OpenFlow paths by NCPS Sensor service collaboration

10 2013/ 8/ 15

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SLIDE 11
  • e

e

  • e

S d

  • d

d PM 2.5 sensor Twitter sensor Wind speed sensor “ wearing mask” topic (e3) Abnormally increasing of PM 2.5 (e1) Abnormally decreasing of wind speed (e2)

O: operators E: event S: situation D: sensors’ data

Air pollution event

Longitude, latitude, time “ filter”

  • perator

(#e3 > a) “aggregation”

  • perator +

“ filter”

  • perator

(#e1 + #e2 > b) TOPIC detection ABNORM AL detection

space time

  • bject

topic Event model func tion

Seamless Processing form Sensor Data to Complex Event

2013/ 8/ 15 (C) NICT 11 Courtesy: Event information management system (UC Irvine, NICT, 2013)

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SLIDE 12

Event Information M anagement System

2013/ 8/ 15 (C) NICT 12

Event detection & correlation C-P-S Event DB

Trigger

Event Warehousing Complex event processing

Sensor data archives Link

Sensor service

Reuse

Creating atomic events from individually-disseminated , heterogeneous sensor data (both streams and archives) Analyzing complex situations by spatio-temporal event stream processing Event Shop (UCI) EvWH (NICT)

Courtesy: Event information management system (UC Irvine, NICT, 2013)

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SLIDE 13

Timestamp Station Temperature precipitation 2012-03-01 11:02:35 AP-Kyoto 2.1 C 3.5mm 2012-03-01 11:03:05 AP-Tokyo 11.2C 0.1 mm 2012-03-01 11:03:35 AP-Osaka 1.0C 2.1mm Datetime Geotag #Tag Text 2012-03-01 11:00:02 35.011636, 135.768029 #Weather Wondering if it snows 2012-03-01 11:02:00 35.681382, 139.766084 # Weather Spring has come 2012-03-01 11:05:45 34.701909, 135.494977 #Weather Not good for laundry

1:2012-03-01 11:02:35, 2:2012-03-01 11:03:05, 3:2012-03-01 11:03:35, 4:AP-Kyoto, 5:AP-Tokyo, 6:2.1C, 7:11.2C, 8:1.0C, 9:3.5mm, 10:0.1mm, 11:2.1mm 1001:[1, 4, 6, 9], 1002: [2, 5, 7, 10], 1003:[3, 4, 8, 11] 21:2012-03-01 11:00:02, 22:2012-03-01 11:02:00, 23:2012-03-01 11:05:45, 24:35.011636, 135.768029, 25:35.681382, 139.766084 , 26:34.701909, 135.494977, 27:#Weather, 28: Wondering if it snows, 29: Spring has come, 30: Not good for laundry 2001:[21, 24, 27, 28], 2002: [22, 25, 27, 29], 2003:[23, 26, 27, 30] Data values tag:value Table structure record_tag:[tag, tag,… ], Data values tag:value Table structure record_tag:[tag, tag,… ],

Correlation corr_tag: (tag, tag, ..)/ corr_coeff … corr_label 101: (1, 2, 3, 21, 22, 23)/ 0.95 … [2012-03-01 noon] 102: (4, 24, 26)/ 0.92 … Kansai area 103: (5, 25)/ 0.80 … Tokyo area 104: (6, 8, 9, 11)/ 0.85 … snow 105: (7, 10)/ 0.9 … sunny 106: (28, 30)/ 0.6 … cheerless day

5001:[101/ 0.95, 102/ 0.92, 104/ 0.85, 106/ 0.6,], 5002: [101/ 0.95, 103/ 0.80, 105/ 0.9, 29:/ 1.0]

Period Area Weather Atmosphere 101/ 0.95: [2012-03-01 noon] 102/ 0.92: Kyoto area 104/ 0.85: snow 106/ 0.6: cheerless day 101/ 0.95: [2012-03-01 noon] 103/ 0.80: Tokyo area 105/ 0.9: sunny 29: Spring has come

Table structure record_tag:[tag/ certanty, tag/ certainty,… ],) Ontological correlation (metrology) Spatiotemporal correlation

Weather sensor data SNS sensor data (weather topic)

Ontological correlation (Atmosphere)

JOIN over Correlation

Event Warehouse (EvWH)

Correlation Database (Value-based Storage approach) Cyber-Physical-Social Event

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SLIDE 14

Correlation Search (Cross-DB Search)

(C) NICT 14 2013/ 8/ 15

Time Lat. Long.

Spatiotemporal Correlation Ontological Correlation Citational Correlation

Complex Correlation Analysis

2013/ 2/ 28 (C) NICT

Event M etadata Search

Optimization Correlation Graph

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SLIDE 15

Correlation Clustering by Evolutional Computing

(C) NICT 15 2013/ 8/ 15

  • Optimize correlation graph cluster by evolutional operations: merge, split,

expansion, crossover, and mutation

mutation crossover mutation expansion split merge mutation

Evolutionary tree of correlation graph

  • The more generation grows, the more correlation graphs become strongly

connected (thick edges = strong correlations) Generation 0 Generation 1 Generation 3 Generation 4 Result 1 Result 2 Result 3

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SLIDE 16

Example of Cross-DB Search

2013/ 8/ 15 (C) NICT 16

Examples of added dataset Query： “ice sheet” in Northern Hemisphere Select spatially- and ontologically- correlating data

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SLIDE 17

Towards Cyber-Physical Cloud Computing

17 2013/ 8/ 15 (C) NICT

Cyber-Physical Cloud Computing (CPCC) is defined as: “a system environment that can rapidly build, modify and provision auto-scale cyber-physical systems composed of a set

  • f cloud computing based sensor, processing, control, and data services”.
  • Efficient use of resources
  • M odular composition
  • Rapid development and scalability of cyber-physical systems
  • Smart adaptation to environment at every scale
  • Scalable reliability and performance

Reference: Cyber-Physical Cloud: its Roots, Architecture, Challenges and Opportunities (NICT, NIST, 2013)

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SLIDE 18

CPCC Scenario

18 2013/ 8/ 15 (C) NICT

  • Geo-Fencing: deliver local

information from global monitoring

  • Emergency Evacuation and Rescue

Systems

  • Person Find Systems
  • Emergency Health Care Systems
  • Emergency Telecommunication

Systems

Reference: Cyber-Physical Cloud: its Roots, Architecture, Challenges and Opportunities (NICT, NIST, 2013)

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SLIDE 19

CPCC Conceptual Architecture

19 2013/ 8/ 15 (C) NICT Reference: Cyber-Physical Cloud: its Roots, Architecture, Challenges and Opportunities (NICT, NIST, 2013)

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SLIDE 20

Conclusions

2013/ 8/ 15 (C) NICT 20

C P S P S C Network Data Event Programmable network (SDN) Collective sensing(& actuation) Event-of- interest Application Ev IM CPSenS S TICKER Correlational data mgmt. Cross-DB Search SCN C P S C P S

E1 E2 Event-specific collaboration

Event-oriented collaboration Scalable C-P-S system

Application for E1 Application for E2