24,795 research outputs found
SQL Query Completion for Data Exploration
Within the big data tsunami, relational databases and SQL are still there and
remain mandatory in most of cases for accessing data. On the one hand, SQL is
easy-to-use by non specialists and allows to identify pertinent initial data at
the very beginning of the data exploration process. On the other hand, it is
not always so easy to formulate SQL queries: nowadays, it is more and more
frequent to have several databases available for one application domain, some
of them with hundreds of tables and/or attributes. Identifying the pertinent
conditions to select the desired data, or even identifying relevant attributes
is far from trivial. To make it easier to write SQL queries, we propose the
notion of SQL query completion: given a query, it suggests additional
conditions to be added to its WHERE clause. This completion is semantic, as it
relies on the data from the database, unlike current completion tools that are
mostly syntactic. Since the process can be repeated over and over again --
until the data analyst reaches her data of interest --, SQL query completion
facilitates the exploration of databases. SQL query completion has been
implemented in a SQL editor on top of a database management system. For the
evaluation, two questions need to be studied: first, does the completion speed
up the writing of SQL queries? Second , is the completion easily adopted by
users? A thorough experiment has been conducted on a group of 70 computer
science students divided in two groups (one with the completion and the other
one without) to answer those questions. The results are positive and very
promising
Data exploration systems for databases
Data exploration systems apply machine learning techniques, multivariate statistical methods, information theory, and database theory to databases to identify significant relationships among the data and summarize information. The result of applying data exploration systems should be a better understanding of the structure of the data and a perspective of the data enabling an analyst to form hypotheses for interpreting the data. This paper argues that data exploration systems need a minimum amount of domain knowledge to guide both the statistical strategy and the interpretation of the resulting patterns discovered by these systems
Interactive Data Exploration with Smart Drill-Down
We present {\em smart drill-down}, an operator for interactively exploring a
relational table to discover and summarize "interesting" groups of tuples. Each
group of tuples is described by a {\em rule}. For instance, the rule tells us that there are a thousand tuples with value in the
first column and in the second column (and any value in the third column).
Smart drill-down presents an analyst with a list of rules that together
describe interesting aspects of the table. The analyst can tailor the
definition of interesting, and can interactively apply smart drill-down on an
existing rule to explore that part of the table. We demonstrate that the
underlying optimization problems are {\sc NP-Hard}, and describe an algorithm
for finding the approximately optimal list of rules to display when the user
uses a smart drill-down, and a dynamic sampling scheme for efficiently
interacting with large tables. Finally, we perform experiments on real datasets
on our experimental prototype to demonstrate the usefulness of smart drill-down
and study the performance of our algorithms
Interactive data exploration with targeted projection pursuit
Data exploration is a vital, but little considered, part of the scientific process; but few visualisation tools can cope with truly complex data. Targeted Projection Pursuit (TPP) is an interactive data exploration technique that provides an intuitive and transparent interface for data exploration. A prototype has been evaluated quantitatively and found to outperform algorithmic techniques on standard visual analysis tasks
- âŠ