MarkLogic is an enterprise NoSQL database platform that can be used for semantic search, data integration, and intelligent recommendation engines. It natively stores XML, JSON, and RDF triples alongside documents. Triples provide context to documents and enable semantic queries over data. MarkLogic also supports full-text and geospatial search, transactions, and flexible indexing and replication of data at scale.

1. © COPYRIGHT 2014 MARKLOGIC CORPORATION. ALL RIGHTS RESERVED.SLIDE: 1
MarkLogic
Overview and Use Cases
Maximize
the
value
of
your
content
John Snelson
Lead Engineer and Semantics Architect

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What is MarkLogic?

3. Geospatial
Support
Full-text
Search
Flexible
Indexes
Native
JSON
Store
Native
XML
Store
Real-time
Alerting
Native RDF
Triple Store
Bitemporal
Tiered
Storage
Fully
Transactional
Server-side
JavaScript
Hadoop
and HDFS
Cloud
Ready
(AWS)
SQL
Support
Scalable
and Elastic
MarkLogic
Content Pump
REST API
Samplestack
Ad-hoc
Queries
Schema
Agnostic
XA
Transactions
24/7
Engineering
Support
LDAP and
Kerberos
Security
Security
Certifications
Configuration
Management
Monitoring and
Management
Performance
at scale
Customizable
Failover
Customizable
Backup
Atomic
Forests
Point-in-time
Recovery
ACID
Transactions
Index Across
Data Types
Flexible
Replication
Semantic
Inference
Multi-OS
Support
POWERFUL AGILE TRUSTED
MarkLogic / Enterprise NoSQL Database Platform

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Harnessing Data & Reimagining Applications
! Reduce Risk
! Manage Compliance
! Create New Value from Data
! Optimize Operations
! Lower TCO / Better IT Economics
! Better Decision-making

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SEARCHDATABASE
APPLICATION
SERVICES

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NoSQL and Semantics: Using CONTEXT to Unlock Content

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MarkLogic: Born a Document Database
Triple StoreDocument Store + Data Store +
Inference
Traversal

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Inside MarkLogic Semantics

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TRIPLE
XQuery Javascript SQL SPARQL
GRAPH
SPARQL

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Triples Live in Documents

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Why Documents?
! Triples have metadata
! Quads, quints… or arbitrary documents
! Documents contain facts
! RDFa, schema.org, microformats
! RDF often exists as documents on the internet
! Many headline RDF projects also use a document database
! Even though they pay a complexity cost for using two databases

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subject predicate object doc ID position
:person4 :first-name "John" 11 5 - 9
:person5 :alma-mater :Brown 4 25 - 40
:person5 :birth-year 1929 9 13 - 17
…
Extending Triples with Context
subject predicate object
:person4 :first-name "John"
:person5 :alma-mater :Brown
:person5 :birth-year 1929
…

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Arbitrary Subsets of Triples
let $query := cts:and-query(
cts:directory-query(“/triples/”),
cts:element-range-query(
xs:QName(“date”),“>”,$date)
)
return sem:sparql(“…”,(),(),(),$query)

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sem:sparql("
select ?country {
<http://example.org/news/Nixon> <http://example.org/wentTo> ?country
} ",(),(),
cts:and-query( (
cts:path-range-query("//sem:triple/@confidence",">",80) ,
cts:path-range-query("//sem:triple/@date","<",xs:date("1974-01-01")),
cts:or-query( (
cts:element-value-query(xs:QName("source"),"AP Newswire"),
cts:element-value-query(xs:QName("source"),"BBC")
) )
) )
)
Which countries did Nixon visit?
! .. before 1974?
! .. only show me answers where I have at least 80%
confidence
! .. and the source is AP Newswire OR BBC

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SPARQL Optimization
! Cost estimation, ie:
! Column cardinality estimates
! Sort order static analysis
! Query plan mutations, ie:
! Multiple orders available in the triple index
! Multiple join implementations
! Join re-ordering
! Simulated annealing
! Guided randomized search for a good query plan

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Use Cases

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Semantic Search
User searches and queries refined by topics and
semantic relationships
" Refine search with topics and concepts
" Geo-location of research institutions,
Semantic Visualization & Tag Clouds
Publishing, Government, Banks (regulatory),
Manufacturing, Healthcare, Pharma

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Search Term Expansion
! "Compliance Navigator"
! Find all the standards I need to read before
building a "cardiac catheter"
! Ex. Search for "cardiac catheters" also
returns results for:
! safety requirements for devices that
stimulate nerves
! sterilization of implantable devices

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Semantics-driven search
Talent
Acted in
Episode 4
Part of
Played
Character
Season 34
Segment
Aired on
Date
Era
Acted in
Includes
Part of

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Intelligent recommendation engine

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Simpler Data Integration, Better Results
How does “Euro zone”
relate to “European
Union”, “Europe
OECD”, or “Europe”?
How does a term such
as “Small States,”
relate to “Least
Developed Countries,”
“Lower Middle
Income,” or “Low &
Middle Income.”

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Benchmarking

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Current LDBC Benchmarks
! Semantic Publishing Benchmark
! Aligns with one of our core use cases
! We’re planning on running it soon
! Omits handling the article content
! Social Network Benchmark
! Not a typical MarkLogic customer use case

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! Recommendation engine
! Incremental addition to SPB?
! Much greater (per user) insert load
! More complex taxonomy +
recommendation queries
! Facet generation
! Broader, narrower, related, tagged
with
! Counts, ranking
! Data integration
! Term thesaurus
! Data transformation (provenance)
! Bridging ontology (subPropertyOf,
subClassOf, sameAs)
! New dataset = new ontology
! Financial Regulation
! Trades
! Bi-temporal
! Often also data integration
Future Benchmark Ideas