  Text reuse is a methodological element of fundamental importance in
humanities research: pieces of text that re-appear across different documents,
verbatim or paraphrased, provide invaluable information about the historical
spread and evolution of ideas. Large modern digitized corpora enable the joint
analysis of text collections that span entire centuries and the detection of
large-scale patterns, impossible to detect with traditional small-scale
analysis. For this opportunity to materialize, it is necessary to develop
efficient data science systems that perform the corresponding analysis tasks.
  In this paper, we share insights from ReceptionReader, a system for analyzing
text reuse in large historical corpora. The system is built upon billions of
instances of text reuses from large digitized corpora of 18th-century texts.
Its main functionality is to perform downstream text reuse analysis tasks, such
as finding reuses that stem from a given article or identifying the most reused
quotes from a set of documents, with each task expressed as a database query.
For the purposes of the paper, we discuss the related design choices including
various database normalization levels and query execution frameworks, such as
distributed data processing (Apache Spark), indexed row store engine (MariaDB
Aria), and compressed column store engine (MariaDB Columnstore). Moreover, we
present an extensive evaluation with various metrics of interest (latency,
storage size, and computing costs) for varying workloads, and we offer insights
from the trade-offs we observed and the choices that emerged as optimal in our
setting. In summary, our results show that (1) for the workloads that are most
relevant to text-reuse analysis, the MariaDB Aria framework emerges as the
overall optimal choice, (2) big data processing (Apache Spark) is irreplaceable
for all processing stages of the system's pipeline.
