Technical feasibility

SECTION I: Testing

1 Has the R & D result been tested?
YES
1a In what mode has the result been tested?

•              Prototype

•              Pilot Application

•              Alpha/BETA testing

The application  has been prototype modular tested in the operation of the following  four components has been tested in various datasets (derived from web 2.0 applications such as Flickr, Del.icio.us, Digg) and the results have been published in scientific conferences and journals. Potential users of the Intelligent Web 2.0 Recommendation Module are any company providing a web 2.0 application.

Data Collection and Preprocessing:Data fields described in the configuration file constitute the basis of information collected from the web 2.0 application and stored in suitable format. Then, efficient preprocessing operations are applied in order to construct convenient data representations in the predefined feature space (described in the configuration file) which will next be used in the mining process.

Data Analysis:Clustering and community detection techniques will be applied on the collected data in order to identify users’ interests and extract their profiles. These techniques will consider the semantic and social aspects of users’ data as well as time, geographical information and means of access. Relations between users will be estimated using metrics that contemplate the former information.

Evaluation of the extracted communities: information analysis methodologies and practices result in communities whose characteristics contribute to:

  • Users’ profile extraction
  • Communities identification
  • Trends detection

Recommendations: based on the extracted communities, an efficient and accurate recommendation mechanism can be supported and in particular, the recommendations may concern:

  • Users: propose information relative to their interests.
  • System Administrators: provide more personalized services.

Further integration tested is still required.

1b. Please describe and discuss the testing results
The testing results for each module separately were positive, as the results were publicized in two papers:

“Leveraging Collective Intelligence through community Detection in Tag Networks”

“Co-Clustering Tags and Social Data Sources”

“Hydra: An open framework for virtual fusion of recommendation filters”

Still this modular testing does not provide any reliable testing outcome for the whole integrated project. Although individual modules could successfully operate, the synthetic results into an integrated solution could raise unforeseen technical and scientific implementation issues.

SECTION 2: Current Stage of Development

2a To what extent does the development team have technical resources for supporting the production of a new product? (Researchers, human resources, hardware, etc. )
The OS-WINDS specializes in Web data mining, content distribution and delivery over the Web, Internet data storage, web 2.0 social network analysis,  modeling and representing Web data, developing clustering and data management techniques that improve data retrieval, formulating policies for content prefetching, storage and delivery, designing models for statistical analysis and processing and analyzing networks for the identification of communities with emphasis on data from the Social Web extracting web users profile. The OSWIND team possesses the skills and competences to gradually integrate the produced modules into a web 2.0 recommendation service. Of course time to market is an important element since the developments in semantic web could drive the project outcomes into new development paths.
2b What are the technical issues that need to be tackled for full deployment, if needed?
The main technical issue is the integration of data collection and pre-processing, data analysis, evaluation of the extracted communities and recommendation modules. Furthermore the following issues are still under research examination:

Poor retrieval in the aforementioned systems remains a major problem mostly due to questionable tag validity and tag ambiguity. Earlier clustering techniques have shown limited improvements, since they were based mostly on tag co-occurrences.

The problem of community detection in tag networks, i.e. networks consisting of associations between tags that are used within Social Tagging Systems (STS) to annotate online resources (e.g. bookmarks, pictures, videos, etc.).

Reducing processing cost of recommendation filtering through the hydra process which enables the virtual fusion of many recommendation algorithms in such distributed manner that algorithm complexities are not summarized but parallelized.

2c What additional technical resources are needed for the production of this new product?
A system integrator for the integration of four modules

A web developer for the synthesis of modules into a single web service

An experience tester to implement integration testing

2d Overall assessment of the current stage of technical development.
The current stage of development is still on experimental stage. The bottom up strategy is based on modular experimental results that will eventually could synthesize the final product. There are important research assumptions that need to be verified in the final outcome. So far the project is generating encouraging results at modular phase, although serous risk management techniques must be applied in the final synthetic effort.

SECTION 3: Deployment

3a Define the demands for large scale production in terms of
  • Materials
There are no plans for mass production.
  • technologies, tools, machineries
  • Staff effort

SECTION 4: Overall Assessment

1 What is you overall assessment of the technical feasibility of the research result?
The functionality of the four proposed components has already been implemented and has been tested. The project aims to integrate the four components to a module and the appropriate definition of a configuration file that will make the module portable and adjustable to several types of Web 2.0 applications. Unit testing most be followed by integration testing and final testing using solid software engineering practices.

KEYWORDS QUANTITATIVE ASSESSMENT (0-5).

Please put X as appropriate. 1 2 3 4 5
Adequacy of testing activity undertaken so far x
Adequacy and availability of technical resources of the development team x
Current development stage x
Overall technical feasibility x

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