processing life science data at scale - using semantic web technologies

© Insight 2014. All Rights Reserved

Processing Life Science Data at Scale – using Semantic Web Technologies

Ali Hasnain, Naoise Dunne, Dietrich Rebholz-Schuhmann

© Insight 2014. All Rights Reserved© Insight 2014. All Rights Reserved

Presenters and Contributors

Ali Hasnain

Dietrich Rebholz-Schuhmann

Naoise Dunne

•Motivation•Query Federation•Infrastructure•Graph Aggregation•Visualization•Hands On Session

Agenda


Session 0: Motivation

The Web is evolving...

WWW (Tim Berners-Lee)“There was a second part of the dream […] we could then use computers to help us analyse it, make sense of what we re doing, where we individually fit in, and how we can better work together.”

© Insight 2014. All Rights Reserved© Insight 2014. All Rights Reserved 6 of 46

Two Key Ingredients

1. RDF – Resource Description FrameworkGraph based Data – nodes and arcs• Identifies objects (URIs)• Interlink information (Relationships)

2. Vocabularies (Ontologies)• provide shared understanding of a domain• organise knowledge in a machine-comprehensible way• give an exploitable meaning to the data


Why Graphs?

TGFβ-3

transforming growth factor, beta 3

Homo sapiens

CCCCGGCGCAGCGCGGCCGCAGCAGCCTCCGCCCCCCGCACGGTGTGAGCGCCCGACGCGGCCGAGGCGG …

14q24

nci:has_description

nih:sequence

nih:organism

nih:location

nih:organism

TGFβ-3

Platelet activation, signalling,aggregation

Response to elevated platelet cytosol Ca2+

Platelet degranulation

rea:process

rea:processrea:process

Gene Database Pathway

Database


Linked Open Data Cloud


Life Sciences….


Relevant trends ongoing in the public

• “Health wearables”• Generating data, private data, data streams

• Personalized medicine• Genomics information available, but also biomarker analysis• Health-related, sports, nutrition, aging

• Treatment according to specific genomic parameters

• Systems medicine, translational medicine

Distributed Biomedical Repositories (Demo)

http://safe.sels.insight-centre.org/

12Insight Centre for Data Analytics


Session 1: Accessing Data- Query Federation

Ali Hasnain, Naoise Dunne, Dietrich Rebholz-Schuhmann


Distributed Biomedical Repositories


SPARQL Query Federation Approaches

•SPARQL Endpoint Federation (SEF)

•Linked Data Federation (LDF)

•Distributed Hash Tables (DHTs)

•Hybrid of SEF+LDF


SPARQL Query Federation Approaches


SPARQL Endpoint Federation Approaches• Most commonly used approaches• Make use of SPARQL endpoints URLs• Fast query execution• RDF data needs to be exposed via SPARQL

endpoints• E.g., HiBISCus, FedX, SPLENDID, ANAPSID, LHD

etc.


Linked Data Federation Approaches• Data needs not be exposed via SPARQL endpoints

• Uses URI lookups at runtime

• Data should follow Linked Data principles

• Slower as compared to previous approaches

• E.g., LDQPS, SIHJoin, WoDQA etc.


Query federation on top of Distributed Hash Tables

• Uses DHT indexing to federate SPARQL queries

• Space efficient

• Cannot deal with whole LOD

• E.g., ATLAS


Hybrid of SEF+LDFFederation over SPARQL endpoints and Linked Data

Can potentially deal with whole LOD

E.g., ADERIS-Hybrid


SPARQL Endpoint Federation

S1

S2

S3

S4

RDF RDF RDF RDF

Parsing/Rewriting

Source Selection

Federator Optimzer

Integrator

Rewrite query and get Individual Triple

Patterns

Identify capable source against Individual Triple

Patterns

Generate optimized sub-query Exe. Plan

Integrate sub-queries results

Execute sub-queries

Curtsey Muhammad Saleem (AKSW)


FedBench (LD3): Return for all US presidents their party membership and news pages about them.

SELECT ?president ?party ?page WHERE {?president rdf:type dbpedia:President .?president dbpedia:nationality dbpedia:United_States .?president dbpedia:party ?party .?x nyt:topicPage ?page .?x owl:sameAs ?president .}

dbpedia

RDF

Source Selection Algorithm

Triple pattern-wise source selection

S1TP1 =

KEGG

RDF

ChEBI

RDF

NYT

RDF

SWDF

RDF

LMDB

RDF

Jamendo

RDF

Geo Names

RDF

DrugBank

RDF

S1 S2 S3 S4 S5 S6 S7 S8 S9

//TP1

//TP3//TP4

//TP5

//TP2

TP2 = S1

Source Selection





dbpedia

RDF



S1TP1 =

KEGG

RDF

ChEBI

RDF

NYT

RDF

SWDF

RDF

LMDB

RDF

Jamendo

RDF

Geo Names

RDF

DrugBank

RDF

S1 S2 S3 S4 S5 S6 S7 S8 S9

//TP1

//TP3//TP4

//TP5

//TP2

TP2 = S1

TP3 = S1

Source Selection





dbpedia

RDF



S1TP1 =

KEGG

RDF

ChEBI

RDF

NYT

RDF

SWDF

RDF

LMDB

RDF

Jamendo

RDF

Geo Names

RDF

DrugBank

RDF

S1 S2 S3 S4 S5 S6 S7 S8 S9

//TP1

//TP3//TP4

//TP5

//TP2

TP2 = S1

TP3 = S1 TP4 = S4

Source Selection





dbpedia

RDF



S1TP1 =

KEGG

RDF

ChEBI

RDF

NYT

RDF

SWDF

RDF

LMDB

RDF

Jamendo

RDF

Geo Names

RDF

DrugBank

RDF

S1 S2 S3 S4 S5 S6 S7 S8 S9

//TP1

//TP3//TP4

//TP5

//TP2

TP2 = S1

TP3 = S1 TP4 = S4

TP5 = S1 S2 S4-S9

Source Selection

Total triple pattern-wise sources selected = 1+1+1+1+8 => 12



SPARQL Query Federation Engine•FedX

•SPLENDID

•HiBISCuS+FedX

•HiBISCuS+SPLENDID

•ANAPSID

•BioFed

•LHD

•DARQ

http://www2.informatik.uni-freiburg.de/~mschmidt/docs/iswc11_fedx.pdf

http://ceur-ws.org/Vol-782/GoerlitzAndStaab_COLD2011.pdf

http://svn.aksw.org/papers/2014/HiBISCuS_ESWC/public.pdf

http://svn.aksw.org/papers/2014/HiBISCuS_ESWC/public.pdf

http://iswc2011.semanticweb.org/fileadmin/iswc/Papers/Research_Paper/03/70310017.pdf

http://srvgal78.deri.ie:8007/

http://events.linkeddata.org/ldow2013/papers/ldow2013-paper-06.pdf

https://informatik.hu-berlin.de/forschung/gebiete/wbi/research/publications/2008/DARQ-FINAL.pdf


Overview of Implementation details of Federated Sparql Query Engines


System Features of Federated Sparql Query Engines


System’s Support for SPARQL Query Construct


Session 2: Infrastructuretrends in big linked data infrastructure


Mixed data sources and query tools

Cancer AtlasCurated Federated cluster(Indexed)

HBASEDistributed Database

DrugBank

Sparql 1.1Ad hocFederated

Federation RDD and MapReduce

MongoDistributed Database

Gremlin

TitianDistributedGraph Database


IntroductionCloud computing frameworks tailored for managing and analyzing big data-sets are powering ever larger clusters of computers.This presentation will describe the infrastructure that is required to serve a linked data flavour of big data


What is Big Data?“Big Data is characterized by High Volume, Velocity

and Variety requiring specific Technology and Analytical Methods for its transformation into Value” - Gartner

• Volume - Data is too big to fit on even largest server

• Velocity - Data need handling based on speed • Variety - heterogeneous Data - comes in

many forms• Veracity - (sometimes) Quality of the data


The nature of applications What is the nature of application this kind of

infrastructure enablesZebra fish (Jeremy Freeman)• needed to map and manipulate the brain at scale

to understand Zebrafish neural activity• Scales too great for one processor


Using spark


How can I get me one?How to enable your research with cutting edge

distributed computing with little effort


DistributedHigh Utilisation & Scalability

The Rise of distributed Datacenter Schedulers


The qualities of a big data Infrastructure• Distributed

• Data and its processing is shared on large cluster multiple cheap commodity servers

• loose coupling, isolation, location transparency, data locality & app-level composition

• High Utilisation• The infrastructure give the best use of computing resources

• Resilience (handling failure)• The infrastructure stays responsive in the face of failure and can “heal”

• Scalability • grows to meet demand (Elastic), responsive under varying workload (Load

balanced)• Operationally efficient

• Needs to be highly automated, be very easy to maintain


Why Datacenter schedulers? Schedulers run your Distributed Apps

• are an operating system kernel for the cloud• Schedulers coordinate execution of work on

cluster• help you to get as many compute resources as

you want whenever you want it• Abstract some scalability and load balancing

issues


Benefits of using a Scheduler• Efficiency - best use of computing resources• Agility - change your application mix with no

turnaround• Scalability - grow to the current demand of your

app• Modularity - 2 level schedulers have plugin

frameworks that allow quick repurposing of core and no reliance on one vendor (more later)


Datacenter schedulersSchedulers help you focus on your own work and not the infrastructure.“its great to be able to focus on what it is you want

to be doing rather than worrying about how do you get what it is you need in order to be able to get stuff done” - John Wilkes (Google)

© Insight 2014. All Rights Reserved Quick history of distributed schedulers

2004 mapreduce paper

2004 Google Borg

2011 Hadoop1.02003 Google filesystem

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

2008 Hadoop released

2013 Yarn

2010 Spark Paper

2010 Nexus (Mesos)

2005 Hadoop started

2013 Mesos Released

2011 Mesos Paper

2014 Kubernetes

2014 Google Omega paper

History of Datacenter Schedulers

2003 Slurm


HadoopMonolithic scheduler: Original opensource datacenter scheduler

• jobs are batched and executed

• Designed only to run Mapreduce jobs

• No concurrency between apps

• Evolving into yarn

Hadoop

Linux Server

Linux Server

hadoop- resource management

mesos slavemesos slave

Linked datat m/r job

Linked datat m/r job

Linked datat app


Mesos2 level scheduler : More flexible

• Can Schedule many kinds of applications

• Frameworks (such as spark) are delegated the per application scheduling

• Mesos responsible for resource distribution between applications and enforcing overall fairness

• Very modular, due to 2 level scheduling. frameworks manage apps as they like

Mesos

Linux Server

Linux Server

Mesos - resource management

Mesos - scheduler jobs

frameworkchronos

mesos slave

frameworkspark

frameworkmarathon

mesos slave

Hadoop M/R job

Linked data job

Linked datat app


Schedulers RecapScheduler allow you to use your cluster as one

machine• ease operations• provide elasticity and load balancing• Can run both batch and longer running jobs• Are Efficient, Agile, scalable and modular


Managing Failure in Infrastructure

“Everything fails all the time”Werner Vogels (CTO Amazon)


Handling FailureThe harsh reality: all distributed infrastructures must deal with failurefor the designers of applications running on distributed infrastructure (even Mesos), a great number of design mistakes need to be avoided...


Fallacies of distributed computing• The most common misconceptions that lead to failure of Network

infrastructure:• The network is reliable.• Latency is zero.• Bandwidth is infinite.• The network is secure.• Topology doesn’t change.• There is one administrator.• Transport cost is zero.• The network is homogeneous

All prove to be false in the long run and all cause big trouble and painful learning experiences.


How successful infrastructure avoids failureNo Single point of failuremultiple masters, multiple copies of data, and redundancy on all services. Use elections between masters, use distributed locks and thresholds.

Design Applications to Expect FailureApplications should continue to function even if the underlying physical hardware fails.Evolving infrastructure eases this by the use schedulers and container managers.

Special Channel for failuresProvides the means to delegate errors as messages on their own channel or service. Techniques include log aggregation and shared monitoring services.


Operationally efficientUsing Containers for better Quality of Service


ContainersContainers run your application in isolation in a portable and repeatable fashion“Because of the way that ... containers separate the application constraints from infrastructure concerns, we help solve that dependency hell.” - Docker


Why Containers

Containers Are: • Small (footprint)• Portable• Fast

Containers Allow:• Resiliency

• can be redeployed in seconds• Operationally efficiency

• The infrastructure can “heal”• Scalability

• on a single server allows resources (such as CPU) to be dialed up or down


Containers vs. VMs

Virtual Machinesemulate a virtual hardware, require considerable overhead in CPU, Disk and Memory

Containersuse shared operating systems much more efficient than hypervisors in system resource terms


Containers vs. VMs


ContainersBut which container to use.

Many choices exist…LXC, Docker, Rocket


Docker - our container of choiceWhy Docker?• Best of breed at the moment• integrates natively with Mesos and Kubernetes • Has great infrastructure including • Docker registry for looking up containers• Docker compose for combining containers

Alternatives• Rockit - More secure as uses init.d but Linux only


Container StandardisationBut choosing a container is not a commitment...Looks like standardisation around the corner via open container:

https://www.opencontainers.org/

Initiative Sponsors: Apcera, AT&T, AWS, Cisco, ClusterHQ, CoreOS, Datera, Docker, EMC, Fujitsu, Google, Goldman Sachs, HP, Huawei, IBM, Intel, Joyent, Kismatic, Kyup, the Linux Foundation, Mesosphere, Microsoft, Midokura, Nutanix, Oracle, Pivotal, Polyverse, Rancher, Red Hat, Resin.io, Suse, Sysdig, Twitter, Verizon, VMWare


Recap: ContainersContainers Are: • Small (footprint), Portable, Fast• Allow you to repeatedly deploy applications• Work well with schedulers such as Mesos• Help with Resiliency and scalability


Putting it all togetherInsights Linked Data infrastructure


Linked Data Infrastructure

Mesos

Mesos - scheduler short jobs Mesos - scheduler long run jobs

Spark Fwk Chronos Fwk

Marathon Framework

OS Monitor

Mesos Monitor

Linux Server

Linux Server

Linux Server


mesos client Docker mesos

client Docker mesos client Docker

Resources cpu mem disk Managed by Mesos

Applications work with frameworks to get resources they need

Frameworks Negotiate with mesos to run their jobs

DatastoresHDT, Neo4JgraphX Granatum RevealedGraph

Jobs

Docker manages isolation on Linux servers


Linked Data Infrastructure

Mesos

Linux Server

Linux Server

Linux Server


Mesos - scheduler short jobs Mesos - scheduler long run jobs

Spark Fwk

Chronos Fwk

DatastoresHDT, Neo4J

Marathon

graphX

mesos client Docker

OS Monitor

Mesos Monitor

mesos client Docker mesos

client Docker

We use graph X for large graph batch jobs

We use both HDT(RDF Store)Neo4J (Graph)

Granatum Revealed

We deploy specialised linked data applicationsto cluster


RecapTo provide linked data at scale and with the right service mix, infrastructures need to consider:

• Services to help application to be Distributed Scalable

• High Utilisation of computing resources • Know how you will handle failure• Operationally efficient

We suggest, using schedulers such as mesos with containers (Docker), use suitable frameworks (GraphX/Spark) and datastores (Neo4J)


Thank youQ & A


Graph Aggregation at scale

Naoise Dunne, Ali Hasnain, Dietrich Rebholz-Schuhmann


Linked DataA method of publishing structured data so that it can be interlinked.

• Normally represented as RDF• Normally queried using

SPARQL

4 principles of linked data1. Use URIs to name (identify) things.2. Use HTTP URIs so can be looked up3. Provide metadata about what thing is.

- use open standards RDF, SPARQL, etc.

4. Link to other things using their HTTP URI-based names


Linked data as a graphLinked data can be considered a Heterogeneous

Graph

What do we mean by Heterogeneous Graph?• Linked data graphs share graph structure but have mixed characteristics • Nodes (Vertices) contain different data• Links (Edges) Mix of directed and undirected• Linked Data Graph can be weighted or not• Can have a mix of classes and types from differing Ontologies

What do these graphs look like?


Comparing AggregationApproaches


Linked data as a graphAs Linked data graphs share graph structure and can be connected we can reason over them using graph algorithms.

Popular approaches to querying graphs are:• Declarative pattern matching - SPARQL • graph traversal languages - Cypher, Gremlin• distributed graph data structures - GraphX


Comparing Graph Query ApproachesGremlin - graph traversalA “pure” graph traversal language based on xpath, allows powerful expression of graph algorithms, but at cost of concisenessCharacteristics

• Great for expressing most graph algorithms

• can be scaled • uses adjacency tables

Prosgreat at localized searches (shortest path for instance)interoperable with most databaseConsCan be difficult to understand

Graph X - message passingDSL rather than language. For “programmers” Scala, Java and Python APIs.Characteristics

• Resilient Distributed Property Graph

• index-free adjacency• Vertex/Edge table

ProsBest (in list) distributed executionPowerful mix of table and traversalhas a set of optimised operators ConsNot a database, data needs to be loaded and stored separately

SPARQL -declarativeDeclarative language, allowing better expression and abstracting the writer from optimisation problemsCharacteristics

• Stores Triples (tuples)• Vendor normally optimise

popular queries.• Often built on RDBMS

ProsIs Expressive languageEasy to express search patternsConsDifficult to scalePoor at traversal


Comparing Graph Query ApproachesGremlin - graph traversalA “pure” graph traversal language based on xpath, allows powerful expression of graph algorithms, but at cost of conciseness

Graph X - message passingDSL rather than language. Scala, Java and Python APIs.

SPARQL -declarativeis a declarative language, allowing better expression and abstracting the writer from optimisation problems

Abstract Concrete

Considerable work (for vendors) to scale Little work to scale

Optimised for aggregation

Optimised for connections

Global Local


Other Graph Query LanguagesGremlin - graph traversalA “pure” graph traversal language based on xpath, allows powerful expression of graph algorithms, but at cost of conciseness

AlternativesNetwork XA python DSL for graphs

Graph X - Message passingDSL rather than language. For “programmers” Scala, Java and Python APIs. Very scaleable.

AlternativesGraphLabvery powerful commercial productGiraffeA hadoop API for graphs

SPARQL -DeclarativeDeclarative language, allowing better expression and abstracting the writer from optimisation problems

AlternativesCypherHas pattern matching constructs, can do much the same as sparql on Neo4J database


Working with linked data with Gremlin


How to Aggregate Graphs with Sparql?


What is Graph AggregationCondenses a large graph into a structurally similar but smaller graph by collapsing vertices and edgesImagine the LOD cloud, and how the aggregate may look


What is Graph AggregationPREFIX : <http://example.org/> PREFIX rdf:<http://www.w3.org/1999/02/22-rdf-syntax-ns#>

CONSTRUCT { _:b0 a ?t1; :count COUNT(?sub.s) . _:b1 a ?t2; :count COUNT(?obj.o). _:b2 a rdf:Statement; rdf:predicate ?p; rdf:subject _:b0; rdf:object _:b1; :count ?prop_count } WHERE { GRAPH_AGGREGATION { ?s ?p ?o {?s a ?t1} GROUP BY ?t1 AS ?sub {?o a ?t2} GROUP BY ?t2 AS ?obj }}

The Famous LOD Cloud

http://lod-cloud.net/

The Famous LOD Cloud (from a different Angel)

Gagg: Two-steps Aggregation

● Relation & measure● Subject dimension(s)

& measure● Object dimension(s) &

measure

Uses aggregation functions and a template similar to CONSTRUCT queries


How do we Aggregate Linked data at scale


Scaling SPARQLSo you still want to use sparql at scaleHow do we query RDF data with SPARQL at Big data scale?Clustering

• Some vendors/platforms provide clustered triple-stores

FederationBecame available in Sparql 1.1 with SERVICE keyword

• Federation emerged with great promises.• For distributed computing, sparql federation has limitations (for

now).• Query planning for SPARQL is NP Complete


Complementing Sparql with Graph algorithmWhen not SPARQL • For distributed computing,

declarative languages such as SPARQL are (for now) problematic

• you cannot know if query is NP or EXP time.

• Difficult to create query plans especially with fast or changing graphs

• Have to rely on federation which is still being researched

Why Graph Traversal• Proven to scale • Graphs traversal lower level

but easier to tune so that it is in P time.

• Most popular algorithm are local (as in) only query neighbouring nodes at any time -thus easier to break up across compute nodes


Thank youQ & A


Sparql Federation example

Q: How are the protein targets of the gleevec drug differentially expressed, which pathways are they involved in?

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>PREFIX cco: <http://rdf.ebi.ac.uk/terms/chembl#>PREFIX chembl_molecule: <http://rdf.ebi.ac.uk/resource/chembl/molecule/>PREFIX biopax3:<http://www.biopax.org/release/biopax-level3.owl#>PREFIX atlasterms: <http://rdf.ebi.ac.uk/terms/atlas/>PREFIX sio: <http://semanticscience.org/resource/>PREFIX dcterms: <http://purl.org/dc/terms/>PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>

SELECT distinct ?dbXref (str(?pathwayname) as ?pathname) ?factorLabel WHERE {

# query chembl for gleevec (CHEMBL941) protein targets ?act a cco:Activity; cco:hasMolecule chembl_molecule:CHEMBL941 ; cco:hasAssay ?assay . ?assay cco:hasTarget ?target . ?target cco:hasTargetComponent ?targetcmpt . ?targetcmpt cco:targetCmptXref ?dbXref . ?targetcmpt cco:taxonomy . ?dbXref a cco:UniprotRef

# query for pathways by those protein targets SERVICE <http://www.ebi.ac.uk/rdf/services/reactome/sparql> { ?protein rdf:type biopax3:Protein . ?protein biopax3:memberPhysicalEntity [biopax3:entityReference ?dbXref] . ?pathway biopax3:displayName ?pathwayname . ?pathway biopax3:pathwayComponent ?reaction . ?reaction ?rel ?protein . }

# get Atlas experiment plus experimental factor where protein is expressed SERVICE <http://www.ebi.ac.uk/rdf/services/atlas/sparql> { ?probe atlasterms:dbXref ?dbXref . ?value atlasterms:isMeasurementOf ?probe . ?value atlasterms:hasFactorValue ?factor . ?value rdfs:label ?factorLabel . }}


Why use Graph algorithms

Polynomial timeA lot of queries on linked data can be expressed as well known graph traversal algo. that work in P time such as Dijkstra for positive weighted directed graph. Because these alog are localised suit distributed computed.


More pure Dijkstra using CypherMATCH (from: Location {LocationName:"x"}), (to: Location

{LocationName:"y"}) , paths = allShortestPaths((from)-[:CONNECTED_TO*]->(to))WITH REDUCE(dist = 0, rel in rels(paths) | dist +

rel.distance) AS distance, pathsRETURN paths, distance ORDER BY distance LIMIT 1

The other approach used an inbuilt function - this shows a closer approximation of the actual algorithm


Dijkstra Psudocodedist[s]←0 (distance to

source vertex is zero)forall v V–{s}∈do dist[v]←∞ (set all other

distances to infinity)S← ∅ (S,the set of visited vert

is initially empty)Q←V (Q,the queue

initially contains all vertices)whileQ≠ ∅ (while the queue

is not empty)dou← mindistance(Q,dist) (select the element of Q with the

min.distance)S←S {u} ∪ (add u to list of

visited vertices)forall v neighbors[u]∈

do if dist[v]>dist[u]+w(u,v) (if new shortest path found)then d[v]←d[u]+w(u,v) (set new value of shortest path)

(if desired,add trace back code)returndist


Thank youQ & A


VisualizationAli Hasnain, Naoise Dunne, Dietrich Rebholz-

Schuhmann


Visualization

• Visualize your Data!• Visualize to easily Query your Data• Example Tools

• ReVeaLD• FedViz


ReVeaLD Search PlatformReVeaLD :- Real-Time Visual Explorer and Aggregator of Linked Data, is a user-driven domain-specific search platform.

Intuitively formulate advanced search queries using a click-input-select mechanism

Visualize the results in a domain–suitable format.

Assembly of the query is governed by a Domain Specific Language (DSL), which in this case is the Granatum Biomedical Semantic Model (CanCO)


ReVeaLD Search PlatformAvailability:http://140.203.155.7:31005/explorer

Demo: https://www.youtube.com/watch?v=6HHK4ASIkJM&hd=1

http://140.203.155.7:31005/explorer

https://www.youtube.com/watch?v=6HHK4ASIkJM&hd=1


DSL Visual RepresentationConcept Map Visualization is used.


Visual Query Builder


Visual Query Model


FedViz: A Visual Interface for SPARQL Queries Formulation and Execution

FedViz is an online application that provides Biologist a flexible visual interface to formulate and execute both federated and non-federated SPARQL queries.

It translates the visually assembled queries into SPARQL equivalent and execute using query engine (FedX).

Availability: http://srvgal86.deri.ie/FedViz/index.html

http://srvgal86.deri.ie/FedViz/index.html


FedViz: A Visual Interface for SPARQL Queries Formulation and Execution


Using FedViz: Step by Step


Hands On SessionNaoise Dunne, Ali Hasnain


Hands on sessionThis tutorial is available from

https://goo.gl/JjfJ0f

https://goo.gl/JjfJ0f

processing life science data at scale - using semantic web technologies

Software