Abstract
On account of the extreme expansion of the scientific research paper databases, the usage of searching and recommender systems in this area increased, as they can help researchers find appropriate papers by searching in enormous indexed datasets. Depending on where the papers are published, there might be stricter policies that force the author to also add the needed metadata, but still there are other for which these metadata are not complete. As a result, many of the current solutions for searching and recommending papers are usually biased to a certain database. This paper proposes a retrieval system that can overcome these problems by aggregating data from different databases in a dynamic and efficient way. Extracting data from different sources dynamically and not only statically, based on a certain database, is important for assuring a complete interrogation, but in the same time incur complex operations that may affect the performance of the system. The performance could be maintained by using carefully designed architecture that relies on tools that allow high level of parallelization. The main original characteristic of the system is represented by the hybrid interrogation of static data (stored in databases) and dynamic data (obtained through real-time web interrogations).
Citare
@Inproceedings{Niculescu2023EfficientAR,
author = {Virginia Niculescu and H. Greblă and Adrian Sterca and Darius Bufnea},
booktitle = {International Conference on Evaluation of Novel Approaches to Software Engineering},
title = {Efficient Academic Retrieval System Based on Aggregated Sources},
year = {2023}
}