This presentation shows an overview of the main concepts introduced in the EDBT2015 Summer School, which took place in Palamos. For each area, we summarize the main issues and current approaches. We also describe the challenges and main activities that were undertaken in the summer school

1. EDBT
Summer School
2015
Badenes, Carlos
Garijo, Daniel
Priyatna, Freddy
Palamos, Spain
31/8 - 4/9 2015

2. The Venue
2
(where we thought we would be) (where we actually were)

3. Overview
3
Graph Data Management
Part I: Theoretical
- Notes about lectures
Part II: Practical
- Sparksee Technology
- Challenges

4. Part I: Theoretical
4
๏ Large Scale Graph Processing System -
(C. Badenes)
Sherif Sakr - National ICT Australia
๏ Graph Visualization - (C. Badenes)
Peter Eades - University of Sydney
๏ Graph Data Management - (F. Priyatna)
Claudio Gutierrez - Universidad de Chile
๏ Applications of Flexible Querying to Graphs - (F.
Priyatna)
Alexandra Poulovassilis - Birkbeck, University of London
๏ Graph Management Benchmarking - (F. Priyatna)
Peter Boncz - CWI and Vrije Universiteit Amsterdam
๏ Graph Algorithms - (D. Garijo)
Dennis Shasha - New York
University
๏ Parallel Processing - (D. Garijo)
Bin Shao - Microsoft Research,
Beijing

5. Graph Data Management
5
Dr. Claudio Gutierrez
Computer Science Department
Universidad de Chile
http://richard.cyganiak.de/blog/2006/06/perez-et-al-semantics-and-
complexity-of-sparql/
(2-2, 1-1)

6. a general view of the main features of current graph
databases
Graph Data Management
6
A hypernode is a directed graph whose nodes can themselves be
graphs (or hypernodes), allowing nesting of graphs.
A property graph is a directed, labelled, attributed multigraph. That is, a graph where the
edges are directed, both nodes and edges are labeled and can have any number of
properties (or attributes), and there can be multiple edges between any two vertices.

7. Applications of Flexible Querying to Graphs
7
Dr. Alexandra Poulovassilis
Department of Computer Science and Information Systems,
Birkbeck, University of London
Reasoning in Event-Based
Distributed Systems
Authors: Helmer, Sven,
Poulovassilis, Alexandra, Xhafa,
Fatos
Adapting to Change in
Content, Size, Topology
and Use
Editors: Levene, Mark,
Poulovassilis, Alexandra (Eds.)
The Functional Approach
to Data Management
Modeling, Analyzing and
Integrating Heterogeneous
Data
Editors: Gray, P.M.D., Kerschberg, L.,
King, P.J.H., Poulovassilis, A. (Eds.)

8. Applications of Flexible Querying to Graphs
8
Query relaxation, which generally returns additional
answers compared to the exact form of the database
query.
Query approximation, which returns potentially
different answers compared to the exact form of the
query.
Q2 = SELECT * WHERE {
?x :actedIn :Tea_with_Mussolini .
RELAX ( ?x :hasFamilyName ?z ) }
Q3 = SELECT * WHERE {
?x :actedIn :Tea_with_Mussolini .
?x :label ?z . }
Q3.1 = SELECT * WHERE {
?x :actedIn :Tea_with_Mussolini .
?x :hasGivenName ?z ) }
Q3.2 = SELECT * WHERE {
?x :actedIn :Tea_with_Mussolini .
?x :hasFamilyName ?z . }
Q1= SELECT * WHERE {
APPROX ( :Battle_of_Waterloo :happenedIn/(:hasLongitude|:hasLatitude) ?x )
}
Q1.1=SELECT * WHERE {
:Battle_of_Waterloo :hasLongitude ?x }
Q1.2=SELECT * WHERE {
:Battle_of_Waterloo :hasLatitude ?x }
hasFamilyNamehasGivenName
label
subPropertyOf subPropertyOf
SparqlAR

9. Graph Management Benchmarking
9
Dr. Peter Boncz
Centrum Wiskunde & Informatica
(CWI)

10. Graph Management Benchmarking
10
Description: Given a start Person, find the Forums which that Person’s friends and friends of
friends (excluding start Person) became Members of after a given date. Return top 20 Forums,
and the number of Posts in each Forum that was Created by any of these Persons. For each
Forum consider only those Persons which joined that particular Forum after the given date. Sort
results descending by the count of Posts, and then ascending by Forum identifier.

11. Graph Management Benchmarking
11
Description: Given a start Person, find the Forums which that Person’s friends and friends of
friends (excluding start Person) became Members of after a given date. Return top 20 Forums,
and the number of Posts in each Forum that was Created by any of these Persons. For each
Forum consider only those Persons which joined that particular Forum after the given date. Sort
results descending by the count of Posts, and then ascending by Forum identifier.

12. Graph Motifs
12

13. Graph Motifs
13

14. Parallel Processing
14

15. Parallel Processing
15

16. Large Scale Graph Processing System
16
Dr. Sherif Sakr
Associate Professor at
College of Public Health and Health Informatics at
King Saud bin Abdul-Aziz University
“Big Data (Graph) Processing Systems
State-of-the-art and open challenges”

17. Large Scale Graph Processing System
17
Pregel Family
Bulk Synchronous Parallel (BSP) model
Valiant. A Bridging Model for Parallel Computation. Commun. ACM, 1990
GraphLab Family
Gather, Apply, Scatter (GAS) model
Y. Low, J. Gonzalez, A. Kyrola, D. Bickson, C. Guestrin, and J. M. Hellerstein
Distributed GraphLab: A Framework for Machine Learning in the Cloud. PVLDB,

18. Graph Visualization
18
Dr. Peter Eades
Research Professor at
School of Information Technologies at
The University of Sidney
Data Drawing Human
Visualization
Function
Perception
Function
faithful + readable

19. Graph Visualization
19
Topology-Shape-Metric approach
Energy-based approach
Clustered Planarity:
Multilevel methods:
Fast Approximations:
scaling to large graphs
scaling to large graphs

20. Part II: Practical
๏ Sparksee - Sparsity Technologies (C.Badenes)
Universitat Politécnica de Catalunya
๏ Challenges :: OEG-Team - (D.Garijo)
Similarities between Wikipedia Articles
20

21. Sparksee
21

22. Sparksee
22
schema
ry: Get common Messages for the given Hashtags
// User Node
int nodeUser = graph.newNodeType("User");
int userNickName = graph.newAttribute(nodeUser, "nickname",
DataType.String, AttributeKind.Unique);
// knows edge
int edgeKnows = graph.newEdgeType("knows", true, true);
// User1
long user1 = graph.newNode(nodeUser);
graph.setAttribute(user1,userNickName,new Value().setString(“User1"));
// edge 'knows'
long knows1 = graph.newEdge(edgeKnows, user1, user2);
// Find out the OID of the Hashtags with the given hastag's texts.
int tag = g.findType("Tag");
int tagName = g.findAttribute(tag, "name");
long tag1 = g.findObject(tagName, new Value().setString(ht1));
long tag2 = g.findObject(tagName, new Value().setString(ht2));
// Retrieve Messages with both hashtags and intersect the retrieved collection of Messages.
int tags = g.findType("tags");
Objects msgs1 = g.neighbors(tag1, tags, EdgesDirection.Ingoing);
Objects msgs2 = g.neighbors(tag2, tags, EdgesDirection.Ingoing);
long nums = msgs1.intersection(msgs2);

23. Challenge
23
Similarities in Wikipedia
- Description
- To Evaluate
- The design
- A good proof of functionality
- The efficiency, in terms of computation time
- The originality of the proposed method
- Technical prerequisites of participants
- Basic programming skills
- To be familiar with some graph library
- Technical support provided to participants
- English Wikipedia data (dump):
- articles_ids.csv
- articles_links.csv
- articles_body.csv
- articles_redirect.csv
- categories_ids.csv
- articles_category.csv
- categories_relations.csv

24. Problem
24
Similarity between Wikipedia Articles
Wikipedia Article:
text
links
categories

25. Hypothesis
25
Wikipedia Article:
text
links
categories
simLinks
simCtg
simTextα·
β·
ɣ·
+
+
simWA(R1,R2) = α·simTxt(R1,R2) + β·simLinks(R1,R2) + ɣ·simCtg(R1,R2)
where α+β+ɣ=1

26. Similarity based on Text
26
…
TOPIC_1
p = [0.5, 0.3,.., 0.7]
q = [0.2, 0.4,.., 0.9]Ri
R
j
TOPIC_2 TOPIC_n
Latent
Dirichlet
Allocation

27. Similarity based on Categories
27
Articles with multiple common categories
are likely to be similar
Noise filtering is necessary (e.g., “All articles lacking in-text citations”).
See https://github.com/cbadenes/siminwikart-challenge4/blob/master/category/wikipedia_bad_categories.txt

28. Similarity based on Links
28
Sim(A,B) = links(A) ∩ links(B) / ( (links(A) U links(B) ) / 2)
Articles with multiple common links
are likely to be similar

29. Proof of Concept
29
Fernando Alonso
Lionel Messi
Iker Casillas Princess Akiko
(simLinks) α = 0.2
(simCtg) β = 0.2
(simTxt) ɣ = 0.6
[1]0.062
[3]0.075
[1]0.666
[3]0.683
[1]0.058
[3]0.069
[1]0.043
[3]0.072
[1]0.019
[3]0.023
[1]0.068
[3]0.069
simTxt = 0.059
simLinks = 0.019
simCtg=[1]0.117
[3]0.181
simTxt = 0.065
simLinks = 0.0
simCtg=[1]0.095
[3]0.161
simTxt = 0.052
simLinks = 0.019
simCtg=[1]0.166
[3]0.172
simTxt = 0.980
simLinks = 0.175
simCtg=[1]0.217
[3]0.302
simTxt = 0.060
simLinks = 0.008
simCtg=[1]0.030
[3]0.172
simTxt = 0.069
simLinks = 0.004
simCtg=[1]0.080
[3]0.134

30. Comparison
30
Lionel Messi
Princess Akiko
simTxt = 0.060 -> <common words>
simLinks = 0.008 -> (England,Buenos_Aires,Chile,Madrid,Argentina)
simCtg=[1]0.030 -> living_person

31. Proposal
31
Graph based on Links Graph based on Similarities

32. Problem
32
Wikipedia links reliability
(missing links)
Wikipedia Article:
text
links
categories

33. Further Refinement
33
Similarities between categories (as topics)
can define relations between articles
Graph based on Links Graph based on Similarities
Subgraph Pattern Matching
+
Topic Model
+

34. Code
34
https://github.com/cbadenes/siminwikart-challenge4

35. Happy Ending
35

36. Kitkat Time
• Suggestions?
• Name for the system?
• Contributors?
36