att_abstract={{Computing mathematical functions or machine learning models
on data streams is difficult: a popular approach is to use the R language.
Unfortunately, R has important limitations: a dynamic runtime
system incompatible with a DBMS, limited by available RAM and no
data management capabilities. On the other hand, SQL is well established
to write queries and manage data, but it is inadequate to perform
mathematical computations. With that motivation in mind, we present
a system that enables analysis in R on a time window, where the DBMS
continuously inserts new records and propagates updates to materialized
views. We explain the low-level integration enabling fast data transfer in
RAM between the DBMS query process and the R runtime. Our system
enables analytic calls in both directions: (1) R calling SQL to evaluate
streaming queries; transferring output streaming tables and analyzing
them with R operators and functions in the R runtime, (2) SQL calling
R, to exploit R mathematical operators and mathematical models,
computed in a streaming fashion inside the DBMS. We discuss analytic
examples, illustrating analytic calls in both directions. We experimentally
show our system achieves streaming speed to transfer data.}},
	att_authors={ds8961, tj1857, vs9593, su2464},
	att_categories={C_IIS.2, C_BB.1},
	att_copyright_notice={{The definitive version was published in 28th International Conference on Database and Expert Systems Applications - DEXA 2017. {{, 2017-08-28}}
	author={Divesh Srivastava and Theodore Johnson and Vladislav Shkapenyuk and Simon Urbanek and Carlos Ordonez},
	institution={{28th International Conference on Database and Expert Systems Applications - DEXA 2017}},
	title={{Integrating the R Language Runtime System with a Data Stream Warehouse}},