  Extracting top-k keywords and documents using weighting schemes are popular
techniques employed in text mining and machine learning for different analysis
and retrieval tasks. The weights are usually computed in the data preprocessing
step, as they are costly to update and keep track of all the modifications
performed on the dataset. Furthermore, computation errors are introduced when
analyzing only subsets of the dataset. Therefore, in a Big Data context, it is
crucial to lower the runtime of computing weighting schemes, without hindering
the analysis process and the accuracy of the machine learning algorithms. To
address this requirement for the task of top-k keywords and documents, it is
customary to design benchmarks that compare weighting schemes within various
configurations of distributed frameworks and database management systems. Thus,
we propose a generic document-oriented benchmark for storing textual data and
constructing weighting schemes (TextBenDS). Our benchmark offers a generic data
model designed with a multidimensional approach for storing text documents. We
also propose using aggregation queries with various complexities and
selectivities for constructing term weighting schemes, that are utilized in
extracting top-k keywords and documents. We evaluate the computing performance
of the queries on several distributed environments set within the Apache Hadoop
ecosystem. Our experimental results provide interesting insights. As an
example, MongoDB proves to have the best overall performance, while Spark's
execution time remains almost the same, regardless of the weighting schemes.
