The tutorial will be of interest to: (a) academic researchers who are incorporating provenance metadata in their research for data quality; (b) developers working on scalable platforms for emerging domain applications, such as IOT, LOD, and healthcare and life sciences. In addition to its meaningful breadth, the tutorial will present key technical topics that have seen significant research. These include, provenance modeling, querying and indexing techniques for W3C RDF datasets for provenance querying, and building complex provenance-enabled healthcare informatics platforms. The tutorial will cover the W3C PROV specifications, which are being used to integrate provenance in information systems, including the PROV Data Model (PROV-DM), PROV Ontology (PROV-O), and the PROV constraints.

1. Provenance Analysis and RDF
Query Processing
Satya S. Sahoo, Praveen Rao
October 12, 2015

2. Plan for the Tutorial
• 09:00 – 10:00: Provenance and its Applications
o What is provenance?
o W3C PROV specifications and applications
• 10:00– 10:30: Provenance Query and Analysis
o Provenance queries
o Graph operations to support provenance queries
• 10:30 – 11:00: Coffee Break, RBC Gallery, First Floor
• 11:00 – 12:15: RDF Query Processing
o Centralized approaches
o Parallel approaches
• 12:15 – 12:30: Discussion

3. Provenance in Application Domains: Healthcare
• Patient treatment often
depends on their
medical history
o Past hospital visits
o Current and past
medications
• Outcome of treatment
also depends on patient
history
• Medical history of
patient - provenance

4. Provenance in Application Domains: Sensor Networks
• Sensor properties
needed for data analysis
o Location of sensor
(geo-spatial)
o Temporal information
of sensor observations
o Sensor capabilities (e.g.,
resolution, modality)
• Provenance in sensor
networks
o Find all sensors located in
geographical location?
o Download data from wind
speed sensors for snowstorm
* Patni, H., Sahoo, S.S., Henson, C., Sheth, A., “Provenance aware linked sensor data”, Proceedings of the Second Workshop on
Trust and Privacy on the Social and Semantic Web, 2010

5. Provenance in Application Domains: In silico Experiments
• Provenance information helps explain how results from in silico
experiments are derived
• Supports scientific reproducibility
• Helps ensure data quality
* Zhao, J., Sahoo, S.S., Missier, P., Sheth, A., Goble, C., “Extending semantic provenance into the web of data”, IEEE Internet
Computing, 15(1), pp. 40-48. 2011

6. Research in Provenance Management
• Provenance: derived from the word provenir - “to come
from”
• Provenance metadata is specific category of metadata
• The W7 model: who, when, why, where, what, which, how
• Provenance tracking in relational databases
o Result set → + query (constraints) + time value
• Provenance in scientific workflow
ID
Name
1
Joe
2
Mary
GeneToPathwayGene Pathway

7. Provenance and Semantic Web Layer Cake
• Proof layer aka
Provenance
• Trust is derived from
provenance
information

8. Provenance Management
• Provenance Modeling using Semantic Web technologies
• Provenance models used as input to the W3C PROV Data Model
! Open Provenance
Model (OPM)
! Provenir Ontology
! Proof Markup
Language (PML)
! Dublin Core
! Provenance
Vocabulary

9. Provenance Management
• Provenance Querying and Access using Semantic Web technologies
• Access provenance of
resources on the Web
using standard Web
protocols (HTTP)
• Two access mechanisms
o Direct access:
Dereferencing URIs
o Provenance query
service
• Mechanism for content
negotiation

10. W3C PROV Family of Specifications: Provenance Modeling
• W3C Recommendations
o PROV Data Model (PROV-
DM)
o PROV Ontology (PROV-O)
o PROV-Constraints
o PROV Notations (PROV-
N)
• PROV Working Group Notes
(selected)
o PROV-Access and Querying (AQ)
o PROV Dictionary
o PROV XML
o PROV and Dublin Core Mappings
(PROV-DC)
o PROV Semantics (using first-order logic)
(PROV-SEM)

11. W3C PROV: PROV Data Model
• Three primary terms
• Entity: A real or
imaging thing with
fixed aspects
• Activity: occurs over
a period of time and
acts on entities
• Agent: bears
responsibility for
activity, entity, or
another agent

12. PROV-DM: Additional Terms
• PROV core terms can be extended to model domain-
specific provenance
o Subtyping: programming is a specific type of activity
• PROV allows modeling provenance of provenance
• Bundles: named set of provenance descriptions
o For example, provenance of medical record is important to
evaluate its accuracy
• Collections: structured entities
o For example, ranked query results

13. PROV-DM: Relationships
• Generation: completion of
production of an entity
• Usage: beginning of utilization of
entity by an activity
• Derivation: transformation of an
entity into another entity
• Attribution: ascribing entity to an
agent
• Association: assignment of
responsibility of an activity to
agent
• Delegation: assignment of
authority or responsibility to agent
…
prefix prov: http://www.w3.org/ns/prov#
prefix tut: <http://www.iswctutorial.com/>
Entity(tut:mapreduceprogram)
Activity (tut:programming)
wasGeneratedBy(tut:mapreduceprogram,
tut:programming, 2015-10-12:09:45)
…

14. A Provenance Graph: Medical History of Patients
• Exercise: Identify subtypes of PROV terms in the graph
ClassInstance

15. PROV Ontology (PROV-O)
• Models the PROV Data Model using OWL2
• Enables creation of domain-specific provenance ontologies

16. PROV-O: Qualified Terms
• Qualified terms are used to model ternary relationships
using the “Qualification Pattern”
• Uses an intermediate class to represent additional
description associated with the relationship
• Additional qualifications:
o Time of generation
o Location

17. PROV Constraints: Provenance Validation and Inference
• PROV Constraints is used to validate PROV instances
using a set of definitions, inferences, and constraints
• Support consistency checking and also reasoning over
PROV dataset
• Also allow normalization of PROV data
• For example,
o Uniqueness constraint: If two PROV statement describe the
birth of a person twice, the two statements will have same
timestamp
o Event ordering constraint: A person cannot be released from
hospital before admission

18. PROV Constraints: Inference
• Support for simple and complex inferences
Inference 15:
IF actedOnBehalfOf(id; ag2, ag1, _a, attrs) THEN
wasInfluencedBy(id; ag2, ag1, attrs)
Inference 13:
IF wasAttributedTo(_att; e, ag, attrs)
THEN wasGeneratedBy(_gen; e, a, _t,
attrs) AND
wasAssociatedWith(_assoc; a, ag, _p1,
[]).

19. Summary of First Session
• We have covered:
! What is provenance?
! Why is provenance important?
! How does it fit into the Semantic Web?
! Which models of provenance can be used by domain
applications?
! When to use PROV Entity, Agent, and Process?
! Who delegates authority or responsibility to Agent
(PROV-DM Relationships)?
! Where can we apply PROV constraints and inference
rules to validate provenance data?

20. Plan for the Tutorial
• 09:00 – 10:00: Provenance and its Applications
o What is provenance?
o W3C PROV specifications and applications
• 10:00 – 10:30: Provenance Query and Analysis
o Provenance queries
o Graph operations to support provenance queries
• 10:30 – 11:00: Coffee Break, RBC Gallery, First Floor
• 11:00 – 12:15: RDF Query Processing
o Centralized approaches
o Parallel approaches
• 12:15 – 12:30: Discussion

21. Provenance Query and Analysis: Data-driven Research
Source: http://renewhamilton.ca
Source: www.comsoc.org/blog
Source: www.nature.com
Human Connectome
Project
PAN-STARRS
Project
Neptune

22. Provenance Query and Analysis
• Challenges in data-driven research
o How to reliably store and transfer data between applications,
users, or across institutions?
o How to integrate data while ensuring consistency and data
quality?
o How to select subsets of data with relevant provenance
attributes
o How to rank results of user queries based on provenance
values?
• Provenance queries
o Directly query provenance
o Query provenance of provenance

23. Classification Scheme for Provenance Queries
• Type 1: Querying for Provenance Metadata
o Has this patient undergone a heart surgery in the
past 1 year?
• Type 2: Querying for Specific Data Set
o Find all financial transactions conducted by John
Doe in the past 3 years involving amount > $1
million?
• Type 3: Operations on Provenance Metadata
o What are the difference in the medical history of two
patients – one had better outcome than other?
23

24. I. Provenance Trails: Query for Provenance of Entity
• Provenance trails consists of all the
provenance related information of
an entity
o Hospital admissions of the patient
o Medication information
o Diagnosis information
• Involves graph traversal
o May involve recursive graph traversal
o All provenance information associated
with specific hospital admission

25. II. Query for Entity Satisfying Provenance
• Retrieve all entities that satisfy
specific provenance constraints
o Involves identification and extraction
of subgraph
o Conforms to the provenance
constraints
• May involve multiple SPARQL
queries
• Require aggregation of result
subgraphs

26. III. Aggregation or Comparison of Provenance
• Compare the provenance trails of two sensor data entities
to identify source of data error
• Provenance graph comparison can be related to subgraph
isomorphism used in SPARQL query execution
o Covered in the RDF query processing segment
• A patient’s medical history spans multiple hospital
admissions
o Requires aggregation of individual provenance graphs
corresponding to hospital admissions

27. RDF Reification Approach
lipoprotein inflammatory_cellsaffects

28. Provenance Context
• Provenance contextual information defines the
interpretation of an entity
• Provenance context is a formal object defined in terms
of Provenir ontology
lipoprotein
inflammatory
_cells
affects
derives_from
PubMed_
Source
derives_from
Entity
rdf:type
derives_from
PROV-O
Provenance
Context
* Sahoo et al., 22nd SSDBM Conference, Heidelberg, Germany, pp. 461-470, Jun 30 - Jul 2, 2010

29. Provenance Context Entity (PaCE) Approach
• A provenance context is used for entity generation - S, P, O of a
RDF triple
• Allows an application to decide the level of provenance
granularity
Exhaus've
approach
(E_PaCE)
Minimal
approach
(M_PaCE)
Intermediate
approach
(I_PaCE)

30. PaCE Inferencing and Evaluation Result
• 85 million fewer
RDF triples using
PaCE
Asserted
Inferred
• Extends existing
RDFS entailment
• Condition:
Equivalence of
provenance context
* Sahoo et al., 22nd SSDBM Conference, Heidelberg, Germany, pp. 461-470, Jun
30
-­‐
Jul
2, 2010

31. Provenance Context Entity (PaCE) Results
Query:
List
all
the
RDF
triples
extracted
from
a
given
journal
ar'cle
Query:
List
all
the
journal
ar'cles
from
which
a
given
RDF
triple
was
extracted
Query:
Count
the
number
of
triples
in
each
source
for
the
therapeu'c
use
of
a
given
drug
Query:
Count
the
number
of
journal
ar'cles
published
between
two
dates
for
a
given
triple
* Sahoo et al., 22nd SSDBM Conference, Heidelberg, Germany, pp. 461-470, Jun
30
-­‐
Jul
2, 2010

32. Time Series Analysis using Provenance Information
Query: Count the number of journal articles published over 10 years
for a given triple (e.g., thalidomide → treats → multiple myeloma)
* Sahoo et al., 22nd SSDBM Conference, Heidelberg, Germany, pp. 461-470, Jun
30
-­‐
Jul
2, 2010

33. Summary of Second Session
• We covered:
! Provenance queries
! Different categories of provenance queries
! Graph operations in context of provenance queries
! Provenance of RDF triples
! Comparison of Provenance Context Entity approach
and RDF Reification approach
! What’s next?

34. Plan for the Tutorial
• 09:00 – 10:00: Provenance and its Applications
o What is provenance?
o W3C PROV specifications and applications
• 10:00 – 10:30: Provenance Query and Analysis
o Provenance queries
o Graph operations to support provenance queries
• 10:30 – 11:00: Coffee Break, RBC Gallery, First Floor
• 11:00 – 12:15: RDF Query Processing
o Centralized approaches
o Parallel approaches
• 12:15 – 12:30: Discussion

35. Semantic Web Layer Cake

36. What will we cover?
• Oracle-RDF, SW-Store
• RDF-3X, Hexastore
• BitMat
• DB2RDF
• TripleBit
• RIQ
Centralized
approaches
• Scalable SPARQL querying
• HadoopRDF
• Trinity.RDF
• H2RDF+
• TriaD
• DREAM
Parallel
approaches
RDF query processing

37. Resource Description Framework (RDF)
• Each RDF statement is a (subject, predicate, object)
triple
o Represents an assertion or a fact
<http://xmlns.com/foaf/0.1/Alice> <http://xmlns.com/foaf/0.1/name> “Alice”

38. RDF Quadruples (Quads)
• A quad is denoted by (subject, predicate, object, context)
o Context (a.k.a. graph name) can be used to capture provenance
information (e.g., origin/source of a statement)
o Triples with the same context belong to the same RDF graph
@prefix foaf: <http://xmlns.com/foaf/0.1/>
foaf:Alice foaf:name “Alice” <http://ex.org/John/foaf.rdf> .
foaf:Bob foaf:name “Bob” <http://ex.org/John/foaf.rdf> .
foaf:Alice foaf:knows foaf:Bob <http://ex.org/graphs/John> .
foaf:Alice foaf:knows foaf:Bob <http://ex.org/graphs/Mary> .

39. SPARQL Query
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
PREFIX movie: <http://data.linkedmdb.org/resource/movie/>
SELECT ?g ?producer ?name ?label ?page ?film WHERE {
GRAPH ?g {
?producer movie:producer_name ?name .
?producer rdfs:label ?label .
OPTIONAL { ?producer foaf:page ?page . }
?film movie:producer ?producer . }}
Basic Graph Pattern (BGP) matching
Triple pattern

40. Open-Source and Commercial Tools
Sesame, Apache Jena, 3store, Mulgara, Kowari,
YARS2, …
Virtuoso, AllegroGraph, Garlik 4store/
5store, …
SYSTAP’s Blazegraph, Stardog, Oracle 12c,
Titan, Neo4j, MarkLogic, Ontotext’s GraphDB

41. Reported Large-scale Deployments
1+ trillion triples
Oracle 12c
8 database nodes (192 cores) and 14
storage nodes (168 cores), 2 TB total
RAM and 44.8 TB Flash Cache
AllegroGraph
240 core Intel x5650, 2.66GHz, 1.28 TB
RAM
10+ billion triples
OpenLink Virtuoso (15+ Billion)
8-node cluster, two quad-core processors
per node, 16 GB RAM
Ontotext’s GraphDB (13 Billion)
Dual-CPU server with Xeon E5-2690
CPUs, 512 GB of RAM and SSD storage
array
Stardog (50 Billion)
Single server, 32 cores, 256 GB RAM
Blazegraph (50 Billion)
Single server, GPU-acceleration
Source: http://www.w3.org/wiki/LargeTripleStores

42. Triples Table, Vertical Partitioning
• SQL-based RDF querying scheme [Chong et.al. VLDB ‘05]
o IDTriples table, URIMap table; use of self-joins; subject-
property matrix
• SW-Store [Abadi et.al., VLDB ’07, VLDBJ ‘09]
o Vertical partitioning of RDF data
• Triples with the same property are grouped together: (S,O)
o Use of a column-store; materialization of frequent joins
• MonetDB/SQL [Sidirourgos et.al., PVLDB ’08]
o Triplestore on a row-store vs vertical partitioning on column-
store
D. J. Abadi, A. Marcus, S. R. Madden, K. Hollenbach, “Scalable Semantic Web Data Management Using Vertical Partitioning,” in Proc. of
the 33rd VLDB Conference, 2007, pp. 411-422.
L. Sidirourgos, R. Goncalves, M. Kersten, N. Nes, S. Manegold, “Column-store support for RDF data management: not all swans are white,”
in PVLDB, 1(2), 2008.
E. I. Chong, S. Das, G. Eadon, J. Srinivasan, “An efficient SQL-based RDF querying scheme,” in Proc. of the 31st VLDB Conference, 2005,
pp. 1216-1227.

43. Exhaustive Indexing
• Early approaches
o Kowari [Wood et.al., XTech ‘05], YARS [Harth et.al., LA-WEB ‘05]
• RDF-3X [Neuman et.al., PVLDB ‘08, VLDBJ ‘10]
o 6 permutations: (SPO), (SOP), (POS), (PSO), (OSP), (OPS)
o Clustered B+-tree indexes; leverages merge joins; compression
o New join ordering method using a cost model based on
selectivity estimates
• Hexastore also builds similar indexes [Weiss et.al., PVLDB ‘08]
o Merge joins; no compression
T. Neumann, G. Weikum, “RDF-3X: a RISC-style engine for RDF,” in Proc. of the VLDB Endowment 1 (1) (2008), pp. 647-659.
C. Weiss, P. Karras, A. Bernstein, “Hexastore: Sextuple indexing for Semantic Web data management,” in Proc. VLDB Endow. 1 (1) (2008),
pp. 1008-1019.

44. Reducing the Cost of Join Processing
• BitMat [Atre et.al., WWW ‘10]
o A triple is uniquely mapped to a cell in a 3D cube
o Compressed bit matrices are loaded and processed in memory
during join processing
• Intermediate join results are not materialized
• DB2RDF [Bornea et.al., SIGMOD ‘13]
o Direct Primary Hash, Reverse Primary Hash
• Wide table layout to reduce joins for star-shaped queries
• Only subject and object indexes
o SPARQL-to-SQL translation
M. A. Bornea, J. Dolby, A. Kementsietsidis, K. Srinivas, P. Dantressangle, O. Udrea, B. Bhattacharjee, “Building an efficient RDF store over a
relational database,” in Proc. of 2013 SIGMOD Conference, 2013, pp. 121-132.
M. Atre, V. Chaoji, M. J. Zaki, J. A. Hendler, “Matrix "Bit" loaded: A scalable lightweight join query processor for RDF data,” in Proc. of the
19th WWW Conference, 2010, pp. 41-50.

45. Reducing the Cost of Join Processing
• TripleBit [Yuan et.al., PVLDB ‘13]
o Represents triples as a 2D bit matrix called Triple Matrix
• Compression for compactness
o For each predicate
• SO and OS ordered buckets of triples
• Conceptually, only two indexes are needed instead of six: POS, PSO
o Reduction in the size of intermediate results during join
processing
P. Yuan, P. Liu, B. Wu, H. Jin, W. Zhang, L. Liu, “TripleBit: A fast and compact system for large scale RDF data,” in Proc. VLDB Endow. 6 (7)
(2013), pp. 517-528.

46. Join Processing on Large, Complex BGPs
Too many join operations "

47. RIQ
• Fast processing of SPARQL queries on RDF quads:
(S,P,O,C)
Decrease-and-conquer
V. Slavov, A. Katib, P. Rao, S. Paturi, D. Barenkala, “Fast Processing of SPARQL Queries on RDF Quadruples,” in Proc. of the 17th International
Workshop on the Web and Databases (WebDB 2014), Snowbird, UT, 2014.

48. RIQ’s Architecture

49. Performance Comparison: Single Large,
Complex BGP
BTC 20121
~ 1.4 billion quads
LUBM
~ 1.4 billion RDF statements
1http://challenge.semanticweb.org
Y. Guo, Z. Pan, J. Heflin, “LUBM: A benchmark for OWL knowledge base systems,” Web Semantics: Science, Services
and Agents on the World Wide Web 3 (2005) 158–182.

50. Performance Comparison: Multiple
BGPs
BTC 20121
~ 1.4 billion quads

51. Parallel RDF Query Processing in a Cluster
• Early approaches
o YARS2 [Harth et.al., ISWC/ASWC ’07], SHARD [Rohloff et.al., PSI
EtA ‘10], Virtuoso1
• Hash partition triples across multiple machines
• Parallel access during query processing
o Work well for simple index lookup queries
o For complex SPARQL queries, need to ship data during query
processing
K. Rohloff and R. Schantz, “High-performance, massively scalable distributed systems using the MapReduce software framework: The
SHARD triple-store.” International Workshop on Programming Support Innovations for Emerging Distributed Applications, 2010.
1OpenLink Software. Towards Web-Scale RDF. http://virtuoso.openlinksw.com/dataspace/dav/wiki/Main/VOSArticleWebScaleRDF.
A. Harth, J. Umbrich, A. Hogan, S. Decker, “YARS2: A Federated Repository for Querying Graph Structured Data from the Web,” in Proc.
of ISWC'07/ASWC'07, pp. 211-224, 2007.

52. Scalable SPARQL Querying
• Vertex partitioning using METIS1
• Triples in each partition are placed together on a
machine
o Replication of triples on the partition boundaries
• n-hop guarantee
o PWOC queries
• No data shuffling between machines
o Uses RDF-3X on each machine
• Uses Hadoop for certain tasks
o E.g., data partitioning, communication during query processing
J. Huang, D. J. Abadi, K. Ren, “Scalable SPARQL querying of large RDF graphs,” in Proc. of VLDB Endow. 4 (11) (2011), pp. 1123-1134.
1METIS. http://glaros.dtc.umn.edu/gkhome/views/metis

53. HadoopRDF
• Split triples by predicate
• For rdf:type, split by distinct objects
• Store the splits as HDFS files
• MapReduce-based join processing to process SPARQL
queries
o Heuristics-based cost model
M. Husain, J. McGlothlin, M. Masud, L. Khan, B. Thuraisingham, “Heuristics-Based Query Processing for Large RDF Graphs Using Cloud
Computing,” in IEEE Transactions on Knowledge and Data Engineering 23(9), pp. 1312-1327 (2011).

54. Trinity.RDF
• Uses a distributed in-memory key-value store
o Hashing on vertex-ids, random partitioning on machines
o RDF graphs are stored natively using key-value pairs
• Parallel graph exploration, optimized exploration
o Lower communication cost
o Reduction in the size of intermediate results
K. Zeng, J. Yang, H. Wang, B. Shao, Z. Wang, “A distributed graph engine for Web Scale RDF data,” Proc. VLDB Endow. 6 (4) (2013),
pp. 265-276.
2models
(vertex-id, <in-adjacency-list, out-adjacency-list>)
(vertex-id, <in1, …, ink, out1, …, outk>), (ini,
<adjacency-listi>), (outi, <adjacency-listi>)
Adjacency list is partitioned on i machines

55. H2RDF+
• Uses HBase to build indexes on triples
o 6 permutations of (SPO)
• Triples are stored as rowkeys
o Aggressive compression
• MapReduce-based multi-way merge and sort-merge joins
o Sort-merge join is used when joining unordered intermediate
results
N. Papailiou, D. Tsoumakos, I. Konstantinou, P. Karras, N. Koziris, “H2RDF+: An Efficient Data Management System for Big RDF
Graphs,” in Proc. of the 2014 SIGMOD Conference, Snowbird, Utah, 2014, pp. 909-912.
N. Papailiou, I. Konstantinou, D. Tsoumakos, P. Karras, N. Koziris, “H2RDF+: High-performance Distributed Joins over Large-
scale RDF Graphs,” in Proc. of the IEEE International Conference on Big Data, 2013.

56. TriAD
• Master node
o Global summary graph – concise summary of RDF data
• Graph partitioning; a supernode per partition
• Worker/slave nodes
o Locality-based sharding
• Triples belonging to a supernode are stored on the same horizontal
partition
o Local indexes – 6 permutations of (SPO)
• Query processing
o Use the summary graph for join-ahead pruning
o Distributed query execution via asynchronous inter-node
communication (MPICH2)
S. Gurajada, S. Seufert, I. Miliaraki, M. Theobald, “TriAD: A Distributed Shared-nothing RDF Engine Based on Asynchronous Asynchronous
Message Passing,” in Proc. of the 2014 SIGMOD Conference, Snowbird, Utah, 2014, pp. 289-300.

57. DREAM
• RDF data is not partitioned across different machines
o Each machine stores the entire RDF data
• Adaptive query planner
o Partitions a query graph into sub-queries
o Sub-queries are executed in parallel on M (≥1) machines
o No data shuffling
• Machines exchange auxiliary data (e.g., ids of triples) for joining
intermediate data and producing the final result
M. Hammoud, D. A. Rabbou, R. Nouri, S.M.R. Beheshti, S. Sakr, “DREAM: Distributed RDF Engine with Adaptive Query Planner and
Minimal Communication,” in Proc. VLDB Endow. 8 (6) (2015), pp. 654-665.

58. What did we cover?
• Oracle-RDF, SW-Store
• RDF-3X, Hexastore
• BitMat
• DB2RDF
• TripleBit
• RIQ
Centralized
approaches
• Scalable SPARQL querying
• HadoopRDF
• Trinity.RDF
• H2RDF+
• TriaD
• DREAM
Parallel
approaches
RDF query processing

59. Open Challenges in Provenance
• Large scale storage of provenance
o Limited work in real world provenance management for Big
Data applications
• Standardization of provenance query APIs
• Integration of provenance analysis with RDF query
processing systems
• Efficient provenance analysis using state of the art
approaches in SPARQL query execution
• Visualization of provenance data

60. Acknowledgement
• Tutorial Website
o https://sites.google.com/site/provenancetutorial/
• Acknowledgements
o National Science Foundation (NSF) Grant No. 1115871
o National Institutes of Health (NIH) Grant No. 1U01EB020955-01
• Contact
o Satya Sahoo, satya.sahoo@case.edu
o Praveen Rao, raopr@umkc.edu