Analysis and Optimization of Information Retrieval Algorithms for Unstructured Data

DOI

10.2991/iccia-19.2019.46How to use a DOI?

Keywords

Information Retrieval; unstructured data; user behavior; file category; optimize algorithm.

Abstract

The Internet has diversified in the form of an explosion in recent years. It has spawned countless forms of Internet branching, and at the same time brought information to the PB level, and massive data is also called big data. More than 85% of the collected data is composed by unstructured and semi-structured data; in order to solve the data group management in the contract system of a large-scale energy enterprise, it aims to realize the interconnection of upstream business data, technology interoperability, research collaboration, and promote the demand for intelligent and massive unstructured data retrieval. This paper proposes a non-institutional data retrieval optimization algorithm based on periodic data heat and category labels. The algorithm is implemented by correlating the user's retrieval behavior in the cycle and combining the defined file category tags. The experimental results show that the method not only can effectively filter and sort unstructured data, but also it can provide strong support for subsequent big data analysis and edge calculation.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Kunying Li
AU  - Dexin Qiao
AU  - Xiaolian Li
AU  - Yu Ding
PY  - 2019/07
DA  - 2019/07
TI  - Analysis and Optimization of Information Retrieval Algorithms for Unstructured Data
BT  - Proceedings of the 3rd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2019)
PB  - Atlantis Press
SP  - 303
EP  - 307
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccia-19.2019.46
DO  - 10.2991/iccia-19.2019.46
ID  - Li2019/07
ER  -