The advent of the Big Data challenge has stimulated research on methods and techniques to deal with the problem of managing data abundance. As a result, effective sense-making of semantically rich and big datasets has received a lot of attention, and new search approaches, such as Exploratory Computing (EC), have seen the light. In this paper we present IQ4EC, a system for data exploration inspired by EC, that supports users in the inspection of huge amounts of relational data through a step-by-step process, providing feedback based on approximate, intensional information expressed in terms of association rules. At each step of the process, the users can choose a portion of data to examine, and the system guides them to the next step by providing synthetic information and visualization of the resulting dataset.
IQ4EC: Intensional answers as a support to exploratory computing
Mazuran M.;Quintarelli E.;Tanca L.
2015-01-01
Abstract
The advent of the Big Data challenge has stimulated research on methods and techniques to deal with the problem of managing data abundance. As a result, effective sense-making of semantically rich and big datasets has received a lot of attention, and new search approaches, such as Exploratory Computing (EC), have seen the light. In this paper we present IQ4EC, a system for data exploration inspired by EC, that supports users in the inspection of huge amounts of relational data through a step-by-step process, providing feedback based on approximate, intensional information expressed in terms of association rules. At each step of the process, the users can choose a portion of data to examine, and the system guides them to the next step by providing synthetic information and visualization of the resulting dataset.File | Dimensione | Formato | |
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