Efficient Multi-View Maintenance in the Social Semantic Web
-
Upload
matthias-broecheler -
Category
Documents
-
view
3.648 -
download
1
description
Transcript of Efficient Multi-View Maintenance in the Social Semantic Web
Efficient Multi-View Maintenance in the Social Semantic Web
Views on Social Networks
Query = Subgraph matching query View = Query for which the answer set is maintained as the social network database is updated
1 Multiple Views
2 Merging Views
3
Example Merge
4 Merge Optimality
5 Optimal Merge
6
7 8 9
Matthias Broecheler, Andrea Pugliese, and VS Subrahmanian
• On large social networks, multiple views are often maintained concurrently. • Maintaining multiple views is very expensive, in particular for rapidly changing databases. • e.g. Twitter has over 340 million tweets / day
IDEA: Very often, view queries have overlapping subgraph structures (bold arcs). If we can overlay the different view queries such that these shared substructures can be matched jointly rather than independently, we can save a lot of time. Social network updates are edge insertions (removals), hence graph merging has to center around the inserted edge type. Developed subgraph matching algorithm that can process merged view queries efficiently.
Many possible ways to merge query graphs. We want high connected overlap which results in most savings at update time. Define merged view score as the sum of edge
overlaps. Finding the optimal view wrt the merged view score is NP–hard.
Our greedy view merging algorithm finds near optimal views in practice.
Experiments
Compared our merged multi-view maintenance algorithm against standard independent view maintenance. 6 real world social network datasets with up to 540 million edges.
Randomly generated 12,000 queries with varying degree of overlap and averaged results over 750 trials.
All algorithm implemented in Java on top of the COSI graph database middleware.
Performance improvement of the Multi-View Maintenance algorithm on 6 different social networks
Applications
Maintaining multiple views jointly as a merged view leads to significant improvements. 477% faster than standard view maintenance
Applications include: Monitoring social networks Fraud, security applications, alerts
Business Analytics Knowledge Discovery Caching frequently asked queries
?v4
?v3 Health Care
?a1
?a2
?v7
?v6
Business Analytics
?v5
topic topic
references
references publish
tweet associated follows
publish
exp
ert
tweet
topic
follows
?v13
?v11
?v12 topic
exp
ert
associated
tweet
references
publish
topic comments
?v9 ?v8 publish
asso
ciat
ed
Edges mapped by 1, 2 and 3
Edges mapped only by 2
Edges mapped only by 3
Edges mapped only by 1
LEGEND
?v4
?v3 Health Care
?a1
?a2 ?v6
Business Analytics
?v5
topic
topic
references
references
publish
tweet associated
follows
publish
exp
ert
tweet
exp
ert
topic comments
?v9 ?v11 publish
?v10
publish ?v8
references topic
associated
Edges mapped by �1, �2 and �3
Edges mapped only by �2 Edges mapped only by �3
Edges mapped only by �1
LEGEND
?v7
?v16
?v14
?v15
topic
topic
references
publish
associated follows
publish
exp
ert
tweet
?v13 ?v12
70.0%%
75.0%%
80.0%%
85.0%%
90.0%%
95.0%%
100.0%%
0%%100%%200%%300%%400%%500%%600%%700%%800%%900%%
Physics%
Enron%
Youtube%
Flickr%
LiveJournal%
Orkut%
Outpe
rforming-
Improvem
ent-
Mul2-View-Maintenance-Performance-
?person
?article1 Health Care
?expert
?msg1
?other
?article2
Business Analytics
?msg2
topic topic
references
references publish
tweet associated follows
publish expert
tweet
Health Care
?expert ?msg
topic
references
associated
comments
expert
?doc ?person
?author ?article publish
publish
tweet
topic