SlideShare a Scribd company logo

Mining and Managing Large-scale Linked Open Data

Linked Open Data (LOD) is about publishing and interlinking data of different origin and purpose on the web. The Resource Description Framework (RDF) is used to describe data on the LOD cloud. In contrast to relational databases, RDF does not provide a fixed, pre-defined schema. Rather, RDF allows for flexibly modeling the data schema by attaching RDF types and properties to the entities. Our schema-level index called SchemEX allows for searching in large-scale RDF graph data. The index can be efficiently computed with reasonable accuracy over large-scale data sets with billions of RDF triples, the smallest information unit on the LOD cloud. SchemEX is highly needed as the size of the LOD cloud quickly increases. Due to the evolution of the LOD cloud, one observes frequent changes of the data. We show that also the data schema changes in terms of combinations of RDF types and properties. As changes cannot capture the dynamics of the LOD cloud, current work includes temporal clustering and finding periodicities in entity dynamics over large-scale snapshots of the LOD cloud with about 100 million triples per week for more than three years.

1 of 47
Download to read offline
Slide 1Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Ansgar Scherp
Mining and Managing
Large-scale Linked Open Data
GVDB, Nörten-Hardenberg, May 25, 2016
Thanks to: Chifumi Nishioka, Renata Dividino, Thomas Gottron,
and many more …
Slide 2Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Team Knowledge Discovery @
Ansgar
Scherp
Ahmed
Saleh
Chifumi
Nishioka
Falk
Böschen
Mohammad
Abdel-Qader
Till Blume
Anke
Koslowski
(Secretariat)
Henrik
Schmidt
(Engineer)
Lukas
Galke
Florian
Mai
&
Slide 3Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Linked Open Data (LOD) Cloud
• Publishing and interlinking data on the web
• Different quality, purpose, and sources
• Using the Resource Description Framework(RDF)
World Wide Web LOD Cloud
Documents Data
Hyperlinks via <a> Typed Links
HTML RDF
Addresses (URIs) Addresses (URIs)
Slide 4Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Relevance of Linked Data?
Slide 5Prof. Ansgar Scherp – asc@informatik.uni-kiel.de1000+ Datasets, 50+ Billion Triples
Media
Geographic
Publications
Web 2.0
eGovernment
Cross-Domain
Life
Sciences
Linked Data: May ‘07  August ‘14
Source: http://lod-cloud.net
Social Networking
Slide 6Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
LOD on One Slide: Example Graph
biglynx:matt-briggs
foaf:Person
rdf:type
Fully qualified URI using vocabulary prefixes:
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix rdf: <http://w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix biglynx: <http://biglynx.co.uk/people/> .
Object
Predicate
Subject
RDF Triple
Slide 7Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
LOD on One Slide: Example Graph
biglynx:matt-briggs
foaf:Person
rdf:type
Fully qualified URI using vocabulary prefixes:
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix rdf: <http://w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix biglynx: <http://biglynx.co.uk/people/> .
biglynx:Director
rdf:type …
…
Slide 8Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
LOD on One Slide: Example Graph
biglynx:matt-briggs
foaf:Person
biglynx:dave-smith
biglynx:Director
rdf:type
foaf:knows
rdf:type
_1:point
wgs84:
lat
wgs84:
long
dp:London
foaf:based_near
……
…
…
ex:loc
“-0.118”
“51.509”
Types
Properties
Entity
Slide 9Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Motivation for the SchemEX Index
• Single entry point to query the LOD cloud
• Search for data sources containing entities like
– ‘Persons, who are Politicians and Actors’
– ‘Research data sets’
– ‘Scientific publications’
Query
SELECT ?x
FROM …
WHERE {
?x rdf:type ex:Actor .
?x rdf:type ex:Politician . }
Index1
2
2
2
Slide 10Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Input Data for SchemEX
• Quads: <subject> <predicate> <object> <context>
• Example:
<http://biglynx.co.uk/people/matt-briggs>
<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>
<http://xmlns.com/foaf/0.1/Person>
<http://biglynx.co.uk/people/matt-briggs.rdf>
<http://biglynx.co.uk/people/
matt-briggs.rdf>
rdf:type
biglynx:
matt-briggs
foaf:
Person
LOD Cloud
Dataset 𝑋
Slide 11Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
SchemEX Idea
• Schema-level index SchemEX
• Assign RDF entities to graph patterns
• Map graph patterns to data sources (context)
• Defined over entities, but store the context
• Construction of schema-level index
• Stream-based for scalability
• Stratified bi-simulation for detecting patterns
• Little loss of accuracy
[KGS+12]
Slide 12Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Building the Index from a Stream
• Stream of quads coming from a LD crawler
… Q16, Q15, Q14, Q13, Q12, Q11, Q10, Q9, Q8, Q7, Q6, Q5, Q4, Q3, Q2, Q1
FiFo
4
3
2
1
1
6
2
3
4
5
C3
C2
C2
C1
+ Reasonable accuracy at cache size of 50k
Slide 13Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Full BTC 2011Data Set: 2.17 Bn Triples
Cache size: 50 k
Winner
BTC’11
+ Linear runtime with respect to number of triples
+ Memory consumption scales with window size
Slide 14Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
[GSK+13] Generalization
Specialization
Result list with
examples
Inspired by
Google
Slide 15Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
LODatio Under the Hood
SPARQL
Snippets
Generalize
Retrieve
Data Sources
Query
translation
Rank
Specialize
Count
Select
Select
• Hybrid database with off-the-shelf components
Slide 16Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
LOD on One Slide: Recap
biglynx:matt-briggs
foaf:Person
biglynx:dave-smith
biglynx:Director
rdf:type
foaf:knows
rdf:type
_1:point
wgs84:
lat
wgs84:
long
dp:London
foaf:based_near
……
…
…
ex:loc
“-0.118”
“51.509”
Type Set (TS)
Property Set (PS)
Information theoretic analyses of LOD
• How much information is encoded in TS and PS?
• … information encoded, once TS or PS is known?
• … to which degree are TS and PS redundant?
• Example: 20% of PLDs do not need TS (6% for PS)
[GKS15]
Slide 17Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
• 29 weekly LOD snapshots of ~100 Mio triples
• Still running since May 2012 (now 200+ weeks)
Käfer et al.’s Temporal Analysis of LOD
• Data on the cloud changes a lot
[Käfer et al., 2013] T. Käfer, A. Abdelrahman, J. Umbrich, P. O'Byrne, A. Hogan: Observing Linked
Data Dynamics. ESWC 2013: 213-227
Changes?
• But vocabularies defining RDF types and
properties are highly static, e.g., RDF, FOAF
LOD cloud ~2012 LOD cloud ~2014
Slide 18Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
𝐻(𝑃𝑆|𝑇𝑆=𝑡𝑠)
𝐻(𝑇𝑆|𝑃𝑆=𝑝𝑠)
But:DoChangesOccurinPS and TS?
• Analysis: expected conditional entropy over time
• 𝐻(𝑃𝑆|𝑇𝑆 = 𝑡𝑠): entropy of 𝑃𝑆 given 𝑇𝑆 is known
• Observation: types become less important
• Changes in the use of TS and PS ? !
Slide 19Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Changes over Time
• Extended characteristic sets: ECS = PS ∪ TS
# of ECS
Avg.: 83.898 ECS per week
# of ECS
[DSG+13]
• Avg. 73% of ECS re-occur next week (orange)
• Avg. 35% of ECS remain unchanged (blue)
• Avg. 20% of entity sets of ECS change / week
[Neumann and Moerkotte, 2011] Thomas Neumann, Guido Moerkotte: Characteristic sets:
Accurate cardinality estimation for RDF queries with multiple joins. ICDE 2011: 984-994
[Neumann and
Moerkotte, 2011]
Slide 20Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Temporal Dynamics of the Entities?
• Notion of entity motivated by ECS: entity is a
set of triples 𝑋 sharing the same subject URI 𝑠
• Example:
–1 entity
–4 triples
w.l.o.g.
• Useful to keep LOD caches up-to-date?
• Can we predict when LOD sources will change?
Slide 21Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Dynamics Function Θ
• Definition of Θ over change rate function 𝑐(𝑋𝑡)
Time
X
𝑡𝑖 𝑡𝑗
Θ
Θ 𝑡 𝑖
𝑋 = Θ(𝑋𝑡 𝑗
) − Θ(𝑋𝑡 𝑖
) = 𝑡 𝑖
𝑡 𝑗
𝑐 𝑋𝑡 d𝑡
[DGS+14]
𝑡𝑗
≈
𝑘=𝑖+1
𝑗
𝛿(𝑋𝑡 𝑘−1
, 𝑋𝑡 𝑘
)
• Approximation as step function over changes
Monotone,
non-negative
c
Slide 22Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Update Strategies for LOD Sources
• Apply strategies from keeping caches of WWW
documents up-to-date to maintain LOD caches
• Assumptions
–LOD is fetched from various sources
–Sources are scored and prioritized based on
strategy
–Data of a source is fetched only when the
operation can be entirely executed
Slide 23Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Scheduling Update Strategies
a) HTTP Header [Dividino et al., 2014a]
b) Age or Last Visited [Dasdan et al., 2009, Cho and
Garcia-Molina, 2000]
c) PageRank [Page et al., 1999, Boldi et al., 2004,
Baeza-Yates et al., 2005]
d) LOD Sources Size
e) Change Ratio [Douglis et al., 1997, Cho et al., 2002.
Tan et al., 2007]
f) Change Rate [Olston et al., 2002, Ntoulas et al.,
2004, Dividino et al., 2013]
g) History Information: Dynamics [Dividino et al., 2014b]
We borrow strategies developed for the WWW and
metrics for data change analysis in the LOD cloud.
Slide 24Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Ranking
Sources which
changed (most)
Sources that not
changed/less changesTimeti tj
e) Change Ratio
• Captures the change
frequency of the data
(freshness)
• Percentage of data items
in the cache that are up-to-date
Slide 25Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
f) Change Rate
• Data from sources which are less similar which their
previous update (snapshot) should be updated first
• Comparison of two RDF data sets
– 𝑋 : Set of triple statements
– 𝛿 : Numeric expression (distance)
𝛿𝐽𝑎𝑐𝑐𝑎𝑟𝑑 𝑋1, 𝑋2 =
1 −
𝑋1 ∩ 𝑋2
𝑋1 ∪ 𝑋2
0,¥[ )
Time𝑡𝑖 𝑡𝑗
𝛿
Example:
Slide 26Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
g) History Information: Dynamics
• Data from sources which most evolve in a given
period of time should be updated first
• Uses both history information and change rate
Θ(𝑋𝑡 𝑗
) − Θ(𝑋𝑡 𝑖
) = 𝑡 𝑖
𝑡 𝑗
𝑐 𝑋𝑡 d𝑡
Time
X
𝑡𝑖 𝑡𝑗
Θ
c
≈
𝑘=𝑖+1
𝑗
𝛿(𝑋𝑡 𝑘−1
, 𝑋𝑡 𝑘
)
Slide 27Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Evaluation
 Idea: simulation of limitations of available
computational resources (network bandwidth,
computation time)
Time
100%
𝑡𝑖 𝑡𝑖+1
Slide 28Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Evaluation: Single Step Update
Time
100%
15%
5%40%
75%
95%60%
𝑡𝑖 𝑡𝑖+1
Slide 29Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Evaluation: Iterative Updates
Time
. . .
15%
5%40%
75%
95%60%
15%
5%40%
75%
95%60%
100%
𝑡𝑖 𝑡𝑖+1 𝑡𝑖+2
Slide 30Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Dataset
• Dynamic Linked Data Observatory
• Weekly snapshots, 14 M triples
 154 snapshots (approx. 3 years)
 590 data sources (PLD)
Top 10 largest data sources Average size
dbpedia.org 3,406,364.5
edgarwrap.ontologycentral.com 982,631.0
dbtune.org 864,107.6
dbtropes.org 787,299.9
data.linkedct.org 498,986.3
aims.fao.org 416,708.9
www.legislation.gov.uk 399,601.6
kent.zpr.fer.hr 387,034.8
identi.ca 278,316.2
webenemasuno.linkeddata.es 250,557.9
Slide 31Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Metrics:Precision & Recall
• Precision: portion of cached data that are
actually up-to-date
• Recall: portion of data in the LOD cloud that
is identical to the cached data
Cached data
Actual data on the LOD cloud
(w.r.t. to the 590 sources considered)
Slide 32Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Results: Single Step Update
Time
t jti
100% 15%
5%40%
75%
95%60%
Slide 33Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Results: Iterative Updates
Time
tjti tj
. . .
15%
5%40%
75%
95%60%
15%
5%
40%
75%
95%60%
100%
Slide 34Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Results: Iterative Updates
Time
tjti tj
. . .
15%
5%40%
75%
95%60%
15%
5%
40%
75%
95%60%
100%
Slide 35Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Results: Iterative Updates
Time
tjti tj
. . .
15%
5%40%
75%
95%60%
15%
5%
40%
75%
95%60%
100%
Slide 36Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Results: Summary
 Best strategies: ones which
capture the change
behaviour over time
 Specially for low relative
bandwidth
Slide 37Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Dynamics Function Θ: Revisited
Time
X
𝑡𝑖 𝑡𝑗
c
• Can we predict when LOD sources will change?
• Notion of dynamics to compute periodicities!
• Dynamics as vector of changes:
< 𝛿(𝑋𝑡1
, 𝑋𝑡2
), … , 𝛿(𝑋𝑡 𝑁−1
, 𝑋𝑡 𝑁
) >
Slide 38Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Temporal Clustering of Entities
• Dynamics as vector: < 𝛿(𝑋𝑡1
, 𝑋𝑡2
), … , 𝛿(𝑋𝑡 𝑁−1
, 𝑋𝑡 𝑁
) >
Time
Change(logscale)
[NS15]
• Clustering with
k-means++ to
find patterns
• 165 snapshots
• 65,044 entities
• 7 patterns (after
optimizing 𝑘)
Slide 39Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Periodicity of Entity Dynamics
• Examples: < 0, 3, 2, 0, 3, 2, 0 >, < 1, 2, 1, 2, 1, 2 >
# of
entities
Most likely
periodicity
C1 12,982 66
C2 168 23
C3 35 1
C4 12 1
C5 1 1
C6 1,541 56
C7 30 37
CS 50,725
[Elfeky et al., 2005] Mohamed G. Elfeky, Walid G. Aref, Ahmed K. Elmagarmid:
Periodicity Detection in Time Series Databases. IEEE Trans. Knowl. Data Eng.
17(7): 875-887 (2005)
• Convolution-based algorithm
[Elfeky et al. 2005]
• Entities of legislation.gov.uk
found in several clusters
(C1,C3,C4,C5,C6)
• No changes (CS): 77.29%
• CS: entities from w3.org and
ontologydesignpatterns.org
Slide 40Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Application Areas: More than One!
• Searching for LOD sources
[GSK+13,KGS+12]
• Strategies for updating data caches [DGS15]
• Programming queries against LOD [SSS12]
• Recommending LOD vocabularies [SGS16]
 Foundation for Future Data-driven Applications
Slide 41Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Summary: KDD in Social Media & DL
How to deal with the vast amount of content related to
research and innovation?
• H2020 INSO-4 project, duration: 04/2016-03/2019
• Data mining & visualization tools enabling information
professionals to deal with large corpora
• Website: http://www.moving-project.eu/
New
Slide 42Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Got Interested?
Knowledge Discovery at ZBW
Contact me!
Prof. Dr. Ansgar Scherp
• Email: a.scherp@zbw.eu
• Twitter: https://twitter.com/ansgarscherp
• Slideshare: http://de.slideshare.net/ascherp
• KD-Website:
http://www.zbw.eu/en/research/knowledge-discovery/
http://www.kd.informatik.uni-kiel.de/en/
Slide 43Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
References
[DGS15] R. Dividino, T. Gottron, A. Scherp: Strategies for Efficiently Keeping Local
Linked Open Data Caches Up-To-Date. International Semantic Web Conference (2)
2015: 356-373
[DGS+14] R. Dividino, T. Gottron, A. Scherp, G. Gröner: From Changes to Dynamics:
Dynamics Analysis of Linked Open Data Sources. PROFILES@ESWC 2014
[GKS15] T. Gottron, M. Knauf, A. Scherp: Analysis of schema structures in the Linked
Open Data graph based on unique subject URIs, pay-level domains, and vocabulary
usage. Distributed and Parallel Databases 33(4): 515-553 (2015)
[DSG+13] R. Dividino, A. Scherp, G. Gröner, T. Gottron: Change-a-LOD: Does the
Schema on the Linked Data Cloud Change or Not? COLD 2013
[GSK+13] T. Gottron, A. Scherp, B. Krayer, A. Peters: LODatio: using a schema-level
index to support users in finding relevant sources of linked data. K-CAP 2013: 105-108
[KGS+12] M. Konrath, T. Gottron, S. Staab, A. Scherp: SchemEX - Efficient construction
of a data catalogue by stream-based indexing of linked data. J. Web Sem. 16: 52-58
(2012)
[NS15] C. Nishioka, A Scherp: Temporal Patterns and Periodicity of Entity Dynamics in
the Linked Open Data Cloud. K-CAP 2015.
[SGS16] J. Schaible, T. Gottron, and A. Scherp: TermPicker Enabling the Reuse of
Vocabulary Terms by Exploiting Data from the Linked Open Data Cloud, ESWC,
Springer, 2016.
[SSS12] S. Scheglmann, A. Scherp, S. Staab: Declarative Representation of
Programming Access to Ontologies. ESWC 2012: 659-673
Slide 44Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
a) HTTP Header
• Data from sources which have been changed
since the last update should be updated first
HTTP Response
HEADER
…
Last-Modified: Tue, 15 Nov 1994 12:45:26
GMT
CONTENT
Slide 45Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
b) Age or Last Visited
• Time elapsed from last
update (the difference
between query time and
last update time)
• It guarantees that every
source is updated after a
period
Ranking
Sources that have been
at longer time updated
Sources that have
been recently updated
Slide 46Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
c) PageRank and d) Source Size
• PageRank captures popularity/
importance of the LOD source
• Data from sources with highest
PageRank are updated first
• LOD source size: data from the
biggest/smallest LOD sources
should be updated first
Ranking
Sources with
higher PR
Sources with
lower PR
Slide 47Prof. Ansgar Scherp – asc@informatik.uni-kiel.de
Results: Single Step Update
Time
t jti
100% 15%
5%40%
75%
95%60%

Recommended

Formalization and Preliminary Evaluation of a Pipeline for Text Extraction Fr...
Formalization and Preliminary Evaluation of a Pipeline for Text Extraction Fr...Formalization and Preliminary Evaluation of a Pipeline for Text Extraction Fr...
Formalization and Preliminary Evaluation of a Pipeline for Text Extraction Fr...Ansgar Scherp
 
SchemEX - Creating the Yellow Pages for the Linked Open Data Cloud
SchemEX - Creating the Yellow Pages for the Linked Open Data CloudSchemEX - Creating the Yellow Pages for the Linked Open Data Cloud
SchemEX - Creating the Yellow Pages for the Linked Open Data CloudAnsgar Scherp
 
A Comparison of Different Strategies for Automated Semantic Document Annotation
A Comparison of Different Strategies for Automated Semantic Document AnnotationA Comparison of Different Strategies for Automated Semantic Document Annotation
A Comparison of Different Strategies for Automated Semantic Document AnnotationAnsgar Scherp
 
Mining and Managing Large-scale Linked Open Data
Mining and Managing Large-scale Linked Open DataMining and Managing Large-scale Linked Open Data
Mining and Managing Large-scale Linked Open DataMOVING Project
 
Knowledge Discovery in Social Media and Scientific Digital Libraries
Knowledge Discovery in Social Media and Scientific Digital LibrariesKnowledge Discovery in Social Media and Scientific Digital Libraries
Knowledge Discovery in Social Media and Scientific Digital LibrariesAnsgar Scherp
 
Real-Time Big Data Stream Analytics
Real-Time Big Data Stream AnalyticsReal-Time Big Data Stream Analytics
Real-Time Big Data Stream AnalyticsAlbert Bifet
 
Keeping Linked Open Data Caches Up-to-date by Predicting the Life-time of RDF...
Keeping Linked Open Data Caches Up-to-date by Predicting the Life-time of RDF...Keeping Linked Open Data Caches Up-to-date by Predicting the Life-time of RDF...
Keeping Linked Open Data Caches Up-to-date by Predicting the Life-time of RDF...MOVING Project
 
Streaming Algorithms
Streaming AlgorithmsStreaming Algorithms
Streaming AlgorithmsJoe Kelley
 

More Related Content

What's hot

Mining Big Data Streams with APACHE SAMOA
Mining Big Data Streams with APACHE SAMOAMining Big Data Streams with APACHE SAMOA
Mining Big Data Streams with APACHE SAMOAAlbert Bifet
 
Data Stream Algorithms in Storm and R
Data Stream Algorithms in Storm and RData Stream Algorithms in Storm and R
Data Stream Algorithms in Storm and RRadek Maciaszek
 
MOA for the IoT at ACML 2016
MOA for the IoT at ACML 2016 MOA for the IoT at ACML 2016
MOA for the IoT at ACML 2016 Albert Bifet
 
Artificial intelligence and data stream mining
Artificial intelligence and data stream miningArtificial intelligence and data stream mining
Artificial intelligence and data stream miningAlbert Bifet
 
5.1 mining data streams
5.1 mining data streams5.1 mining data streams
5.1 mining data streamsKrish_ver2
 
Massive Simulations In Spark: Distributed Monte Carlo For Global Health Forec...
Massive Simulations In Spark: Distributed Monte Carlo For Global Health Forec...Massive Simulations In Spark: Distributed Monte Carlo For Global Health Forec...
Massive Simulations In Spark: Distributed Monte Carlo For Global Health Forec...Jen Aman
 
Mining Big Data in Real Time
Mining Big Data in Real TimeMining Big Data in Real Time
Mining Big Data in Real TimeAlbert Bifet
 
Accelerating the Experimental Feedback Loop: Data Streams and the Advanced Ph...
Accelerating the Experimental Feedback Loop: Data Streams and the Advanced Ph...Accelerating the Experimental Feedback Loop: Data Streams and the Advanced Ph...
Accelerating the Experimental Feedback Loop: Data Streams and the Advanced Ph...Ian Foster
 
Parikshit Ram – Senior Machine Learning Scientist, Skytree at MLconf ATL
Parikshit Ram – Senior Machine Learning Scientist, Skytree at MLconf ATLParikshit Ram – Senior Machine Learning Scientist, Skytree at MLconf ATL
Parikshit Ram – Senior Machine Learning Scientist, Skytree at MLconf ATLMLconf
 
Signals from outer space
Signals from outer spaceSignals from outer space
Signals from outer spaceGraphAware
 
Mining high speed data streams: Hoeffding and VFDT
Mining high speed data streams: Hoeffding and VFDTMining high speed data streams: Hoeffding and VFDT
Mining high speed data streams: Hoeffding and VFDTDavide Gallitelli
 
Magellan-Spark as a Geospatial Analytics Engine by Ram Sriharsha
Magellan-Spark as a Geospatial Analytics Engine by Ram SriharshaMagellan-Spark as a Geospatial Analytics Engine by Ram Sriharsha
Magellan-Spark as a Geospatial Analytics Engine by Ram SriharshaSpark Summit
 
Efficient Online Evaluation of Big Data Stream Classifiers
Efficient Online Evaluation of Big Data Stream ClassifiersEfficient Online Evaluation of Big Data Stream Classifiers
Efficient Online Evaluation of Big Data Stream ClassifiersAlbert Bifet
 
Latent Semantic Analysis of Wikipedia with Spark
Latent Semantic Analysis of Wikipedia with SparkLatent Semantic Analysis of Wikipedia with Spark
Latent Semantic Analysis of Wikipedia with SparkSandy Ryza
 
Moa: Real Time Analytics for Data Streams
Moa: Real Time Analytics for Data StreamsMoa: Real Time Analytics for Data Streams
Moa: Real Time Analytics for Data StreamsAlbert Bifet
 
Strata NYC 2015: Sketching Big Data with Spark: randomized algorithms for lar...
Strata NYC 2015: Sketching Big Data with Spark: randomized algorithms for lar...Strata NYC 2015: Sketching Big Data with Spark: randomized algorithms for lar...
Strata NYC 2015: Sketching Big Data with Spark: randomized algorithms for lar...Databricks
 
Introduction to Data streaming - 05/12/2014
Introduction to Data streaming - 05/12/2014Introduction to Data streaming - 05/12/2014
Introduction to Data streaming - 05/12/2014Raja Chiky
 
Sebastian Schelter – Distributed Machine Learing with the Samsara DSL
Sebastian Schelter – Distributed Machine Learing with the Samsara DSLSebastian Schelter – Distributed Machine Learing with the Samsara DSL
Sebastian Schelter – Distributed Machine Learing with the Samsara DSLFlink Forward
 
Apache con big data 2015 magellan
Apache con big data 2015 magellanApache con big data 2015 magellan
Apache con big data 2015 magellanRam Sriharsha
 

What's hot (20)

Mining Big Data Streams with APACHE SAMOA
Mining Big Data Streams with APACHE SAMOAMining Big Data Streams with APACHE SAMOA
Mining Big Data Streams with APACHE SAMOA
 
Data Stream Algorithms in Storm and R
Data Stream Algorithms in Storm and RData Stream Algorithms in Storm and R
Data Stream Algorithms in Storm and R
 
MOA for the IoT at ACML 2016
MOA for the IoT at ACML 2016 MOA for the IoT at ACML 2016
MOA for the IoT at ACML 2016
 
Artificial intelligence and data stream mining
Artificial intelligence and data stream miningArtificial intelligence and data stream mining
Artificial intelligence and data stream mining
 
5.1 mining data streams
5.1 mining data streams5.1 mining data streams
5.1 mining data streams
 
Massive Simulations In Spark: Distributed Monte Carlo For Global Health Forec...
Massive Simulations In Spark: Distributed Monte Carlo For Global Health Forec...Massive Simulations In Spark: Distributed Monte Carlo For Global Health Forec...
Massive Simulations In Spark: Distributed Monte Carlo For Global Health Forec...
 
Mining Big Data in Real Time
Mining Big Data in Real TimeMining Big Data in Real Time
Mining Big Data in Real Time
 
Accelerating the Experimental Feedback Loop: Data Streams and the Advanced Ph...
Accelerating the Experimental Feedback Loop: Data Streams and the Advanced Ph...Accelerating the Experimental Feedback Loop: Data Streams and the Advanced Ph...
Accelerating the Experimental Feedback Loop: Data Streams and the Advanced Ph...
 
Parikshit Ram – Senior Machine Learning Scientist, Skytree at MLconf ATL
Parikshit Ram – Senior Machine Learning Scientist, Skytree at MLconf ATLParikshit Ram – Senior Machine Learning Scientist, Skytree at MLconf ATL
Parikshit Ram – Senior Machine Learning Scientist, Skytree at MLconf ATL
 
Signals from outer space
Signals from outer spaceSignals from outer space
Signals from outer space
 
Mining high speed data streams: Hoeffding and VFDT
Mining high speed data streams: Hoeffding and VFDTMining high speed data streams: Hoeffding and VFDT
Mining high speed data streams: Hoeffding and VFDT
 
Magellan-Spark as a Geospatial Analytics Engine by Ram Sriharsha
Magellan-Spark as a Geospatial Analytics Engine by Ram SriharshaMagellan-Spark as a Geospatial Analytics Engine by Ram Sriharsha
Magellan-Spark as a Geospatial Analytics Engine by Ram Sriharsha
 
CLIM Program: Remote Sensing Workshop, High Performance Computing and Spatial...
CLIM Program: Remote Sensing Workshop, High Performance Computing and Spatial...CLIM Program: Remote Sensing Workshop, High Performance Computing and Spatial...
CLIM Program: Remote Sensing Workshop, High Performance Computing and Spatial...
 
Efficient Online Evaluation of Big Data Stream Classifiers
Efficient Online Evaluation of Big Data Stream ClassifiersEfficient Online Evaluation of Big Data Stream Classifiers
Efficient Online Evaluation of Big Data Stream Classifiers
 
Latent Semantic Analysis of Wikipedia with Spark
Latent Semantic Analysis of Wikipedia with SparkLatent Semantic Analysis of Wikipedia with Spark
Latent Semantic Analysis of Wikipedia with Spark
 
Moa: Real Time Analytics for Data Streams
Moa: Real Time Analytics for Data StreamsMoa: Real Time Analytics for Data Streams
Moa: Real Time Analytics for Data Streams
 
Strata NYC 2015: Sketching Big Data with Spark: randomized algorithms for lar...
Strata NYC 2015: Sketching Big Data with Spark: randomized algorithms for lar...Strata NYC 2015: Sketching Big Data with Spark: randomized algorithms for lar...
Strata NYC 2015: Sketching Big Data with Spark: randomized algorithms for lar...
 
Introduction to Data streaming - 05/12/2014
Introduction to Data streaming - 05/12/2014Introduction to Data streaming - 05/12/2014
Introduction to Data streaming - 05/12/2014
 
Sebastian Schelter – Distributed Machine Learing with the Samsara DSL
Sebastian Schelter – Distributed Machine Learing with the Samsara DSLSebastian Schelter – Distributed Machine Learing with the Samsara DSL
Sebastian Schelter – Distributed Machine Learing with the Samsara DSL
 
Apache con big data 2015 magellan
Apache con big data 2015 magellanApache con big data 2015 magellan
Apache con big data 2015 magellan
 

Viewers also liked

About Multimedia Presentation Generation and Multimedia Metadata: From Synthe...
About Multimedia Presentation Generation and Multimedia Metadata: From Synthe...About Multimedia Presentation Generation and Multimedia Metadata: From Synthe...
About Multimedia Presentation Generation and Multimedia Metadata: From Synthe...Ansgar Scherp
 
Events in Multimedia - Theory, Model, Application
Events in Multimedia - Theory, Model, ApplicationEvents in Multimedia - Theory, Model, Application
Events in Multimedia - Theory, Model, ApplicationAnsgar Scherp
 
Олег Антонов "Как подключить и использовать систему Расчет (ЕРИП)
Олег Антонов "Как подключить и использовать систему Расчет (ЕРИП)Олег Антонов "Как подключить и использовать систему Расчет (ЕРИП)
Олег Антонов "Как подключить и использовать систему Расчет (ЕРИП)dealby_seminar
 
Linked Open Data (Entwurfsprinzipien und Muster für vernetzte Daten)
Linked Open Data (Entwurfsprinzipien und Muster für vernetzte Daten)Linked Open Data (Entwurfsprinzipien und Muster für vernetzte Daten)
Linked Open Data (Entwurfsprinzipien und Muster für vernetzte Daten)Ansgar Scherp
 
A Framework for Iterative Signing of Graph Data on the Web
A Framework for Iterative Signing of Graph Data on the WebA Framework for Iterative Signing of Graph Data on the Web
A Framework for Iterative Signing of Graph Data on the WebAnsgar Scherp
 
SchemEX -- Building an Index for Linked Open Data
SchemEX -- Building an Index for Linked Open DataSchemEX -- Building an Index for Linked Open Data
SchemEX -- Building an Index for Linked Open DataAnsgar Scherp
 
Smart photo selection: interpret gaze as personal interest
Smart photo selection: interpret gaze as personal interestSmart photo selection: interpret gaze as personal interest
Smart photo selection: interpret gaze as personal interestAnsgar Scherp
 

Viewers also liked (7)

About Multimedia Presentation Generation and Multimedia Metadata: From Synthe...
About Multimedia Presentation Generation and Multimedia Metadata: From Synthe...About Multimedia Presentation Generation and Multimedia Metadata: From Synthe...
About Multimedia Presentation Generation and Multimedia Metadata: From Synthe...
 
Events in Multimedia - Theory, Model, Application
Events in Multimedia - Theory, Model, ApplicationEvents in Multimedia - Theory, Model, Application
Events in Multimedia - Theory, Model, Application
 
Олег Антонов "Как подключить и использовать систему Расчет (ЕРИП)
Олег Антонов "Как подключить и использовать систему Расчет (ЕРИП)Олег Антонов "Как подключить и использовать систему Расчет (ЕРИП)
Олег Антонов "Как подключить и использовать систему Расчет (ЕРИП)
 
Linked Open Data (Entwurfsprinzipien und Muster für vernetzte Daten)
Linked Open Data (Entwurfsprinzipien und Muster für vernetzte Daten)Linked Open Data (Entwurfsprinzipien und Muster für vernetzte Daten)
Linked Open Data (Entwurfsprinzipien und Muster für vernetzte Daten)
 
A Framework for Iterative Signing of Graph Data on the Web
A Framework for Iterative Signing of Graph Data on the WebA Framework for Iterative Signing of Graph Data on the Web
A Framework for Iterative Signing of Graph Data on the Web
 
SchemEX -- Building an Index for Linked Open Data
SchemEX -- Building an Index for Linked Open DataSchemEX -- Building an Index for Linked Open Data
SchemEX -- Building an Index for Linked Open Data
 
Smart photo selection: interpret gaze as personal interest
Smart photo selection: interpret gaze as personal interestSmart photo selection: interpret gaze as personal interest
Smart photo selection: interpret gaze as personal interest
 

Similar to Mining and Managing Large-scale Linked Open Data

On the need for a W3C community group on RDF Stream Processing
On the need for a W3C community group on RDF Stream ProcessingOn the need for a W3C community group on RDF Stream Processing
On the need for a W3C community group on RDF Stream ProcessingPlanetData Network of Excellence
 
OrdRing 2013 keynote - On the need for a W3C community group on RDF Stream Pr...
OrdRing 2013 keynote - On the need for a W3C community group on RDF Stream Pr...OrdRing 2013 keynote - On the need for a W3C community group on RDF Stream Pr...
OrdRing 2013 keynote - On the need for a W3C community group on RDF Stream Pr...Oscar Corcho
 
OSDC 2016 - Chronix - A fast and efficient time series storage based on Apach...
OSDC 2016 - Chronix - A fast and efficient time series storage based on Apach...OSDC 2016 - Chronix - A fast and efficient time series storage based on Apach...
OSDC 2016 - Chronix - A fast and efficient time series storage based on Apach...NETWAYS
 
A Fast and Efficient Time Series Storage Based on Apache Solr
A Fast and Efficient Time Series Storage Based on Apache SolrA Fast and Efficient Time Series Storage Based on Apache Solr
A Fast and Efficient Time Series Storage Based on Apache SolrQAware GmbH
 
Chronix: A fast and efficient time series storage based on Apache Solr
Chronix: A fast and efficient time series storage based on Apache SolrChronix: A fast and efficient time series storage based on Apache Solr
Chronix: A fast and efficient time series storage based on Apache SolrFlorian Lautenschlager
 
Real-Time Analytics with Apache Cassandra and Apache Spark
Real-Time Analytics with Apache Cassandra and Apache SparkReal-Time Analytics with Apache Cassandra and Apache Spark
Real-Time Analytics with Apache Cassandra and Apache SparkGuido Schmutz
 
Spark Under the Hood - Meetup @ Data Science London
Spark Under the Hood - Meetup @ Data Science LondonSpark Under the Hood - Meetup @ Data Science London
Spark Under the Hood - Meetup @ Data Science LondonDatabricks
 
Time to Science/Time to Results: Transforming Research in the Cloud
Time to Science/Time to Results: Transforming Research in the CloudTime to Science/Time to Results: Transforming Research in the Cloud
Time to Science/Time to Results: Transforming Research in the CloudAmazon Web Services
 
Using the Open Science Data Cloud for Data Science Research
Using the Open Science Data Cloud for Data Science ResearchUsing the Open Science Data Cloud for Data Science Research
Using the Open Science Data Cloud for Data Science ResearchRobert Grossman
 
Ehtsham Elahi, Senior Research Engineer, Personalization Science and Engineer...
Ehtsham Elahi, Senior Research Engineer, Personalization Science and Engineer...Ehtsham Elahi, Senior Research Engineer, Personalization Science and Engineer...
Ehtsham Elahi, Senior Research Engineer, Personalization Science and Engineer...MLconf
 
Materials Project computation and database infrastructure
Materials Project computation and database infrastructureMaterials Project computation and database infrastructure
Materials Project computation and database infrastructureAnubhav Jain
 
RISELab:Enabling Intelligent Real-Time Decisions
RISELab:Enabling Intelligent Real-Time DecisionsRISELab:Enabling Intelligent Real-Time Decisions
RISELab:Enabling Intelligent Real-Time DecisionsJen Aman
 
RISELab: Enabling Intelligent Real-Time Decisions keynote by Ion Stoica
RISELab: Enabling Intelligent Real-Time Decisions keynote by Ion StoicaRISELab: Enabling Intelligent Real-Time Decisions keynote by Ion Stoica
RISELab: Enabling Intelligent Real-Time Decisions keynote by Ion StoicaSpark Summit
 
Chronix Time Series Database - The New Time Series Kid on the Block
Chronix Time Series Database - The New Time Series Kid on the BlockChronix Time Series Database - The New Time Series Kid on the Block
Chronix Time Series Database - The New Time Series Kid on the BlockQAware GmbH
 
Leveraging the Power of Solr with Spark: Presented by Johannes Weigend, QAware
Leveraging the Power of Solr with Spark: Presented by Johannes Weigend, QAwareLeveraging the Power of Solr with Spark: Presented by Johannes Weigend, QAware
Leveraging the Power of Solr with Spark: Presented by Johannes Weigend, QAwareLucidworks
 
Leveraging the Power of Solr with Spark
Leveraging the Power of Solr with SparkLeveraging the Power of Solr with Spark
Leveraging the Power of Solr with SparkQAware GmbH
 
Apache Solr as a compressed, scalable, and high performance time series database
Apache Solr as a compressed, scalable, and high performance time series databaseApache Solr as a compressed, scalable, and high performance time series database
Apache Solr as a compressed, scalable, and high performance time series databaseFlorian Lautenschlager
 
Big data distributed processing: Spark introduction
Big data distributed processing: Spark introductionBig data distributed processing: Spark introduction
Big data distributed processing: Spark introductionHektor Jacynycz García
 

Similar to Mining and Managing Large-scale Linked Open Data (20)

On the need for a W3C community group on RDF Stream Processing
On the need for a W3C community group on RDF Stream ProcessingOn the need for a W3C community group on RDF Stream Processing
On the need for a W3C community group on RDF Stream Processing
 
OrdRing 2013 keynote - On the need for a W3C community group on RDF Stream Pr...
OrdRing 2013 keynote - On the need for a W3C community group on RDF Stream Pr...OrdRing 2013 keynote - On the need for a W3C community group on RDF Stream Pr...
OrdRing 2013 keynote - On the need for a W3C community group on RDF Stream Pr...
 
OSDC 2016 - Chronix - A fast and efficient time series storage based on Apach...
OSDC 2016 - Chronix - A fast and efficient time series storage based on Apach...OSDC 2016 - Chronix - A fast and efficient time series storage based on Apach...
OSDC 2016 - Chronix - A fast and efficient time series storage based on Apach...
 
A Fast and Efficient Time Series Storage Based on Apache Solr
A Fast and Efficient Time Series Storage Based on Apache SolrA Fast and Efficient Time Series Storage Based on Apache Solr
A Fast and Efficient Time Series Storage Based on Apache Solr
 
Chronix: A fast and efficient time series storage based on Apache Solr
Chronix: A fast and efficient time series storage based on Apache SolrChronix: A fast and efficient time series storage based on Apache Solr
Chronix: A fast and efficient time series storage based on Apache Solr
 
Real-Time Analytics with Apache Cassandra and Apache Spark,
Real-Time Analytics with Apache Cassandra and Apache Spark,Real-Time Analytics with Apache Cassandra and Apache Spark,
Real-Time Analytics with Apache Cassandra and Apache Spark,
 
Real-Time Analytics with Apache Cassandra and Apache Spark
Real-Time Analytics with Apache Cassandra and Apache SparkReal-Time Analytics with Apache Cassandra and Apache Spark
Real-Time Analytics with Apache Cassandra and Apache Spark
 
Spark Under the Hood - Meetup @ Data Science London
Spark Under the Hood - Meetup @ Data Science LondonSpark Under the Hood - Meetup @ Data Science London
Spark Under the Hood - Meetup @ Data Science London
 
Time to Science/Time to Results: Transforming Research in the Cloud
Time to Science/Time to Results: Transforming Research in the CloudTime to Science/Time to Results: Transforming Research in the Cloud
Time to Science/Time to Results: Transforming Research in the Cloud
 
Using the Open Science Data Cloud for Data Science Research
Using the Open Science Data Cloud for Data Science ResearchUsing the Open Science Data Cloud for Data Science Research
Using the Open Science Data Cloud for Data Science Research
 
Ehtsham Elahi, Senior Research Engineer, Personalization Science and Engineer...
Ehtsham Elahi, Senior Research Engineer, Personalization Science and Engineer...Ehtsham Elahi, Senior Research Engineer, Personalization Science and Engineer...
Ehtsham Elahi, Senior Research Engineer, Personalization Science and Engineer...
 
Materials Project computation and database infrastructure
Materials Project computation and database infrastructureMaterials Project computation and database infrastructure
Materials Project computation and database infrastructure
 
RISELab:Enabling Intelligent Real-Time Decisions
RISELab:Enabling Intelligent Real-Time DecisionsRISELab:Enabling Intelligent Real-Time Decisions
RISELab:Enabling Intelligent Real-Time Decisions
 
RISELab: Enabling Intelligent Real-Time Decisions keynote by Ion Stoica
RISELab: Enabling Intelligent Real-Time Decisions keynote by Ion StoicaRISELab: Enabling Intelligent Real-Time Decisions keynote by Ion Stoica
RISELab: Enabling Intelligent Real-Time Decisions keynote by Ion Stoica
 
Chronix Time Series Database - The New Time Series Kid on the Block
Chronix Time Series Database - The New Time Series Kid on the BlockChronix Time Series Database - The New Time Series Kid on the Block
Chronix Time Series Database - The New Time Series Kid on the Block
 
Leveraging the Power of Solr with Spark: Presented by Johannes Weigend, QAware
Leveraging the Power of Solr with Spark: Presented by Johannes Weigend, QAwareLeveraging the Power of Solr with Spark: Presented by Johannes Weigend, QAware
Leveraging the Power of Solr with Spark: Presented by Johannes Weigend, QAware
 
Leveraging the Power of Solr with Spark
Leveraging the Power of Solr with SparkLeveraging the Power of Solr with Spark
Leveraging the Power of Solr with Spark
 
The new time series kid on the block
The new time series kid on the blockThe new time series kid on the block
The new time series kid on the block
 
Apache Solr as a compressed, scalable, and high performance time series database
Apache Solr as a compressed, scalable, and high performance time series databaseApache Solr as a compressed, scalable, and high performance time series database
Apache Solr as a compressed, scalable, and high performance time series database
 
Big data distributed processing: Spark introduction
Big data distributed processing: Spark introductionBig data distributed processing: Spark introduction
Big data distributed processing: Spark introduction
 

More from Ansgar Scherp

Analysis of GraphSum's Attention Weights to Improve the Explainability of Mul...
Analysis of GraphSum's Attention Weights to Improve the Explainability of Mul...Analysis of GraphSum's Attention Weights to Improve the Explainability of Mul...
Analysis of GraphSum's Attention Weights to Improve the Explainability of Mul...Ansgar Scherp
 
STEREO: A Pipeline for Extracting Experiment Statistics, Conditions, and Topi...
STEREO: A Pipeline for Extracting Experiment Statistics, Conditions, and Topi...STEREO: A Pipeline for Extracting Experiment Statistics, Conditions, and Topi...
STEREO: A Pipeline for Extracting Experiment Statistics, Conditions, and Topi...Ansgar Scherp
 
Text Localization in Scientific Figures using Fully Convolutional Neural Netw...
Text Localization in Scientific Figures using Fully Convolutional Neural Netw...Text Localization in Scientific Figures using Fully Convolutional Neural Netw...
Text Localization in Scientific Figures using Fully Convolutional Neural Netw...Ansgar Scherp
 
A Comparison of Approaches for Automated Text Extraction from Scholarly Figures
A Comparison of Approaches for Automated Text Extraction from Scholarly FiguresA Comparison of Approaches for Automated Text Extraction from Scholarly Figures
A Comparison of Approaches for Automated Text Extraction from Scholarly FiguresAnsgar Scherp
 
Can you see it? Annotating Image Regions based on Users' Gaze Information
Can you see it? Annotating Image Regions based on Users' Gaze InformationCan you see it? Annotating Image Regions based on Users' Gaze Information
Can you see it? Annotating Image Regions based on Users' Gaze InformationAnsgar Scherp
 
Linked open data - how to juggle with more than a billion triples
Linked open data - how to juggle with more than a billion triplesLinked open data - how to juggle with more than a billion triples
Linked open data - how to juggle with more than a billion triplesAnsgar Scherp
 
SchemEX -- Building an Index for Linked Open Data
SchemEX -- Building an Index for Linked Open DataSchemEX -- Building an Index for Linked Open Data
SchemEX -- Building an Index for Linked Open DataAnsgar Scherp
 
A Model of Events for Integrating Event-based Information in Complex Socio-te...
A Model of Events for Integrating Event-based Information in Complex Socio-te...A Model of Events for Integrating Event-based Information in Complex Socio-te...
A Model of Events for Integrating Event-based Information in Complex Socio-te...Ansgar Scherp
 
strukt - A Pattern System for Integrating Individual and Organizational Knowl...
strukt - A Pattern System for Integrating Individual and Organizational Knowl...strukt - A Pattern System for Integrating Individual and Organizational Knowl...
strukt - A Pattern System for Integrating Individual and Organizational Knowl...Ansgar Scherp
 
Identifying Objects in Images from Analyzing the User‘s Gaze Movements for Pr...
Identifying Objects in Images from Analyzing the User‘s Gaze Movements for Pr...Identifying Objects in Images from Analyzing the User‘s Gaze Movements for Pr...
Identifying Objects in Images from Analyzing the User‘s Gaze Movements for Pr...Ansgar Scherp
 

More from Ansgar Scherp (10)

Analysis of GraphSum's Attention Weights to Improve the Explainability of Mul...
Analysis of GraphSum's Attention Weights to Improve the Explainability of Mul...Analysis of GraphSum's Attention Weights to Improve the Explainability of Mul...
Analysis of GraphSum's Attention Weights to Improve the Explainability of Mul...
 
STEREO: A Pipeline for Extracting Experiment Statistics, Conditions, and Topi...
STEREO: A Pipeline for Extracting Experiment Statistics, Conditions, and Topi...STEREO: A Pipeline for Extracting Experiment Statistics, Conditions, and Topi...
STEREO: A Pipeline for Extracting Experiment Statistics, Conditions, and Topi...
 
Text Localization in Scientific Figures using Fully Convolutional Neural Netw...
Text Localization in Scientific Figures using Fully Convolutional Neural Netw...Text Localization in Scientific Figures using Fully Convolutional Neural Netw...
Text Localization in Scientific Figures using Fully Convolutional Neural Netw...
 
A Comparison of Approaches for Automated Text Extraction from Scholarly Figures
A Comparison of Approaches for Automated Text Extraction from Scholarly FiguresA Comparison of Approaches for Automated Text Extraction from Scholarly Figures
A Comparison of Approaches for Automated Text Extraction from Scholarly Figures
 
Can you see it? Annotating Image Regions based on Users' Gaze Information
Can you see it? Annotating Image Regions based on Users' Gaze InformationCan you see it? Annotating Image Regions based on Users' Gaze Information
Can you see it? Annotating Image Regions based on Users' Gaze Information
 
Linked open data - how to juggle with more than a billion triples
Linked open data - how to juggle with more than a billion triplesLinked open data - how to juggle with more than a billion triples
Linked open data - how to juggle with more than a billion triples
 
SchemEX -- Building an Index for Linked Open Data
SchemEX -- Building an Index for Linked Open DataSchemEX -- Building an Index for Linked Open Data
SchemEX -- Building an Index for Linked Open Data
 
A Model of Events for Integrating Event-based Information in Complex Socio-te...
A Model of Events for Integrating Event-based Information in Complex Socio-te...A Model of Events for Integrating Event-based Information in Complex Socio-te...
A Model of Events for Integrating Event-based Information in Complex Socio-te...
 
strukt - A Pattern System for Integrating Individual and Organizational Knowl...
strukt - A Pattern System for Integrating Individual and Organizational Knowl...strukt - A Pattern System for Integrating Individual and Organizational Knowl...
strukt - A Pattern System for Integrating Individual and Organizational Knowl...
 
Identifying Objects in Images from Analyzing the User‘s Gaze Movements for Pr...
Identifying Objects in Images from Analyzing the User‘s Gaze Movements for Pr...Identifying Objects in Images from Analyzing the User‘s Gaze Movements for Pr...
Identifying Objects in Images from Analyzing the User‘s Gaze Movements for Pr...
 

Recently uploaded

Why Choose Canada for your next film project
Why Choose Canada for your next film projectWhy Choose Canada for your next film project
Why Choose Canada for your next film projectnubreed2019
 
Choose your perfect jacket.pdf
Choose your perfect jacket.pdfChoose your perfect jacket.pdf
Choose your perfect jacket.pdfAlexia Trejo
 
Ratio analysis, Formulas, Advantage PPt.pptx
Ratio analysis, Formulas, Advantage PPt.pptxRatio analysis, Formulas, Advantage PPt.pptx
Ratio analysis, Formulas, Advantage PPt.pptxSugumarVenkai
 
AWS_projects related AWS services such as feature store store and clarify
AWS_projects related AWS services such as feature store store and clarifyAWS_projects related AWS services such as feature store store and clarify
AWS_projects related AWS services such as feature store store and clarifyVarun Garg
 
introduction-to-crimean-congo-haemorrhagic-fever.pdf
introduction-to-crimean-congo-haemorrhagic-fever.pdfintroduction-to-crimean-congo-haemorrhagic-fever.pdf
introduction-to-crimean-congo-haemorrhagic-fever.pdfSalamaAdel
 
Basics of Creating Graphs / Charts using Microsoft Excel
Basics of Creating Graphs / Charts using Microsoft ExcelBasics of Creating Graphs / Charts using Microsoft Excel
Basics of Creating Graphs / Charts using Microsoft ExcelTope Osanyintuyi
 
HayleyDerby_Market_Research_Spotify.docx
HayleyDerby_Market_Research_Spotify.docxHayleyDerby_Market_Research_Spotify.docx
HayleyDerby_Market_Research_Spotify.docxHayleyDerby
 
Pereira_EBM_Assign3.docx1234123412341234
Pereira_EBM_Assign3.docx1234123412341234Pereira_EBM_Assign3.docx1234123412341234
Pereira_EBM_Assign3.docx1234123412341234LR1709MUSIC
 
Customer Satisfaction Data - Multiple Linear Regression Model.pdf
Customer Satisfaction Data -  Multiple Linear Regression Model.pdfCustomer Satisfaction Data -  Multiple Linear Regression Model.pdf
Customer Satisfaction Data - Multiple Linear Regression Model.pdfruwanp2000
 
Cousera Cap Course Datasets containing datasets from a Fictional Fitness Trac...
Cousera Cap Course Datasets containing datasets from a Fictional Fitness Trac...Cousera Cap Course Datasets containing datasets from a Fictional Fitness Trac...
Cousera Cap Course Datasets containing datasets from a Fictional Fitness Trac...Samuel Chukwuma
 
Artificial Intelligence for Vision: A walkthrough of recent breakthroughs
Artificial Intelligence for Vision:  A walkthrough of recent breakthroughsArtificial Intelligence for Vision:  A walkthrough of recent breakthroughs
Artificial Intelligence for Vision: A walkthrough of recent breakthroughsNikolas Markou
 
Growing through Artificial Intelligence (AI)
Growing  through Artificial Intelligence (AI)Growing  through Artificial Intelligence (AI)
Growing through Artificial Intelligence (AI)OmChou6
 
Prometheus Grafana Dashboard for Cassandra 5
Prometheus Grafana Dashboard for Cassandra 5Prometheus Grafana Dashboard for Cassandra 5
Prometheus Grafana Dashboard for Cassandra 5Sarma Pydipally
 
presentation big data analytics on Apache spark
presentation big data analytics on Apache sparkpresentation big data analytics on Apache spark
presentation big data analytics on Apache sparkVarun Garg
 
EXCEL-VLOOKUP-AND-HLOOKUP LECTURE NOTES ALL EXCEL VLOOKUP NOTES PDF
EXCEL-VLOOKUP-AND-HLOOKUP LECTURE NOTES ALL EXCEL VLOOKUP NOTES PDFEXCEL-VLOOKUP-AND-HLOOKUP LECTURE NOTES ALL EXCEL VLOOKUP NOTES PDF
EXCEL-VLOOKUP-AND-HLOOKUP LECTURE NOTES ALL EXCEL VLOOKUP NOTES PDFProject Cubicle
 
EIS-Webinar-Info-Governance-Age-AI-2024-02-27-for-distr.pdf
EIS-Webinar-Info-Governance-Age-AI-2024-02-27-for-distr.pdfEIS-Webinar-Info-Governance-Age-AI-2024-02-27-for-distr.pdf
EIS-Webinar-Info-Governance-Age-AI-2024-02-27-for-distr.pdfEarley Information Science
 
TainoMoreiraPereiraLuis_Assignment_4.docx
TainoMoreiraPereiraLuis_Assignment_4.docxTainoMoreiraPereiraLuis_Assignment_4.docx
TainoMoreiraPereiraLuis_Assignment_4.docxLR1709MUSIC
 
Introduction to data science.pdf-Definition,types and application of Data Sci...
Introduction to data science.pdf-Definition,types and application of Data Sci...Introduction to data science.pdf-Definition,types and application of Data Sci...
Introduction to data science.pdf-Definition,types and application of Data Sci...DrSumathyV
 
Discover the Best Free Web Hosting Services with SSL in 2023
Discover the Best Free Web Hosting Services with SSL in 2023Discover the Best Free Web Hosting Services with SSL in 2023
Discover the Best Free Web Hosting Services with SSL in 2023maker Money
 
WOMEN IN TECH EVENT : Explore Salesforce Metadata.pptx
WOMEN IN TECH EVENT : Explore Salesforce Metadata.pptxWOMEN IN TECH EVENT : Explore Salesforce Metadata.pptx
WOMEN IN TECH EVENT : Explore Salesforce Metadata.pptxyosra Saidani
 

Recently uploaded (20)

Why Choose Canada for your next film project
Why Choose Canada for your next film projectWhy Choose Canada for your next film project
Why Choose Canada for your next film project
 
Choose your perfect jacket.pdf
Choose your perfect jacket.pdfChoose your perfect jacket.pdf
Choose your perfect jacket.pdf
 
Ratio analysis, Formulas, Advantage PPt.pptx
Ratio analysis, Formulas, Advantage PPt.pptxRatio analysis, Formulas, Advantage PPt.pptx
Ratio analysis, Formulas, Advantage PPt.pptx
 
AWS_projects related AWS services such as feature store store and clarify
AWS_projects related AWS services such as feature store store and clarifyAWS_projects related AWS services such as feature store store and clarify
AWS_projects related AWS services such as feature store store and clarify
 
introduction-to-crimean-congo-haemorrhagic-fever.pdf
introduction-to-crimean-congo-haemorrhagic-fever.pdfintroduction-to-crimean-congo-haemorrhagic-fever.pdf
introduction-to-crimean-congo-haemorrhagic-fever.pdf
 
Basics of Creating Graphs / Charts using Microsoft Excel
Basics of Creating Graphs / Charts using Microsoft ExcelBasics of Creating Graphs / Charts using Microsoft Excel
Basics of Creating Graphs / Charts using Microsoft Excel
 
HayleyDerby_Market_Research_Spotify.docx
HayleyDerby_Market_Research_Spotify.docxHayleyDerby_Market_Research_Spotify.docx
HayleyDerby_Market_Research_Spotify.docx
 
Pereira_EBM_Assign3.docx1234123412341234
Pereira_EBM_Assign3.docx1234123412341234Pereira_EBM_Assign3.docx1234123412341234
Pereira_EBM_Assign3.docx1234123412341234
 
Customer Satisfaction Data - Multiple Linear Regression Model.pdf
Customer Satisfaction Data -  Multiple Linear Regression Model.pdfCustomer Satisfaction Data -  Multiple Linear Regression Model.pdf
Customer Satisfaction Data - Multiple Linear Regression Model.pdf
 
Cousera Cap Course Datasets containing datasets from a Fictional Fitness Trac...
Cousera Cap Course Datasets containing datasets from a Fictional Fitness Trac...Cousera Cap Course Datasets containing datasets from a Fictional Fitness Trac...
Cousera Cap Course Datasets containing datasets from a Fictional Fitness Trac...
 
Artificial Intelligence for Vision: A walkthrough of recent breakthroughs
Artificial Intelligence for Vision:  A walkthrough of recent breakthroughsArtificial Intelligence for Vision:  A walkthrough of recent breakthroughs
Artificial Intelligence for Vision: A walkthrough of recent breakthroughs
 
Growing through Artificial Intelligence (AI)
Growing  through Artificial Intelligence (AI)Growing  through Artificial Intelligence (AI)
Growing through Artificial Intelligence (AI)
 
Prometheus Grafana Dashboard for Cassandra 5
Prometheus Grafana Dashboard for Cassandra 5Prometheus Grafana Dashboard for Cassandra 5
Prometheus Grafana Dashboard for Cassandra 5
 
presentation big data analytics on Apache spark
presentation big data analytics on Apache sparkpresentation big data analytics on Apache spark
presentation big data analytics on Apache spark
 
EXCEL-VLOOKUP-AND-HLOOKUP LECTURE NOTES ALL EXCEL VLOOKUP NOTES PDF
EXCEL-VLOOKUP-AND-HLOOKUP LECTURE NOTES ALL EXCEL VLOOKUP NOTES PDFEXCEL-VLOOKUP-AND-HLOOKUP LECTURE NOTES ALL EXCEL VLOOKUP NOTES PDF
EXCEL-VLOOKUP-AND-HLOOKUP LECTURE NOTES ALL EXCEL VLOOKUP NOTES PDF
 
EIS-Webinar-Info-Governance-Age-AI-2024-02-27-for-distr.pdf
EIS-Webinar-Info-Governance-Age-AI-2024-02-27-for-distr.pdfEIS-Webinar-Info-Governance-Age-AI-2024-02-27-for-distr.pdf
EIS-Webinar-Info-Governance-Age-AI-2024-02-27-for-distr.pdf
 
TainoMoreiraPereiraLuis_Assignment_4.docx
TainoMoreiraPereiraLuis_Assignment_4.docxTainoMoreiraPereiraLuis_Assignment_4.docx
TainoMoreiraPereiraLuis_Assignment_4.docx
 
Introduction to data science.pdf-Definition,types and application of Data Sci...
Introduction to data science.pdf-Definition,types and application of Data Sci...Introduction to data science.pdf-Definition,types and application of Data Sci...
Introduction to data science.pdf-Definition,types and application of Data Sci...
 
Discover the Best Free Web Hosting Services with SSL in 2023
Discover the Best Free Web Hosting Services with SSL in 2023Discover the Best Free Web Hosting Services with SSL in 2023
Discover the Best Free Web Hosting Services with SSL in 2023
 
WOMEN IN TECH EVENT : Explore Salesforce Metadata.pptx
WOMEN IN TECH EVENT : Explore Salesforce Metadata.pptxWOMEN IN TECH EVENT : Explore Salesforce Metadata.pptx
WOMEN IN TECH EVENT : Explore Salesforce Metadata.pptx
 

Mining and Managing Large-scale Linked Open Data

  • 1. Slide 1Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Ansgar Scherp Mining and Managing Large-scale Linked Open Data GVDB, Nörten-Hardenberg, May 25, 2016 Thanks to: Chifumi Nishioka, Renata Dividino, Thomas Gottron, and many more …
  • 2. Slide 2Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Team Knowledge Discovery @ Ansgar Scherp Ahmed Saleh Chifumi Nishioka Falk Böschen Mohammad Abdel-Qader Till Blume Anke Koslowski (Secretariat) Henrik Schmidt (Engineer) Lukas Galke Florian Mai &
  • 3. Slide 3Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Linked Open Data (LOD) Cloud • Publishing and interlinking data on the web • Different quality, purpose, and sources • Using the Resource Description Framework(RDF) World Wide Web LOD Cloud Documents Data Hyperlinks via <a> Typed Links HTML RDF Addresses (URIs) Addresses (URIs)
  • 4. Slide 4Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Relevance of Linked Data?
  • 5. Slide 5Prof. Ansgar Scherp – asc@informatik.uni-kiel.de1000+ Datasets, 50+ Billion Triples Media Geographic Publications Web 2.0 eGovernment Cross-Domain Life Sciences Linked Data: May ‘07  August ‘14 Source: http://lod-cloud.net Social Networking
  • 6. Slide 6Prof. Ansgar Scherp – asc@informatik.uni-kiel.de LOD on One Slide: Example Graph biglynx:matt-briggs foaf:Person rdf:type Fully qualified URI using vocabulary prefixes: @prefix foaf: <http://xmlns.com/foaf/0.1/> . @prefix rdf: <http://w3.org/1999/02/22-rdf-syntax-ns#> . @prefix biglynx: <http://biglynx.co.uk/people/> . Object Predicate Subject RDF Triple
  • 7. Slide 7Prof. Ansgar Scherp – asc@informatik.uni-kiel.de LOD on One Slide: Example Graph biglynx:matt-briggs foaf:Person rdf:type Fully qualified URI using vocabulary prefixes: @prefix foaf: <http://xmlns.com/foaf/0.1/> . @prefix rdf: <http://w3.org/1999/02/22-rdf-syntax-ns#> . @prefix biglynx: <http://biglynx.co.uk/people/> . biglynx:Director rdf:type … …
  • 8. Slide 8Prof. Ansgar Scherp – asc@informatik.uni-kiel.de LOD on One Slide: Example Graph biglynx:matt-briggs foaf:Person biglynx:dave-smith biglynx:Director rdf:type foaf:knows rdf:type _1:point wgs84: lat wgs84: long dp:London foaf:based_near …… … … ex:loc “-0.118” “51.509” Types Properties Entity
  • 9. Slide 9Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Motivation for the SchemEX Index • Single entry point to query the LOD cloud • Search for data sources containing entities like – ‘Persons, who are Politicians and Actors’ – ‘Research data sets’ – ‘Scientific publications’ Query SELECT ?x FROM … WHERE { ?x rdf:type ex:Actor . ?x rdf:type ex:Politician . } Index1 2 2 2
  • 10. Slide 10Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Input Data for SchemEX • Quads: <subject> <predicate> <object> <context> • Example: <http://biglynx.co.uk/people/matt-briggs> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://xmlns.com/foaf/0.1/Person> <http://biglynx.co.uk/people/matt-briggs.rdf> <http://biglynx.co.uk/people/ matt-briggs.rdf> rdf:type biglynx: matt-briggs foaf: Person LOD Cloud Dataset 𝑋
  • 11. Slide 11Prof. Ansgar Scherp – asc@informatik.uni-kiel.de SchemEX Idea • Schema-level index SchemEX • Assign RDF entities to graph patterns • Map graph patterns to data sources (context) • Defined over entities, but store the context • Construction of schema-level index • Stream-based for scalability • Stratified bi-simulation for detecting patterns • Little loss of accuracy [KGS+12]
  • 12. Slide 12Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Building the Index from a Stream • Stream of quads coming from a LD crawler … Q16, Q15, Q14, Q13, Q12, Q11, Q10, Q9, Q8, Q7, Q6, Q5, Q4, Q3, Q2, Q1 FiFo 4 3 2 1 1 6 2 3 4 5 C3 C2 C2 C1 + Reasonable accuracy at cache size of 50k
  • 13. Slide 13Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Full BTC 2011Data Set: 2.17 Bn Triples Cache size: 50 k Winner BTC’11 + Linear runtime with respect to number of triples + Memory consumption scales with window size
  • 14. Slide 14Prof. Ansgar Scherp – asc@informatik.uni-kiel.de [GSK+13] Generalization Specialization Result list with examples Inspired by Google
  • 15. Slide 15Prof. Ansgar Scherp – asc@informatik.uni-kiel.de LODatio Under the Hood SPARQL Snippets Generalize Retrieve Data Sources Query translation Rank Specialize Count Select Select • Hybrid database with off-the-shelf components
  • 16. Slide 16Prof. Ansgar Scherp – asc@informatik.uni-kiel.de LOD on One Slide: Recap biglynx:matt-briggs foaf:Person biglynx:dave-smith biglynx:Director rdf:type foaf:knows rdf:type _1:point wgs84: lat wgs84: long dp:London foaf:based_near …… … … ex:loc “-0.118” “51.509” Type Set (TS) Property Set (PS) Information theoretic analyses of LOD • How much information is encoded in TS and PS? • … information encoded, once TS or PS is known? • … to which degree are TS and PS redundant? • Example: 20% of PLDs do not need TS (6% for PS) [GKS15]
  • 17. Slide 17Prof. Ansgar Scherp – asc@informatik.uni-kiel.de • 29 weekly LOD snapshots of ~100 Mio triples • Still running since May 2012 (now 200+ weeks) Käfer et al.’s Temporal Analysis of LOD • Data on the cloud changes a lot [Käfer et al., 2013] T. Käfer, A. Abdelrahman, J. Umbrich, P. O'Byrne, A. Hogan: Observing Linked Data Dynamics. ESWC 2013: 213-227 Changes? • But vocabularies defining RDF types and properties are highly static, e.g., RDF, FOAF LOD cloud ~2012 LOD cloud ~2014
  • 18. Slide 18Prof. Ansgar Scherp – asc@informatik.uni-kiel.de 𝐻(𝑃𝑆|𝑇𝑆=𝑡𝑠) 𝐻(𝑇𝑆|𝑃𝑆=𝑝𝑠) But:DoChangesOccurinPS and TS? • Analysis: expected conditional entropy over time • 𝐻(𝑃𝑆|𝑇𝑆 = 𝑡𝑠): entropy of 𝑃𝑆 given 𝑇𝑆 is known • Observation: types become less important • Changes in the use of TS and PS ? !
  • 19. Slide 19Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Changes over Time • Extended characteristic sets: ECS = PS ∪ TS # of ECS Avg.: 83.898 ECS per week # of ECS [DSG+13] • Avg. 73% of ECS re-occur next week (orange) • Avg. 35% of ECS remain unchanged (blue) • Avg. 20% of entity sets of ECS change / week [Neumann and Moerkotte, 2011] Thomas Neumann, Guido Moerkotte: Characteristic sets: Accurate cardinality estimation for RDF queries with multiple joins. ICDE 2011: 984-994 [Neumann and Moerkotte, 2011]
  • 20. Slide 20Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Temporal Dynamics of the Entities? • Notion of entity motivated by ECS: entity is a set of triples 𝑋 sharing the same subject URI 𝑠 • Example: –1 entity –4 triples w.l.o.g. • Useful to keep LOD caches up-to-date? • Can we predict when LOD sources will change?
  • 21. Slide 21Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Dynamics Function Θ • Definition of Θ over change rate function 𝑐(𝑋𝑡) Time X 𝑡𝑖 𝑡𝑗 Θ Θ 𝑡 𝑖 𝑋 = Θ(𝑋𝑡 𝑗 ) − Θ(𝑋𝑡 𝑖 ) = 𝑡 𝑖 𝑡 𝑗 𝑐 𝑋𝑡 d𝑡 [DGS+14] 𝑡𝑗 ≈ 𝑘=𝑖+1 𝑗 𝛿(𝑋𝑡 𝑘−1 , 𝑋𝑡 𝑘 ) • Approximation as step function over changes Monotone, non-negative c
  • 22. Slide 22Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Update Strategies for LOD Sources • Apply strategies from keeping caches of WWW documents up-to-date to maintain LOD caches • Assumptions –LOD is fetched from various sources –Sources are scored and prioritized based on strategy –Data of a source is fetched only when the operation can be entirely executed
  • 23. Slide 23Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Scheduling Update Strategies a) HTTP Header [Dividino et al., 2014a] b) Age or Last Visited [Dasdan et al., 2009, Cho and Garcia-Molina, 2000] c) PageRank [Page et al., 1999, Boldi et al., 2004, Baeza-Yates et al., 2005] d) LOD Sources Size e) Change Ratio [Douglis et al., 1997, Cho et al., 2002. Tan et al., 2007] f) Change Rate [Olston et al., 2002, Ntoulas et al., 2004, Dividino et al., 2013] g) History Information: Dynamics [Dividino et al., 2014b] We borrow strategies developed for the WWW and metrics for data change analysis in the LOD cloud.
  • 24. Slide 24Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Ranking Sources which changed (most) Sources that not changed/less changesTimeti tj e) Change Ratio • Captures the change frequency of the data (freshness) • Percentage of data items in the cache that are up-to-date
  • 25. Slide 25Prof. Ansgar Scherp – asc@informatik.uni-kiel.de f) Change Rate • Data from sources which are less similar which their previous update (snapshot) should be updated first • Comparison of two RDF data sets – 𝑋 : Set of triple statements – 𝛿 : Numeric expression (distance) 𝛿𝐽𝑎𝑐𝑐𝑎𝑟𝑑 𝑋1, 𝑋2 = 1 − 𝑋1 ∩ 𝑋2 𝑋1 ∪ 𝑋2 0,¥[ ) Time𝑡𝑖 𝑡𝑗 𝛿 Example:
  • 26. Slide 26Prof. Ansgar Scherp – asc@informatik.uni-kiel.de g) History Information: Dynamics • Data from sources which most evolve in a given period of time should be updated first • Uses both history information and change rate Θ(𝑋𝑡 𝑗 ) − Θ(𝑋𝑡 𝑖 ) = 𝑡 𝑖 𝑡 𝑗 𝑐 𝑋𝑡 d𝑡 Time X 𝑡𝑖 𝑡𝑗 Θ c ≈ 𝑘=𝑖+1 𝑗 𝛿(𝑋𝑡 𝑘−1 , 𝑋𝑡 𝑘 )
  • 27. Slide 27Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Evaluation  Idea: simulation of limitations of available computational resources (network bandwidth, computation time) Time 100% 𝑡𝑖 𝑡𝑖+1
  • 28. Slide 28Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Evaluation: Single Step Update Time 100% 15% 5%40% 75% 95%60% 𝑡𝑖 𝑡𝑖+1
  • 29. Slide 29Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Evaluation: Iterative Updates Time . . . 15% 5%40% 75% 95%60% 15% 5%40% 75% 95%60% 100% 𝑡𝑖 𝑡𝑖+1 𝑡𝑖+2
  • 30. Slide 30Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Dataset • Dynamic Linked Data Observatory • Weekly snapshots, 14 M triples  154 snapshots (approx. 3 years)  590 data sources (PLD) Top 10 largest data sources Average size dbpedia.org 3,406,364.5 edgarwrap.ontologycentral.com 982,631.0 dbtune.org 864,107.6 dbtropes.org 787,299.9 data.linkedct.org 498,986.3 aims.fao.org 416,708.9 www.legislation.gov.uk 399,601.6 kent.zpr.fer.hr 387,034.8 identi.ca 278,316.2 webenemasuno.linkeddata.es 250,557.9
  • 31. Slide 31Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Metrics:Precision & Recall • Precision: portion of cached data that are actually up-to-date • Recall: portion of data in the LOD cloud that is identical to the cached data Cached data Actual data on the LOD cloud (w.r.t. to the 590 sources considered)
  • 32. Slide 32Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Results: Single Step Update Time t jti 100% 15% 5%40% 75% 95%60%
  • 33. Slide 33Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Results: Iterative Updates Time tjti tj . . . 15% 5%40% 75% 95%60% 15% 5% 40% 75% 95%60% 100%
  • 34. Slide 34Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Results: Iterative Updates Time tjti tj . . . 15% 5%40% 75% 95%60% 15% 5% 40% 75% 95%60% 100%
  • 35. Slide 35Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Results: Iterative Updates Time tjti tj . . . 15% 5%40% 75% 95%60% 15% 5% 40% 75% 95%60% 100%
  • 36. Slide 36Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Results: Summary  Best strategies: ones which capture the change behaviour over time  Specially for low relative bandwidth
  • 37. Slide 37Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Dynamics Function Θ: Revisited Time X 𝑡𝑖 𝑡𝑗 c • Can we predict when LOD sources will change? • Notion of dynamics to compute periodicities! • Dynamics as vector of changes: < 𝛿(𝑋𝑡1 , 𝑋𝑡2 ), … , 𝛿(𝑋𝑡 𝑁−1 , 𝑋𝑡 𝑁 ) >
  • 38. Slide 38Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Temporal Clustering of Entities • Dynamics as vector: < 𝛿(𝑋𝑡1 , 𝑋𝑡2 ), … , 𝛿(𝑋𝑡 𝑁−1 , 𝑋𝑡 𝑁 ) > Time Change(logscale) [NS15] • Clustering with k-means++ to find patterns • 165 snapshots • 65,044 entities • 7 patterns (after optimizing 𝑘)
  • 39. Slide 39Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Periodicity of Entity Dynamics • Examples: < 0, 3, 2, 0, 3, 2, 0 >, < 1, 2, 1, 2, 1, 2 > # of entities Most likely periodicity C1 12,982 66 C2 168 23 C3 35 1 C4 12 1 C5 1 1 C6 1,541 56 C7 30 37 CS 50,725 [Elfeky et al., 2005] Mohamed G. Elfeky, Walid G. Aref, Ahmed K. Elmagarmid: Periodicity Detection in Time Series Databases. IEEE Trans. Knowl. Data Eng. 17(7): 875-887 (2005) • Convolution-based algorithm [Elfeky et al. 2005] • Entities of legislation.gov.uk found in several clusters (C1,C3,C4,C5,C6) • No changes (CS): 77.29% • CS: entities from w3.org and ontologydesignpatterns.org
  • 40. Slide 40Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Application Areas: More than One! • Searching for LOD sources [GSK+13,KGS+12] • Strategies for updating data caches [DGS15] • Programming queries against LOD [SSS12] • Recommending LOD vocabularies [SGS16]  Foundation for Future Data-driven Applications
  • 41. Slide 41Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Summary: KDD in Social Media & DL How to deal with the vast amount of content related to research and innovation? • H2020 INSO-4 project, duration: 04/2016-03/2019 • Data mining & visualization tools enabling information professionals to deal with large corpora • Website: http://www.moving-project.eu/ New
  • 42. Slide 42Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Got Interested? Knowledge Discovery at ZBW Contact me! Prof. Dr. Ansgar Scherp • Email: a.scherp@zbw.eu • Twitter: https://twitter.com/ansgarscherp • Slideshare: http://de.slideshare.net/ascherp • KD-Website: http://www.zbw.eu/en/research/knowledge-discovery/ http://www.kd.informatik.uni-kiel.de/en/
  • 43. Slide 43Prof. Ansgar Scherp – asc@informatik.uni-kiel.de References [DGS15] R. Dividino, T. Gottron, A. Scherp: Strategies for Efficiently Keeping Local Linked Open Data Caches Up-To-Date. International Semantic Web Conference (2) 2015: 356-373 [DGS+14] R. Dividino, T. Gottron, A. Scherp, G. Gröner: From Changes to Dynamics: Dynamics Analysis of Linked Open Data Sources. PROFILES@ESWC 2014 [GKS15] T. Gottron, M. Knauf, A. Scherp: Analysis of schema structures in the Linked Open Data graph based on unique subject URIs, pay-level domains, and vocabulary usage. Distributed and Parallel Databases 33(4): 515-553 (2015) [DSG+13] R. Dividino, A. Scherp, G. Gröner, T. Gottron: Change-a-LOD: Does the Schema on the Linked Data Cloud Change or Not? COLD 2013 [GSK+13] T. Gottron, A. Scherp, B. Krayer, A. Peters: LODatio: using a schema-level index to support users in finding relevant sources of linked data. K-CAP 2013: 105-108 [KGS+12] M. Konrath, T. Gottron, S. Staab, A. Scherp: SchemEX - Efficient construction of a data catalogue by stream-based indexing of linked data. J. Web Sem. 16: 52-58 (2012) [NS15] C. Nishioka, A Scherp: Temporal Patterns and Periodicity of Entity Dynamics in the Linked Open Data Cloud. K-CAP 2015. [SGS16] J. Schaible, T. Gottron, and A. Scherp: TermPicker Enabling the Reuse of Vocabulary Terms by Exploiting Data from the Linked Open Data Cloud, ESWC, Springer, 2016. [SSS12] S. Scheglmann, A. Scherp, S. Staab: Declarative Representation of Programming Access to Ontologies. ESWC 2012: 659-673
  • 44. Slide 44Prof. Ansgar Scherp – asc@informatik.uni-kiel.de a) HTTP Header • Data from sources which have been changed since the last update should be updated first HTTP Response HEADER … Last-Modified: Tue, 15 Nov 1994 12:45:26 GMT CONTENT
  • 45. Slide 45Prof. Ansgar Scherp – asc@informatik.uni-kiel.de b) Age or Last Visited • Time elapsed from last update (the difference between query time and last update time) • It guarantees that every source is updated after a period Ranking Sources that have been at longer time updated Sources that have been recently updated
  • 46. Slide 46Prof. Ansgar Scherp – asc@informatik.uni-kiel.de c) PageRank and d) Source Size • PageRank captures popularity/ importance of the LOD source • Data from sources with highest PageRank are updated first • LOD source size: data from the biggest/smallest LOD sources should be updated first Ranking Sources with higher PR Sources with lower PR
  • 47. Slide 47Prof. Ansgar Scherp – asc@informatik.uni-kiel.de Results: Single Step Update Time t jti 100% 15% 5%40% 75% 95%60%