The value of Big Data largely relies on its analytical outcomes; and MapReduce has so far been the most efficient tool for performing analysis on the data. However, the low level nature of MapReduce programming necessitates the development of High-level abstractions, i.e., High Level Query Languages (HLQL), such as Hive, Pig, JAQL and others. These languages can be categorized as either dataflow based or OLAP-based. For OLAP-based HLQL, in particular Hive, at the moment, the speed of retrieval of big data for the analysis is still requiring improvement. Hence, indexing is one of the techniques used for this purpose. Yet, the indexing approach still has its loopholes since it is performed manually and externally using the approach of index inclusion and two-way data scanning. It requires huge computational time and space and hence not scalable for future potential scale of big data. Thus, an adaptive indexing framework is proposed for improving both the computational time and memory usage of the indexing process. The technique shall check the user queries to determine the necessity for indexing and use internal indexing with one-way data scanning approach for the indexing strategy. In this paper, the initial framework of the technique is presented and discussed. © 2015 IEEE.