Talk to Your Data: a Chatbot System for Multidimensional Datasets

Maria Helena Franciscatto, Marcos Didonet Del Fabro, Celio Trois, Luis Carlos Erpen De Bona, Jordi Cabot, Leon Augusto Okida Gonçalves

Abstract: Finding information may be a complex task for end users, either due to the format in which data is stored, or difficulty in formulating a query that fits the database structure. Conversational interfaces, such as chatbots, can minimize this issue, by facilitating query formulation through natural language. Despite the several applications of chatbots for data querying, the multidimensional aspect of data is rarely addressed in the literature, making the search for information even more challenging. Chatbots can be used for allowing the user to “talk to the data” by adding metrics and dimensions to a query, without relying on technical expertise. Thus, this paper presents a chatbot approach for querying multidimensional data, which captures users intentions and links them to the multidimensional metadata. This linking process allows the bot to set query parameters, using them for accessing the data. The chatbot was implemented for querying an open database containing about 2.5 billions records and over 1700 attributes (including dimensions and metrics), and it was evaluated through an empirical user study involving a group of participants performing a set of search tasks. The evaluation results supported the usefulness of the proposed approach in querying multidimensional data and retrieving information.