Nowadays, searching for a topic on the Internet can be a frustrating experience because of all the excessive information. Thus, a strategy for automatically classifying the results can improve user experience and work efficiency. Latent Semantic Indexing (LSI) algorithm is used to classify documents by meaning due to its effectiveness. However, there is a problem with the implementation of this algorithm. LSI is computationally intensive because the cost is directly related to the number of documents. In particular, the Singular Value Decomposition (SVD) that is mainly used in LSI is unscalable in terms of both memory and computation time. One possible solution is to use more powerful computational resources, such as multiple computing nodes. In this paper, a novel distributed architecture for the LSI algorithm is proposed. It is based on the use of microservices in a Google Cloud environment. We evaluated the performances of the proposed Cloud-based LSI, and comparison is made with standalone LSI. The results show the benefits of using distributed systems based on runtime, concurrency, and processing.
|Title of host publication||Smart Technologies, Systems and Applications - 1st International Conference, SmartTech-IC 2019, Proceedings|
|Editors||Fabián R. Narváez, Diego F. Vallejo, Paulina A. Morillo, Julio R. Proaño|
|Number of pages||12|
|State||Published - 1 Jan 2020|
|Event||1st International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2019 - Quito, Ecuador|
Duration: 2 Dec 2019 → 4 Dec 2019
|Name||Communications in Computer and Information Science|
|Conference||1st International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2019|
|Period||2/12/19 → 4/12/19|
Bibliographical noteFunding Information:
Acknowledgments. This work was supported by IDEIAGEOCA Research Group of Universidad Politécnica Salesiana in Quito, Ecuador.
This work was supported by IDEIAGEOCA Research Group of Universidad Polit?cnica Salesiana in Quito, Ecuador.
© Springer Nature Switzerland AG 2020.
Copyright 2020 Elsevier B.V., All rights reserved.
- Distributed computing
- Latent Semantic Index
- Micro services