The presentation of new hybrid compression techniques to optimize memory usage and speed of access in cloud database

Enterprises and cloud service providers face dramatic increase in the amount of data stored in private
and public clouds. Thus, data storage costs are growing hastily because they use only one single high-
performance storage tier for storing all cloud data. There,s considerable potential to reduce cloud costs
by classifying data into active (hot) and inactive (cold). In the main-memory databases research,
recent work focus on approaches to identify hot/cold data. Most of these approaches track tuple
accesses to identify hot/cold tuples. In contrast, we introduce a novel LOAD DATA INFILE that
tracks both tuples and columns accesses in secondary storage databases. Our objective is to enhance
the performance in terms of three dimensions: storage space, query elapsed time and CPU usage. In
order to validate the effectiveness of our approach, we realized its concrete implementation on LOAD
DATA INFILE Approach (LDA) that reads rows from a text file into a table at a very high speed by
using the well-known qps and TPC-H benchmark. Experimental results show that the proposed LOAD
DATA approach outperforms prepare_data in respect of two performance dimensions. In specific,
LOAD DATA reduces the storage space by average of 14-62% and reduces the query elapsed time by
average of 280-440 times compared to the traditional database approach.