  Modern data-driven applications require that databases support fast
cross-model analytical queries. Achieving fast analytical queries in a database
system is challenging since they are usually scan-intensive (i.e., they need to
intensively scan over a large number of records) which results in huge I/O and
memory costs. And it becomes tougher when the analytical queries are
cross-model. It is hard to accelerate cross-model analytical queries in
existing databases due to the lack of appropriate storage layout and efficient
query processing techniques. In this paper, we present QUEST (QUery Evaluation
Scheme Towards scan-intensive cross-model analysis) to push scan-intensive
queries down to unified columnar storage layout and seamlessly deliver payloads
across different data models. QUEST employs a columnar data layout to unify the
representation of multi-model data. Then, a novel index structure named
Skip-Tree is developed for QUEST to enable the query evaluation more efficient.
With the help of two pairwise bitset-based operations coupled with Skip-Tree,
the scan of most irrelevant instances can be pruned so as to avoid the giant
intermediate result, thus reducing query response latency and saving the
computational resources significantly when evaluating scan-intensive
cross-model analysis. The proposed methods are implemented on an open-source
platform. Through comprehensive theoretical analysis and extensive experiments,
we demonstrate that QUEST improves the performance by 3.7x - 178.2x compared to
state-of-the-art multi-model databases when evaluating scan-intensive
cross-model analytical queries.
