MarkLogic Overview and Use Cases

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MarkLogic
Overview and Use Cases
Maximize
the
value

of
your
content

John Snelson
Lead Engineer and Semantics Architect

What is MarkLogic?

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

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

SEARCHDATABASE
APPLICATION
SERVICES

NoSQL and Semantics: Using CONTEXT to Unlock Content

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

Inside MarkLogic Semantics

TRIPLE
XQuery Javascript SQL SPARQL
GRAPH
SPARQL

Triples Live in Documents

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

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
…

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)

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

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

Use Cases

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

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

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

Intelligent recommendation engine

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.”

Benchmarking

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

!  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

MarkLogic Overview and Use Cases

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