Daq

Abstract

Many modern applications deal with exponentially increasing data volumes and aid business-critical decisions in near real-time. Particularly in exploratory data analysis, the focus is on interactive querying and some degree of error in estimated results is tolerable. A common response to this challenge is approximate query processing, where the user is presented with a quick confidence interval estimate based on a sample of the data. In this work, we highlight some of the problems that are associated with this probabilistic approach when extended to more complex queries, both in semantic interpretation and the lack of a formal algebra. As an alternative, we propose deterministic approximate querying (DAQ) schemes, formalize a closed deterministic approximation algebra, and outline some design principles for DAQ schemes. We also illustrate the utility of this approach with an example deterministic online approximation scheme which uses a bitsliced index representation and computes the most significant bits of the result first. Our prototype scheme delivers speedups over exact aggregation and predicate evaluation, and outperforms sampling-based schemes for extreme value aggregations.

Interactive Data Exploration of Distributed Raw Files: A Systematic Mapping Study

“…It is worth mentioning that sampling tends to fail when the query interest is focused on extreme values (outliers) [22], [28], [29].…”

In‐Memory Database Query

“…The latency and energy associated with moving data to the processor is a key performance bottleneck. Conventional acceleration methods range from software‐oriented schemes that solve queries approximately [ 15 ] to hardware‐oriented systems that parallelize operations using graphics processing units. [ 16 ] However, these approaches do not tackle the main bottleneck of data movement caused by the limited cache capacity of processing units and limited memory bandwidth.…”