Thesis Poster

Improving Online Social Network collection and processing mechanisms
Dimitris Tsagkarakis, Alexandros Vavakos, Vasilis Stavrou, Miltiadis Kandias
{d.tsagkarakis, alexandros.vavakos, stavrouv, kandias}@aueb.gr
Information Security and Critical Infrastructure Protection Laboratory
Dept. of Informatics, Athens University of Economics & Business (AUEB)
Introduction
Problems
 Rapid explosion of Online Social Networks.
 Time-consuming data management due to conventional relational databases.
 Users transfer their offline behavior to the online world.
 Delays in the data mining mechanisms due to lack of parallel processing.
Extraction of information from social networks contributes to the profiling of users.
 Need to upgrade existing mechanisms in order to make use of the latest API versions.
Open Source INTelligence (OSINT) to mitigate the insider threat.
OLTP vs. OLAP
Inserts and Updates
Queries
Hadoop Ecosystem
OLTP System
OLAP System
Short and fast inserts and
Periodic long-running batch jobs refresh
updates initiated by end users
the data
Relatively standardized and
Often complex queries involving
simple queries that return
aggregations
relatively few records
Processing Speed
Typically very fast
Depends on the amount of data involved
Space Requirements
Relatively small
Relatively large
Database Design
Highly normalized with many
Typically de-normalized with fewer
tables
tables; use of star and snowflake
schemas
Figure 3: Hadoop ecosystem
Final Twitter Crawler
Figure 1: OLTP vs OLAP Systems
Twitter
 User Privacy:
 Ability to identify a user from a comment or image by third
parties.
 Option to display the geographical location where a comment
or image was posted from.
 Utilization of users’ personal information in order to associate
certain advertisements with them.
 Improvements:
 Parallelization using multithreading.
 Design of a Graphical User Interface.
 Crawler update to sequentially gather users using a file.
 Crawler update to modify the tool’s configuration from within the application.
 Crawler update to store incidents in a log file for later use (analysis or debugging).
Conclusions
Figure 4: Twitter Crawler root window
Figure 5: Twitter Crawler configuration window
Youtube
 User Privacy:
 Ability to display user’s activity to third parties.
 Ability to display video’s information (view count, likes, etc).
 Connection with Google accounts.
 Shared accounts with Facebook and Twitter.
 Improvements:
 Updates and improvements on YouTube’s API responses.
 Parallelization using multithreading.
 Changes on the data stored in the data warehouse.
References
Figure 2: Social media connectivity
 Use of a distributed cluster of machines to store and manage large amounts of data.
 Need for parallelized data collection due to the constantly increasing amounts of
data that social networks process.
 Ability to connect to a social network using accounts from different networks.
 Ability to simultaneously collect user’s data from all the social networks in which
they use the same account.
 Proactive critical infrastructure protection capability.
 Ability to enhance organizational monitoring systems to mitigate the insider threat.
Athens University of Economics and Business
1. Amichai-Hamburger, Y., Vinitzky, G., Social Network Use and Personality”, 2010.
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Improving Online Social Network collection and processing mechanisms
D. Tsagkarakis, A. Vavakos, V. Stavrou, M. Kandias