## 4 Results and Discussions
### Discussion  
The systems for processing textual bases are present in most Public Administration agencies, but we can observe that the solution architecture model for implementing these systems is not consensual and does not indicate paths of best practices and the standards adopted. However, the quantitative analysis presented in the survey shows the technologies and techniques used by these systems that are in line with the model proposed by CADE, such as the predominance of Apache Lucene as a text search library, since this technology allows high-performance searches in large volumes of information.  
In the Jurisprudence Search System, the proposed solution architecture uses Apache Solr to process data from the SEI database for data indexing purposes, responsible for accessing Apache Lucene resources. The Java language appears as predominant in the survey results and is used in CADE's system because it is the native language of Apache Lucene.  
Concerning the availability of search resources applied to the surveyed systems, most of them have similarities with the functionalities implemented in their search systems. For example, many of the respondents cited the use of filters, logical operators, PDF file treatment, and export. Therefore, the solution proposed by CADE, besides implementing these functionalities and resources, presents the differential of basket resources, search history, and highlights.  
In the development of CADE's Jurisprudence Search system, we used Artificial Intelligence techniques in conjunction with statistical techniques to perform natural language processing and discourse analysis techniques to form a supplementary knowledge base, text mining, and machine learning. Unfortunately, most of the agencies participating in the survey did not use any Artificial Intelligence techniques. However, some agencies mentioned the use of Machine Learning techniques for data classification and clustering. Thus, we can infer that CADE's Jurisprudence Search system differs from other systems used by Brazilian public administration agencies by using Artificial Intelligence and Ontology in the proposed solution.  
It is noteworthy that Machine Learning techniques can be categorized into supervised and unsupervised and applied alone or combined, depending on the needs and according to the defined database. Therefore, it is necessary to perform a preliminary analysis to identify the appropriate techniques for each scenario. The technologies used in this research were Artificial Intelligence using Machine Learning and Text mining. The ML methods and techniques used in this work are for information retrieval, such as extraction (usingfacets), classification (clustering, summarization, named entities), indexing (by extracting n-grams), and natural language processing.  
The open questions of the survey brought important information and perceptions about the opinions of the experts of each organization that use search systems, such as (i) improve usability, accessibility, user experience in the use of search systems; (ii) improve text indexing; (iii) index other types of content such as audio and video of plenary sessions; (iv) perform a search by relevance, keywords, metadata, advanced filters, treatment of synonyms; (v) export the documents and the search results; (vi) recommend other documents; (vii) identify documents with similar content, and (viii) incorporate other textual bases. These insights were essential for the continuous improvement of the Jurisprudence Search systems.