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Interactive Data Exploration (IDE) is a key ingredient of a diverse set of discovery-oriented applications, including ones from scientific computing and evidence-based medicine. In these applications, data discovery is a highly ad hoc interactive process where users execute numerous exploration queries using varying predicates aiming to balance the(More)
Traditional DBSMs are suited for applications in which the structure, meaning and contents of the database, as well as the questions to be asked are already well understood. There is, however, a class of applications that we will collectively refer to as Interactive Data Exploration (IDE) applications, in which this is not the case. IDE is a key ingredient(More)
In this paper, we argue that database systems be augmented with an automated data exploration service that methodically steers users through the data in a meaningful way. Such an automated system is crucial for deriving insights from complex datasets found in many big data applications such as scientific and healthcare applications as well as for reducing(More)
Data analysts often engage in data exploration tasks to discover interesting data patterns, without knowing exactly what they are looking for. Such exploration tasks can be very labor-intensive because they often require the user to review many results of ad-hoc queries and adjust the predicates of subsequent queries to balance the trade-off between(More)
This article provides an overview of our research on data exploration. Our work aims to facilitate interactive exploration tasks in many big data applications in the scientific, biomedical and healthcare domains. We argue for a shift towards learning-based exploration techniques that automatically steer the user towards interesting data areas based on(More)
Interactive Data Exploration (IDE) applications typically involve users that aim to discover interesting objects by it-eratively executing numerous ad-hoc exploration queries. Therefore, IDE can easily become an extremely labor and resource intensive process. To support these applications, we introduce a framework that assists users by automatically(More)
—In this paper, we argue that database systems be augmented with an automated data exploration service that methodically steers users through the data in a meaningful way. Such an automated system is crucial for deriving insights from complex datasets found in many big data applications such as scientific and healthcare applications as well as for reducing(More)
The amount of data that have flooded databases during the last few years have created several new problems for the data management community to address. One of the most prominent is the discovery of new and interesting information that is hidden in the underlying big data sets. As of now, in order to explore these data sets users begin with a few general(More)
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