Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Moment-Based Quantile Sketches for Efficient High Cardinality
  Aggregation Queries   Interactive analytics increasingly involves querying for quantiles over
sub-populations of high cardinality datasets. Data processing engines such as
Druid and Spark use mergeable summaries to estimate quantiles, but summary
merge times can be a bottleneck during aggregation. We show how a compact and
efficiently mergeable quantile sketch can support aggregation workloads. This
data structure, which we refer to as the moments sketch, operates with a small
memory footprint (200 bytes) and computationally efficient (50ns) merges by
tracking only a set of summary statistics, notably the sample moments. We
demonstrate how we can efficiently and practically estimate quantiles using the
method of moments and the maximum entropy principle, and show how the use of a
cascade further improves query time for threshold predicates. Empirical
evaluation on real-world datasets shows that the moments sketch can achieve
less than 1 percent error with 15 times less merge overhead than comparable
summaries, improving end query time in the MacroBase engine by up to 7 times
and the Druid engine by up to 60 times.

19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat