SlideShare a Scribd company logo
Turning	
  agro-­‐biodiversity	
  open	
  data	
  to	
  
real	
  services:	
  an	
  intro	
  to	
  technologies	
  
Giannis	
  Stoitsis	
  
Agro-­‐Know	
  Technologies	
  
	
  
AIM	
  OF	
  THE	
  COURSE	
  
How	
  to	
  get	
  as	
  much	
  as	
  possible	
  from	
  data?	
  
Which	
  technologies	
  and	
  how	
  they	
  can	
  be	
  used?	
  
Course	
  syllabus	
  
•  Intro	
  
•  Technologies	
  
•  Sharing	
  data	
  
•  Linking	
  data	
  
•  Architectural	
  aspects	
  
•  Building	
  a	
  simple	
  case	
  
•  Some	
  suggesFons	
  
ARE	
  WE	
  READY	
  TO	
  BUILD	
  REAL	
  
SERVICES	
  USING	
  AGRO	
  AND	
  
BIODIVERSITY	
  OPEN	
  DATA?	
  
What	
  we	
  need	
  
data	
   technology	
  
and	
  of	
  course	
  a	
  good	
  idea	
  
As	
  for	
  the	
  data	
  
Thanks	
  Nikos	
  	
  
“Intro	
  to	
  green	
  data	
  ecosystems:	
  why	
  is	
  a	
  data-­‐
powered	
  tech	
  start	
  up	
  for	
  agriculture	
  &	
  
biodiversity	
  going	
  to	
  be	
  profitable?”	
  
TECHNOLOGIES:	
  THERE	
  IS	
  REALLY	
  
GREAT	
  STAFF	
  OUT	
  THERE	
  
Infrastructure	
  
•  We	
  have	
  the	
  cloud	
  
•  Cloud	
  is	
  geJng	
  cheaper,	
  beLer	
  and	
  more	
  
development	
  friendly	
  	
  
•  OpFons	
  
–  Amazon	
  
–  Rackspace	
  
–  Linode	
  
–  MicrosoQ	
  
–  Google	
  
–  Okeanos	
  
–  Other	
  Private	
  or	
  even	
  yours	
  (e.g.	
  OpenStack,	
  OpenNebula)	
  
Storage and Processing Monitoring/Management/Allocation layer
Virtualization of Infrastructure Layer
Virtual Machines
Virtualization of Infrastructure LayerVirtualized Infrastractures Management Layer
GUI tools and APIs
Cloud provider A Cloud provider B Cloud provider B
What	
  does	
  this	
  mean?	
  
Quick	
  prototyping	
  
Get	
  your	
  running	
  instance	
  in	
  minutes	
  
REST	
  
•  AbstracFng	
  the	
  architectural	
  elements	
  
•  Close	
  to	
  the	
  way	
  we	
  are	
  working	
  in	
  the	
  web	
  
•  Standard	
  hLp	
  methods	
  
–  GET	
  
–  PUT	
  
–  POST	
  
–  DELETE	
  
•  easy	
  to	
  be	
  built	
  especially	
  with	
  MVC	
  frameworks	
  
e.g.	
  Rails	
  	
  
•  easy	
  to	
  be	
  consumed	
  with	
  any	
  language	
  	
  
GET	
  hLp://www.example.com/User/1234	
  
POST	
  hLp://www.example	
  .com/User/	
  ‘{“user”:”{“”id”:”55”,	
  “name”:”John”}’	
  
DELETE	
  hLp://www.example.com/User/1234	
  
REST	
  clients	
  
•  easy	
  to	
  be	
  	
  developed	
  with	
  any	
  language	
  
•  check	
  before	
  start	
  developing	
  
Data	
  exchange	
  formats	
  
•  XML,	
  RDF	
  or	
  JSON?	
  
•  They	
  all	
  work	
  but	
  we	
  always	
  have	
  preferences	
  
•  Why	
  we	
  love	
  JSON?	
  
– can	
  be	
  parsed	
  by	
  any	
  front	
  end	
  (web,	
  mobile)	
  
– simple	
  
– can	
  be	
  used	
  to	
  represent	
  data	
  models	
  
– beLer	
  in	
  processing	
  e.g.	
  transformaFon	
  
– used	
  widely	
  by	
  all	
  document	
  based	
  repositories	
  
(e.g.	
  MongoDB,	
  elasFcsearch)	
  
Sensors	
  
•  Are	
  everywhere	
  
•  Two	
  categories	
  
– The	
  one	
  that	
  we	
  hold	
  i.e.	
  from	
  smartphones	
  today	
  
from	
  watch	
  tomorrow	
  
– The	
  one	
  installed	
  at	
  sites	
  e.g.	
  fields	
  
•  We	
  can	
  get	
  and	
  use	
  data	
  from	
  both	
  
get	
  data	
  from	
  a	
  smartphone	
  
•  sensing	
  data	
  
– MoFon	
  sensor/accelerometer	
  
– Proximity	
  
– gyroscope	
  (for	
  gaming)	
  
•  GPS:	
  	
  Core	
  LocaFon	
  Framework	
  to	
  get	
  the	
  
coordinates	
  and	
  tracking	
  user’s	
  locaFon	
  
GeJng	
  real	
  Fme	
  weather	
  data	
  
GeJng	
  real	
  Fme	
  weather	
  data	
  
	
  
h:p://api.openweathermap.org/data/2.5/weather?q=London,uk	
  	
  
Big	
  data	
  technologies	
  
•  A	
  trend?	
  	
  
•  Some	
  of	
  them	
  
– NoSQL	
  dbs	
  e.g.	
  MongoDB,	
  CouchDB	
  
– elasFcsearch	
  
– MapReduce	
  framework	
  e.g.	
  run	
  processes	
  over	
  a	
  
big	
  amount	
  of	
  data	
  
– Hadoop	
  
– Cassandra	
  
– and	
  more	
  ….	
  
Natural	
  Language	
  Processing	
  
•  Dbpedia	
  Spotlight	
  
•  EnFty	
  recogniFon	
  (idenFfy	
  names	
  and	
  places	
  in	
  a	
  text)	
  
•  DisambiguaFon	
  (apple	
  can	
  be	
  a	
  company,	
  a	
  fruit	
  or	
  a	
  river)	
  
Image	
  processing	
  
APIs	
  for	
  mobile	
  apps	
   Open	
  Source	
  Libraries	
  	
  
how	
  image	
  processing	
  can	
  be	
  used?	
  
•  Similar	
  to	
  how	
  Google	
  Goggles	
  works	
  but	
  trained	
  for	
  your	
  data	
  
Machine	
  Learning	
  
how	
  machine	
  learning	
  can	
  be	
  used?	
  
Provide	
  a	
  recommendaFon	
  service	
   Mine	
  and	
  Cluster	
  Data	
  
JavaScript	
  
•  can	
  do	
  almost	
  everything	
  
•  even	
  image	
  processing	
  e.g.	
  PixasFc	
  for	
  image	
  processing	
  
•  powerful	
  libraries	
  e.g.	
  D3	
  for	
  real	
  Fme	
  data	
  visualizaFon	
  
AnalyFcs	
  
•  Track	
  the	
  users	
  and	
  events	
  of	
  your	
  applicaFon	
  
– ElasFc	
  Search,	
  MongoDB	
  
– Google	
  AnalyFcs	
  
Follow	
  a	
  well	
  accepted	
  framework	
  
•  Model	
  View	
  Controller	
  (MVC)	
  
•  There	
  are	
  many	
  nice	
  MVC	
  based	
  frameworks	
  
– Rails	
  
– Spring	
  
– Zend	
  
– Yii	
  
– Laravel	
  
– and	
  more	
  …	
  
SHARING	
  DATA	
  
  	
   	
   	
  
	
   	
   	
   	
  	
  
Publisher
Date Catalog
Subject
ID
Author
Title
we actually share metadata
the	
  value	
  is	
  sFll	
  in	
  metadata	
  
•  Metadata	
  connect	
  us	
  to	
  the	
  real	
  data	
  	
  
– Search	
  metadata	
  records	
  not	
  raw	
  data	
  
•  Metadata	
  gives	
  the	
  context	
  
– themaFc	
  classificaFon	
  
– target	
  users	
  
•  Well	
  defined	
  standards	
  (e.g.	
  DC,	
  IEEE	
  LOM,	
  
Darwin	
  Core,	
  ABCD)	
  
•  RDF,	
  XML	
  and	
  JSON	
  binding	
  
AGGREGATING	
  METADATA	
  
…	
  the	
  power	
  is	
  at	
  the	
  back	
  end	
  
CulQvaQon	
   HarvesQng	
   Blossom	
  
Unorganized	
  content	
  in	
  
local	
  and	
  remote	
  sites	
  
Organized	
  and	
  
structured	
  content	
  in	
  
local	
  and	
  remote	
  
databases	
  
EducaQonal	
  
Bibliographic	
  
EducaQonal	
  
Geographical	
  
Bibliographic	
  
Agricultural	
  
Data	
  
PlaVorm	
  
Aggregate	
  
data	
  from	
  
diverse	
  
sources	
  
Work	
  with	
  
different	
  
type	
  of	
  data	
  
Prepare	
  
data	
  for	
  
meaningful	
  
services	
  
IngesQon	
  Enrichment	
  TranslaQon	
  Publishing	
  
Data	
  discovery	
  
services	
  
Widgets	
  
Authoring	
  
services	
  
AnalyQcs	
  	
  
services	
  
Why	
  to	
  aggregate?	
  
•  Enrich	
  metadata	
  
•  Provide	
  developer	
  friendly	
  APIs	
  that	
  can	
  be	
  
used	
  to	
  build	
  good	
  services	
  
•  Data	
  providers	
  rarely	
  have	
  APIs	
  
	
  
LINKING	
  DATA	
  
Why	
  we	
  sFll	
  have	
  data	
  silos?	
  
•  CompeFng	
  metadata	
  standards	
  (e.g.	
  DC,	
  IEEE	
  LOM)	
  
•  Diversity	
  of	
  web	
  interfaces	
  (e.g.	
  REST,	
  OAI-­‐PMH,	
  SOAP,	
  SPI,	
  SQI)	
  
•  Different	
  exchange	
  format	
  (e.g.	
  XML,	
  RDF,	
  JSON)	
  
•  Fragmented	
  use	
  of	
  texonomies	
  
LD for educational data/resource sharing
Overview
Approaches for LD in educational data sharing
 On the-fly/automated integration of heterogeneous APIs and data (http://www.meducator.net)
 Dataset (transformation and) cataloging (http://linkedup-project.eu)
?
	
  We	
  are	
  sFll	
  here	
  …	
   …	
  and	
  not	
  here	
  …	
  
What	
  is	
  Linked	
  Data?	
  
•  A	
  set	
  of	
  principles	
  and	
  technologies	
  for	
  the	
  Web	
  
of	
  Data	
  
•  Principles	
  
–  Put	
  the	
  data	
  online	
  with	
  permanent	
  address	
  (URIs)	
  
–  Describe	
  the	
  data	
  with	
  a	
  standard	
  representaFon	
  (RDF)	
  
–  Link	
  to	
  other	
  data	
  through	
  published	
  taxonomies	
  
•  Technologies	
  
–  Triple	
  stores	
  
–  SPARQL	
  
–  RDF,	
  OWL	
  	
  
–  SWRL	
  
Seman&c	
  Web	
  /	
  Linked	
  Data	
  Technologies	
  by	
  Mathieu	
  d'Aquin	
  on	
  Sep	
  11,	
  2013	
  
ApplicaFons	
  
Mash	
  up	
  and	
  Linked	
  Data:	
  FAO	
  case	
  
How	
  it	
  works?	
  
•  Based	
  on	
  AGROVOC	
  
•  Using	
  AGROVOC	
  mappings	
  to	
  other	
  ontologies	
  e.g.	
  GeopoliFcal	
  and	
  DBpedia	
  
<hLp://aims.fao.org/aos/agrovoc/c_690>	
  <hLp://www.w3.org/2004/02/skos/core#closeMatch>	
  <hLp://dbpedia.org/resource/Salmon>	
  	
  
<hLp://dbpedia.org/page/Salmon>	
  	
  
replace	
  resource	
  with	
  page	
  and	
  get	
  rdf	
  
Why	
  to	
  rely	
  on	
  linked	
  data	
  
•  Easy	
  to	
  be	
  processed	
  e.g.	
  microdata,	
  rdf	
  
•  Get	
  more	
  out	
  of	
  data	
  through	
  linking	
  e.g.	
  get	
  the	
  staFsFcs	
  for	
  a	
  country	
  
through	
  GeoPoliFcal	
  Ontology	
  
Web	
  resource	
  about	
  Greece	
  annotated	
  with	
  Agrovoc	
  term	
  “Greece”	
   hLp://www.fao.org/countryprofiles/geoinfo/geopoliFcal/data/Greece	
  
Country	
  GDP	
  
and	
  even	
  more	
  …	
  
•  Data	
  that	
  you	
  can	
  get	
  from	
  World	
  Bank	
  based	
  on	
  ISO3	
  
country	
  code	
  
–  Agricultural	
  irrigated	
  land	
  (%	
  of	
  total	
  agricultural	
  land)	
  
–  Cereal	
  yield	
  (kg	
  per	
  hectare)	
  
–  Rural	
  populaFon	
  (%	
  of	
  total	
  populaFon)	
  
–  Poverty	
  headcount	
  raFo	
  at	
  rural	
  poverty	
  line	
  (%	
  of	
  rural	
  
populaFon)	
  
–  Agricultural	
  machinery,	
  tractors	
  per	
  100	
  sq.	
  km	
  of	
  arable	
  
land	
  
–  Arable	
  land	
  (%	
  of	
  land	
  area)	
  
–  CO2	
  emissions	
  (kt)	
  
–  Fish	
  species,	
  threatened	
  
DECOUPLING	
  FRONT	
  END	
  FROM	
  
BACK	
  END	
  
how	
  it	
  works	
  
Template	
  customizaQon	
  
html,	
  css,	
  Ajax,	
  JS	
  
Data	
  mash	
  up	
  
Search	
  API	
  
Cloud	
  
Edu	
  resources	
  
Data	
  processing	
  and	
  enrichment	
  
e.g.	
  semanQc	
  enrichment	
  or	
  clustering	
  
Search	
  API	
  
Cultural	
  resources	
  
from	
  Museums	
  
how	
  it	
  works	
  
Template	
  customizaQon	
  
html,	
  css,	
  Ajax,	
  JS	
  
widget	
  in	
  Facebook	
  page	
  
Data	
  mash	
  up	
  
Search	
  API	
  
Cloud	
  
Edu	
  resources	
  
Data	
  processing	
  and	
  enrichment	
  
e.g.	
  semanQc	
  enrichment	
  or	
  clustering	
  
Search	
  API	
  
Cultural	
  resources	
  
from	
  Museums	
  
AUTHENTICATION	
  
Use	
  authenFcaFon	
  APIs	
  
NATIVE	
  OR	
  WEB	
  APP?	
  
NaFve	
  vs	
  Web	
  app	
  
•  Depends	
  on	
  the	
  business	
  case	
  
•  Responsive	
  web	
  app	
  are	
  cross	
  plaworm	
  
•  Responsive	
  design	
  is	
  great	
  but	
  it	
  is	
  not	
  so	
  
friendly	
  as	
  naFve	
  app	
  
•  NaFve	
  app	
  has	
  nice	
  workflow	
  
•  NaFve	
  app	
  can	
  use	
  device	
  sensors	
  
•  In	
  many	
  data-­‐powered	
  cases	
  you	
  need	
  both	
  
BUILDING	
  A	
  BASIC	
  APP	
  IN	
  HOURS	
  
THE	
  IDEA	
  
A	
  mobile	
  app	
  for	
  discovering	
  green	
  
learning	
  resources	
  
•  Similar	
  apps	
  
– OER	
  commons	
  
– iMarine	
  
•  Similar	
  web	
  app	
  
– hLp://www.greenlearningnetwork.org/
organicedunet/	
  
Which	
  data	
  
•  Learning	
  Resources	
  about	
  environment,	
  
agriculture	
  and	
  biodiversity	
  
•  API	
  end	
  point	
  hLp://83.212.96.219:8080/
glnRepo/api/ariadne/restp?json=Χ&callback=Y	
  
Recipe	
  
•  Mobile	
  app	
  SDK	
  e.g.	
  XCODE	
  
•  Mobile	
  simulator	
  
•  Some	
  reading	
  
•  Similar	
  examples	
  e.g.	
  Searching	
  in	
  twiLer	
  
•  A	
  workflow	
  
•  JSON	
  parser	
  
•  No	
  beauFes	
  at	
  this	
  stage	
  
Development	
  is	
  friendly	
  
Defining	
  the	
  workflow	
  is	
  a	
  game	
  
ConnecFng	
  to	
  the	
  data	
  
Use	
  NSJSONSerializaFon	
  Class	
  to	
  get	
  the	
  data	
  
and	
  finally	
  
SuggesFons	
  
•  focus	
  on	
  the	
  idea	
  and	
  business	
  value	
  
•  re-­‐use	
  the	
  workflow	
  of	
  successful	
  apps	
  
•  processing	
  gives	
  the	
  value	
  
•  do	
  not	
  create	
  unnecessary	
  procedures	
  e.g.	
  RegistraFon	
  page	
  
•  re-­‐use	
  exisFng	
  data	
  
•  put	
  emphasis	
  on	
  the	
  value	
  of	
  your	
  app	
  and	
  not	
  on	
  the	
  
complexity	
  of	
  the	
  technology	
  
•  always	
  check	
  first	
  if	
  there	
  is	
  open	
  source	
  code	
  
•  design	
  maLers	
  
•  go	
  beyond	
  trivial	
  and	
  benefit	
  from	
  innovaFon	
  
 
	
  
thank	
  you!	
  
stoitsis@agroknow.gr	
  	
  

More Related Content

What's hot

Oak meeting 18/09/2014
Oak meeting 18/09/2014Oak meeting 18/09/2014
Oak meeting 18/09/2014
INRIA-OAK
 
Semantic Technologies and Triplestores for Business Intelligence
Semantic Technologies and Triplestores for Business IntelligenceSemantic Technologies and Triplestores for Business Intelligence
Semantic Technologies and Triplestores for Business Intelligence
Marin Dimitrov
 
2012 09 aos-workshop-johanneskeizer
2012 09 aos-workshop-johanneskeizer2012 09 aos-workshop-johanneskeizer
2012 09 aos-workshop-johanneskeizer
Johannes Keizer
 
OU RSE Tutorial Big Data Cluster
OU RSE Tutorial Big Data ClusterOU RSE Tutorial Big Data Cluster
OU RSE Tutorial Big Data Cluster
Enrico Daga
 
Family tree of data – provenance and neo4j
Family tree of data – provenance and neo4jFamily tree of data – provenance and neo4j
Family tree of data – provenance and neo4j
M. David Allen
 
IGeLU 2014
IGeLU 2014IGeLU 2014
IGeLU 2014
jhkrug
 
SUNYLA2018_Opening_Pandora’s_Box
SUNYLA2018_Opening_Pandora’s_BoxSUNYLA2018_Opening_Pandora’s_Box
SUNYLA2018_Opening_Pandora’s_Box
hebertm3308
 
Hadoop User Group - Status Apache Drill
Hadoop User Group - Status Apache DrillHadoop User Group - Status Apache Drill
Hadoop User Group - Status Apache Drill
MapR Technologies
 
Analyzing Web Archives
Analyzing Web ArchivesAnalyzing Web Archives
Analyzing Web Archives
vinaygo
 
Introduction to the Semantic Web
Introduction to the Semantic WebIntroduction to the Semantic Web
Introduction to the Semantic Web
Nuxeo
 
IPython Notebook as a Unified Data Science Interface for Hadoop
IPython Notebook as a Unified Data Science Interface for HadoopIPython Notebook as a Unified Data Science Interface for Hadoop
IPython Notebook as a Unified Data Science Interface for Hadoop
DataWorks Summit
 
Arabidopsis Information Portal, Developer Workshop 2014, Introduction
Arabidopsis Information Portal, Developer Workshop 2014, IntroductionArabidopsis Information Portal, Developer Workshop 2014, Introduction
Arabidopsis Information Portal, Developer Workshop 2014, Introduction
JasonRafeMiller
 
NISO Webinar: Library Linked Data: From Vision to Reality
NISO Webinar: Library Linked Data: From Vision to RealityNISO Webinar: Library Linked Data: From Vision to Reality
NISO Webinar: Library Linked Data: From Vision to Reality
National Information Standards Organization (NISO)
 
Large-Scale Data Storage and Processing for Scientists with Hadoop
Large-Scale Data Storage and Processing for Scientists with HadoopLarge-Scale Data Storage and Processing for Scientists with Hadoop
Large-Scale Data Storage and Processing for Scientists with Hadoop
Evert Lammerts
 
Introduction to hadoop V2
Introduction to hadoop V2Introduction to hadoop V2
Introduction to hadoop V2
TarjeiRomtveit
 
Coordinating the Many Tools of Big Data - Apache HCatalog, Apache Pig and Apa...
Coordinating the Many Tools of Big Data - Apache HCatalog, Apache Pig and Apa...Coordinating the Many Tools of Big Data - Apache HCatalog, Apache Pig and Apa...
Coordinating the Many Tools of Big Data - Apache HCatalog, Apache Pig and Apa...
Big Data Spain
 
Apache Arrow - An Overview
Apache Arrow - An OverviewApache Arrow - An Overview
Apache Arrow - An Overview
Dremio Corporation
 
What are Hadoop Components? Hadoop Ecosystem and Architecture | Edureka
What are Hadoop Components? Hadoop Ecosystem and Architecture | EdurekaWhat are Hadoop Components? Hadoop Ecosystem and Architecture | Edureka
What are Hadoop Components? Hadoop Ecosystem and Architecture | Edureka
Edureka!
 
Linked Data as an enabling framework for resource discovery across libraries,...
Linked Data as an enabling framework for resource discovery across libraries,...Linked Data as an enabling framework for resource discovery across libraries,...
Linked Data as an enabling framework for resource discovery across libraries,...
Andy Powell
 
Apache Arrow: Cross-language Development Platform for In-memory Data
Apache Arrow: Cross-language Development Platform for In-memory DataApache Arrow: Cross-language Development Platform for In-memory Data
Apache Arrow: Cross-language Development Platform for In-memory Data
Wes McKinney
 

What's hot (20)

Oak meeting 18/09/2014
Oak meeting 18/09/2014Oak meeting 18/09/2014
Oak meeting 18/09/2014
 
Semantic Technologies and Triplestores for Business Intelligence
Semantic Technologies and Triplestores for Business IntelligenceSemantic Technologies and Triplestores for Business Intelligence
Semantic Technologies and Triplestores for Business Intelligence
 
2012 09 aos-workshop-johanneskeizer
2012 09 aos-workshop-johanneskeizer2012 09 aos-workshop-johanneskeizer
2012 09 aos-workshop-johanneskeizer
 
OU RSE Tutorial Big Data Cluster
OU RSE Tutorial Big Data ClusterOU RSE Tutorial Big Data Cluster
OU RSE Tutorial Big Data Cluster
 
Family tree of data – provenance and neo4j
Family tree of data – provenance and neo4jFamily tree of data – provenance and neo4j
Family tree of data – provenance and neo4j
 
IGeLU 2014
IGeLU 2014IGeLU 2014
IGeLU 2014
 
SUNYLA2018_Opening_Pandora’s_Box
SUNYLA2018_Opening_Pandora’s_BoxSUNYLA2018_Opening_Pandora’s_Box
SUNYLA2018_Opening_Pandora’s_Box
 
Hadoop User Group - Status Apache Drill
Hadoop User Group - Status Apache DrillHadoop User Group - Status Apache Drill
Hadoop User Group - Status Apache Drill
 
Analyzing Web Archives
Analyzing Web ArchivesAnalyzing Web Archives
Analyzing Web Archives
 
Introduction to the Semantic Web
Introduction to the Semantic WebIntroduction to the Semantic Web
Introduction to the Semantic Web
 
IPython Notebook as a Unified Data Science Interface for Hadoop
IPython Notebook as a Unified Data Science Interface for HadoopIPython Notebook as a Unified Data Science Interface for Hadoop
IPython Notebook as a Unified Data Science Interface for Hadoop
 
Arabidopsis Information Portal, Developer Workshop 2014, Introduction
Arabidopsis Information Portal, Developer Workshop 2014, IntroductionArabidopsis Information Portal, Developer Workshop 2014, Introduction
Arabidopsis Information Portal, Developer Workshop 2014, Introduction
 
NISO Webinar: Library Linked Data: From Vision to Reality
NISO Webinar: Library Linked Data: From Vision to RealityNISO Webinar: Library Linked Data: From Vision to Reality
NISO Webinar: Library Linked Data: From Vision to Reality
 
Large-Scale Data Storage and Processing for Scientists with Hadoop
Large-Scale Data Storage and Processing for Scientists with HadoopLarge-Scale Data Storage and Processing for Scientists with Hadoop
Large-Scale Data Storage and Processing for Scientists with Hadoop
 
Introduction to hadoop V2
Introduction to hadoop V2Introduction to hadoop V2
Introduction to hadoop V2
 
Coordinating the Many Tools of Big Data - Apache HCatalog, Apache Pig and Apa...
Coordinating the Many Tools of Big Data - Apache HCatalog, Apache Pig and Apa...Coordinating the Many Tools of Big Data - Apache HCatalog, Apache Pig and Apa...
Coordinating the Many Tools of Big Data - Apache HCatalog, Apache Pig and Apa...
 
Apache Arrow - An Overview
Apache Arrow - An OverviewApache Arrow - An Overview
Apache Arrow - An Overview
 
What are Hadoop Components? Hadoop Ecosystem and Architecture | Edureka
What are Hadoop Components? Hadoop Ecosystem and Architecture | EdurekaWhat are Hadoop Components? Hadoop Ecosystem and Architecture | Edureka
What are Hadoop Components? Hadoop Ecosystem and Architecture | Edureka
 
Linked Data as an enabling framework for resource discovery across libraries,...
Linked Data as an enabling framework for resource discovery across libraries,...Linked Data as an enabling framework for resource discovery across libraries,...
Linked Data as an enabling framework for resource discovery across libraries,...
 
Apache Arrow: Cross-language Development Platform for In-memory Data
Apache Arrow: Cross-language Development Platform for In-memory DataApache Arrow: Cross-language Development Platform for In-memory Data
Apache Arrow: Cross-language Development Platform for In-memory Data
 

Similar to Intro to-technologies-Green-City-Hackathon-Athens

Why I don't use Semantic Web technologies anymore, event if they still influe...
Why I don't use Semantic Web technologies anymore, event if they still influe...Why I don't use Semantic Web technologies anymore, event if they still influe...
Why I don't use Semantic Web technologies anymore, event if they still influe...
Gautier Poupeau
 
Exploring the Semantic Web
Exploring the Semantic WebExploring the Semantic Web
Exploring the Semantic Web
Roberto García
 
Bio2RDF presentation at Combine 2012
Bio2RDF presentation at Combine 2012Bio2RDF presentation at Combine 2012
Bio2RDF presentation at Combine 2012
François Belleau
 
“Publishing and Consuming Linked Data. (Lessons learnt when using LOD in an a...
“Publishing and Consuming Linked Data. (Lessons learnt when using LOD in an a...“Publishing and Consuming Linked Data. (Lessons learnt when using LOD in an a...
“Publishing and Consuming Linked Data. (Lessons learnt when using LOD in an a...
Marta Villegas
 
S. Bartoli & F. Pompermaier – A Semantic Big Data Companion
S. Bartoli & F. Pompermaier – A Semantic Big Data CompanionS. Bartoli & F. Pompermaier – A Semantic Big Data Companion
S. Bartoli & F. Pompermaier – A Semantic Big Data Companion
Flink Forward
 
NISO/DCMI September 25 Webinar: Implementing Linked Data in Developing Countr...
NISO/DCMI September 25 Webinar: Implementing Linked Data in Developing Countr...NISO/DCMI September 25 Webinar: Implementing Linked Data in Developing Countr...
NISO/DCMI September 25 Webinar: Implementing Linked Data in Developing Countr...
National Information Standards Organization (NISO)
 
Drupal and Apache Stanbol
Drupal and Apache StanbolDrupal and Apache Stanbol
Drupal and Apache Stanbol
Alkuvoima
 
RDF-Gen: Generating RDF from streaming and archival data
RDF-Gen: Generating RDF from streaming and archival dataRDF-Gen: Generating RDF from streaming and archival data
RDF-Gen: Generating RDF from streaming and archival data
Giorgos Santipantakis
 
State of the Semantic Web
State of the Semantic WebState of the Semantic Web
State of the Semantic Web
Ivan Herman
 
SFScon21 - Sander Van Dooren - Joinup: Maintaining an Open catalogue of reusa...
SFScon21 - Sander Van Dooren - Joinup: Maintaining an Open catalogue of reusa...SFScon21 - Sander Van Dooren - Joinup: Maintaining an Open catalogue of reusa...
SFScon21 - Sander Van Dooren - Joinup: Maintaining an Open catalogue of reusa...
South Tyrol Free Software Conference
 
Scaling up Linked Data
Scaling up Linked DataScaling up Linked Data
Scaling up Linked Data
Marin Dimitrov
 
Integrating an electronic lab notebook with a data repository; American Chemi...
Integrating an electronic lab notebook with a data repository; American Chemi...Integrating an electronic lab notebook with a data repository; American Chemi...
Integrating an electronic lab notebook with a data repository; American Chemi...
rmacneil88
 
Elns and repositories, American Chemical Society, Dallas, March 2014
Elns and repositories, American Chemical Society, Dallas, March 2014Elns and repositories, American Chemical Society, Dallas, March 2014
Elns and repositories, American Chemical Society, Dallas, March 2014
ResearchSpace
 
Linked Data at the OU - the story so far
Linked Data at the OU - the story so farLinked Data at the OU - the story so far
Linked Data at the OU - the story so far
Enrico Daga
 
How e-infrastructure can contribute to Linked Germplasm Data
How e-infrastructure can contribute to Linked Germplasm DataHow e-infrastructure can contribute to Linked Germplasm Data
How e-infrastructure can contribute to Linked Germplasm Data
Stoitsis Giannis
 
Ontologies & linked open data
Ontologies & linked open dataOntologies & linked open data
Ontologies & linked open data
João Rocha da Silva
 
Data Integration And Visualization
Data Integration And VisualizationData Integration And Visualization
Data Integration And Visualization
Ivan Ermilov
 
GoodRelations Tutorial Part 2
GoodRelations Tutorial Part 2GoodRelations Tutorial Part 2
GoodRelations Tutorial Part 2
guestecacad2
 
ISWC GoodRelations Tutorial Part 2
ISWC GoodRelations Tutorial Part 2ISWC GoodRelations Tutorial Part 2
ISWC GoodRelations Tutorial Part 2
Martin Hepp
 
Advances in Scientific Workflow Environments
Advances in Scientific Workflow EnvironmentsAdvances in Scientific Workflow Environments
Advances in Scientific Workflow Environments
Carole Goble
 

Similar to Intro to-technologies-Green-City-Hackathon-Athens (20)

Why I don't use Semantic Web technologies anymore, event if they still influe...
Why I don't use Semantic Web technologies anymore, event if they still influe...Why I don't use Semantic Web technologies anymore, event if they still influe...
Why I don't use Semantic Web technologies anymore, event if they still influe...
 
Exploring the Semantic Web
Exploring the Semantic WebExploring the Semantic Web
Exploring the Semantic Web
 
Bio2RDF presentation at Combine 2012
Bio2RDF presentation at Combine 2012Bio2RDF presentation at Combine 2012
Bio2RDF presentation at Combine 2012
 
“Publishing and Consuming Linked Data. (Lessons learnt when using LOD in an a...
“Publishing and Consuming Linked Data. (Lessons learnt when using LOD in an a...“Publishing and Consuming Linked Data. (Lessons learnt when using LOD in an a...
“Publishing and Consuming Linked Data. (Lessons learnt when using LOD in an a...
 
S. Bartoli & F. Pompermaier – A Semantic Big Data Companion
S. Bartoli & F. Pompermaier – A Semantic Big Data CompanionS. Bartoli & F. Pompermaier – A Semantic Big Data Companion
S. Bartoli & F. Pompermaier – A Semantic Big Data Companion
 
NISO/DCMI September 25 Webinar: Implementing Linked Data in Developing Countr...
NISO/DCMI September 25 Webinar: Implementing Linked Data in Developing Countr...NISO/DCMI September 25 Webinar: Implementing Linked Data in Developing Countr...
NISO/DCMI September 25 Webinar: Implementing Linked Data in Developing Countr...
 
Drupal and Apache Stanbol
Drupal and Apache StanbolDrupal and Apache Stanbol
Drupal and Apache Stanbol
 
RDF-Gen: Generating RDF from streaming and archival data
RDF-Gen: Generating RDF from streaming and archival dataRDF-Gen: Generating RDF from streaming and archival data
RDF-Gen: Generating RDF from streaming and archival data
 
State of the Semantic Web
State of the Semantic WebState of the Semantic Web
State of the Semantic Web
 
SFScon21 - Sander Van Dooren - Joinup: Maintaining an Open catalogue of reusa...
SFScon21 - Sander Van Dooren - Joinup: Maintaining an Open catalogue of reusa...SFScon21 - Sander Van Dooren - Joinup: Maintaining an Open catalogue of reusa...
SFScon21 - Sander Van Dooren - Joinup: Maintaining an Open catalogue of reusa...
 
Scaling up Linked Data
Scaling up Linked DataScaling up Linked Data
Scaling up Linked Data
 
Integrating an electronic lab notebook with a data repository; American Chemi...
Integrating an electronic lab notebook with a data repository; American Chemi...Integrating an electronic lab notebook with a data repository; American Chemi...
Integrating an electronic lab notebook with a data repository; American Chemi...
 
Elns and repositories, American Chemical Society, Dallas, March 2014
Elns and repositories, American Chemical Society, Dallas, March 2014Elns and repositories, American Chemical Society, Dallas, March 2014
Elns and repositories, American Chemical Society, Dallas, March 2014
 
Linked Data at the OU - the story so far
Linked Data at the OU - the story so farLinked Data at the OU - the story so far
Linked Data at the OU - the story so far
 
How e-infrastructure can contribute to Linked Germplasm Data
How e-infrastructure can contribute to Linked Germplasm DataHow e-infrastructure can contribute to Linked Germplasm Data
How e-infrastructure can contribute to Linked Germplasm Data
 
Ontologies & linked open data
Ontologies & linked open dataOntologies & linked open data
Ontologies & linked open data
 
Data Integration And Visualization
Data Integration And VisualizationData Integration And Visualization
Data Integration And Visualization
 
GoodRelations Tutorial Part 2
GoodRelations Tutorial Part 2GoodRelations Tutorial Part 2
GoodRelations Tutorial Part 2
 
ISWC GoodRelations Tutorial Part 2
ISWC GoodRelations Tutorial Part 2ISWC GoodRelations Tutorial Part 2
ISWC GoodRelations Tutorial Part 2
 
Advances in Scientific Workflow Environments
Advances in Scientific Workflow EnvironmentsAdvances in Scientific Workflow Environments
Advances in Scientific Workflow Environments
 

More from Stoitsis Giannis

Food, agriculture and open data – the world is changing
Food, agriculture and open data – the world is changing�Food, agriculture and open data – the world is changing�
Food, agriculture and open data – the world is changing
Stoitsis Giannis
 
Agroknow and FREME presentation @Linda workshop-20-11-2015
Agroknow and FREME presentation @Linda workshop-20-11-2015Agroknow and FREME presentation @Linda workshop-20-11-2015
Agroknow and FREME presentation @Linda workshop-20-11-2015
Stoitsis Giannis
 
The Open Data Stakeholders’ Ecosystem
The Open Data Stakeholders’ EcosystemThe Open Data Stakeholders’ Ecosystem
The Open Data Stakeholders’ Ecosystem
Stoitsis Giannis
 
Open Data in the agrifood sector
Open Data in the agrifood sectorOpen Data in the agrifood sector
Open Data in the agrifood sector
Stoitsis Giannis
 
Open-data-in-agrifood-sector-challenges-opportunities
Open-data-in-agrifood-sector-challenges-opportunitiesOpen-data-in-agrifood-sector-challenges-opportunities
Open-data-in-agrifood-sector-challenges-opportunities
Stoitsis Giannis
 
How internet and open data transforms the agricultural sector (in greek)
How internet and open data transforms the agricultural sector (in greek)How internet and open data transforms the agricultural sector (in greek)
How internet and open data transforms the agricultural sector (in greek)
Stoitsis Giannis
 
Facilitating regional growth through they use of open agricultural data
Facilitating regional growth through they use of open agricultural dataFacilitating regional growth through they use of open agricultural data
Facilitating regional growth through they use of open agricultural data
Stoitsis Giannis
 
Open data: Showcases from agricultural domain
Open data: Showcases from agricultural domainOpen data: Showcases from agricultural domain
Open data: Showcases from agricultural domain
Stoitsis Giannis
 
Open Data Working Group - Agricultural Showcase
Open Data Working Group - Agricultural ShowcaseOpen Data Working Group - Agricultural Showcase
Open Data Working Group - Agricultural Showcase
Stoitsis Giannis
 
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Stoitsis Giannis
 
Cetaf ISTC Meeting: Natural-Europe Presentation
Cetaf ISTC Meeting: Natural-Europe PresentationCetaf ISTC Meeting: Natural-Europe Presentation
Cetaf ISTC Meeting: Natural-Europe Presentation
Stoitsis Giannis
 
E services for learning in agriculture-stevia-event-dec-2012
E services for learning in agriculture-stevia-event-dec-2012E services for learning in agriculture-stevia-event-dec-2012
E services for learning in agriculture-stevia-event-dec-2012Stoitsis Giannis
 
Requirements for Processing Datasets for Recommender Systems
Requirements for Processing Datasets for Recommender SystemsRequirements for Processing Datasets for Recommender Systems
Requirements for Processing Datasets for Recommender Systems
Stoitsis Giannis
 
Organic.lingua presentation cer_organic
Organic.lingua presentation cer_organicOrganic.lingua presentation cer_organic
Organic.lingua presentation cer_organic
Stoitsis Giannis
 

More from Stoitsis Giannis (15)

Food, agriculture and open data – the world is changing
Food, agriculture and open data – the world is changing�Food, agriculture and open data – the world is changing�
Food, agriculture and open data – the world is changing
 
Agroknow and FREME presentation @Linda workshop-20-11-2015
Agroknow and FREME presentation @Linda workshop-20-11-2015Agroknow and FREME presentation @Linda workshop-20-11-2015
Agroknow and FREME presentation @Linda workshop-20-11-2015
 
The Open Data Stakeholders’ Ecosystem
The Open Data Stakeholders’ EcosystemThe Open Data Stakeholders’ Ecosystem
The Open Data Stakeholders’ Ecosystem
 
Open Data in the agrifood sector
Open Data in the agrifood sectorOpen Data in the agrifood sector
Open Data in the agrifood sector
 
Open-data-in-agrifood-sector-challenges-opportunities
Open-data-in-agrifood-sector-challenges-opportunitiesOpen-data-in-agrifood-sector-challenges-opportunities
Open-data-in-agrifood-sector-challenges-opportunities
 
How internet and open data transforms the agricultural sector (in greek)
How internet and open data transforms the agricultural sector (in greek)How internet and open data transforms the agricultural sector (in greek)
How internet and open data transforms the agricultural sector (in greek)
 
Facilitating regional growth through they use of open agricultural data
Facilitating regional growth through they use of open agricultural dataFacilitating regional growth through they use of open agricultural data
Facilitating regional growth through they use of open agricultural data
 
City to-farm agro-know
City to-farm agro-knowCity to-farm agro-know
City to-farm agro-know
 
Open data: Showcases from agricultural domain
Open data: Showcases from agricultural domainOpen data: Showcases from agricultural domain
Open data: Showcases from agricultural domain
 
Open Data Working Group - Agricultural Showcase
Open Data Working Group - Agricultural ShowcaseOpen Data Working Group - Agricultural Showcase
Open Data Working Group - Agricultural Showcase
 
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
 
Cetaf ISTC Meeting: Natural-Europe Presentation
Cetaf ISTC Meeting: Natural-Europe PresentationCetaf ISTC Meeting: Natural-Europe Presentation
Cetaf ISTC Meeting: Natural-Europe Presentation
 
E services for learning in agriculture-stevia-event-dec-2012
E services for learning in agriculture-stevia-event-dec-2012E services for learning in agriculture-stevia-event-dec-2012
E services for learning in agriculture-stevia-event-dec-2012
 
Requirements for Processing Datasets for Recommender Systems
Requirements for Processing Datasets for Recommender SystemsRequirements for Processing Datasets for Recommender Systems
Requirements for Processing Datasets for Recommender Systems
 
Organic.lingua presentation cer_organic
Organic.lingua presentation cer_organicOrganic.lingua presentation cer_organic
Organic.lingua presentation cer_organic
 

Recently uploaded

220711130100 udita Chakraborty Aims and objectives of national policy on inf...
220711130100 udita Chakraborty  Aims and objectives of national policy on inf...220711130100 udita Chakraborty  Aims and objectives of national policy on inf...
220711130100 udita Chakraborty Aims and objectives of national policy on inf...
Kalna College
 
How to Fix [Errno 98] address already in use
How to Fix [Errno 98] address already in useHow to Fix [Errno 98] address already in use
How to Fix [Errno 98] address already in use
Celine George
 
Oliver Asks for More by Charles Dickens (9)
Oliver Asks for More by Charles Dickens (9)Oliver Asks for More by Charles Dickens (9)
Oliver Asks for More by Charles Dickens (9)
nitinpv4ai
 
欧洲杯下注-欧洲杯下注押注官网-欧洲杯下注押注网站|【​网址​🎉ac44.net🎉​】
欧洲杯下注-欧洲杯下注押注官网-欧洲杯下注押注网站|【​网址​🎉ac44.net🎉​】欧洲杯下注-欧洲杯下注押注官网-欧洲杯下注押注网站|【​网址​🎉ac44.net🎉​】
欧洲杯下注-欧洲杯下注押注官网-欧洲杯下注押注网站|【​网址​🎉ac44.net🎉​】
andagarcia212
 
Gender and Mental Health - Counselling and Family Therapy Applications and In...
Gender and Mental Health - Counselling and Family Therapy Applications and In...Gender and Mental Health - Counselling and Family Therapy Applications and In...
Gender and Mental Health - Counselling and Family Therapy Applications and In...
PsychoTech Services
 
Temple of Asclepius in Thrace. Excavation results
Temple of Asclepius in Thrace. Excavation resultsTemple of Asclepius in Thrace. Excavation results
Temple of Asclepius in Thrace. Excavation results
Krassimira Luka
 
BPSC-105 important questions for june term end exam
BPSC-105 important questions for june term end examBPSC-105 important questions for june term end exam
BPSC-105 important questions for june term end exam
sonukumargpnirsadhan
 
adjectives.ppt for class 1 to 6, grammar
adjectives.ppt for class 1 to 6, grammaradjectives.ppt for class 1 to 6, grammar
adjectives.ppt for class 1 to 6, grammar
7DFarhanaMohammed
 
Contiguity Of Various Message Forms - Rupam Chandra.pptx
Contiguity Of Various Message Forms - Rupam Chandra.pptxContiguity Of Various Message Forms - Rupam Chandra.pptx
Contiguity Of Various Message Forms - Rupam Chandra.pptx
Kalna College
 
The basics of sentences session 7pptx.pptx
The basics of sentences session 7pptx.pptxThe basics of sentences session 7pptx.pptx
The basics of sentences session 7pptx.pptx
heathfieldcps1
 
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...
TechSoup
 
220711130088 Sumi Basak Virtual University EPC 3.pptx
220711130088 Sumi Basak Virtual University EPC 3.pptx220711130088 Sumi Basak Virtual University EPC 3.pptx
220711130088 Sumi Basak Virtual University EPC 3.pptx
Kalna College
 
Educational Technology in the Health Sciences
Educational Technology in the Health SciencesEducational Technology in the Health Sciences
Educational Technology in the Health Sciences
Iris Thiele Isip-Tan
 
Information and Communication Technology in Education
Information and Communication Technology in EducationInformation and Communication Technology in Education
Information and Communication Technology in Education
MJDuyan
 
Pharmaceutics Pharmaceuticals best of brub
Pharmaceutics Pharmaceuticals best of brubPharmaceutics Pharmaceuticals best of brub
Pharmaceutics Pharmaceuticals best of brub
danielkiash986
 
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) Curriculum
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumPhilippine Edukasyong Pantahanan at Pangkabuhayan (EPP) Curriculum
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) Curriculum
MJDuyan
 
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...
indexPub
 
NIPER 2024 MEMORY BASED QUESTIONS.ANSWERS TO NIPER 2024 QUESTIONS.NIPER JEE 2...
NIPER 2024 MEMORY BASED QUESTIONS.ANSWERS TO NIPER 2024 QUESTIONS.NIPER JEE 2...NIPER 2024 MEMORY BASED QUESTIONS.ANSWERS TO NIPER 2024 QUESTIONS.NIPER JEE 2...
NIPER 2024 MEMORY BASED QUESTIONS.ANSWERS TO NIPER 2024 QUESTIONS.NIPER JEE 2...
Payaamvohra1
 
Observational Learning
Observational Learning Observational Learning
Observational Learning
sanamushtaq922
 
78 Microsoft-Publisher - Sirin Sultana Bora.pptx
78 Microsoft-Publisher - Sirin Sultana Bora.pptx78 Microsoft-Publisher - Sirin Sultana Bora.pptx
78 Microsoft-Publisher - Sirin Sultana Bora.pptx
Kalna College
 

Recently uploaded (20)

220711130100 udita Chakraborty Aims and objectives of national policy on inf...
220711130100 udita Chakraborty  Aims and objectives of national policy on inf...220711130100 udita Chakraborty  Aims and objectives of national policy on inf...
220711130100 udita Chakraborty Aims and objectives of national policy on inf...
 
How to Fix [Errno 98] address already in use
How to Fix [Errno 98] address already in useHow to Fix [Errno 98] address already in use
How to Fix [Errno 98] address already in use
 
Oliver Asks for More by Charles Dickens (9)
Oliver Asks for More by Charles Dickens (9)Oliver Asks for More by Charles Dickens (9)
Oliver Asks for More by Charles Dickens (9)
 
欧洲杯下注-欧洲杯下注押注官网-欧洲杯下注押注网站|【​网址​🎉ac44.net🎉​】
欧洲杯下注-欧洲杯下注押注官网-欧洲杯下注押注网站|【​网址​🎉ac44.net🎉​】欧洲杯下注-欧洲杯下注押注官网-欧洲杯下注押注网站|【​网址​🎉ac44.net🎉​】
欧洲杯下注-欧洲杯下注押注官网-欧洲杯下注押注网站|【​网址​🎉ac44.net🎉​】
 
Gender and Mental Health - Counselling and Family Therapy Applications and In...
Gender and Mental Health - Counselling and Family Therapy Applications and In...Gender and Mental Health - Counselling and Family Therapy Applications and In...
Gender and Mental Health - Counselling and Family Therapy Applications and In...
 
Temple of Asclepius in Thrace. Excavation results
Temple of Asclepius in Thrace. Excavation resultsTemple of Asclepius in Thrace. Excavation results
Temple of Asclepius in Thrace. Excavation results
 
BPSC-105 important questions for june term end exam
BPSC-105 important questions for june term end examBPSC-105 important questions for june term end exam
BPSC-105 important questions for june term end exam
 
adjectives.ppt for class 1 to 6, grammar
adjectives.ppt for class 1 to 6, grammaradjectives.ppt for class 1 to 6, grammar
adjectives.ppt for class 1 to 6, grammar
 
Contiguity Of Various Message Forms - Rupam Chandra.pptx
Contiguity Of Various Message Forms - Rupam Chandra.pptxContiguity Of Various Message Forms - Rupam Chandra.pptx
Contiguity Of Various Message Forms - Rupam Chandra.pptx
 
The basics of sentences session 7pptx.pptx
The basics of sentences session 7pptx.pptxThe basics of sentences session 7pptx.pptx
The basics of sentences session 7pptx.pptx
 
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...
 
220711130088 Sumi Basak Virtual University EPC 3.pptx
220711130088 Sumi Basak Virtual University EPC 3.pptx220711130088 Sumi Basak Virtual University EPC 3.pptx
220711130088 Sumi Basak Virtual University EPC 3.pptx
 
Educational Technology in the Health Sciences
Educational Technology in the Health SciencesEducational Technology in the Health Sciences
Educational Technology in the Health Sciences
 
Information and Communication Technology in Education
Information and Communication Technology in EducationInformation and Communication Technology in Education
Information and Communication Technology in Education
 
Pharmaceutics Pharmaceuticals best of brub
Pharmaceutics Pharmaceuticals best of brubPharmaceutics Pharmaceuticals best of brub
Pharmaceutics Pharmaceuticals best of brub
 
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) Curriculum
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumPhilippine Edukasyong Pantahanan at Pangkabuhayan (EPP) Curriculum
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) Curriculum
 
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...
 
NIPER 2024 MEMORY BASED QUESTIONS.ANSWERS TO NIPER 2024 QUESTIONS.NIPER JEE 2...
NIPER 2024 MEMORY BASED QUESTIONS.ANSWERS TO NIPER 2024 QUESTIONS.NIPER JEE 2...NIPER 2024 MEMORY BASED QUESTIONS.ANSWERS TO NIPER 2024 QUESTIONS.NIPER JEE 2...
NIPER 2024 MEMORY BASED QUESTIONS.ANSWERS TO NIPER 2024 QUESTIONS.NIPER JEE 2...
 
Observational Learning
Observational Learning Observational Learning
Observational Learning
 
78 Microsoft-Publisher - Sirin Sultana Bora.pptx
78 Microsoft-Publisher - Sirin Sultana Bora.pptx78 Microsoft-Publisher - Sirin Sultana Bora.pptx
78 Microsoft-Publisher - Sirin Sultana Bora.pptx
 

Intro to-technologies-Green-City-Hackathon-Athens

  • 1. Turning  agro-­‐biodiversity  open  data  to   real  services:  an  intro  to  technologies   Giannis  Stoitsis   Agro-­‐Know  Technologies    
  • 2. AIM  OF  THE  COURSE  
  • 3. How  to  get  as  much  as  possible  from  data?   Which  technologies  and  how  they  can  be  used?  
  • 4. Course  syllabus   •  Intro   •  Technologies   •  Sharing  data   •  Linking  data   •  Architectural  aspects   •  Building  a  simple  case   •  Some  suggesFons  
  • 5. ARE  WE  READY  TO  BUILD  REAL   SERVICES  USING  AGRO  AND   BIODIVERSITY  OPEN  DATA?  
  • 6. What  we  need   data   technology   and  of  course  a  good  idea  
  • 7. As  for  the  data   Thanks  Nikos     “Intro  to  green  data  ecosystems:  why  is  a  data-­‐ powered  tech  start  up  for  agriculture  &   biodiversity  going  to  be  profitable?”  
  • 8. TECHNOLOGIES:  THERE  IS  REALLY   GREAT  STAFF  OUT  THERE  
  • 9. Infrastructure   •  We  have  the  cloud   •  Cloud  is  geJng  cheaper,  beLer  and  more   development  friendly     •  OpFons   –  Amazon   –  Rackspace   –  Linode   –  MicrosoQ   –  Google   –  Okeanos   –  Other  Private  or  even  yours  (e.g.  OpenStack,  OpenNebula)  
  • 10. Storage and Processing Monitoring/Management/Allocation layer Virtualization of Infrastructure Layer Virtual Machines Virtualization of Infrastructure LayerVirtualized Infrastractures Management Layer GUI tools and APIs Cloud provider A Cloud provider B Cloud provider B
  • 11. What  does  this  mean?   Quick  prototyping   Get  your  running  instance  in  minutes  
  • 12. REST   •  AbstracFng  the  architectural  elements   •  Close  to  the  way  we  are  working  in  the  web   •  Standard  hLp  methods   –  GET   –  PUT   –  POST   –  DELETE   •  easy  to  be  built  especially  with  MVC  frameworks   e.g.  Rails     •  easy  to  be  consumed  with  any  language     GET  hLp://www.example.com/User/1234   POST  hLp://www.example  .com/User/  ‘{“user”:”{“”id”:”55”,  “name”:”John”}’   DELETE  hLp://www.example.com/User/1234  
  • 13. REST  clients   •  easy  to  be    developed  with  any  language   •  check  before  start  developing  
  • 14. Data  exchange  formats   •  XML,  RDF  or  JSON?   •  They  all  work  but  we  always  have  preferences   •  Why  we  love  JSON?   – can  be  parsed  by  any  front  end  (web,  mobile)   – simple   – can  be  used  to  represent  data  models   – beLer  in  processing  e.g.  transformaFon   – used  widely  by  all  document  based  repositories   (e.g.  MongoDB,  elasFcsearch)  
  • 15. Sensors   •  Are  everywhere   •  Two  categories   – The  one  that  we  hold  i.e.  from  smartphones  today   from  watch  tomorrow   – The  one  installed  at  sites  e.g.  fields   •  We  can  get  and  use  data  from  both  
  • 16. get  data  from  a  smartphone   •  sensing  data   – MoFon  sensor/accelerometer   – Proximity   – gyroscope  (for  gaming)   •  GPS:    Core  LocaFon  Framework  to  get  the   coordinates  and  tracking  user’s  locaFon  
  • 17. GeJng  real  Fme  weather  data  
  • 18. GeJng  real  Fme  weather  data     h:p://api.openweathermap.org/data/2.5/weather?q=London,uk    
  • 19. Big  data  technologies   •  A  trend?     •  Some  of  them   – NoSQL  dbs  e.g.  MongoDB,  CouchDB   – elasFcsearch   – MapReduce  framework  e.g.  run  processes  over  a   big  amount  of  data   – Hadoop   – Cassandra   – and  more  ….  
  • 20. Natural  Language  Processing   •  Dbpedia  Spotlight   •  EnFty  recogniFon  (idenFfy  names  and  places  in  a  text)   •  DisambiguaFon  (apple  can  be  a  company,  a  fruit  or  a  river)  
  • 21. Image  processing   APIs  for  mobile  apps   Open  Source  Libraries    
  • 22. how  image  processing  can  be  used?   •  Similar  to  how  Google  Goggles  works  but  trained  for  your  data  
  • 24. how  machine  learning  can  be  used?   Provide  a  recommendaFon  service   Mine  and  Cluster  Data  
  • 25. JavaScript   •  can  do  almost  everything   •  even  image  processing  e.g.  PixasFc  for  image  processing   •  powerful  libraries  e.g.  D3  for  real  Fme  data  visualizaFon  
  • 26. AnalyFcs   •  Track  the  users  and  events  of  your  applicaFon   – ElasFc  Search,  MongoDB   – Google  AnalyFcs  
  • 27. Follow  a  well  accepted  framework   •  Model  View  Controller  (MVC)   •  There  are  many  nice  MVC  based  frameworks   – Rails   – Spring   – Zend   – Yii   – Laravel   – and  more  …  
  • 29.                   Publisher Date Catalog Subject ID Author Title we actually share metadata
  • 30. the  value  is  sFll  in  metadata   •  Metadata  connect  us  to  the  real  data     – Search  metadata  records  not  raw  data   •  Metadata  gives  the  context   – themaFc  classificaFon   – target  users   •  Well  defined  standards  (e.g.  DC,  IEEE  LOM,   Darwin  Core,  ABCD)   •  RDF,  XML  and  JSON  binding  
  • 32. …  the  power  is  at  the  back  end   CulQvaQon   HarvesQng   Blossom   Unorganized  content  in   local  and  remote  sites   Organized  and   structured  content  in   local  and  remote   databases   EducaQonal   Bibliographic   EducaQonal   Geographical   Bibliographic   Agricultural   Data   PlaVorm   Aggregate   data  from   diverse   sources   Work  with   different   type  of  data   Prepare   data  for   meaningful   services   IngesQon  Enrichment  TranslaQon  Publishing   Data  discovery   services   Widgets   Authoring   services   AnalyQcs     services  
  • 33. Why  to  aggregate?   •  Enrich  metadata   •  Provide  developer  friendly  APIs  that  can  be   used  to  build  good  services   •  Data  providers  rarely  have  APIs    
  • 35. Why  we  sFll  have  data  silos?   •  CompeFng  metadata  standards  (e.g.  DC,  IEEE  LOM)   •  Diversity  of  web  interfaces  (e.g.  REST,  OAI-­‐PMH,  SOAP,  SPI,  SQI)   •  Different  exchange  format  (e.g.  XML,  RDF,  JSON)   •  Fragmented  use  of  texonomies   LD for educational data/resource sharing Overview Approaches for LD in educational data sharing  On the-fly/automated integration of heterogeneous APIs and data (http://www.meducator.net)  Dataset (transformation and) cataloging (http://linkedup-project.eu) ?  We  are  sFll  here  …   …  and  not  here  …  
  • 36. What  is  Linked  Data?   •  A  set  of  principles  and  technologies  for  the  Web   of  Data   •  Principles   –  Put  the  data  online  with  permanent  address  (URIs)   –  Describe  the  data  with  a  standard  representaFon  (RDF)   –  Link  to  other  data  through  published  taxonomies   •  Technologies   –  Triple  stores   –  SPARQL   –  RDF,  OWL     –  SWRL  
  • 37. Seman&c  Web  /  Linked  Data  Technologies  by  Mathieu  d'Aquin  on  Sep  11,  2013   ApplicaFons  
  • 38. Mash  up  and  Linked  Data:  FAO  case  
  • 39. How  it  works?   •  Based  on  AGROVOC   •  Using  AGROVOC  mappings  to  other  ontologies  e.g.  GeopoliFcal  and  DBpedia   <hLp://aims.fao.org/aos/agrovoc/c_690>  <hLp://www.w3.org/2004/02/skos/core#closeMatch>  <hLp://dbpedia.org/resource/Salmon>     <hLp://dbpedia.org/page/Salmon>     replace  resource  with  page  and  get  rdf  
  • 40. Why  to  rely  on  linked  data   •  Easy  to  be  processed  e.g.  microdata,  rdf   •  Get  more  out  of  data  through  linking  e.g.  get  the  staFsFcs  for  a  country   through  GeoPoliFcal  Ontology   Web  resource  about  Greece  annotated  with  Agrovoc  term  “Greece”   hLp://www.fao.org/countryprofiles/geoinfo/geopoliFcal/data/Greece   Country  GDP  
  • 41. and  even  more  …   •  Data  that  you  can  get  from  World  Bank  based  on  ISO3   country  code   –  Agricultural  irrigated  land  (%  of  total  agricultural  land)   –  Cereal  yield  (kg  per  hectare)   –  Rural  populaFon  (%  of  total  populaFon)   –  Poverty  headcount  raFo  at  rural  poverty  line  (%  of  rural   populaFon)   –  Agricultural  machinery,  tractors  per  100  sq.  km  of  arable   land   –  Arable  land  (%  of  land  area)   –  CO2  emissions  (kt)   –  Fish  species,  threatened  
  • 42. DECOUPLING  FRONT  END  FROM   BACK  END  
  • 43. how  it  works   Template  customizaQon   html,  css,  Ajax,  JS   Data  mash  up   Search  API   Cloud   Edu  resources   Data  processing  and  enrichment   e.g.  semanQc  enrichment  or  clustering   Search  API   Cultural  resources   from  Museums  
  • 44. how  it  works   Template  customizaQon   html,  css,  Ajax,  JS   widget  in  Facebook  page   Data  mash  up   Search  API   Cloud   Edu  resources   Data  processing  and  enrichment   e.g.  semanQc  enrichment  or  clustering   Search  API   Cultural  resources   from  Museums  
  • 47. NATIVE  OR  WEB  APP?  
  • 48. NaFve  vs  Web  app   •  Depends  on  the  business  case   •  Responsive  web  app  are  cross  plaworm   •  Responsive  design  is  great  but  it  is  not  so   friendly  as  naFve  app   •  NaFve  app  has  nice  workflow   •  NaFve  app  can  use  device  sensors   •  In  many  data-­‐powered  cases  you  need  both  
  • 49. BUILDING  A  BASIC  APP  IN  HOURS  
  • 51. A  mobile  app  for  discovering  green   learning  resources   •  Similar  apps   – OER  commons   – iMarine   •  Similar  web  app   – hLp://www.greenlearningnetwork.org/ organicedunet/  
  • 52. Which  data   •  Learning  Resources  about  environment,   agriculture  and  biodiversity   •  API  end  point  hLp://83.212.96.219:8080/ glnRepo/api/ariadne/restp?json=Χ&callback=Y  
  • 53. Recipe   •  Mobile  app  SDK  e.g.  XCODE   •  Mobile  simulator   •  Some  reading   •  Similar  examples  e.g.  Searching  in  twiLer   •  A  workflow   •  JSON  parser   •  No  beauFes  at  this  stage  
  • 55. Defining  the  workflow  is  a  game  
  • 56. ConnecFng  to  the  data   Use  NSJSONSerializaFon  Class  to  get  the  data  
  • 58. SuggesFons   •  focus  on  the  idea  and  business  value   •  re-­‐use  the  workflow  of  successful  apps   •  processing  gives  the  value   •  do  not  create  unnecessary  procedures  e.g.  RegistraFon  page   •  re-­‐use  exisFng  data   •  put  emphasis  on  the  value  of  your  app  and  not  on  the   complexity  of  the  technology   •  always  check  first  if  there  is  open  source  code   •  design  maLers   •  go  beyond  trivial  and  benefit  from  innovaFon  
  • 59.     thank  you!   stoitsis@agroknow.gr