Scaling up Linked Data

Scaling up Linked Data
Presented by:
Marin Dimitrov (Ontotext)

EUCLID Objective
2
Visualization
Module
Metadata
Streaming providers
Physical Wrapper
Downloads
Dataacquisition
R2R Transf.LD Wrapper
Musical Content
Application
Analysis &
Mining Module
LDDatasetAccess
LD Wrapper
RDF/
XML
Integrated
Dataset
Interlinking Cleansing
Vocabulary
Mapping
SPARQL
Endpoint
Publishing
RDFa
Other content
EUCLID – Scaling up Linked Data

• Our aim: build a music-based portal using Linked
Data technologies
• So far, we have studied different mechanisms for:
• Linked Data management via SPARQL queries
• Reasoning over Linked Data
• Linked Data access (RDF dumps, endpoints, RDFa)
• Linked Data storage in repositories
• In this chapter, we will study current research and
technologies to scale up to very large volumes of
Linked Data
Motivation: Music!
EUCLID – Scaling up Linked Data 3
CH 2
CH 3
CH 1
CH 5

Agenda
1. Introduction to Big (Linked) Data
2. NoSQL databases for Linked Data
3. Hadoop for Linked Data
4. Stream processing for Linked Data
5. … and more
4EUCLID – Scaling up Linked Data

INTRODUCTIONTO BIG (LINKED)
DATA

Introduction to Big Data
6
Big
Data
Management of data which is “too
complex” for being processed with
traditional solutions
• Big does not stand primarily for size,
but as an analogy for “overwhelming”
• Big can mean “high variety”, “high
volume” or “high velocity”

The 3Vs of Big Data
7
Big
Data
Variety
Velocity
Volume
Different forms of data
Petabytes of data
Real-time data streams
Big
Data

Variety Volume Velocity
Data
characteristic
Structured, semi-
structured and
unstructured
Large volumes of
data
Streams, sensors,
near real-time
data, IoT
Challenge Data integration Reasoning and
querying
Reasoning &
querying
Solution Semantic
technologies are
a good fit
Distributed
storage &
processing,
parallel
processing
Stream reasoning
& querying
The 3Vs of Big Data
8
time

The ExtendedVs of Big Data
9
• Veracity: Uncertainty of the data
• Variability: Variation in meaning in different contexts
• Value: turning data into information into insight
• Not easy measure
• Depend on context and intended use
• Linked Data & Semantic Technologies can help
Variety VelocityVolume

Beyond Big Data

11
Source: Gartner Inc. “Gartner Identifies Top Technology Trends Impacting Information
Infrastructure in 2013”
Semantic Technologies
Semantic technologies extract meaning from data, ranging from quantitative
data and text, to video, voice and images. Many of these techniques have
existed for years and are based on advanced statistics, data mining, machine
learning and knowledge management. One reason they are garnering more
interest is the renewed business requirement for monetizing information as a
strategic asset. Even more pressing is the technical need. Increasing volumes,
variety and velocity — big data — in IM and business operations, requires
semantic technology that makes sense out of data for humans, or
automates decisions
Beyond Big Data (2)

Towards Big Linked Data
12
• This characteristic is the most inherent to Linked Data
• Agile data model
• Different vocabularies
Variety
Velocity
Volume
2007 2008 2009 2010 2011
• RDF Streams
• Semantic Sensors

Towards Big Linked Data (2)

Big Linked Data &
Linked Big Data
14
• Exponential growth of Linked
Data in the last five years
• Big Data approach adopted by
the Linked Data community,
especially to handle
Source: M. Dimitrov. “Semantic Technologies for Big Data”
VelocityVolume
Big Linked Data Linked Big Data
• Linked Data approach
adopted by the Big Data
community
• RDF data model for
• Enrich Big Data with metadata
and semantics
• Interlink Big Data sets &
reduce duplication
• Simplify data access,
discovery & integration
Variety

NOSQL DATABASES FOR
LINKED DATA

RDF Databases
16
• Native or RDBMS based RDF databases
– OWLIM (http://www.ontotext.com/owlim)
– Virtuoso Universal Server (http://virtuoso.openlinksw.com/ )
– Stardog (http://stardog.com)
– AllegroGraph (http://www.franz.com/agraph/allegrograph/ )
– Systap Bigdata (http://www.systap.com/)
– Jena TDB (http://jena.apache.org/documentation/tdb/)
– Oracle, DB2

RDF Database Advantages
17
• RDF (graph) based data model
– Global identifies of resources/entities
– Agile schema
• Inference of implicit facts
– Forward, backward, hybrid reasoning strategy
• Expressive query language (SPARQL)
• Compliance to standards

NoSQL Databases
18
• “Not Only SQL”
• a group of databases technologies which don’t
follow the relational data model
• Typical requirements
– Distributed
– High availability
– Handle big data & query volumes (scalability)
– Hierarchical or graph data structures
– Flexible schema

NoSQLTaxonomy
19
• Key/value stores
– Each key associated with a value (DHT)
• Wide-column stores
– Each key is associated with many attributes,
columns are stored together
• Document databases
– Each key associated with a complex data
structure
• Graph databases
– Data is represented as nodes and edges
ValueKey
Data Data
Relationship
Structured-
document
Key
Structured-
document
Key
Conceptual structures
Artist Album Song
The
Beatles
Let it be Get back
Queen Jazz Fun it

Key/Value Stores
20
• Efficient key/value lookups
• Schema-less
• Simpler read/write operations
– Low latency & high throughput
• Examples
– DynamoDB, Azure Table Storage, Riak, Redis, MemcacheDB,
Voldemort
ValueKey

Wide-Column Stores
21
• A key is associated with several attributes
• Data in the same column is stored together
• Efficient for complex aggregations over data
• Schema-less / dynamic schema
• Easy to add new columns
• Columns can be grouped together (column family)
• Examples:
– HBase (http://hbase.apache.org)
– Cassandra (http://cassandra.apache.org)
Artist Album Song
The
Beatles
Let it be Get back
Queen Jazz Fun it

HBase
22
• Open source column-oriented store
• Based on Google’s BigTable
• Built on top of HDFS and Hadoop
• Horizontally scalable, automatic sharding
• high availability / automatic failover
• Strongly consistent reads/writes
• Java/REST API

Document Databases
23
• Each key associated with a complex data structure
(document)
• Documents can contain key/value pairs, key/array
pairs, or even nested structures
• Schema-less / dynamic schema
– New fields can be easily added to the document structure
• Typical document formats
– JSON, XML
• Examples:
– Couchbase (http://www.couchbase.com)
– MongoDB (http://www.mongodb.org)
Structured-
document
Key
Structured-
document
Key

Document Databases (2)
24
Example:
{
Homepage: "thebeatles.com",
Origin: "Liverpool",
Albums: [
{Title: "Let it be", Year: "1970", Duration: "35:16"},
{Title: "Help!", Year: "1965"},
{Title: "Revolver", Year: "1966", Duration: "35:01"}
]
}
The Beatles
{
FullName: "Elvis Aaron Presley",
Homepage: "elvis.com",
Origin: "Memphis"
Albums: [
{Title: "Blue Hawaii", Year: "1961", Duration:
"32:02"}
]
}
Elvis Presley

Couchbase
25
• Document-oriented database
– Documents are stored as JSON
• Flexible schema
– Document structure easy to change
• Optimised to run in-memory and on several
nodes
– Ejection and eventual persistence
• Incremental views & indexes
• Scalability, rebalancing, replication, failover
• RESTful API

Network of Friends in a High School
26
Graph Databases
Motivation
Relationship among artists in Last.fm
http://sixdegrees.hu/last.fm/
A Fragment of Facebook Relationships between Tweets
Graphs: Representation of highly connected data

Graph Databases
27
• Based on the property graph model
• Support for query languages and core graph-based
tasks
– reachability, traversal, adjacency and pattern matching
• Examples
– Neo4j (http://neo4j.org)
– Dex (http://sparsity-technologies.com/dex.php)
– HyperGraphDB (http://www.hypergraphdb.org)
Data Data
Relationship

Graph Databases
28
Example: Property Graph Model
• Nodes and edges may have properties
• Properties: Key-value pairs
The Beatles
Let it be
Revolver
Help!
created
Year: 1970
Duration: 35:16
Year: 1965
Year: 1966
Duration: 35:01
Homepage:
thebeatles.com
Origin: Liverpool
Elvis Presley Revolver
created
Year: 1961
Duration: 32:02
Fullname: Elvis Aaron
Presley
Homepage: elvis.com
Origin: Memphis

Neo4j
29
• Graph database
– Nodes, Relationships, Properties, Paths
– Indexes over properties
• Flexible schema
• Cypher graph query language
• ACID transactions
• High availability, distributed clusters
• RESTful and Java APIs

Rya
30
• RDF store based on Accumulo
– Column-store, HDFS
– Sesame query parser, SAIL
implementation
• 3 table index
– SPO, POS, OSP
– Sufficient for all triple patterns
– All triple parts (S, P, O) encoded in
the RowID
– Clustered index
Source: R. Punnoose, A. Crainiceanu, D. Rapp “Rya: A Scalable RDF Triple Store for the Clouds”

Rya (2)
31
• Query processing
– Sesame (SPARQL) query plan translated to Accumulo range
scans & lookups
– Parallel scans for joins (x10-20 speedup)
– Batch scans (Accumulo) to reduce number of range scans
– Statistics for triple patterns selectivity, query re-ordering
• Performance evaluation (LUBM)
– No significant degradation when data grows with 2-3 orders
of magnitude
Source: R. Punnoose, A. Crainiceanu, D. Rapp “Rya: A Scalable RDF Triple Store for the Clouds”

“NoSQL Databases f0r RDF: An
Empirical Evaluation”
32
• Goal
– Store RDF data in HBase, Couchbase, Hive & Cassandra
– Benchmark query performance against a native
distributed RDF database (4store)
• HBase prototype
– Jena for SPARQL queries
– 3 index tables (SPO, POS, OSP)
– Row key encodes S+P+O, cells are empty
– Jena query plan translated to HBase filters & lookups
Source: Cudre-Mauroux et al. “NoSQL Databases for RDF: An Empirical Evaluation”

Empirical Evaluation” (2)
33
• Hive+HBase prototype
– SPARQL to HiveQL translation
– Property table
• Row key is S
• a column for each P
• cell value stores O
• Multi-valued attributes have different timestamps

34
• CumulusRDF prototype
– Sesame for SPARQL queries, Cassandra for data management
– 3 index tables (SPO, POS, OSP)
– Sesame query plan translated to Cassandra index lookups
• Couchbase prototype
– Map RDF into JSON documents
• all triples with the same S stored in the same document (molecule)
• 2 JSON arrays for Ps and Os
– Jena as a SPARQL query engine
– 3 indexes (Couchbase views): SPO, POS, OSP

35
• Benchmarks
– BSBM 10M, 100M
and 1B triples
– 1, 2, 4, 8, 16 node
cluster
– AWS cost & query
execution time

36
• Results
– Simple SPARQL queries can be executed more
efficiently on a NoSQL datastore
– Data loading time for some NoSQL datastores
comparable or better than the native RDF store
– Complex SPARQL queries perform significantly slower
on NoSQL systems
• Query optimisations are required
– MapReduce operations (Hive & Couchbase) introduce
high latency for view maintenance / query execution

HADOOP FOR LINKED DATA

• Apache Hadoop (http://hadoop.apache.org) is an open source
implementation of MapReduce
• MapReduce
– Distributed batch processing
– Map phase partitions the input set (K/V pairs), Reduce phase performs
aggregated processing over the partitions in parallel
– Shuffle intermediate results (from Map nodes to Reduce nodes)
• Allows for the processing of distributed large data sets across
clusters of computers
– On a distributed file system (HDFS)
– Scales up to thousands of nodes, each offering local processing power
and storage
38
Working with Distributed Data

“Scalable Distributed Reasoning
with MapReduce”
39
• Goal
– Utilise Hadoop for large scale reasoning
• Approach
– Implement each RDFS rule (join) via a Map & Reduce function
– Map outputs original triple as value, and the join term as key
– Reducer receives all needed triples to perform the join
Source: Urbani et al. “Scalable Distributed Reasoning with MapReduce”

with MapReduce” (2)

41
• Challenge
– Too many duplicates (unique to derived
triple ratio of 1:50)
• Optimisations
– Replicate schema triples on each mode
(in memory)
• Needed for each join; usually a small set
– Rule re-ordering
• Which rule may be triggered by another
rule?
• Reduce the number of required iterations

42
• Results
– Throughput of 4.5M triples / sec on a 16-node cluster
– 16+ nodes do not improve the performance
significantly

Lessons Learned from Large-
scale Reasoning (J. Urbani)
43
• 1st Law: Treat schema triples differently
– Replicate on all nodes to minimise subsequent data transfer
• 2nd Law: Data skew dominates data distribution
– No universal partitioning scheme for input data
– Computation tasks moved to the nodes storing the data
(data locality)
• 3rd Law: Certain problems only appear at a very large
scale
– Proof-of-concept prototypes are often not representative
Source: Jacopo Urbani “Three Laws Learned from Web-scale Reasoning”

STREAM PROCESSING FOR
LINKED DATA

Streaming Data
• A large amount of new data is constantly being created or
data is being updated at a rapid rate
– Traffic data, sensor networks, social networks, financial markets
• Many data sources create a constant “stream of information”
– Not always practical to store all data and then query it
– Continuous queries over transient data
• More recent data is more important
– Describes the current state of a dynamic system
46
time

Stream Processing
• Streams are observed through windows
• Continuous queries can be registered over the stream
• Continuous queries are iteratively evaluated over the data in the
current window
– Can leverage static background knowledge (e.g., schema information)
• Generates a stream of answers
47
Window
Stream of answers
Background
Knowledge
time
Continuous
Query

Linked Stream Data
48
• A representation of sensor/stream data following the
Linked Data principles
– Sensor data can be enriched with semantics
– Facilitates data discovery and integration of heterogeneous data
sources
• Challenges
– RDF Triples must be annotated with timestamps
– Extensions to the SPARQL language – windows, continuous queries,
streaming operators
– Continuous semantics
– Scalability (Volume)
– High throughput and low latency (Velocity)
– Approximate reasoning

Querying Streams with
SPARQL Extensions
49
• The mechanism to evaluate queries over streaming data is the
specification of continuous queries
• The corresponding results to the continuous query are
updated while new data arrives
• Several SPARQL extensions with streaming operators based on
CQL (Continuous Query Language)
– C-SPARQL
– SPARQLStream
– EP-SPARQL, CQELS, Instants

C-SPARQL (1)
50
C-SPARQL is an extension of SPARQL 1.1
FromStrClause  'FROM' ['NAMED'] 'STREAM' StreamIRI
' [ RANGE' Window ']'
Window  LogicalWindow | PhysicalWindow
LogicalWindow  Number TimeUnit WindowOverlap
TimeUnit  'MSEC' | 'SEC' | 'MIN' | 'HOUR' |
'DAY'
WindowOverlap  'STEP' Number TimeUnit | 'TUMBLING'
PhysicalWindow  'TRIPLES' Number
1. RDF Streams: Sequence of RDF triples annotated with timestamps:
<(s,p,o), timestamp>
2. FROM STREAM extension for stream sources and windows

C-SPARQL (2)
51
3. Registration
• Creates a continuous query over the data source
• The query output is variable bindings, RDF graph, or a
new stream
Registration  'REGISTER' ('QUERY'|'STREAM') QName 'AS' Query

C-SPARQL (3)
52
Example
REGISTER QUERY CarsEnteringInDistricts AS
SELECT DISTINCT ?district ?car
FROM STREAM <www.uc.eu/tollgates.trdf> [RANGE 40 SEC STEP 10 SEC]
WHERE {
?toll t:registers ?car .
?toll c:placedIn ?street .
?district c:contains ?street . }
Query: Retrieve the cars and districts, where the car was registered in a toll.
Source: Barbieri, Davide Francesco, et al. "Querying rdf streams with c-sparql." ACM SIGMOD
Record 39.1 (2010): 20-26.

C-SPARQL (4)
Source: M. Balduini et al. “Tutorial on Stream Reasoning for Linked Data (ISWC’2013)”

SPARQLStream(1)
54
• Utilizes the same definition of RDF streams as in C-SPARQL:
• The language is defined as follows:
<(s,p,o), timestamp>
NamedStream  'FROM' ['NAMED'] 'STREAM' StreamIRI ' [' Window ']'
Window  'NOW-' Integer TimeUnit [UpperBound] [Slide]
UpperBound  'TO NOW-' Integer TimeUnit
Slide  'SLIDE' Integer TimeUnit
TimeUnit  'MS' | 'S' | 'MINUTES' | 'HOURS' | 'DAY'
Select  'SELECT' [XStream] [DISTINCT | REDUCED] …
Xstream  'ISTREAM' | 'DSTREAM' | 'RSTREAM'
Source: Jean-Paul Calbimonte and Oscar Corcho. ”SPARQLStream: Ontology-based access to data
streams." Tutorial at ISWC 2013

SPARQLStream(2)
55
Example
Query: Retrieve a rstream with the observations captured by all sensors in the last
10 minutes.
PREFIX ssn: <http://purl.oclc.org/NET/ssnx/ssn>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns/#>
SELECT RSTREAM ?sensor ?observation
FROM STREAM <www.semsorgrid4env.eu/SensorReadings.srdf>
[FROM NOW – 10 MINUTES TO NOW STEP 1 MINUTE]
WHERE {
?observation a ssn:Observation;
ssn:observedBy ?sensor .
}

Classification of Existing
Systems
Source: M. Balduini et al. “Tutorial on Stream Reasoning for Linked Data (ISWC’2013)”

W3C Semantic Sensor Networks
57
• SSN Ontology
– http://www.w3.org/2005/Incubator/ssn/ssnx/ssn
– OWL DL ontology
– used to semantically describe sensors and sensor networks & data
– Recommendations for applying the ontology for Linked Sensor Data

W3C Semantic Sensor Networks
(2)
58
• Different perspectives
– Sensor, data/observation, system

… AND MORE

ATrillion RDFTriples
60
• Use case
– Use RDF and Linked Data for the customer management
database of a big telecom
– Franz Inc / AllegroGraph

uRiKA Appliance
61
• YarcData
• Big Data appliance for graph
analytics
– 8K processors, 1TB RAM
– In-memory RDF database
– SPARQL 1.1 support

RDFS Reasoning on GPUs
62
• Similar approach to Urbani et al. for large scale
reasoning with Hadoop
– Handle rules with 2 antecedents
– Rule reordering
– Dictionary encoding
• Shared-memory architecture
– Efficient GPU algorithm implementation is challenging
Source: Norman Heino & Jeff Z. Pan ”RDFS Reasoning on Massively Parallel Hardware" ISWC 2012

RDFS Reasoning on GPUs (2)
63
• Data parallelism
– Apply one rule (thread) on one instance triple, join to a schema triple
if possible
– Hundreds / thousands of threads working on parallel
• Challenge
– Duplicate removal
• Benchmark
– x5 speedup of computation
– But… memory transfer overhead is significant
Source: Norman Heino & Jeff Z. Pan ”RDFS Reasoning on Massively Parallel Hardware" ISWC 2012

Benchmarks
64
• BSBM v3.1 (April 2013)
– http://wifo5-03.informatik.uni-
mannheim.de/bizer/berlinsparqlbenchmark/results/V7/
– Includes benchmarks with up to 150 billion triples
– x750 scale increase since the last BSBM result (200M triples)
• LDBC
– Industry neutral, non-profit organisation
– Benchmarks for RDF and graph databases, similar to TPC
– Big data volume, complex queries

SUMMARY

Summary
66
• Linked Data is a good fit for the Variety
challenge of Big Data
• Linked Data can simplify data discovery, data
access, data integration challenges for Big Data
• Exponential growth of Linked Data
• Linked Data benchmarks target bigger
workloads

Summary (2)
67
• Ongoing R&D towards scaling up Linked Data
for high data Volume and Velocity
– NoSQL datastores for RDF data management
– Hadoop for scalable RDF reasoning
– GPUs for scalable RDF reasoning
• Adapting Linked Data & SPARQL for streaming
data scenarios

For exercises, quiz and further material visit our website:
68
@euclid_project euclidproject euclidproject
http://www.euclid-project.eu
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