May 14, 2014 RDF Analytics Lenses over Semantic Graphs Dario Colazzo 3,1 Franc¸oisGoasdou´ e 4,1 Ioana Manolescu 1,2 Alexandra Roatis ¸ 2,1 1 OAK – Inria, France 2 LRI – Universit´ e Paris-Sud, France 3 LAMSADE – Universit´ e Paris Dauphine, France 4 PILGRIM – Universit´ e Rennes 1, France
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May 14, 2014
RDF AnalyticsLenses over Semantic GraphsDario Colazzo 3,1 Francois Goasdoue 4,1
Ioana Manolescu 1,2 Alexandra Roatis 2,1
1OAK – Inria, France2LRI – Universite Paris-Sud, France3LAMSADE – Universite Paris Dauphine, France4PILGRIM – Universite Rennes 1, France
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 2
RDF data warehousing scenario
þAlice
software engineer
IT companybuilds user applications
open RDF data (Grenoble)
worksFor
DS: Restaurants
(i) heterogeneous data
App: clickable mapm#restaurants
region & average ratingtype of cuisine
build
RDW: relational data warehouseextract tabular data (SPARQL queries)
merge
(ii) new central concepts
DS3: MuseumsDS2: Shops
RDW2 RDW3
(iii) other missing relationships?
Bug: landmarks , museums
find
redesign
Feature: query relationshipsregion � famous people
(iv) query schema
add
Feature: new type of aggregationfor each landmark, show how many restaurants are nearby
(v) impossible ! (separate star schema; restaurants and landmarks – central entities)
add
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 2
RDF data warehousing scenario
þAlice
software engineer
IT companybuilds user applications
open RDF data (Grenoble)
worksFor
DS: Restaurants
(i) heterogeneous data
App: clickable mapm#restaurants
region & average ratingtype of cuisine
build
RDW: relational data warehouseextract tabular data (SPARQL queries)
merge
(ii) new central concepts
DS3: MuseumsDS2: Shops
RDW2 RDW3
(iii) other missing relationships?
Bug: landmarks , museums
find
redesign
Feature: query relationshipsregion � famous people
(iv) query schema
add
Feature: new type of aggregationfor each landmark, show how many restaurants are nearby
(v) impossible ! (separate star schema; restaurants and landmarks – central entities)
add
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 2
RDF data warehousing scenario
þAlice
software engineer
IT companybuilds user applications
open RDF data (Grenoble)
worksFor
DS: Restaurants
(i) heterogeneous data
App: clickable mapm#restaurants
region & average ratingtype of cuisine
build
RDW: relational data warehouseextract tabular data (SPARQL queries)
merge
(ii) new central concepts
DS3: MuseumsDS2: Shops
RDW2 RDW3
(iii) other missing relationships?
Bug: landmarks , museums
find
redesign
Feature: query relationshipsregion � famous people
(iv) query schema
add
Feature: new type of aggregationfor each landmark, show how many restaurants are nearby
(v) impossible ! (separate star schema; restaurants and landmarks – central entities)
add
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 2
RDF data warehousing scenario
þAlice
software engineer
IT companybuilds user applications
open RDF data (Grenoble)
worksFor
DS: Restaurants
(i) heterogeneous data
App: clickable mapm#restaurants
region & average ratingtype of cuisine
build
RDW: relational data warehouseextract tabular data (SPARQL queries)
merge
(ii) new central concepts
DS3: MuseumsDS2: Shops
RDW2 RDW3
(iii) other missing relationships?
Bug: landmarks , museums
find
redesign
Feature: query relationshipsregion � famous people
(iv) query schema
add
Feature: new type of aggregationfor each landmark, show how many restaurants are nearby
(v) impossible ! (separate star schema; restaurants and landmarks – central entities)
add
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 2
RDF data warehousing scenario
þAlice
software engineer
IT companybuilds user applications
open RDF data (Grenoble)
worksFor
DS: Restaurants
(i) heterogeneous data
App: clickable mapm#restaurants
region & average ratingtype of cuisine
build
RDW: relational data warehouseextract tabular data (SPARQL queries)
merge
(ii) new central concepts
DS3: MuseumsDS2: Shops
RDW2 RDW3
(iii) other missing relationships?
Bug: landmarks , museums
find
redesign
Feature: query relationshipsregion � famous people
(iv) query schema
add
Feature: new type of aggregationfor each landmark, show how many restaurants are nearby
(v) impossible ! (separate star schema; restaurants and landmarks – central entities)
add
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 2
RDF data warehousing scenario
þAlice
software engineer
IT companybuilds user applications
open RDF data (Grenoble)
worksFor
DS: Restaurants
(i) heterogeneous data
App: clickable mapm#restaurants
region & average ratingtype of cuisine
build
RDW: relational data warehouseextract tabular data (SPARQL queries)
merge
(ii) new central concepts
DS3: MuseumsDS2: Shops
RDW2 RDW3
(iii) other missing relationships?
Bug: landmarks , museums
find
redesign
Feature: query relationshipsregion � famous people
(iv) query schema
add
Feature: new type of aggregationfor each landmark, show how many restaurants are nearby
(v) impossible ! (separate star schema; restaurants and landmarks – central entities)
add
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 2
RDF data warehousing scenario
þAlice
software engineer
IT companybuilds user applications
open RDF data (Grenoble)
worksFor
DS: Restaurants
(i) heterogeneous data
App: clickable mapm#restaurants
region & average ratingtype of cuisine
build
RDW: relational data warehouseextract tabular data (SPARQL queries)
merge
(ii) new central concepts
DS3: MuseumsDS2: Shops
RDW2 RDW3
(iii) other missing relationships?
Bug: landmarks , museums
find
redesign
Feature: query relationshipsregion � famous people
(iv) query schema
add
Feature: new type of aggregationfor each landmark, show how many restaurants are nearby
(v) impossible ! (separate star schema; restaurants and landmarks – central entities)
add
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 2
RDF data warehousing scenario
þAlice
software engineer
IT companybuilds user applications
open RDF data (Grenoble)
worksFor
DS: Restaurants
(i) heterogeneous data
App: clickable mapm#restaurants
region & average ratingtype of cuisine
build
RDW: relational data warehouseextract tabular data (SPARQL queries)
merge
(ii) new central concepts
DS3: MuseumsDS2: Shops
RDW2 RDW3
(iii) other missing relationships?
Bug: landmarks , museums
find
redesign
Feature: query relationshipsregion � famous people
(iv) query schema
add
Feature: new type of aggregationfor each landmark, show how many restaurants are nearby
(v) impossible ! (separate star schema; restaurants and landmarks – central entities)
add
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 2
RDF data warehousing scenario
þAlice
software engineer
IT companybuilds user applications
open RDF data (Grenoble)
worksFor
DS: Restaurants
(i) heterogeneous data
App: clickable mapm#restaurants
region & average ratingtype of cuisine
build
RDW: relational data warehouseextract tabular data (SPARQL queries)
merge
(ii) new central concepts
DS3: MuseumsDS2: Shops
RDW2 RDW3
(iii) other missing relationships?
Bug: landmarks , museums
find
redesign
Feature: query relationshipsregion � famous people
(iv) query schema
add
Feature: new type of aggregationfor each landmark, show how many restaurants are nearby
(v) impossible ! (separate star schema; restaurants and landmarks – central entities)
add
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 2
RDF data warehousing scenario
þAlice
software engineer
IT companybuilds user applications
open RDF data (Grenoble)
worksFor
DS: Restaurants
(i) heterogeneous data
App: clickable mapm#restaurants
region & average ratingtype of cuisine
build
RDW: relational data warehouseextract tabular data (SPARQL queries)
merge
(ii) new central concepts
DS3: MuseumsDS2: Shops
RDW2 RDW3
(iii) other missing relationships?
Bug: landmarks , museums
find
redesign
Feature: query relationshipsregion � famous people
(iv) query schema
add
Feature: new type of aggregationfor each landmark, show how many restaurants are nearby
(v) impossible ! (separate star schema; restaurants and landmarks – central entities)
add
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 3
RDF data warehousing
Application needs:(i) support of heterogeneous data(ii) multiple central concepts(iii) support for RDF semantics when querying(iv) possibility to query the relationships between entities (the schema)(v) flexible choice of aggregation dimensions
This work:I redesign the core data analytics concepts and tools for RDFI formal framework for warehouse-style analytics on RDF data
suited to heterogeneous, semantic-rich corpora of Linked Data
Summary
1. RDF Graphs & BGP Queries2. RDF Graph Analysis3. On-Line Analytical Processing4. Empirical Evaluation5. Sum Up
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 4
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 5
RDF Graphs & BGP Queries– recall –
The Resource Description Framework (RDF)
RDF graph – set of triples
Assertion Triple Relational notationClass s rdf:type o o(s)Property s p o p(s, o)
user1
user2
worksWith
Bill hasName28 hasAge
Madrid inCity
Studentrdf:type:b1wrote
blog1
inBlogresource (URI)
blank node
literal (string)
property
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 6
RDF Schema (RDFS)
– declare semantic constraints between classes and properties
Constraint Triple Relational notationSubclass s rdfs:subClassOf o s ⊆ o
Subproperty s rdfs:subPropertyOf o s ⊆ o
Domain typing s rdfs:domain o Πdomain(s) ⊆ oRange typing s rdfs:range o Πrange(s) ⊆ o
Person
Student
rdfs:subClassOf
knowsrdfs:rangerdfs:domain
worksWith
rdfs:subPropertyOf
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 7
Open-world assumption and RDF entailmentRDF data model – based on the open-world assumption.→ deductive constraints – implicitly propagate tuples
Entailment – reasoning mechanismset of explicit triples
+ → derive implicit triplessome entailment rules
Exhaustive application of entailment → saturation (closure)
The semantics of an RDF graph is its saturation.
user1 Student
Person
rdfs:subClassOf
rdf:type
rdf:type
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 8
Basic Graph Pattern (BGP) queries
→ subset of SPARQL; BGP – conjunctions of triple patterns
q(y) :- x rdf:type Person, x hasName y
query evaluation , query answeringI the evaluation of a query only uses the graph’s explicit triplesI (complete) answer set – evaluate q against the graph’s saturation
user1 Student
Person
rdfs:subClassOf
rdf:type
rdf:type
Bill
hasName
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 9
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 10
RDF Graph Analysis– formal framework for warehousing RDF data –
Analytical schema (AnS) and instance (I)RDF graph:
Personuser1
user2
rdf:type
rdf:type
BillhasNamepost1
post2
wrote
wroteblog1
inBlog
inBlog
Code BloghasName
Analytical schema:→ labeled directed graph
n1
λ(n1) ← Blogger
δ(n1) ←q(x) :- x rdf:type Person,
x wrote y ,y inBlog z
n2
λ(n2) ← Nameδ(n2) ← q(x) :- y hasName x
e2
λ(e2) ← identifiedBy
δ(e2) ←q(x , y) :- x rdf:type Person,
x hasName y
Instance of the analytical schema w.r.t. the graph
x rdf:type λ(n1)
user1 rdf:type Bloggeruser2 rdf:type Blogger
x λ(e2) yuser1 identifiedBy Bill
x rdf:type λ(n2)
Bill rdf:type NameCode Blog rdf:type Name
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 11
Analytical schema (AnS) and instance (I)RDF graph:
Personuser1
user2
rdf:type
rdf:type
BillhasNamepost1
post2
wrote
wroteblog1
inBlog
inBlog
Code BloghasName
Analytical schema:→ labeled directed graph
n1
λ(n1) ← Blogger
δ(n1) ←q(x) :- x rdf:type Person,
x wrote y ,y inBlog z
n2
λ(n2) ← Nameδ(n2) ← q(x) :- y hasName x
e2
λ(e2) ← identifiedBy
δ(e2) ←q(x , y) :- x rdf:type Person,
x hasName y
Instance of the analytical schema w.r.t. the graphx rdf:type λ(n1)
user1 rdf:type Bloggeruser2 rdf:type Blogger
x λ(e2) yuser1 identifiedBy Bill
x rdf:type λ(n2)
Bill rdf:type NameCode Blog rdf:type Name
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 11
Analytical schema (AnS) and instance (I)RDF graph:
Personuser1
user2
rdf:type
rdf:type
BillhasNamepost1
post2
wrote
wroteblog1
inBlog
inBlog
Code BloghasName
Analytical schema:→ labeled directed graph
n1
λ(n1) ← Blogger
δ(n1) ←q(x) :- x rdf:type Person,
x wrote y ,y inBlog z
n2
λ(n2) ← Nameδ(n2) ← q(x) :- y hasName x
e2
λ(e2) ← identifiedBy
δ(e2) ←q(x , y) :- x rdf:type Person,
x hasName y
Instance of the analytical schema w.r.t. the graphx rdf:type λ(n1)
user1 rdf:type Bloggeruser2 rdf:type Blogger
x λ(e2) yuser1 identifiedBy Bill
x rdf:type λ(n2)
Bill rdf:type NameCode Blog rdf:type Name
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 11
Analytical schema (AnS) and instance (I)RDF graph:
Personuser1
user2
rdf:type
rdf:type
BillhasNamepost1
post2
wrote
wroteblog1
inBlog
inBlog
Code BloghasName
Analytical schema:→ labeled directed graph
n1
λ(n1) ← Blogger
δ(n1) ←q(x) :- x rdf:type Person,
x wrote y ,y inBlog z
n2
λ(n2) ← Nameδ(n2) ← q(x) :- y hasName x
e2
λ(e2) ← identifiedBy
δ(e2) ←q(x , y) :- x rdf:type Person,
x hasName y
Instance of the analytical schema w.r.t. the graphx rdf:type λ(n1)
user1 rdf:type Bloggeruser2 rdf:type Blogger
x λ(e2) yuser1 identifiedBy Bill
x rdf:type λ(n2)
Bill rdf:type NameCode Blog rdf:type Name
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 11
Analytical schema (AnS) and instance (I)RDF graph:
Personuser1
user2
rdf:type
rdf:type
BillhasNamepost1
post2
wrote
wroteblog1
inBlog
inBlog
Code BloghasName
Analytical schema:→ labeled directed graph
n1
λ(n1) ← Blogger
δ(n1) ←q(x) :- x rdf:type Person,
x wrote y ,y inBlog z
n2
λ(n2) ← Nameδ(n2) ← q(x) :- y hasName x
e2
λ(e2) ← identifiedBy
δ(e2) ←q(x , y) :- x rdf:type Person,
x hasName y
! data heterogeneity preserved !
Instance of the analytical schema w.r.t. the graphx rdf:type λ(n1)
user1 rdf:type Bloggeruser2 rdf:type Blogger
x λ(e2) yuser1 identifiedBy Bill
x rdf:type λ(n2)
Bill rdf:type NameCode Blog rdf:type Name
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 11
Analytical schema (AnS) and instance (I)RDF graph:
Personuser1
user2
rdf:type
rdf:type
BillhasNamepost1
post2
wrote
wroteblog1
inBlog
inBlog
Code BloghasName
Analytical schema:→ labeled directed graph
n1
λ(n1) ← Blogger
δ(n1) ←q(x) :- x rdf:type Person,
x wrote y ,y inBlog z
n2
λ(n2) ← Nameδ(n2) ← q(x) :- y hasName x
e2
λ(e2) ← identifiedBy
δ(e2) ←q(x , y) :- x rdf:type Person,
x hasName y
! easy to extend !
Instance of the analytical schema w.r.t. the graphx rdf:type λ(n1)
user1 rdf:type Bloggeruser2 rdf:type Blogger
x λ(e2) yuser1 identifiedBy Bill
x rdf:type λ(n2)
Bill rdf:type NameCode Blog rdf:type Name
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 11
Analytical query (AnQ)
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 12
Analytical schema: Instance:
n1 : Blogger n2 : Citye2 : from
n3 : Valuee3 : age
n4 : BlogPost
e4 : posted
n5 : Site e5 : on
user1
user2
user3
28 age
Madrid from
40 age
35 age
New York from
post1
post2
post3
post4
postedposted
posted
posted
blog1
blog2
on
ononon
Query: Find the number of sites where each blogger posts,classified by the blogger’s age and city.
c(x , d1, d2) :- x age d1, x from d2m(x , v) :- x posted y , y on vcount
Analytical query (AnQ)
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 12
Analytical schema: Instance:
n1 : Blogger n2 : Citye2 : from
n3 : Valuee3 : age
n4 : BlogPost
e4 : posted
n5 : Site e5 : on
user1
user2
user3
28 age
Madrid from
40 age
35 ageNew York from
post1
post2
post3
post4
postedposted
posted
posted
blog1
blog2
on
ononon
Query: Find the number of sites where each blogger posts,classified by the blogger’s age and city.
c(x , d1, d2) :- x age d1, x from d2{ 〈user1, “28”, “Madrid”〉 , 〈user3, “35”, “New York”〉 }
m(x , v) :- x posted y , y on vcount
Analytical query (AnQ)
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 12
Analytical schema: Instance:
n1 : Blogger n2 : Citye2 : from
n3 : Valuee3 : age
n4 : BlogPost
e4 : posted
n5 : Site e5 : on
user1
user2
user3
28 age
Madrid from
40 age
35 age
New York from
post1
post2
post3
post4
postedposted
posted
posted
blog1
blog2
on
ononon
Query: Find the number of sites where each blogger posts,classified by the blogger’s age and city.
c(x , d1, d2) :- x age d1, x from d2{ 〈user1, “28”, “Madrid”〉 , 〈user3, “35”, “New York”〉 }
m(x , v) :- x posted y , y on v{〈user1, blog1〉, 〈user1, blog2〉, 〈user2, blog2〉, 〈user3, blog2〉}
count
Analytical query (AnQ)
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 12
Analytical schema: Instance:
n1 : Blogger n2 : Citye2 : from
n3 : Valuee3 : age
n4 : BlogPost
e4 : posted
n5 : Site e5 : on
user1
user2
user3
28 age
Madrid from
40 age
35 age
New York from
post1
post2
post3
post4
postedposted
posted
posted
blog1
blog2
on
ononon
Query: Find the number of sites where each blogger posts,classified by the blogger’s age and city.
c(x , d1, d2) :- x age d1, x from d2{ 〈user1, “28”, “Madrid”〉 , 〈user3, “35”, “New York”〉 }
m(x , v) :- x posted y , y on v{〈user1, blog1〉, 〈user1, blog2〉, 〈user2, blog2〉, 〈user3, blog2〉}
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 15
Query: Find the number of sites where each blogger posts,classified by the blogger’s age and city.c(x , d1, d2) :- x age d1, x from d2m(x , v) :- x posted y , y on vcount
Slice, dice, drill-in and drill-out
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 15
Query: Find the number of sites where each blogger posts,classified by the blogger’s age and city.c(x , d1, d2) :- x age d1, x from d2m(x , v) :- x posted y , y on vcount
Slice: bind an aggregation dimension to a single valuecΣ′(x , d1, d2) :- x age d1, x from d2Σ′ = { d1 ← “35” }
Slice, dice, drill-in and drill-out
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 15
Query: Find the number of sites where each blogger posts,classified by the blogger’s age and city.c(x , d1, d2) :- x age d1, x from d2m(x , v) :- x posted y , y on vcount
Slice: bind an aggregation dimension to a single valuecΣ′(x , d1, d2) :- x age d1, x from d2Σ′ = { d1 ← “35” }
Dice: bind several aggregation dimensions to sets of valuescΣ′(x , d1, d2) :- x age d1, x from d2Σ′ = { d1 ← {“28”}, d2 ← {“Madrid”, “Kyoto”} }
Slice, dice, drill-in and drill-out
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 15
Query: Find the number of sites where each blogger posts,classified by the blogger’s age and city.c(x , d1, d2) :- x age d1, x from d2m(x , v) :- x posted y , y on vcount
Slice: bind an aggregation dimension to a single valuecΣ′(x , d1, d2) :- x age d1, x from d2Σ′ = { d1 ← “35” }
Dice: bind several aggregation dimensions to sets of valuescΣ′(x , d1, d2) :- x age d1, x from d2Σ′ = { d1 ← {“28”}, d2 ← {“Madrid”, “Kyoto”} }
Drill-in: remove a dimension from the classifierc ′(x , d2) :- x from d2
Slice, dice, drill-in and drill-out
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 15
Query: Find the number of sites where each blogger posts,classified by the blogger’s age and city.c(x , d1, d2) :- x age d1, x from d2m(x , v) :- x posted y , y on vcount
Slice: bind an aggregation dimension to a single valuecΣ′(x , d1, d2) :- x age d1, x from d2Σ′ = { d1 ← “35” }
Dice: bind several aggregation dimensions to sets of valuescΣ′(x , d1, d2) :- x age d1, x from d2Σ′ = { d1 ← {“28”}, d2 ← {“Madrid”, “Kyoto”} }
Drill-in: remove a dimension from the classifierc ′(x , d2) :- x from d2
Drill-out: add a dimension to the classifierc ′(x , d1, d2, d3) :- x age d1, x from d2, x acquaintedWith d3
Roll-up and drill-down
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 16
Query: Find the number of sites where each blogger posts,classified by the blogger’s age and city.
c(x , d1, d2) :- x age d1, x from d2m(x , v) :- x posted y , y on vcount
nextLevel relationship – hierarchies among nodes or edges
Dataset: DBpedia Download 3.8Ontology and Ontology Infobox datasets
Hardware: 8-core DELL server at 2.13 GHz16 GB of RAMrunning Linux 2.6.31.14
Results: linear scale-up w.r.t. the data sizefor instance materialization and query answering
Analytical query answering12 patterns c number of triple patterns in the classifier query
1,097 queries v number of dimension variables in the classifier querym number of triple patterns in the measure query
c1v1
m1
c1v1
m2
c1v1
m3
c2v1
m3
c3v2
m3
c4v3
m3
c5v1
m3
c5v2
m3
c5v3
m3
c5v4
m1
c5v4
m2
c5v4
m3
0
1
10
average minimum maximum
c1v1m1 (73)
c1v1m2 (53)
c1v1m3 (62)
c2v1m3 (71)
c3v2m3 (76)
c4v3m3 (130)
c5v1m3 (144)
c5v2m3 (216)
c5v3m3 (144)
c5v4m1 (28)
c5v4m2 (64)
c5v4m3 (36)
0
1
10
100
1,000
10,000
100,000
evaluation time (s)
number of results
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 19
Java GUI using the Prefuse toolkit(collaboration with Tushar Ghosh)
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 20
Java GUI using the Prefuse toolkit(collaboration with Tushar Ghosh)
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 20
Java GUI using the Prefuse toolkit(collaboration with Tushar Ghosh)
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 20
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 21
Sum Up
Related works
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 22
I Graph cube: on warehousing and OLAP multidimensional networks [SIGMOD 2011]→ do not handle heterogeneous graphs, nor data semantics, both central in RDF→ only focus on counting edges in contrast with our flexible analytical queries
I Business intelligence on complex graph data [EDBT/ICDT 2012 Workshops]→ graph data aggregated in a spatial fashion (group connected nodes into regions)→ our framework – RDF-specific + more general aggregation
I No Size Fits All – Running the Star Schema Benchmark with SPARQL andRDF Aggregate Views [ESWC 2013]→ techniques for transforming OLAP queries into SPARQL→ could be used to further optimize analytical query answering in our framework
I The MD-join: An Operator for Complex OLAP [ICDE 2001]→ separation between grouping and aggregation present in our analytical queries
is similar to the MD-join operator for RDWsI W3C’s SPARQL 1.1 Query Language
→ features SQL-style grouping and aggregation→ efficient SPARQL 1.1 platforms – ideal for deploying our framework
Sum up and perspectives
RDF Analytics: Lenses over Semantic Graphs May 14, 2014 – 23
Sum up:Approach for specifying and exploiting an RDF data warehouseI define an analytical schema that captures the information of
interestI formalize analytical queries (or cubes) over the analytical schema
Instances of analytical schemas are RDF graphs themselves, whichallows to exploit the rich semantics and heterogeneous structure.
Perspectives:I semi-automatic analytical schema designI optimized OLAP operation on analytical queries resultsI efficient methods for deploying analytical schemas and analytical