0
Scaling up Linked Data
Presented by:
Marin Dimitrov (Ontotext)
Analysis &
Mining Module

Visualization
Module

RDFa

Data acquisition

LD Dataset

Access

Application

EUCLID Objective
...
Motivation: Music!
• Our aim: build a music-based portal using Linked
CH 1
Data technologies
• So far, we have studied dif...
Agenda
1. Introduction to Big (Linked) Data
2. NoSQL databases for Linked Data
3. Hadoop for Linked Data
4. Stream process...
INTRODUCTION TO BIG (LINKED)
DATA
EUCLID – Scaling up Linked Data

5
Introduction to Big Data

Big
Data

Management of data which is “too
complex” for being processed with
traditional solutio...
The 3 Vs of Big Data
Variety

Big
Big
Data
Data

Different forms of data

Volume

Petabytes of data

Velocity

Real-time d...
The 3 Vs of Big Data
Variety

Volume

Velocity

time

Data
characteristic

Structured, semi- Large volumes of Streams, sen...
The Extended Vs of Big Data
Variety

Volume

Velocity

• Veracity: Uncertainty of the data
• Variability: Variation in mea...
Beyond Big Data

EUCLID – Scaling up Linked Data

10
Beyond Big Data (2)

Semantic Technologies
Semantic technologies extract meaning from data, ranging from quantitative
data...
Towards Big Linked Data
• This characteristic is the most inherent to Linked Data

Variety

• Agile data model
• Different...
Towards Big Linked Data (2)

EUCLID – Scaling up Linked Data

13
Big Linked Data &
Linked Big Data
Big Linked Data

Linked Big Data

• Exponential growth of Linked
Data in the last five y...
NOSQL DATABASES FOR
LINKED DATA
EUCLID – Scaling up Linked Data

15
RDF Databases
• Native or RDBMS based RDF databases
– OWLIM (http://www.ontotext.com/owlim)
– Virtuoso Universal Server (h...
RDF Database Advantages
• RDF (graph) based data model
– Global identifies of resources/entities

– Agile schema

• Infere...
NoSQL Databases
• “Not Only SQL”
• a group of databases technologies which don’t
follow the relational data model

• Typic...
NoSQL Taxonomy
Conceptual structures

• Key/value stores

Value

Key

– Each key associated with a value (DHT)

• Wide-col...
Key/Value Stores
• Efficient key/value lookups

Key

Value

• Schema-less

• Simpler read/write operations
– Low latency &...
Wide-Column Stores
•
•
•
•
•
•
•

A key is associated with several attributes
Data in the same column is stored together
E...
HBase
•
•
•
•
•
•
•

Open source column-oriented store
Based on Google’s BigTable
Built on top of HDFS and Hadoop
Horizont...
Document Databases
• Each key associated with a complex data structure
(document)
• Documents can contain key/value pairs,...
Document Databases (2)
Example:
{
Homepage: "thebeatles.com",
Origin: "Liverpool",
Albums: [
{Title: "Let it be", Year: "1...
Couchbase
• Document-oriented database
– Documents are stored as JSON

• Flexible schema
– Document structure easy to chan...
Graph Databases
Motivation
Graphs: Representation of highly connected data

Network of Friends in a High School

Relations...
Graph Databases
• Based on the property graph model
• Support for query languages and core graph-based
tasks
– reachabilit...
Graph Databases
Example: Property Graph Model
Year: 1970
Duration: 35:16

Let it be
Homepage:
thebeatles.com
Origin: Liver...
Neo4j
• Graph database
– Nodes, Relationships, Properties, Paths
– Indexes over properties

•
•
•
•
•

Flexible schema
Cyp...
Rya
• RDF store based on Accumulo
– Column-store, HDFS
– Sesame query parser, SAIL
implementation

• 3 table index
– SPO, ...
Rya (2)
• Query processing
– Sesame (SPARQL) query plan translated to Accumulo range
scans & lookups

– Parallel scans for...
“NoSQL Databases f0r RDF: An
Empirical Evaluation”
• Goal
– Store RDF data in HBase, Couchbase, Hive & Cassandra
– Benchma...
“NoSQL Databases f0r RDF: An
Empirical Evaluation” (2)
• Hive+HBase prototype
– SPARQL to HiveQL translation

– Property t...
“NoSQL Databases f0r RDF: An
Empirical Evaluation” (3)
• CumulusRDF prototype
– Sesame for SPARQL queries, Cassandra for d...
“NoSQL Databases f0r RDF: An
Empirical Evaluation” (4)
• Benchmarks
– BSBM 10M, 100M
and 1B triples
– 1, 2, 4, 8, 16 node
...
“NoSQL Databases f0r RDF: An
Empirical Evaluation” (5)
• Results
– Simple SPARQL queries can be executed more
efficiently ...
HADOOP FOR LINKED DATA

EUCLID – Scaling up Linked Data

37
Working with Distributed Data
• Apache Hadoop (http://hadoop.apache.org) is an open source
implementation of MapReduce
• M...
“Scalable Distributed Reasoning
with MapReduce”
• Goal
– Utilise Hadoop for large scale reasoning

• Approach
– Implement ...
“Scalable Distributed Reasoning
with MapReduce” (2)

Source: Urbani et al. “Scalable Distributed Reasoning with MapReduce”...
“Scalable Distributed Reasoning
with MapReduce” (3)
• Challenge
– Too many duplicates (unique to derived
triple ratio of 1...
“Scalable Distributed Reasoning
with MapReduce” (4)
• Results
– Throughput of 4.5M triples / sec on a 16-node cluster
– 16...
Lessons Learned from Largescale Reasoning (J. Urbani)
• 1st Law: Treat schema triples differently
– Replicate on all nodes...
STREAM PROCESSING FOR
LINKED DATA
EUCLID – Scaling up Linked Data

45
Streaming Data
• A large amount of new data is constantly being created or
data is being updated at a rapid rate
– Traffic...
Stream Processing
• Streams are observed through windows
• Continuous queries can be registered over the stream
• Continuo...
Linked Stream Data
• A representation of sensor/stream data following the
Linked Data principles
– Sensor data can be enri...
Querying Streams with
SPARQL Extensions
• The mechanism to evaluate queries over streaming data is the
specification of co...
C-SPARQL (1)
C-SPARQL is an extension of SPARQL 1.1
1. RDF Streams: Sequence of RDF triples annotated with timestamps:
<(s...
C-SPARQL (2)
3. Registration
• Creates a continuous query over the data source
• The query output is variable bindings, RD...
C-SPARQL (3)
Example
Query:

Retrieve the cars and districts, where the car was registered in a toll.

REGISTER QUERY Cars...
C-SPARQL (4)

Source: M. Balduini et al. “Tutorial on Stream Reasoning for Linked Data (ISWC’2013)”
EUCLID – Scaling up Li...
SPARQLStream (1)
• Utilizes the same definition of RDF streams as in C-SPARQL:
<(s,p,o), timestamp>

• The language is def...
SPARQLStream (2)
Example
Query:

Retrieve a rstream with the observations captured by all sensors in the last
10 minutes.
...
Classification of Existing
Systems

Source: M. Balduini et al. “Tutorial on Stream Reasoning for Linked Data (ISWC’2013)”
...
W3C Semantic Sensor Networks
• SSN Ontology
–
–
–
–

http://www.w3.org/2005/Incubator/ssn/ssnx/ssn
OWL DL ontology
used to...
W3C Semantic Sensor Networks
(2)
• Different perspectives
– Sensor, data/observation, system

EUCLID – Scaling up Linked D...
… AND MORE

EUCLID – Scaling up Linked Data

59
A Trillion RDF Triples
• Use case
– Use RDF and Linked Data for the customer management
database of a big telecom

– Franz...
uRiKA Appliance
• YarcData
• Big Data appliance for graph
analytics
– 8K processors, 1TB RAM

– In-memory RDF database
– S...
RDFS Reasoning on GPUs
• Similar approach to Urbani et al. for large scale
reasoning with Hadoop
– Handle rules with 2 ant...
RDFS Reasoning on GPUs (2)
• Data parallelism
– Apply one rule (thread) on one instance triple, join to a schema triple
if...
Benchmarks
• BSBM v3.1 (April 2013)
– http://wifo5-03.informatik.unimannheim.de/bizer/berlinsparqlbenchmark/results/V7/
– ...
SUMMARY

EUCLID – Scaling up Linked Data

65
Summary
• Linked Data is a good fit for the Variety
challenge of Big Data
• Linked Data can simplify data discovery, data
...
Summary (2)
• Ongoing R&D towards scaling up Linked Data
for high data Volume and Velocity
– NoSQL datastores for RDF data...
For exercises, quiz and further material visit our website:

http://www.euclid-project.eu

Course

eBook

Other channels:
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Scaling up Linked Data

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This presentation addresses the main issues of Linked Data and scalability. In particular, it provides gives details on approaches and technologies for clustering, distributing, sharing, and caching data. Furthermore, it addresses the means for publishing data trough could deployment and the relationship between Big Data and Linked Data, exploring how some of the solutions can be transferred in the context of Linked Data.

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Transcript of "Scaling up Linked Data"

  1. 1. Scaling up Linked Data Presented by: Marin Dimitrov (Ontotext)
  2. 2. Analysis & Mining Module Visualization Module RDFa Data acquisition LD Dataset Access Application EUCLID Objective SPARQL Endpoint Publishing Vocabulary Mapping Interlinking LD Wrapper Physical Wrapper Integrated Dataset Cleansing R2R Transf. LD Wrapper RDF/ XML Streaming providers Downloads Musical Content Metadata EUCLID – Scaling up Linked Data Other content 2
  3. 3. Motivation: Music! • Our aim: build a music-based portal using Linked CH 1 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 CH 5 CH 2 CH 3 • In this chapter, we will study current research and technologies to scale up to very large volumes of Linked Data EUCLID – Scaling up Linked Data 3
  4. 4. 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 EUCLID – Scaling up Linked Data 4
  5. 5. INTRODUCTION TO BIG (LINKED) DATA EUCLID – Scaling up Linked Data 5
  6. 6. Introduction to Big Data 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” EUCLID – Scaling up Linked Data 6
  7. 7. The 3 Vs of Big Data Variety Big Big Data Data Different forms of data Volume Petabytes of data Velocity Real-time data streams EUCLID – Scaling up Linked Data 7
  8. 8. The 3 Vs of Big Data Variety Volume Velocity time Data characteristic Structured, semi- Large volumes of Streams, sensors, structured and data near real-time unstructured 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 EUCLID – Scaling up Linked Data 8
  9. 9. The Extended Vs of Big Data Variety Volume Velocity • 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 EUCLID – Scaling up Linked Data 9
  10. 10. Beyond Big Data EUCLID – Scaling up Linked Data 10
  11. 11. Beyond Big Data (2) 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 Source: Gartner Inc. “Gartner Identifies Top Technology Trends Impacting Information Infrastructure in 2013” EUCLID – Scaling up Linked Data 11
  12. 12. Towards Big Linked Data • This characteristic is the most inherent to Linked Data Variety • Agile data model • Different vocabularies Volume 2007 Velocity 2008 2009 2010 2011 • RDF Streams • Semantic Sensors EUCLID – Scaling up Linked Data 12
  13. 13. Towards Big Linked Data (2) EUCLID – Scaling up Linked Data 13
  14. 14. Big Linked Data & Linked Big Data Big Linked Data Linked Big Data • Exponential growth of Linked Data in the last five years • Big Data approach adopted by the Linked Data community, especially to handle Volume Velocity • Linked Data approach adopted by the Big Data community • RDF data model for Variety • Enrich Big Data with metadata and semantics • Interlink Big Data sets & reduce duplication • Simplify data access, discovery & integration Source: M. Dimitrov. “Semantic Technologies for Big Data” EUCLID – Scaling up Linked Data 14
  15. 15. NOSQL DATABASES FOR LINKED DATA EUCLID – Scaling up Linked Data 15
  16. 16. RDF Databases • 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 EUCLID – Scaling up Linked Data 16
  17. 17. RDF Database Advantages • 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 EUCLID – Scaling up Linked Data 17
  18. 18. NoSQL Databases • “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 EUCLID – Scaling up Linked Data 18
  19. 19. NoSQL Taxonomy Conceptual structures • Key/value stores Value Key – Each key associated with a value (DHT) • Wide-column stores Artist – Each key is associated with many attributes, columns are stored together Album Song The Beatles Let it be Get back Queen Jazz Fun it • Document databases – Each key associated with a complex data structure Key Structureddocument Key Structureddocument • Graph databases – Data is represented as nodes and edges Data EUCLID – Scaling up Linked Data Relationship Data 19
  20. 20. Key/Value Stores • Efficient key/value lookups Key Value • Schema-less • Simpler read/write operations – Low latency & high throughput • Examples – DynamoDB, Azure Table Storage, Riak, Redis, MemcacheDB, Voldemort EUCLID – Scaling up Linked Data 20
  21. 21. Wide-Column Stores • • • • • • • A key is associated with several attributes Data in the same column is stored together Efficient for complex aggregations over data Artist Album Song Schema-less / dynamic schema The Let it be Get back Beatles Easy to add new columns Queen Jazz Fun it Columns can be grouped together (column family) Examples: – HBase (http://hbase.apache.org) – Cassandra (http://cassandra.apache.org) EUCLID – Scaling up Linked Data 21
  22. 22. HBase • • • • • • • 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 EUCLID – Scaling up Linked Data 22
  23. 23. Document Databases • 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: Key – Couchbase (http://www.couchbase.com) – MongoDB (http://www.mongodb.org) EUCLID – Scaling up Linked Data Structureddocument Key Structureddocument 23
  24. 24. Document Databases (2) 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 } { Elvis Presley FullName: "Elvis Aaron Presley", Homepage: "elvis.com", Origin: "Memphis" Albums: [ {Title: "Blue Hawaii", Year: "1961", Duration: "32:02"} ] } EUCLID – Scaling up Linked Data 24
  25. 25. Couchbase • 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 EUCLID – Scaling up Linked Data 25
  26. 26. Graph Databases Motivation Graphs: Representation of highly connected data Network of Friends in a High School Relationship among artists in Last.fm http://sixdegrees.hu/last.fm/ A Fragment of Facebook EUCLID – Scaling up Linked Data Relationships between Tweets 26
  27. 27. Graph Databases • Based on the property graph model • Support for query languages and core graph-based tasks – reachability, traversal, adjacency and pattern matching • Examples Relationship Data – Neo4j (http://neo4j.org) – Dex (http://sparsity-technologies.com/dex.php) – HyperGraphDB (http://www.hypergraphdb.org) EUCLID – Scaling up Linked Data Data 27
  28. 28. Graph Databases Example: Property Graph Model Year: 1970 Duration: 35:16 Let it be Homepage: thebeatles.com Origin: Liverpool The Beatles created Year: 1961 Duration: 32:02 Year: 1965 Elvis Presley created Revolver Revolver Year: 1966 Duration: 35:01 Fullname: Elvis Aaron Presley Homepage: elvis.com Origin: Memphis Help! • Nodes and edges may have properties • Properties: Key-value pairs EUCLID – Scaling up Linked Data 28
  29. 29. Neo4j • 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 EUCLID – Scaling up Linked Data 29
  30. 30. Rya • 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” EUCLID – Scaling up Linked Data 30
  31. 31. Rya (2) • 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” EUCLID – Scaling up Linked Data 31
  32. 32. “NoSQL Databases f0r RDF: An Empirical Evaluation” • 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” EUCLID – Scaling up Linked Data 32
  33. 33. “NoSQL Databases f0r RDF: An Empirical Evaluation” (2) • 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 Source: Cudre-Mauroux et al. “NoSQL Databases for RDF: An Empirical Evaluation” EUCLID – Scaling up Linked Data 33
  34. 34. “NoSQL Databases f0r RDF: An Empirical Evaluation” (3) • 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 Source: Cudre-Mauroux et al. “NoSQL Databases for RDF: An Empirical Evaluation” EUCLID – Scaling up Linked Data 34
  35. 35. “NoSQL Databases f0r RDF: An Empirical Evaluation” (4) • Benchmarks – BSBM 10M, 100M and 1B triples – 1, 2, 4, 8, 16 node cluster – AWS cost & query execution time Source: Cudre-Mauroux et al. “NoSQL Databases for RDF: An Empirical Evaluation” EUCLID – Scaling up Linked Data 35
  36. 36. “NoSQL Databases f0r RDF: An Empirical Evaluation” (5) • 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 Source: Cudre-Mauroux et al. “NoSQL Databases for RDF: An Empirical Evaluation” EUCLID – Scaling up Linked Data 36
  37. 37. HADOOP FOR LINKED DATA EUCLID – Scaling up Linked Data 37
  38. 38. Working with Distributed 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 EUCLID – Scaling up Linked Data 38
  39. 39. “Scalable Distributed Reasoning with MapReduce” • 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” EUCLID – Scaling up Linked Data 39
  40. 40. “Scalable Distributed Reasoning with MapReduce” (2) Source: Urbani et al. “Scalable Distributed Reasoning with MapReduce” EUCLID – Scaling up Linked Data 40
  41. 41. “Scalable Distributed Reasoning with MapReduce” (3) • 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 Source: Urbani et al. “Scalable Distributed Reasoning with MapReduce” EUCLID – Scaling up Linked Data 41
  42. 42. “Scalable Distributed Reasoning with MapReduce” (4) • Results – Throughput of 4.5M triples / sec on a 16-node cluster – 16+ nodes do not improve the performance significantly Source: Urbani et al. “Scalable Distributed Reasoning with MapReduce” EUCLID – Scaling up Linked Data 42
  43. 43. Lessons Learned from Largescale Reasoning (J. Urbani) • 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” EUCLID – Scaling up Linked Data 43
  44. 44. STREAM PROCESSING FOR LINKED DATA EUCLID – Scaling up Linked Data 45
  45. 45. 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 time • 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 EUCLID – Scaling up Linked Data 46
  46. 46. 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 Window time Background Knowledge Continuous Query EUCLID – Scaling up Linked Data Stream of answers 47
  47. 47. Linked Stream Data • 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 EUCLID – Scaling up Linked Data 48
  48. 48. Querying Streams with SPARQL Extensions • 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 EUCLID – Scaling up Linked Data 49
  49. 49. C-SPARQL (1) C-SPARQL is an extension of SPARQL 1.1 1. RDF Streams: Sequence of RDF triples annotated with timestamps: <(s,p,o), timestamp> 2. FROM STREAM extension for stream sources and windows FromStrClause  'FROM' ['NAMED'] 'STREAM' StreamIRI ' [ RANGE' Window ']' Window  LogicalWindow | PhysicalWindow LogicalWindow  Number TimeUnit WindowOverlap TimeUnit 'DAY'  'MSEC' | 'SEC' | 'MIN' | 'HOUR' | WindowOverlap  'STEP' Number TimeUnit | 'TUMBLING' PhysicalWindow  'TRIPLES' Number EUCLID – Scaling up Linked Data 50
  50. 50. C-SPARQL (2) 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 EUCLID – Scaling up Linked Data 51
  51. 51. C-SPARQL (3) Example Query: Retrieve the cars and districts, where the car was registered in a toll. 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 . } Source: Barbieri, Davide Francesco, et al. "Querying rdf streams with c-sparql." ACM SIGMOD Record 39.1 (2010): 20-26. EUCLID – Scaling up Linked Data 52
  52. 52. C-SPARQL (4) Source: M. Balduini et al. “Tutorial on Stream Reasoning for Linked Data (ISWC’2013)” EUCLID – Scaling up Linked Data 53
  53. 53. SPARQLStream (1) • Utilizes the same definition of RDF streams as in C-SPARQL: <(s,p,o), timestamp> • The language is defined as follows: 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 EUCLID – Scaling up Linked Data 54
  54. 54. SPARQLStream (2) 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 . } EUCLID – Scaling up Linked Data 55
  55. 55. Classification of Existing Systems Source: M. Balduini et al. “Tutorial on Stream Reasoning for Linked Data (ISWC’2013)” EUCLID – Scaling up Linked Data 56
  56. 56. W3C Semantic Sensor Networks • 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 EUCLID – Scaling up Linked Data 57
  57. 57. W3C Semantic Sensor Networks (2) • Different perspectives – Sensor, data/observation, system EUCLID – Scaling up Linked Data 58
  58. 58. … AND MORE EUCLID – Scaling up Linked Data 59
  59. 59. A Trillion RDF Triples • Use case – Use RDF and Linked Data for the customer management database of a big telecom – Franz Inc / AllegroGraph EUCLID – Scaling up Linked Data 60
  60. 60. uRiKA Appliance • YarcData • Big Data appliance for graph analytics – 8K processors, 1TB RAM – In-memory RDF database – SPARQL 1.1 support EUCLID – Scaling up Linked Data 61
  61. 61. RDFS Reasoning on GPUs • 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 EUCLID – Scaling up Linked Data 62
  62. 62. RDFS Reasoning on GPUs (2) • 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 EUCLID – Scaling up Linked Data 63
  63. 63. Benchmarks • BSBM v3.1 (April 2013) – http://wifo5-03.informatik.unimannheim.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 EUCLID – Scaling up Linked Data 64
  64. 64. SUMMARY EUCLID – Scaling up Linked Data 65
  65. 65. Summary • 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 EUCLID – Scaling up Linked Data 66
  66. 66. Summary (2) • 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 EUCLID – Scaling up Linked Data 67
  67. 67. For exercises, quiz and further material visit our website: http://www.euclid-project.eu Course eBook Other channels: @euclid_project euclidproject EUCLID – Scaling up Linked Data euclidproject 68
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