Social Curation of Large Multimedia Collections on a Microsoft Azure Cloud Dazhi Chong, Samuel Coppage, Xiangyi Gu, Harris Wu Kurt Maly, Mohammad Zubair

DH 2012 Hamburg 19 July 2012 1

Social Curation of Large Multimedia Collections on a Microsoft Azure Cloud

Dazhi Chong, Samuel Coppage, Xiangyi Gu, Harris WuKurt Maly, Mohammad [email protected]

Old Dominion UniversityDepartment of Computer Science

mailto:[email protected]


Outline

Faceted Classification System and scalability issues

Implementation and deployment on a cloud Evaluation and user studies Conclusions


Faceted Classification Systemand scalability issues Web based application Allows users collaboratively organize

multimedia collections into faceted classification

Social application - must handle Many users Various network traffic levels

Traditional on-premises deployment can’t handle Increasing number of users Numerous evolving classification schemas Large document collections


Faceted Classification Systemand scalability issues



New features require even more resources Personal classification schema History feature – evolution of

classification over time Decision – move to a cloud platform


The click-and-drag classification screen


Global and personal (or local) schemas




Microsoft Windows Azure vs. Amazon Elastic Compute

Microsoft Windows Azure Cloud Platform as a Service (PaaS) cloud

Hides management and operational side from users

Focus on development and solving business problems

Amazon Elastic Compute Cloud Infrastructure as a Service (IaaS) cloud Allows to deploy new technologies and

adopt new capabilities


Microsoft Windows Azure vs. Amazon Elastic Compute

Both offer reliability and scalability Windows Azure more suitable for

applications with variable load, short or unpredicted lifetime

Azure platform was chosen because of the most managed environment

Choice of either platform – best fit for a company, developers and users


Implementation and deployment on Azure cloud platform

First step – conversion of Joomla 1.6.3 to work with Azure SQL

Second step – converting Faceted Classification System packages to Azure SQL (from MSQL)

Third step – full configuration of the system Last step – configuration of the whole

project and deployment






Design of the cloud-based web application

Final design of current deployment Web role can run by default 20

instances (more if needed) Azure manages load-balancing

(round-robin algorithm, performance and failover in beta) and seamlessly redirects users

All data stored now on Azure SQL


Design of the cloud-based web application


Advantages and disadvantages of deployment on the cloud platform

Advantages High availability, reliability and

scalability Disadvantages Azure SQL is a new product

Lacks features of the full MSSQL DB No profiler Import, export are rudimentary


Advantages and disadvantages of deployment on the cloud platform

Biggest drawback – performance of Microsoft SQL Driver for PHP

Measured query statements – no unusual delays

Fetching results with sqlsrv_fetch_array() sqlsrv_fetch_object()

delays in rendering web pages up to 20 seconds

Deployment of web application should consider all benefits and drawbacks


Evaluation

User studies with classes on information technologies (Spring and Fall 2011) Students had to develop personal facet

schemas Personal schemas were merged into

global schema

Initial Page with only few facets

Page without & with user facets

Item detail screen without & with faces and tags

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Merging of Personal Facets

Global Personal

- Good facet/category definition - Useful for most users - Optimized - Wide coverage

- Personal use - May contain non-facet schemas - Personal wording for facet/category/tag - Narrow coverage

Approach:

Evaluating all the personal schemas, find most widely used facets/category/items,

use similarity of concepts, enrich/reconstruct the global schema.

Sample algorithm component

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Popularity Description

New facet (1) It does not existed in the global schema;(2) is used in more than half of the personal schema

New category (1)It or a ‘similar’ category does not exist in the global old facet;

(2)the personal facet containing the global new category is similar to the global old facet

(3)more than half of the users who have the (‘similar’) global facet have the new category under it.

“Similar”: when two entities are either Wordnet similar or structure similar

Example-1:

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Event

- Group action

- Competition

- Wreck

Location

- Alabama

- Virginia

Source

- Newspaper

- Internet

Space Quality Time

- VA - Good - 1998

- New York - Bad - 2006

- Alabama Event - 2010

Position - Activity Tom

- NY - Crash - OK

- Virginia - Happening - Not Ok

Jason Year

- Favorite - before 2000

- Dislike - after 2000

Global schema (old):

Personal schema:

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Example-2:

Event Source

- Group action - Newspaper

- Competition - Internet

- Wreck Year

Location - Before 2000

- Alabama - After 2000

- Virginia

- New York

Similarity: S(year, time) =0.5528, S(crash, wreck) =1,

S(New York, NY)=1, S(Virginia, VA)=1

New global schema


Conclusions

A cloud can solve the scalability issue of: compute intensive features such as schema

merging and history (schema evolution) many simultaneous users

Porting a complex application to the cloud is a daunting task – not for the uninitiated

Documents

Social Curation of Large Multimedia Collections on a Microsoft Azure Cloud Dazhi Chong, Samuel Coppage, Xiangyi Gu, Harris Wu Kurt Maly, Mohammad Zubair