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In todays digital age, data is everything. Dealing with and accessing a lot of data is a common task for almost every individual, be it for personal or for professional purposes and they expect applications to provide fast and accurate search capabilities to access and view their data. But all this data becomes meaningless if it cannot be accessed easily when needed or within a certain amount of time. This study aims to implement a powerful and customizable search feature in applications using the open-source search technologies, Lucene and Solr. The system will use Lucenes efficient indexing and querying capabilities to create an inverted index of the applications content, enabling fast and accurate full-text search. Solr will provide a web interface for the search functionality, allowing users to easily perform searches and filter results by various criteria such as date, author, category, and more. By incorporating Lucene and Solr, the system will be able to overcome the previous mentioned issues by providing search results significantly faster and deliver a robust, fast, and user-friendly search experience for applications that can be easily integrated into any application.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 6 | |
| downloads | 11 |

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