  The number of scientific publications continues to rise exponentially,
especially in Computer Science (CS). However, current solutions to analyze
those publications restrict access behind a paywall, offer no features for
visual analysis, limit access to their data, only focus on niches or
sub-fields, and/or are not flexible and modular enough to be transferred to
other datasets. In this thesis, we conduct a scientometric analysis to uncover
the implicit patterns hidden in CS metadata and to determine the state of CS
research. Specifically, we investigate trends of the quantity, impact, and
topics for authors, venues, document types (conferences vs. journals), and
fields of study (compared to, e.g., medicine). To achieve this we introduce the
CS-Insights system, an interactive web application to analyze CS publications
with various dashboards, filters, and visualizations. The data underlying this
system is the DBLP Discovery Dataset (D3), which contains metadata from 5
million CS publications. Both D3 and CS-Insights are open-access, and
CS-Insights can be easily adapted to other datasets in the future. The most
interesting findings of our scientometric analysis include that i) there has
been a stark increase in publications, authors, and venues in the last two
decades, ii) many authors only recently joined the field, iii) the most cited
authors and venues focus on computer vision and pattern recognition, while the
most productive prefer engineering-related topics, iv) the preference of
researchers to publish in conferences over journals dwindles, v) on average,
journal articles receive twice as many citations compared to conference papers,
but the contrast is much smaller for the most cited conferences and journals,
and vi) journals also get more citations in all other investigated fields of
study, while only CS and engineering publish more in conferences than journals.
