Publication: Improving Big Data Technologies with Visual Faceted Search
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Date
2020
Authors
Najah Mahdi M.
Rahim Ahmad A.
Ismail R.
Subhi M.A.
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
Because of the quantity, complexity and speed measurements of big data, access to the data needed for big data applications becomes ever more challenging for both end-users and IT-experts. access. This has however led to problems of overloading information. While progress has been made in search engines, finding the right information still is a struggle due to the exponential increase in the everyday information provided. The need for Big Data Analytic now powers other aspects of modern society, because they can build new linkages and discoveries that help drive tomorrow's innovation. Data visualization of big data applications is an important tool to interpret and understand immense data, making the technology more enticing to use. Visualization of a search result with faceted search is increasingly popular in search engines. This seeks to allow users to easily and effectively find their way through large document collections. FS strategies presume that correct information is required so that the value, importance, and expense of achieving the requested information is optimized. In this work, we propose a new FS framework for visualizing browsing and refinements of search results to allow users to visually build complex search queries. The proposed FS can also solve the problem of lexical uncertainty in current search engines and give users more interest. � 2020 IEEE.
Description
Advanced Analytics; Big data; Data visualization; Digital storage; Visualization; Big data applications; Complex searches; Data technologies; Document collection; End users; Exponential increase; Faceted search; Speed measurement; Search engines