Giovanni Tummarello, Ph.D Data Intensive Infrastructure UNIT - DERI.ie

CEO SindiceTech

Real Time Semantic Warehousing: Sindice.com

technology for the enterprise

How we started : Sindice.com

80 Billions triple, 500,000,000 RDF Graphs, 5 TB of data. The Sindice Suite powers Sindice.com. Online with 99,9%+

uptime.

Semantic Sandboxes on: Sindice.com

Data Sandboxes in Sindice.com – Powered by CloudSpaces

And then we met people asking can you do it for us

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Example story (Pharmaceutical company0

To stay competitive, Pharmaceutical companies need to leverage all the data available from inside sources as well as from the increasingly many public HCLS data sources available. Due to the diversity of this data with respect to nature, formats, quality, there are complex integration issues. Traditional data warehousing technology require big upfront thinking and is handled within a company in the “go via the IT department” approach. This does not meet the need of data scientists who are the only ones that can do the complex cross-use case thinking required. Via Real Time Semantic Data Warehousing (RETIS) data scientist expect to get:

• The ability to speed up “In silico” scientific workflows (interrelation of diverse large datasets) by orders of magnitude by relying on a data warehousing approach.

• The ability to create large scale “data maps” or “aggregated views” which would allow researchers to see “trends” and gather insights at high level which would not be possible by data accessed via single lookups.

• The ability to receive recommendations and suggestions for new data connections based on an ever evolving ecosystem of available experimental datasets.

• Provide their R&D departments with superior tools for investigating their internal knowledge; search engines and data browsing tools which provide unified views of multiple, evolving, live datasets without leakage of specific “queries” to the outside world which would reveal internal research trends

• The ability to leverage the ever increasing body of public, crowd curated open data

Linked Data clouds for the Enterprise

– Strategic knowledge spaces, where new databases can be added and “leveraged” with an unprecedented ease

– Integration “Pay as you go” : explore now, fine tune later.

– Its BigData (Cluster+Clouds) meets RDF and Semantic Technologies

Sindice.com

Because you need Semantic SandBoxes

A Dataspace Template

Semantic Web Data A typical implementation template.

Dataspaces own:• Resources• Services• Datasets for others to reuse

Dataspace Composition

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Scalable cascading semantic ‘Dataspaces”• Resources allocated in public/private clouds• Allow to get Sindice Data and mix it/ process it for private purposes

Cloud powered!

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<dataspace id= “iphonedataspace”>

<dependencies>   http://ecommerce01.dataspace.sindice.net/</dataspace>   http://price01.dataspace.sindice.net/</dependencies>

<resources>  <mysql name=“sql”>  <hbase size=“10g”>  <siren name=“index”> <triplestore name=“sparql” kind=“virtuoso” /> </resources>

<retention> (see later) <update-rate>1D</update-rate> <timeout>1D</timeout></retention></dataspace>

Scale is only 1 dimension

Multiple dimensions of WeD data integration• RDF tool stack flexibility• Cluster scalable processing scalability• “Cloud” Pipelines dynamicity

Full Json Like Search.On Solr.

All operators supported.

What is SIREn ?

• Plugin to Solr• Built for searching and operating on

semistructured data and relational datastructures

SIREn: Semantic IR Engine

• Extension to Enterprise Search Engine Solr• Semantic, full-text, incremental updates,

distributed searchSemantic Database

sSIREn

Constant time

Limitations of Apache Solr

• Not efficient with highly heterogeneous structured data sources– Limitation on the number of attributes:

Dictionary size explosion

Dictionary Size Explosion

Record 1label Renaud Delbru

name Renaud Delbru

Dictionary Size Explosion

Record 1label Renaud Delbru

name Renaud Delbru

Dictionarylabel:renaud

label:delbru

name:renaud

name:delbru

Dictionary construction Concatenation of attribute name and term N * M complexity (worst case)

2 attributes * 2 terms = 4 dictionary entries 100K attributes * 1B terms = 100B entries

Limitations of Apache Solr

• Not efficient with highly heterogeneous structured data sources– Limitation on the number of attributes:

Dictionary size explosionQuery clause explosion when searching across all

attributes

Limitations of Apache Solr

• Not efficient with highly heterogeneous structured data sources– Limitation on the number of attributes:

Dictionary size explosionQuery clause explosion when searching across all

attributes

• Limited support for structured query– Multi-valued attributes

Multi-valued attributes

Record 1

label man's best friend

pooch

Record 2

label man's worst enemy

friend to no one

• No support in Solr for "all words must match in the same value of a multi-valued field".

• A field value is a bag of words– No distinction between multiple values

Multi-valued attributes

Record 1

label man's best friend pooch

Record 2

label man's worst enemy friend to no one

• No support in Solr for "all words must match in the same value of a multi-valued field".

• A field value is a bag of words– No distinction between multiple values

• Query example– label : man’s friend– Solr returns Record 1 & 2 as results

Limitations of Apache Solr

• Not efficient with highly heterogeneous structured data sources– Limitation on the number of attributes:

Dictionary size explosionQuery clause explosion when searching across all

attributes

• Limited support for structured query– Multi-valued attributes– No full-text search on attribute names

Full-text search on attribute names

Record 1

rdfs:label Renaud Delbru

• No support in Solr for “keyword search in attribute names".

• Query example– (name OR label) = “Renaud Delbru”– Solr is unable to find the records without the exact

attribute nameRecord 2

foaf:name Renaud Delbru

Record 3

sioc:name Renaud Delbru

Record 4

full_name Renaud Delbru

Limitations of Apache Solr• Not efficient with highly heterogeneous

structured data sources– Limitation on the number of attributes:

Dictionary size explosionQuery clause explosion when searching across all

attributes

• Limited support for structured query– Multi-valued attributes– No full-text search on attribute names– No 1:N relationship materialisation

Relationship materialization

• Its Json like indexing and searching

• Materialize the relationships between your entities and others.

Some numbers: Siren on Sindice

Data Collection 500M web data documents

(RDF, RDFa, Microformat, etc.) 200K datasets 50B triples

Settings Cluster of 4 nodes

2 nodes for indexing 2 nodes for querying

Replication

Indexing Performance Full index construction takes

approx 24 hours 436K triples / second

Services Keyword and structured

queries Dataset search >> 99% uptime

Large scale RDF ‘Summaries”

Introducing large scale RDF ‘Summaries”

We do it for:• Data exploration

– How to find datasets about movies ?• Assisted SPARQL Query Editor

– What is the data structure ?• Dataset Quality

– How to differentiate relevant form irrelevant dataset ?

Large Scale RDF summaries

10B relationships

12M relationshipsClass Level

Sindice Analytics Widget Demo

• http://test01.sindice.net:9001/sindice-stats-webapp/

• http://test01.sindice.net/szydan/dataset-view/dataset/default/www.bbc.co.uk

Relational Faceted Browsing. At speed of light

Patent Pending

SparQL is awesome. And now your guys can actually use it.

Thank you

Sindice.com team April 2012

With the contribution of