A Survey on Feature Recommendation Techniques

Recommendation systems are a very common now days and it is used in a variety of applications. A recommender system that is designed to reduce the human effort of performing domain analysis. Domain analysis is the task in which we can find the commonality and difference between the different software’s of same domain ‘feature recommendation is very useful now a days. This approach relies on data mining techniques to discover common features across products as well as the relationship among these common features. In this paper we used different techniques which are used for domain analysis and feature recommendation. This approach mines descriptions of product from publicly available online product descriptions, uses a text mining and a novel incremental diffusive clustering algorithm to discover features in specific domain , uses association rule mining to know latent relationships between the features within the products of same domain and uses KNN algorithm which generates a probabilistic feature model that represents commonalities, variant. DOI: 10.17762/ijritcc2321-8169.150316

Domain Orientation by Feature Modeling and Recommendation for Product Classification

Domain Analysis says that activity occurring before system analysis provides domain model. Domain model is input to the system analysis to the designer‘s tasks. Domain analysis is the procedure of identifying, organizing, analyzing, and modeling features common to a specific domain. The complete process is very time consuming and more man power is required for it. There are various projects which require extensive domain analysis activities. In proposed method, recommended system is used to reduce human efforts of performing domain analysis. It is not easy to discover relationship between items in a large database of sales transactions but there are some algorithms for solving this problem. Data mining techniques are used to discover common features across products as well as relationships among those features .Incremental diffusive algorithm is used to extract features. Bi-Partity Distribution technique is used for feature recommendations during the domain analysis process DOI: 10.17762/ijritcc2321-8169.150512

Program on Earth Observation Data Management Systems (EODMS), appendixes

The needs of state, regional, and local agencies involved in natural resources management in Illinois, Iowa, Minnesota, Missouri, and Wisconsin are investigated to determine the design of satellite remotely sensed derivable information products. It is concluded that an operational Earth Observation Data Management System (EODMS) will be most beneficial if it provides a full range of services - from raw data acquisition to interpretation and dissemination of final information products. Included is a cost and performance analysis of alternative processing centers, and an assessment of the impacts of policy, regulation, and government structure on implementing large scale use of remote sensing technology in this community of users

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Program on stimulating operational private sector use of Earth observation satellite data

There are no author-identified significant results in this report