A Progressive Query Materialization for Interactive Data Exploration

Data analysis is task of competently digging out data insights even if user is uncertain but database systems unable to meet. User’s Iterative interactions with system might be an alternative. Interactive data exploration (IDE) is one such system, supports exploration by incorporating user intention. IDE is key ingredient of many discovery applications, such as scientific computing, financial analysis, Social Analytics etc. IDE supports user’s navigation via query to query transition, via ‘exploratory session’. A session often consists of long, complex and analytical queries, when processing against a large and multi-faceted data, hence consume huge time. Although, decomposing an original query into ‘checkpoint queries’ and materialization can significantly reduce time, extensively used in query navigation. We proposed, checkpoint queries selection in greedy manner considering various heuristics. Although, selection of checkpoint queries and improving query reuse are two key challenges in this process.