  In-situ processing has been proposed as a novel data exploration solution in
many domains generating massive amounts of raw data, e.g., astronomy, since it
provides immediate SQL querying over raw files. The performance of in-situ
processing across a query workload is, however, limited by the speed of full
scan, tokenizing, and parsing of the entire data. Online aggregation (OLA) has
been introduced as an efficient method for data exploration that identifies
uninteresting patterns faster by continuously estimating the result of a
computation during the actual processing---the computation can be stopped as
early as the estimate is accurate enough to be deemed uninteresting. However,
existing OLA solutions have a high upfront cost of randomly shuffling and/or
sampling the data. In this paper, we present OLA-RAW, a bi-level sampling
scheme for parallel online aggregation over raw data. Sampling in OLA-RAW is
query-driven and performed exclusively in-situ during the runtime query
execution, without data reorganization. This is realized by a novel
resource-aware bi-level sampling algorithm that processes data in random chunks
concurrently and determines adaptively the number of sampled tuples inside a
chunk. In order to avoid the cost of repetitive conversion from raw data,
OLA-RAW builds and maintains a memory-resident bi-level sample synopsis
incrementally. We implement OLA-RAW inside a modern in-situ data processing
system and evaluate its performance across several real and synthetic datasets
and file formats. Our results show that OLA-RAW chooses the sampling plan that
minimizes the execution time and guarantees the required accuracy for each
query in a given workload. The end result is a focused data exploration process
that avoids unnecessary work and discards uninteresting data.
