## 2 Background
### Case Study  
In order to assist the legal process managers of the Administrative Council for Economic Defense (CADE) developed to retrieve information stored in the Electronic Information System (SEI) and other databases, the technical solution named Jurisprudence Search System, which can index and search the information requested by the user within the scope of processes already defined by CADE. Furthermore, the data indexing and search system use the concept of forming textually indexed, classificatory knowledge bases, which allows the consolidation, on a single platform, of judged cases and decisions made by CADE, as well as other collections of documents of interest, in the consolidation of the search to support the formation of specific jurisprudence knowledge. As a result, the system favors the homogeneity and predominance of trends in decision-making by CADE's Commissioners and supports managers in competition matters. During the development of the Jurisprudence System, the project researchers analyzed and determined five important evolutionary axes in the understanding and treatment of the research problem of research and development (R&D), which are:  
* Infrastructure, APIS, and Interfaces: It deals with the infrastructure requirements (deployment and configuration), interface, navigation, and Apache Solr [40] handling in its searches;Collection, Retrieval, and Indexing: Deals with the specific configuration for the \"Jurisprudence\" collection;
* Information Structure: Development of a suitable ontology for the formation of shared or unshared classificatory data, in order to incorporate new data collections in the short and medium-term, to increase the investigative capacity of the intended system;
* Analysis and Morphology: To inform CADE of the results of the statistical analysis of the incorporated documents;
* Machine Learning, Research and Investigation: Support to models, mechanisms, and techniques of adaptive and evolutionary analysis of the classifications and researches, by the automated use of the results obtained in the treatment of Analysis and Morphology. We used machine learning classification because we considered it adequate for: (a) Performing more restrictive filters through more qualified aggregations than simple text search; (b) formation of clusters in the identification of groups of documents by interest from a given group of reserved words; (c) expansion of visual interpretation capabilities through document relation graphs and word cloud formation techniques; (d) application of advanced automatic summarization techniques; (e) interpretation of named entities and their relations across several documents, and (f) support for the formation of controlled vocabularies through n-gram validation. We chose as system development platform, Apache Solr [15; 40; 41]. Solr allows for indexing and scalable searches and facets for managing searches, occurrence highlighting, and advanced analysis capabilities. Through this tool, the search system can provide advanced search filters, which can be conditional (where it adds specific fields to return an exact answer from the system), search with specific characters/terms, by proximity or Boolean operators, search by relevance, phonetic search with spell checker and auto-suggestion. As a search result, the system features word highlighting, pagination and sorting, controlled vocabulary synonyms, the definition of term-stopwords, and document standardization. In addition, the Jurisprudence system allows the indexing of various file extensions, such as PDF with OCR, Word documents, and Excel Spreadsheets. Figure 1 shows an example of a search using the developed system. Collections can perform the search in the system's database, i.e., judgments from the Federal Audit Court, guides and publications, jurisprudence, legislation, news, and technical opinions, selected according to the end user's needs. The search results show all documents with the word searched in highlight (\"Cartel\").