While diversification of query results, like other data summarization techniques, provides users with quick insights into the huge query answer space, it adds additional complexity to an already computationally expensive data exploration task.
Furthermore, we present a research infrastructure which is adapted to this new process model and demonstrate how the domain-expert can be deeply integrated even into the highly complex data-mining process and data-exploration tasks.
The integration of the domain-expert directly into data exploration and data mining tasks is a relatively recent approach, and it should be emphasized that data mining is only one step of the whole interactive knowledge-discovery process chain (see Fig. 2 in [2]).
A relational database is well suited for the task of data exploration and can be used to collate analyses from multiple sequence datasets.
During the experiment, we asked 12 participants to perform 5 tasks related to data exploration and fill a questionnaire about their experience using the tool.
CIESIN is seeking highly motivated, detail-oriented interns to support tasks related to GIS data exploration, analysis, and documentation for the Geo-Referenced Infrastructure and Demographic Data for Development (GRID³) project.
Data exploration is largely manual and labor intensive.
This chapter shows how to easily build tools to cover aspects of data exploration, model building, model validation, and model and task management.
The tasks that may potentially arise in data exploration are, in the turn, dependent on the data.
However, none of these novel systems has been demonstrated to efficiently perform multi-dimensional range queries incorporating many boolean operators, a task which is commonly used in scientific data exploration, data warehousing and business analytics.
