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.
Valuable information for decision-making can be obtained by collecting and analyzing opinions from diverse stakeholder or respondent groups, which usually have different backgrounds and are variously affected by the topics under survey. For this to succeed, it is necessary to manage the uncertainty of respondents’ opinions, different number of filled questionnaires among groups, different number of questions for each stakeholder group, and relevance of subsets of respondent groups. This work proposes handling the hesitance of respondents’ opinions for the rating scale questions. To evaluate the collected opinions, a three-level aggregation model is developed. In the first level, the overall opinion of each respondent is computed as a mean of fuzzy numbers covering uncertain answers and their respective hesitance. In the second level, stakeholder groups are considered as a whole. Aggregation by a relative quantifier is applied to calculate the validity of a proposition the majority of respondents have a positive or negative opinion. At the third level, the consensus among diverse subsets of stakeholder groups is calculated considering the relevance of each group independently as well as their so-called coalitions by Choquet integral. Finally, the proposed model is illustrated by a real-life case study.
Choosing a proper representation of textual data is an important part of natural language processing. One option is using Word2Vec embeddings, i.e., dense vectors whose properties can to a degree capture the "meaning" of each word. One of the main disadvantages of Word2Vec is its inability to distinguish between antonyms. Motivated by this deficiency, this paper presents a Word2Vec extension for incorporating domain-specific labels. The goal is to improve the ability to differentiate between embeddings of words associated with different document labels or classes. This improvement is demonstrated on word embeddings derived from tweets related to a publicly traded company. Each tweet is given a label depending on whether its publication coincides with a stock price increase or decrease. The extended Word2Vec model then takes this label into account. The user can also set the weight of this label in the embedding creation process. Experiment results show that increasing this weight leads to a gradual decrease in cosine similarity between embeddings of words associated with different labels. This decrease in similarity can be interpreted as an improvement of the ability to distinguish between these words.
Valuable information for decision making can be obtained by collecting and analyzing opinions from diverse stakeholder or respondent groups, which usually have different backgrounds and are variously affected by the topics under survey. For this to succeed, it is necessary to manage the uncertainty of respondents’ opinions, different number of filled questionnaires among groups, different number of questions for each stakeholder group, and relevance of subsets of respondent groups. This work proposes handling the hesitance of respondents’ opinions for the rating scale questions. To evaluate the collected opinions, a three-level aggregation model is developed. In the first level, the overall opinion of each respondent is computed as a mean of fuzzy numbers covering uncertain answers and their respective hesitance. In the second level, stakeholder groups are considered as a whole. Aggregation by a relative quantifier is applied to calculate the validity of a proposition the majority of respondents have a positive or negative opinion . At the third level, the consensus among diverse subsets of stakeholder groups is calculated considering the relevance of each group independently as well as their so-called coalitions by Choquet integral. Finally, the proposed model is illustrated by a real-life case study.
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