Linguistic Summaries in Evaluating Elementary Conditions, Summarizing Data and Managing Nested Queries

Abstract

Data users are generally interested in two types of aggregated information: summarization of the selected attribute(s) for all considered entities, and retrieval and evaluation of entities by the requirements posed on the relevant attributes. Less statistically literate users (e.g. domain experts) and the business intelligence strategic dashboards can benefit from the linguistic summarization, i.e. a summary like the most of customers are middle-aged can be understood immediately. Evaluation of the mandatory and optional requirements of the structure P 1 and most of the other posed predicates should be satisfied is beneficial for analytical business intelligence dashboards and search engines in general. This work formalizes the integration of aforementioned quantified summaries and quantified evaluation into the concept of database queries to empower their flexibility by, e.g. the nested quantified query conditions on hierarchical data structures. Next, this approach contributes to the mitigation of the empty answer problem in data retrieval tasks. Thus, the strategic and analytical dashboards as well as query engines might benefit from the proposed approach. Finally, the obtained results are illustrated on examples, the internal and external trustworthiness is elaborated, and the future research topics and applicability are discussed.

Fuzzy Aggregation for Ranking in Business

“…The geometric mean provides the result between the best and worst evaluation for all cases, whereas uni-norms provide such a solution only for entities that are badly evaluated in several attributes and positively in the other ones. ▪ The second, when all attributes are optional, and anyone is preferred, so it means the most of attributes should be satisfied in Sojka et al (2020) ▪ The third, when some attributes are mandatory, and others are optional in Sojka et al (2020) ▪ The fourth, when we speak about "nested queries" and the result of the finding nested queries within the database is a list of entities that fully or partially meet the condition ranked downward from the best by the intensity of matching degrees in Sojka et al (2020) The last three cases are described in Sojka et al (2020), where authors count quantified summaries of predicates in databases. The predicates express the attributes, which have an impact on further decisions.…”

“…The final solution of quantified summaries is a value that represents the validity or truth value of the evaluated quantified sentence expressed within the unit interval, which allows us to compare the values for ranking entities by one evaluator. So, in the second case, we assume that all attributes are connected by the conjunction and are optional; we can count the aggregation the same way as Sojka et al (2020). To avoid the problems as "empty answer values" and "evaluating by the number of attributes" that are better (the entity with more attributes with better values are preferred in ranking) we replace the conjunction with relative quantifier: most of the attributes should be satisfied.…”

“…The third case represents the situation, where some attributes are mandatory and others are optional. To covered these requirements Sojka et al(2020) modified (2) and used conjunction in the fuzzy logic, especially the minimum t-norms in Beliakov et al (2007) and quantified summary in such case is expressed by…”

“…This case allows to make the direct ranking within the specific group from the database but is not able to compare the entities from various groups. The procedure for calculating validities is created straightforwardly as the extension of (2) Sojka et al (2020), and the method is integrated with the vertical and horizontal aggregation Sojka et al (2020).…”

Linguistic Summaries in Evaluating Elementary Conditions and Their Application in Software Environment

“…Based on our previous research (Sojka et al, 2020), we decided to apply our work to the real world scenario described later in this paper. Datasets in databases usually contain a large number of entities and their attributes.…”