The information search performance efficiency evaluation

DOI: 10.31673/2412-4338.2020.014552

Abstract

Information is becoming a strategic resource of a new high-tech society. In the conditions of scientific and technological progress, there is a task is to develop fundamentally new methods of processing, storage and control of information. Information system is a combination of information flows, means of information collection, processing and data management. The main behavioral incentive for the implementation of information retrieval is the information need, expressed in the form of an information request. Since the requirements for search speed, the relevance of information every day are becoming higher, so are the requirements for methods and algorithms for finding information are continuously growing as well. The main purpose of information retrieval is to help the user to find information in which he is interested. There are a significant number of methods and algorithms for information retrieval, but the growth of data volumes requires continuous improvement of existing methods and the development of new approaches.
The most important task while developing an information retrieval system is to evaluate a searching efficiency. A searching quality depends on the efficiency of the used searching methods and algorithms, determining types of information sources correctly and implementing an efficiency assessment system.
The paper covers the main aspects of information retrieval, searching methods, classification of information sources, the list of modern searching algorithms. The definition of relevancy and its criteria are given along with relevancy grades based on ranking of how well results matches the query. Moreover, different estimates for evaluating the relevance of search results have been introduced in the paper.

Keywords: search performance efficiency, information retrieval system, search algorithm, formal relevance, ontological relevance, pertinence.

References
1. Tsvetkov V. Y. (2015) “Information certainty and uncertainty in information sciences”. Information technologies. № 1. P.3-7.
2. Vasyutinska S. Y. (2016) “Information asymmetry in educational technologies”. Educational resources and technologies.. № 4 (16). P.14-20.
3. Ion A. L. (2009) “Algorithms for reducing the semantic gap in image retrieval systems”. 2nd Conference on Human System Interactions. IEEE. P. 97-102.
4. Tsvetkov V. Y. (2015) “Cognitive Science of Information Retrieval”. European Journal of Psychological Studies.. Vol. 5. Iss. 1. P. 37-44.
5. Ozherelieva T. A. (2016) “Information situation as a management tool”. Slavic Forum. № 4 (14). P. 176-181.
6. Tsvetkov V. Y. (2014) “Information models of objects, processes and situations”. Distant and virtual learning. № 5. P. 4-11.
7. Zagidulin I. M. (2011) “Use of context in information search”. Management systems and information technologies. Т. 45. № 3.2. P. 297-301.
8. Sharapov R. V., Sharapova E. V. (2009) “Application of fuzzy set theory in information retrieval”. Methods and devices for information transfer and processing. № 11. P. 427-432.
9. Romanov V. P. (1996) “Theoretical Foundations of Informatics. Information structures and factual search for information”. M. REA. 190 p.
10. Bolbakov R. G. (2013) “Development and “application of cognitive-semantic methods and algorithms in multimedia educational portal systems. P. 184.
11. Tsvetkov V. Y (2105) “Descriptive and prescriptive information models”. Remote and virtual training. № 7. P. 48-54.
12. Stoeva D. R. (2015) “Systematization of information models”. Prospects of science and education.. № 4. P. 13-18.