Social Curation of Large Multimedia Collections on a Microsoft Azure Cloud
Dazhi Chong, Samuel Coppage, Xiangyi Gu, Harris Wu*
College of Business and Public Administration
Old Dominion University
Norfolk, VA 23529
dchong@odu.edu, scoppage@odu.edu, xxxgu001@odu.edu, hwu@odu.edu
Kurt Maly, Mohammad Zubair
Department of Computer Science
Old Dominion University
Norfolk, VA 23529
maly@cs.odu.edu, zubair@cs.odu.edu
* Author designated to receive proofs and correspondence
Abstract
The explosion of digital media and decreasing computing costs enable people to create large digital
multimedia collections. With cloud computing, the size of such collections seems to be virtually
unlimited. However, useful digital collections require a large amount of classification and other
curation efforts. This paper shares the experience of deploying a social curation system on a
Microsoft Azure cloud. The system is designed to be used by users who lack the expertise of
librarians and are not technically savvy. And yet, the system deployed on the cloud builds metadata
and navigable structures automatically using individual users’ personal organization and tagging of
documents. The talk will highlight several key scalability challenges, solutions and evaluation results.
Introduction and Prior Work
In previous Digital Humanities conferences we presented the Facet System, a system that improves
browsing and searching of a large collection by supporting users to build a faceted (multi-perspective)
classification schema collaboratively (Georges et al., 2008; Fu et al., 2010). The system is targeted for
multimedia collections that have little textual metadata. In summary, our system (a) allows users to
build and maintain a faceted classification collaboratively, (b) enriches the user-created facet
schema systematically, and (c) classifies documents into an evolving facet schema automatically. It is
hoped that through users’ collective efforts the faceted classification schema will evolve along with
the users’ interests and thus help them navigate the collection. For the browsing and classification
interfaces of the Facet System see Fig. 1 and Fig. 2. This paper discusses scalability challenges and
improvements made to the system, cloud deployment, user studies and evaluation results.
Figure 1. Browsing of the system enabled by user-evolved faceted classification
Figure 2. The click-and-drag classification screen
Scalability Issues and System Improvement
Our Facet System allows users to collaboratively evolve a common, global schema that contains
facets (perspectives such as colour) and categories (such as red and green in the colour facet) for a
large multimedia collection, using a wiki-inspired interface. The global schema can then used by any
users for browsing or searching the collection. The variety of user perspectives increases with the
number of users. Even though we have taken measures to cure and reduce the global schema, we
find that the global schema alone faces limitations in supporting a large number of users. Many
users prefer to focus on a smaller subset of the schema or collection. To address this issue, we have
improved the user interface to allow users switch between global and personal schemas. The
personal schema allows users to organize digital documents in their own way. A group of people can
share the same personal schema which applies to a subset of the large digital collection. On the
backend, we developed a “merging” algorithm that automatically enriches the global schema by
merging items, categories and facets from personal schema into the global schema. The algorithm
applies statistical techniques to item-category associations, category-subcategory and category-facet
associations, and Wordnet-based similarities. Figure 3 shows both global and personal schemas that
can be utilized by individual users or user groups. More technical details will be presented at the
conference.
Figure 3. Global and personal (or local) schemas
Cloud Deployment
A large repository with millions of multimedia items may consume terabytes of storage. To support
millions of users, the system needs to be partitioned, mirrored, or load balanced across an array of
servers. When user traffic and growth trends are not perfectly predicable, cloud computing is not
only the most cost effective but also likely the only viable option.
After evaluating several cloud service providers, we find that Microsoft Azure Cloud provides the
best cost-effectiveness for us to host large digital repositories that require scalable database support
as well as network bandwidth. Our Facet System was built on an open source technology stack
including Joomla, PHP and MySQL. Joomla is a leading open source content management system
implemented in PHP. Initially we deployed the system on two server layers, Web (Joomla/PHP) and
Database (MySQL) layers, utilizing Web and Worker roles in the Azure cloud (see Fig. 4). The
scalability and manageability of the MySQL database turned out to be prohibitive. We found that
true database scalability can be accomplished only by cloud-tailored solutions such as Microsoft SQL
Azure. We spent significant efforts in porting Joomla from MySQL to Microsoft SQL Azure. In
addition, we moved Joomla’s session management from the default storage in operating system files
to Azure storage, to enable stateless load balancing across many servers on the Web layer. During
the conference we will present more technical details which may benefit users of Joomla and other
open source content management systems.
Figure 4. The deployment architecture in Microsoft Azure
Evaluation and user studies
We evaluated the system using 15 classes (different sessions of a university-wide undergraduate
introductory information technology class) during Spring 2011, and continue the evaluation with 22
classes during Fall 2011 semester using the cloud deployment of our system. Each class has around
30 students. The system was used to teach the digital humanities and social computing component
of the class, by asking students to collaboratively curate a special library collection of un-cataloged
local newspaper clippings and images on African American activism in United States. We captured
detailed usage information as users completed a series of tasks involving searching, classification,
tagging and voting (providing feedback on digital items and metadata). Each class of students work
as a group using a single personal schema, which may be better labeled as “local schema” in the
future to avoid confusion. A backend algorithm periodically merges categories and facets from these
local schemas into the global schema. While global schema is useful, we find that the students spend
a lot of efforts on curating the collection through building the local schemas for their own classes.
Therefore we refer to our current system a Social Curation System. The performance of the system
and cloud deployment proves to be satisfactory and actually exceeded our expectation in certain
aspects.
A common challenge to curating digital collections is the lack of expertise necessary to classify,
catalog and create metadata structures for such collections. The evaluation shows the feasibility of
social curation by non-expert users supplemented by suitable algorithms for reinforcing, combining
and judging user created associations and tags. From a social science perspective, we are interested
in the relationship between student’s prior experience and attitudes about social computing and
their current behavior in a social curation environment. We used a pre-usage instrument to assess
the users’ prior experience with common social sites, then a post-usage instrument to measure postusage attitudes and conceptual understanding of the resulting metadata among the users. We also
measured students’ knowledge in digital humanities and African American activism before and after
the system usage. The change in perceived usefulness of the archive from the beginning of the
experiment to the end was also measured. We will present the result of the data analysis at the
conference and hope that it will help future designs and implementations of socially curated
archives.
Related Work and Conclusion
There are many large scale multimedia collections on the Internet. Few of them, however, contain
library-grade metadata for digital items. Researchers have proposed or studied how to build large,
curated collections on the Internet. Recent Digital Humanities conferences have showcased efforts
in trying to utilize multiple organizations’ collaborative efforts in building federated or related
collections (Timothy et al.,2011). Our project utilizes cloud computing to explore the scalability
limitations of collaborative curation, and implement algorithmic approaches to synthesize individual
users’ or groups’ curation efforts. Our work on backend algorithms are build upon recent
advancements in ontology building, text categorization and link mining (e.g. Wu et al., 2006a; Wu et
al., 2006b; Heymann et al., 2006; Joachims et al., 1998;Schmitz et al., 2006). Evaluation results show
the promise of our social curation approach.
Funding
This work was supported by the National Science Foundation [ grant number 0713290]
References
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Figure legends
Figure 1. Browsing of the system enabled by user-evolved faceted classification
Figure 2. The click-and-drag classification screen
Figure 3. Global and personal (or local) schemas
Figure 4. The deployment architecture in Microsoft Azure