EDBT 2015: Summer School Overview

EDBTSummer

School 2015

Badenes, CarlosGarijo, Daniel

Priyatna, Freddy

Palamos, Spain31/8 - 4/9 2015

The Venue

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(where we thought we would be) (where we actually were)

Overview

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Graph Data Management Part I: Theoretical

- Notes about lectures Part II: Practical- Sparksee Technology - Challenges

Part I: Theoretical

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๏ 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

Graph Data Management

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Dr. Claudio GutierrezComputer Science DepartmentUniversidad de Chile

http://richard.cyganiak.de/blog/2006/06/perez-et-al-semantics-and-complexity-of-sparql/

(2-2, 1-1)



a general view of the main features of current graph databases

Graph Data Management

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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.

Applications of Flexible Querying to Graphs

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Dr. Alexandra PoulovassilisDepartment 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.)

Applications of Flexible Querying to Graphs

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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 :label ?z . }

Q3.1 = SELECT * WHERE {


?x :hasGivenName ?z ) }

Q3.2 = SELECT * WHERE {


?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

labelsubPropertyOf subPropertyOf

SparqlAR

Graph Management Benchmarking

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Dr. Peter BonczCentrum Wiskunde & Informatica (CWI)


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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.


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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.

Graph Motifs

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Graph Motifs

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Parallel Processing

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Parallel Processing

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Large Scale Graph Processing System

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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”

Large Scale Graph Processing System

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Pregel FamilyBulk Synchronous Parallel (BSP) model

L. G. Valiant. A Bridging Model for Parallel Computation. Commun. ACM, 1990

GraphLab FamilyGather, 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, 2012

Graph Visualization

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Dr. Peter Eades Research Professor at

School of Information Technologies at The University of Sidney

Data Drawing Human

VisualizationFunction

PerceptionFunction

faithful + readable

Graph Visualization

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Topology-Shape-Metric approach

Energy-based approach

Clustered Planarity:

Multilevel methods:

Fast Approximations:

scaling to large graphs

scaling to large graphs

Part II: Practical

๏ Sparksee - Sparsity Technologies (C.Badenes)Universitat Politécnica de Catalunya

๏ Challenges :: OEG-Team - (D.Garijo)Similarities between Wikipedia Articles

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Sparksee

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Sparksee

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schema

query: Get common Messages for the given Hashtags

// User Nodeint nodeUser = graph.newNodeType("User");int userNickName = graph.newAttribute(nodeUser, "nickname", DataType.String, AttributeKind.Unique);

// knows edgeint edgeKnows = graph.newEdgeType("knows", true, true);

// User1long 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);

Challenge

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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

Problem

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Similarity between Wikipedia Articles

Wikipedia Article: text

links

categories

Hypothesis

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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

Similarity based on Text

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…

TOPIC_1

p = [0.5, 0.3,.., 0.7]q = [0.2, 0.4,.., 0.9]Ri R

j

TOPIC_2 TOPIC_n

LatentDirichletAllocation

Similarity based on Categories

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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

https://github.com/cbadenes/siminwikart-challenge4/blob/master/category/wikipedia_bad_categories.txt

Similarity based on Links

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Sim(A,B) = links(A) ∩ links(B) / ( (links(A) U links(B) ) / 2)

Articles with multiple common linksare likely to be similar

Proof of Concept

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Fernando Alonso

Lionel Messi

Iker CasillasPrincess 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 = 0.059simLinks = 0.019simCtg=[1]0.117

[3]0.181


[3]0.161


[3]0.172


[3]0.302


[3]0.172


[3]0.134

Comparison

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Lionel Messi

Princess Akiko

simTxt = 0.060 -> <common words>simLinks = 0.008 -> (England,Buenos_Aires,Chile,Madrid,Argentina)simCtg=[1]0.030 -> living_person

Proposal

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Graph based on Links Graph based on Similarities

Problem

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Wikipedia links reliability(missing links)


links

categories

Further Refinement

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Similarities between categories (as topics) can define relations between articles

Graph based on Links Graph based on Similarities

Subgraph Pattern Matching

+Topic Model

+

Code

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

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

Happy Ending

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Kitkat Time

• Suggestions?

• Name for the system?

• Contributors?

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EDBT 2015: Summer School Overview

Education

Transcript of EDBT 2015: Summer School Overview