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


SEARCH DATABASE

APPLICATION SERVICES


NoSQL and Semantics: Using CONTEXT to Unlock Content


MarkLogic: Born a Document Database

Triple Store Document 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

Date post:	15-Apr-2017
Category:	Technology
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MarkLogic Overview and Use Cases

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