  Selective bulk analyses, such as statistical learning on temporal/spatial
data, are fundamental to a wide range of contemporary data analysis. However,
with the increasingly larger data-sets, such as weather data and marketing
transactions, the data organization/access becomes more challenging in
selective bulk data processing with the use of current big data processing
frameworks such as Spark or keyvalue stores. In this paper, we propose a method
to optimize selective bulk analysis in big data processing and referred to as
Oseba. Oseba maintains a super index for the data organization in memory to
support fast lookup through targeting the data involved with each selective
analysis program. Oseba is able to save memory as well as computation in
comparison to the default data processing frameworks.
