INVISQUE - INteractive VIsual Search and Query Environment Using visual analytics to make sense of online search results A JISC funded research project conducted by staff from EIS and Learning Resources Nazlin Bhimani School Liaison Manager (EIS & iWBL) Learning Resources Academic Support Middlesex University Visual Analytics in the Library Environment Visual Analytics (VA) allows us to make sense of large datasets through the use of an interactive visuali- sation interface. VA will show the visual representations of keyword associations and the relationships between different information resources in large and loosely coupled datasets such as the library catalogue and journal in- dexing databases, repositories and other such digital information resources. The visual search results will allow users to rotate, re- order and re-collate, facilitating the chance discovery of un-anticipated information resources. The Problem Intelligent keyword associations are not made in many of the search engines that exist today – including the new vertical search engines employing Web 2.0 tag clouds that are in use in some UK HE libraries. Ad- ditionally, bibliometric information such as number of citations, relevance of keyword to article is not drawn out and there is a lack of a common standard for bibliographic database design. This means that not all solutions work in a systematic manner resulting in an uneven display of search results. The Project The project is to apply VA in addressing the prob- lems outlined above. It is JISC funded and led by EIS and Learning Resources. The research team has ex- pertise in interaction design and digital libraries. The project will lead to the development of a prototype for a computer interface that will provide students and researchers with an interactive visual presentation of their bibliographic search results. Speciﬁcally INVISQUE will show a network of key- words, allowing interaction with associated bibliographic resources. The prototype will use the Learning Resources library catalogue test database. The Future The INVISQUE prototype will provide a platform from which to develop an innovative searching interface. In the longer term, a study of how learners use this will provide a reﬁned version. It is hoped that they will respond positively to the visual presentation of their search results in an ever-changing, multi- disciplinary information landscape. More broadly the INVISQUE project and the process and prototype resulting from this six-month project will open up new areas of research and innovation. The results of a combined application of visual analytics, semantic extraction, heterogeneous data transformation and integration in support of visual- ization and human sense making will provide holders of large data-sets a way in which to present the data visually and make bet-ter sense of data which origi- nates from disparate data sets.

INVISQUE - Interactive Visual Search and Query Environment

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The INVISQUE poster was created for the 9th Annual Teaching and Learning Conference at Middlesex University. The purpose was to disseminate information on research projects involving teaching fellows at the university.

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Page 1: INVISQUE - Interactive Visual Search and Query Environment

INVISQUE - INteractive VIsual Search and Query Environment Using visual analytics to make sense of online search results

A JISC funded research project conducted by staff from EIS and Learning Resources

Nazlin Bhimani School Liaison Manager (EIS & iWBL) Learning Resources Academic Support

Middlesex University

Visual Analytics in the Library Environment Visual Analytics (VA) allows us to make sense of large datasets through the use of an interactive visuali-sation interface.

VA will show the visual representations of keyword associations and the relationships between different information resources in large and loosely coupled datasets such as the library catalogue and journal in-dexing databases, repositories and other such digital information resources.

The visual search results will allow users to rotate, re-order and re-collate, facilitating the chance discovery of un-anticipated information resources.

The Problem

Intelligent keyword associations are not made in many of the search engines that exist today – including the new vertical search engines employing Web 2.0 tag clouds that are in use in some UK HE libraries. Ad-ditionally, bibliometric information such as number of citations, relevance of keyword to article is not drawn out and there is a lack of a common standard for bib-liographic database design. This means that not all solutions work in a systematic manner resulting in an uneven display of search results.

The Project

The project is to apply VA in addressing the prob-lems outlined above. It is JISC funded and led by EIS and Learning Resources. The research team has ex-pertise in interaction design and digital libraries.

The project will lead to the development of a prototype for a computer interface that will provide students and researchers with an interactive visual presenta-tion of their bibliographic search results.

Specifi cally INVISQUE will show a network of key-words, allowing interaction with associated biblio-graphic resources. The prototype will use the Learning Resources library catalogue test database.

The Future The INVISQUE prototype will provide a platform from which to develop an innovative searching interface. In the longer term, a study of how learners use this will provide a refi ned version.

It is hoped that they will respond positively to the visual presentation of their search results in an ever-changing, multi-disciplinary information landscape.

More broadly the INVISQUE project and the process and proto-type resulting from this six-month project will open up new areas of research and innovation. The results of a combined application of visual analytics, semantic extraction, heterogeneous data transformation and integration in support of visual-ization and human sense making will provide holders of large data-sets a way in which to present the data visually and make bet-ter sense of data which origi-nates from disparate data sets.