Seishi Ninomiya, Institute of Sustainable Agro-ecosystem Services, The University of Tokyo, at RDA 5th Plenary Meeting, IG Agriculture Data Interoperability Session in San Diego (CA, US) on the 9th of March 2015

1. Development of Data Integration &
Analysis System in Japan
Activities toward interoperability
Seishi Ninomiya
Institute of Sustainable Agro-ecosystem Services,
The University of Tokyo

2. Productivity
Quality/Safety
Farmers’
benefits
Food
loss/waste
Minimum
emission
Limited
resources/water
/land/energy
Ecosystem/
Biodiversity
Food
distribution
Diet
transition
Climatic
change/disaster
Toward Sustainable and sufficient food productionToward Sustainable and sufficient food production
Security
Food
Security

3. What we need for data-centric science in agriculture
• Utilization of legacy data
o Yield data, variety data, quality data, soil data, market data, …………
o Need to rescue such data
• Sensor innovation IOT
o To efficiently monitor the facts in fields, market, demands, logistics, processing,….
o To collect knowledge of farmers, tacit knowledge
• Data integration and efficient usage/ Interoperability
o Common platform for seamless data exchange with standard
o Agricultural cloud and database
• Tools for analysis/analytics and for supporting decisions
o Statistics, data-mining , knowledge extraction, risk managements
o Big data-based optimization
o Enrichment of commonly usable APIs
• Communication innovation
o Efficient Knowledge transfer to farmers
• Service science
Big data
+ Advancement of Agricultural Science

4. DIAS
Data Integration & Analysis System

5. © GEO Secretariat
• The Group on Earth Observations is coordinating efforts to build a
Global Earth Observation System of Systems (GEOSS).
• GEO was launched in response to calls for action by the 2002 World
Summit on Sustainable Development and by the G8 leading
industrialized countries.
GEO/GEOSS

6. S&D strategy in Japan
RECCA2
S&T Basic plan -5
will start since 2016

7. Data Integration and Analysis System (DIAS)
• DIAS was launched in 2006 as a Japanese contribution to
GEOSS
• one of five National Key Technologies defined by the 3rd Basic
Program for Science and Technology of Japan.
o Total of USD 60 million for 10 years by MEXT from 2006
• The missions are:
o to coordinate the cutting-edge information science and
technology and the various research fields addressing the earth
environment;
o to construct data infrastructure that can integrate earth
observation data, numerical model outputs, and socio-
economic data effectively;
o to create knowledge enabling us to solve the sustainable world
o to generate socio-economic benefits

8. 20PB by 2014

9. Data Storage Core System
User Communication & Management
Search &
Discovery
System
Science
Societal Benefit
Data
Mata Data
Document
Data
Cleansing
System
(QC)
Processed Data
Data
Loading
System
Data Integration & Analysis System
Data
Archive
Data
Download
System
Analysis
Systems
By User
Communication Tool Management Tool
Data Provider
Meta data
Registration
Document
Registration
Meta data
Standard
Inter-
operability
Portal
User
Authentication
User
Authorized
User

10. 10
DB
DB
0
50
100
150
200
250
300
350
19
92
19
94
19
96
19
98
20
00
20
02
20
04
20
06
20
08
20
10
20
12
20
14
20
16
20
18
20
20
A1:高度成長社会
A2：多元化型
B1:持続発展型
B2：地域共存型
DB
DB
DB
DB
DB
0
50
100
150
200
250
300
350
199
2
199
4
199
6
199
8
200
0
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2
200
4
200
6
200
8
201
0
201
2
201
4
201
6
201
8
202
0
A1:高度成長社会
A2：多元化型
B1:持続発展型
B2：地域共存型
DB
DB
0
50
100
150
200
250
300
350
199
2
199
4
199
6
199
8
200
0
200
2
200
4
200
6
200
8
201
0
201
2
201
4
201
6
201
8
202
0
A1:高度成長社会
A2：多元化型
B1:持続発展型
B2：地域共存型
DB
DB
D
B
Meteorology
Ecosystem
Agriculture
Hydrology
Land
Use
Climatology
Health
农业
เกษตรกรร
ม
농업
Where is data?
How to access?
Technical Terms
among
Different
Disciplines
Quality?
Reliability?

11. Upload
Meta Data
Meta Data Meta Data
Data Integration and Analysis System (DA-09-02a)
Quality
Control
Data Provider (Observer)
User
Meta Data
Registration
•Search with Metadata
•Data Download
•Document Generation from Meta Data
•Data Visualization
・・
Observation
Data
Meta Data
Data Upload
＋(part of )Meta Data
Observation
Data
Meta Data
Data Quality
Control Process
Meta Data
Post-QC
Observation Data
Input
Meta Data
Data DownloadSearch IF Document Generator Visualization System
DataArchivingDataIntegration
Web-based Data Archiving & Integration System
Basic
Information
Observation Point Inf.,
Contact Person Inf.,……

20. Ontology development in DIAS

21. Challenge for Data Management & Fusion
Syntax Interoperability
Proposal of Standard Schema and Interface.
It is not enough for diversified Geo-spatial Information.
e.g. legacy data.
New Challenge of Data
Management and Fusion
Registry
Visualizing diversified data,
Helping data convergency .
One stop service for data utilization.
Very Large& Heterogeneous
Data Management and Fusion
Data quality checking, Emergency response for
disaster, Automatic processing and fusion, Change
detection, etc.
Integrating
Observation Data and
Model Simulation
Semantic Interoperability (Ontology )
Semantic Interoperability for geo-spatial data by
using data definitions, terminologies, relations,
landnames, etc.
By Msahiko Nagai, U..Tokyo & AIT, 2015

22. Semantics Activity
Existing Glossaries
1 WMO Glossary
2 CEOS Missions, Instruments and
Measurements(MIM) Database
3 CEOS Systems Engineering
Office(SEO)
4 GEMET
5 INSPIRE Feature Concept
Dictionary
6 SWEET
7 CUAHSI
8 CF Standard Names
9 GCMD
10 Eurovoc Thesaurus
11 International Glossary of
Hydrology/UNESCO
12 Marine Metadata Interoperability
Close Match
We are forcing on the one hand the
implementation of semantic
interoperability arrangement with
ontological information.
DIAS Vocabulary Registry
SKOS
146 observation parameters with SBA
Define and associate with EO Vocabulary and Existing Glossaries
By Msahiko Nagai, U..Tokyo & AIT, 2015

23. Vocabulary Registry to Find Similar Technical Term
Input Keywords, “precipitation”
Similarity score
with the input keywords
By Msahiko Nagai, U..Tokyo & AIT, 2015

24. INDEX DB RESULTS
- Scholarly Journals
- Social Data
- Lat / Lng
- Scientific Values
- Keywords
- Date
Scientific Data
[ Remotely Sensed Data, Meta - Data ]
- Description
- Images
- Date
- Geo - Tags
- Videos
]
Social Data
[Ushahidi, Google News, Social
Networks]
INDEXED
DATABASE
User
Interface
QUERY PARAMETERS
- Location
- COP / SBA
- Date
Scholarly Journal Data
[ Sci-Verse HUB, Mendeley api ]
• Keywords
• Abstract / Full Text
• Author(s)
• Published Date
• Scientific Models Used
• Input Data / Output Data
• Geo - Tags
ONTOLOGY DEVELOPMENT / CONCEPT TAGGING
EXTRACTION / INDEXING
ONTOLOGY UPDATE
SEARCH REQUEST
SEARCH RESULTS
ONTOLOGY
Application Service
EXTRACTOR
SEARCH ENGINE
Journals
Knowledge based Ontology
Development / Update
JSP Service
24
By Msahiko Nagai, U..Tokyo & AIT, 2015

25. MetBroker
Weather data integration

26. • Heterogeneity among data sources is a big issue in the Internet.
(data structure, access methods, etc.)
• Data brokers provide consistent access to those heterogeneous
data sources
MetBroker for various weather databases
Meta
Data
Heterogeneous and
Autonomous DBs
Rice Growth Prediction
Farm Management
MetBroker
Pesticide Prediction
Heterogeneity is absorbed by brokers (mediators)
B-DB
C-DB
A-DB

27. MetBroker Since 2000 -Spatial integration of weather data
• MetBroker provides applications consistent access to heterogeneous
weather databases and covers 30,000 weather stations of 25 DBs
• API MetXML

28. Crop model + MetBroker = Potential Rice Yield
28

29. IOT/Sensor data

30. Giving Interoperability to heterogeneous sensing data
via OGC Standard Web Service, SOS
OGCAPI
GetCapabilities
List of authorized
SOS stations with its
sensors
GetObservation
Sensor Data with
Timestamp
1
2
Simulation System
User Interface for Famers
Kiyoshi HONDA, R. Chinnachodteeranun, A. Witayangkurn,
APAN Meeting 4 Mar 2015

31. Sensor Infra. And Multi-Layered Web Service
Sensor Infra
Water
Level/Temp
via NICT
NARO 1km Mesh
Agri Weather (Past
and 2w Forecast)
NIAES Point
Agri. Weather
Other
Sensor
Interpolation, Statistics,
Visualization
Weather Generator
Rice Crop Simulation
Standard APIStandard API
SOS
Open API
Open API
Application
Open APIOpen API
Application
Visualization
Analysis Appli.
Crop Simulation
Developed
by single
developer
Obtain
necessary
functionalities
via Web
Service
Anyone can
access to
high-level
functionalities
Sensor Virtualization, New
sensor, sensor transfer will
be reflected application
automatically
Kiyoshi HONDA, R. Chinnachodteeranun, A.
Witayangkurn, APAN Meeting 4 Mar 2015

32. Farm data

33. 33
Conceptual sketch of “CLOP”
APAN 39th in FukuokaAPAN 39th in Fukuoka T. Yoshida, NARO
USD 10 million for 5 years by MAFF from 2014

34. • Current range FIX-pms covers is limited to farm
work and production process management.
34
Cover range of ‘FIX-pms’
APAN 39th in Fukuoka T. Yoshida, NARO

35. • Defined based on agroXML.
35
Outline structure of ‘FIX-pms’
APAN 39th in Fukuoka T. Yoshida, NARO

36. Structure of API mashup : 4 Layers
36
Term, Code Layer
Data Content
Layer
Data Format
(Container)
Layer
API
Layer
agroXML, Sensor ML, GML/KML, GPX, …
FarmXML(FIX-pms), BIX-pp, GPXX, …
Data structure?
Data meaning?
Data relation?
V
V
V
RDF, UML, …
SOS, WMTS, WMS, WFS, …
MetXML, PDS, …
Content list in certain region of interest
among certain stakeholders, …
Terminology, ontology, …
Code system definition, …
(Language / Localization)
APAN 39th in Fukuoka T. Yoshida, NARO

37. International collaboration

38. W3C Agriculture CG
W3C Team | Posted on: October 15, 2014
The Agriculture Community Group has been launched:
The initial mission of the Agriculture Community Group is to gather and categories
existing user scenarios, which use Web APIs and services, in the agriculture industry
from around the world, and to serve as a portal which helps both web developers and
agricultural stakeholders create smarter devices, Web applications & services, and to
provide bird’s eye view map of this domain which enables W3C and other SDOs to
find overlaps and gaps of user scenarios and the Open Web Platform.
We’ll try to collect facts and knowledge from around the world through crowd-
sourcing, while, at the same time, build a scaffold for it by quickly gathering key topics
from Japanese agricultural stakeholders. Smart Platform Forum supports this early
stages by connecting relevant stakeholders in Japan and organizing face-to-face
meetings if needed to proceed faster.
https://www.w3.org/community/agri/

39. Future Internet PPP – 300 M€ EU
funding
3
9

40. 40
二宮正士 snino@isas.a.u-tokyo.ac.jp
Thank you very much