BigDataEurope @BDVA Summit2016 2: Societal Pilots

BIG DATA EUROPE
AND THE 7 SOCIETAL PILOTS
BDVA Summit 2016, Valencia1 December 2016
Summit 2016

Talk outline
 The BigDataEurope Project & Mission [2 slides]
 The Big Data Integrator (BDI) platform [3 slides]
 7 Pilots for the 7 Societal Challenge Domains
o Overview
o SC4 (Transport: Traffic Conditions Estimation)
o SC7 (Security: Event Detection) [DEMO]
6-déc.-16www.big-data-europe.eu

Supporting the Societal Domains with Big Data Technology
BigDataEurope Project

BigDataEurope Action
 EC Horizon 2020 Coord. & Support Action
o ~5mio €, 2015-2017
 Lower barrier for using BD technologies
o Setup & deploy use-case workflows, lack of expertise
 Show societal value of Big Data
o Across 7 H2020 societal challenges
o Establish data value chains across domains & orgs.

Data Value Chain Evolution
6-déc.-16
Extraction, Curation Quality, Linking,
Integration
Publication,
Visualization, Analysis
Extraction, Curation, Quality,
Linking, Integration, Publication,
Visualization, Analysis
Health
Transport
Security
Extraction Curation Quality Linking Integration Publication Visualization Analysis
Data
Repositories
Linked
Open Data
TIME
Food SocietiesClimate Energy
Proprietary,
‘locked-in’
solutions
OS Solutions,
Big Data Stacks
www.big-data-europe.eu

A flexible, generic platform for (Big) Data Value
Chain Deployment
Big Data Integrator

• Must be considered at: data acquisition, data processing and data
display level
• A need to find a solution to accommodate all 3 levels
• It is an important concern to most SCs
• Common feeling “better integration solution of wider variety of data
leads to better statistics”
• Most help in this direction is needed by SC1 and SC5, remains an
important aspect for All SCs
• Decisions depend on results of statistics which are as good as the
data quality which is used
SC1 SC2 SC3 SC4 SC5 SC6 SC7
Societal Perception of the 4 V’s
Platform
Requirements

Big Data Integrator: Architecture
 Key points
o Stacks Open Source solutions (Free)
o Dockerization
o Facilitates integration and deployment
o Plug-and-play BD Platform
o Cloud-deployment ready
 Key BDE additions
o Support layer: integrated UI
o Semantification layer

Big Data Integrator: In-Use
 Big Data Integrator:
https://github.com/big-data-europe
WIKI : extensive documentation, information on
supported components, instructions, etc.

Demonstrating the Societal Value through 7 Pilot
‘Real-world’ use-cases
1. Overview
BigDataEurope Pilots

Pilots: Overview
 SC1: Health & Pharm.
 SC2: Food & Agr.
 SC3: Energy
 SC4: Transport
 SC5: Climate
 SC6: Social Sciences
 SC7: Security

7 Pilots
◎ BDI Platform Instantiations
o Allow end-users to easily deploy functionality in own system environment
o Modularized Docker approach - easier to replace components
o Reduces effort to keep 3rd party software updated & integrated
◎ 7 Societal Challenge Pilots
o Aligned with 7 European Commision H2020 Societal Challenges
o Real-world use-cases (Data, Objectives, Solutions)
o Some pilots have different data & objectives but a similar solution

SC1: Pharmacology research
6-déc.-16
Life
Sciences
& Health
• Query a large
number of datasets,
some large
• Existing elaborate
ingestion and
homogenization by
OpenPHACTS
• Extensive toolset
developed by OPF
and others
Objective: Large-scale heterogeneous pharma-
research data linking & integration

SC1: Architecture & Components
• Replicate Open PHACTS
functionality on the BDE
infrastructure using OS solutions
• Based on Virtuoso, proprietary
distributed database
• Apply to other domains (e.g.
Agriculture)
• Porting to BDI gives flexibility
and enables new functionalities
• Logging & system health monitoring

SC2: Viticulture resources
Food and
Agriculture
Objective: Automate publication ingestion and
thematic classification
• AgInfra is a major
infrastructure for
agriculture
researchers, serving
cross-linked
bibliography, data,
and processing
services

• BDI deployed as an external
infrastructure for processing
text (viticulture publications)
• Storing and processing text at
a larger scale than AgInfra
can currently manage

SC3: Predictive maintenance
Energy
• Wind turbine monitoring
applies computational
models to sensor data
streams
• Models are weekly re-
parameterized using
week’s data from multiple
turbines
Objective: Real-time turbine monitoring stream
processing and analytics

• Existing in-house non-scalable solution for model
parameterization
• Reliable Fortran software for data analysis
• Efficient, but not scalable to data volume
• Developing a BDI orchestrator
• Re-uses existing software unmodified
• Makes it easy to apply in parallel to many
datasets and manage the outputs

SC4: Traffic conditions estimation
Transport
• Combines:
• Traffic modelling from
historical data
• Current measurements from a
taxi fleet of 1200 vehicles
Objective: Estimation of real-time traffic
conditions in Thessaloniki

• New Flink implementations
of map matching and
traffic prediction algorithms
• BDI provides access to
varied data sources
• PostGIS database with
city map
• ElasticSearch database
of historical data
• Kafka stream of real-
time data

SC5: Climate modelling
Climate
• Preparing modelling experiments
• Slicing, transforming, combining datasets
• Submission and retrieval from modelling
infrastructure
• Discovering and re-using previously
computed derivatives
• Lineage annotation: computer derivatives
from datasets and model parameters
• Finding appropriate past runs avoids
repeating weeks-long modelling runs
Objective: Supporting data-intensive climate research

• BDI offers:
• Hive for managing data
in a way that can be
retrieved and
manipulated, rather
than file blocks
• Cassandra stores
structured and textual
metadata for searching
headers and lineage
• Existing infrastructure; stable, reliable software for parallel computation of models
• BDI is deployed as an external infrastructure for preparing and managing datasets

SC6: Municipality budgets
Social
Sciences
• Ingestion of budget and
budget execution data
• Multiple municipalities in
varied formats and data
models
Objective: Homogenized Budgetary data made
available for analysis and comparison

• BDI deployed as ingestion
and storage infrastructure
for external tools
• Homogenizes variety of
data (JSON, CSV, XML,
etc.)
• Exposes data as SPARQL
endpoint serving
homogenized data
• Existing analytics and visualization tools
• Use SPARQL queries to retrieve only the relevant slices of the overall data

SC7: Change detection & verification
Secure
Societies
• Events are extracted from text
published by news agencies and
on social networking sites
• Events are geo-located and
relevant changes are detected by
comparing current and previous
satellite images
Objective: Detect and Verify Events based on Satellite
Imagery, News and Social Media

Event Detection
Change Detection
• Re-implementation of change
detection algorithms for Spark
• Parallel orchestrator for text analytics
• Re-uses existing software
• Scales to many input streams
• BDI provides:
• Cassandra for text content and
metadata
• Strabon GIS store for detected
change location
• Homogeneous access to both for
analysis and visualization

2. In-depth look at the Transport Pilot

Transport Pilot: Architecture & Objectives
“A scalable, fault-tolerant and flexible platform based on open source
frameworks that can process unbounded data sets and graphs.”

Message Broker: Kafka Cluster
 L. Selmi - BDE - Tech. Workshop
Apache Kafka is a high-throughput distributed
durable messaging system
Apache Kafka

Stream and Batch Processor: Flink Cluster
Apache Flink is an open source platform for
distributed stream and batch data processing.
Apache Flink

Storage and Indexing: Elasticsearch Cluster
Elasticsearch is a distributed open source document
database built on top of Apache Lucene

Map-Matching & Prediction: Rserve
R is a free software environment for
statistical computing. It is used in the
pilot to run the map-matching and the
prediction algorithms.
The R Project

Transport Pilot: Architecture (High-level)

Transport Pilot: BDE Components in Docker Swarm

Transport Pilot: The BDE Platform Stack

Visualization
L. Selmi - BDE - Tech. Workshop
SC4 Pilot 1 can process real-
time FCD data for map-
matching and simple road
segments classification
(normal/congested)

3. Demonstration of the Security Pilot

Architecture for SC 7
38
Stack

Security Pilot in Practice
 Demonstration

Free Workshops, Hangouts & Webinars
BigDataEurope Activities

2nd round of Societal Workshops
Transport 22 September 2016 Brussels Collocated with Big Data for
Transport, Tisa workshop
Food&Agri 30 September 2016 Brussels Collocated with DG AGRI WP2018-
20 stakeholder consultation
Energy 4 October 2016 Brussels Collocated with EC H2020 Info Day
on “Smart Grids and Storage”
Climate 11 October 2016 Brussels Collocated with Melodies Project
Event – Exploiting Open Data
Security 18 October 2016 Brussels Standalone Workshop
Societies 5 December 2016 Cologne Collocated with EDDI16- 8th Annual
European DDI User Conference
Health 9 December 2016 Brussels Standalone Workshop

Other Activities
 Fresh set (7) of Societal Workshops in 2017
 Various SC-focussed and general hangouts, follow!
o Apache Flink & BDE (20 Oct) – available online
o BDVA & BDE Webinar planned early next year
o Keep track on BDE Website (Events)

WEB: www.big-data-europe.eu EMAIL: info@big-data-europe.eu
BIG DATA INTEGRATOR
www.github.com/big-data-europe
PROJECT COORDINATION (Fraunhofer IAIS)
Prof. Sören Auer, auer © cs.uni-bonn · de
> Dr. Simon Scerri, scerri © cs.uni-bonn · de
EIS Department/Group,
Fraunhofer IAIS & CS Department Uni-Bonn, Bonn, Germany
Questions & Contacts
6-déc.-16
#BigDataEurope
leads the Fraunhofer
Big Data Alliance

BigDataEurope @BDVA Summit2016 2: Societal Pilots

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