  Evaluation in empirical computer science is essential to show progress and
assess technologies developed. Several research domains such as information
retrieval have long relied on systematic evaluation to measure progress: here,
the Cranfield paradigm of creating shared test collections, defining search
tasks, and collecting ground truth for these tasks has persisted up until now.
In recent years, however, several new challenges have emerged that do not fit
this paradigm very well: extremely large data sets, confidential data sets as
found in the medical domain, and rapidly changing data sets as often
encountered in industry. Also, crowdsourcing has changed the way that industry
approaches problem-solving with companies now organizing challenges and handing
out monetary awards to incentivize people to work on their challenges,
particularly in the field of machine learning.
  This white paper is based on discussions at a workshop on
Evaluation-as-a-Service (EaaS). EaaS is the paradigm of not providing data sets
to participants and have them work on the data locally, but keeping the data
central and allowing access via Application Programming Interfaces (API),
Virtual Machines (VM) or other possibilities to ship executables. The objective
of this white paper are to summarize and compare the current approaches and
consolidate the experiences of these approaches to outline the next steps of
EaaS, particularly towards sustainable research infrastructures.
  This white paper summarizes several existing approaches to EaaS and analyzes
their usage scenarios and also the advantages and disadvantages. The many
factors influencing EaaS are overviewed, and the environment in terms of
motivations for the various stakeholders, from funding agencies to challenge
organizers, researchers and participants, to industry interested in supplying
real-world problems for which they require solutions.
