1. Introduction
An Event-Centric Model
Summary
An Event-Centric Provenance Model for Digital
Libraries
C. Tang D. Castelli L. Candela P. Manghi
P. Pagano C. Thanos
Istituto di Scienza e Tecnologie dell’Informazione “A. Faedo” – CNR, Pisa - Italy
name.surname@isti.cnr.it
6th Italian Research Conference on Digital Libraries
Padua, Italy, 28-29 January 2010
C. Tang et al. An Event-Centric Provenance Model

2. Introduction
An Event-Centric Model
Summary
Outline
1 Introduction
Motivations
2 An Event-Centric Model
The Constituents
Exploiting the Model
C. Tang et al. An Event-Centric Provenance Model

3. Introduction
An Event-Centric Model Motivations
Summary
What is Provenance?
Some pseudo-definitions:
“a summary of the history and context of the data”
“the parts of the input that influenced (or that explain) a
part of the output”
“the part of the input that shows where a part of the output
came from”
“a causal graph that shows how a result was computed”
C. Tang et al. An Event-Centric Provenance Model

4. Introduction
An Event-Centric Model Motivations
Summary
What is Provenance?
Provenance is thus information about
source, derivation, influences, history
. . . of an object
program result, database query
In e-Science (thus in DLs), it is essential for
efficiency, reproducibility, accountability, explanation, data
cleaning, certifying scientific value of data
C. Tang et al. An Event-Centric Provenance Model

5. Introduction
An Event-Centric Model Motivations
Summary
What is the Problem?
Many models are being developed
Where-provenance, links output parts to equal input parts
Why-provenance, explains “why” some data appears in the
result
How-provenance, explains “how” a result was calculated
Workflow, describes result of a parallel/distributed program
. . . using different assumptions, e.g. system scope, program,
granularity
Our goal: develop a “non invasive” and “open” model
supporting “provenance generation”
C. Tang et al. An Event-Centric Provenance Model

6. Introduction
The Constituents
An Event-Centric Model
Exploiting the Model
Summary
The Idea
Add a layer dedicated to capture provenance-oriented data
Reference Objects
Information Objects
Events
C. Tang et al. An Event-Centric Provenance Model

7. Introduction
The Constituents
An Event-Centric Model
Exploiting the Model
Summary
The Model
Event is a happening having an effect on a Reference Object
<happenedTo> an Object
C. Tang et al. An Event-Centric Provenance Model

8. Introduction
The Constituents
An Event-Centric Model
Exploiting the Model
Summary
The Model
Each Event has a Type for filtering purposes
C. Tang et al. An Event-Centric Provenance Model

9. Introduction
The Constituents
An Event-Centric Model
Exploiting the Model
Summary
The Model
Description captures the “how” of the Event
C. Tang et al. An Event-Centric Provenance Model

10. Introduction
The Constituents
An Event-Centric Model
Exploiting the Model
Summary
The Model
Place captures the “where” of the Event
C. Tang et al. An Event-Centric Provenance Model

11. Introduction
The Constituents
An Event-Centric Model
Exploiting the Model
Summary
The Model
Time captures the “when” of the Event
C. Tang et al. An Event-Centric Provenance Model

12. Introduction
The Constituents
An Event-Centric Model
Exploiting the Model
Summary
The Model
The Agent controls the Event
C. Tang et al. An Event-Centric Provenance Model

13. Introduction
The Constituents
An Event-Centric Model
Exploiting the Model
Summary
The Model
Rationale captures the “why” of the Event
C. Tang et al. An Event-Centric Provenance Model

14. Introduction
The Constituents
An Event-Centric Model
Exploiting the Model
Summary
The Model
The Parameter is any additional information
C. Tang et al. An Event-Centric Provenance Model

15. Introduction
The Constituents
An Event-Centric Model
Exploiting the Model
Summary
The Model
Don’t reinvent the wheel!!!
C. Tang et al. An Event-Centric Provenance Model

16. Introduction
The Constituents
An Event-Centric Model
Exploiting the Model
Summary
Computing the provenance
4 1
5
3 2
C. Tang et al. An Event-Centric Provenance Model

17. Introduction
The Constituents
An Event-Centric Model
Exploiting the Model
Summary
The granularity issue
High flexibility by relying on the Information Object relationships
Reference Objects
part-of
Information Objects
Events
C. Tang et al. An Event-Centric Provenance Model

18. Introduction
The Constituents
An Event-Centric Model
Exploiting the Model
Summary
The AquaMaps scenario
AquaMaps is one of the VRE supported by the D4Science
e-Infrastructure
Aggregate data on species from multiple and evolving data
sources (e.g. OBIS, GBIF)
Curate aggregated data
Generate species distribution and biodiversity prediction
maps
C. Tang et al. An Event-Centric Provenance Model

19. Introduction
The Constituents
An Event-Centric Model
Exploiting the Model
Summary
Example 1
Find the events occurred to the Salmon object
C. Tang et al. An Event-Centric Provenance Model

20. Introduction
The Constituents
An Event-Centric Model
Exploiting the Model
Summary
Example 2
Find the contributors to the Salmon object
C. Tang et al. An Event-Centric Provenance Model

21. Introduction
The Constituents
An Event-Centric Model
Exploiting the Model
Summary
Example 3
How to explain the existence of the Salmon object
C. Tang et al. An Event-Centric Provenance Model

22. Introduction
An Event-Centric Model
Summary
Summary
Provenance is an essential feature in Digital Libraries and
eScience scenarios
Many provenance models are being developed using
different assumptions
A DL oriented provenance model that is event-based,
“open” and “non invasive”
Future steps
validation and consolidation of the model in the context of
new DLs application scenarios
implementation of a infrastructural service realising the
model in the D4Science infrastructure
C. Tang et al. An Event-Centric Provenance Model

23. Introduction
An Event-Centric Model
Summary
Summary
Provenance is an essential feature in Digital Libraries and
eScience scenarios
Many provenance models are being developed using
different assumptions
A DL oriented provenance model that is event-based,
“open” and “non invasive”
Future steps
validation and consolidation of the model in the context of
new DLs application scenarios
implementation of a infrastructural service realising the
model in the D4Science infrastructure
http://www.d4science.eu
http://www.dlorg.eu
C. Tang et al. An Event-Centric Provenance Model