  Materialized model query aims to find the most appropriate materialized model
as the initial model for model reuse. It is the precondition of model reuse,
and has recently attracted much attention. {Nonetheless, the existing methods
suffer from the need to provide source data, limited range of applications, and
inefficiency since they do not construct a suitable metric to measure the
target-related knowledge of materialized models. To address this, we present
\textsf{MMQ}, a source-data free, general, efficient, and effective
materialized model query framework.} It uses a Gaussian mixture-based metric
called separation degree to rank materialized models. For each materialized
model, \textsf{MMQ} first vectorizes the samples in the target dataset into
probability vectors by directly applying this model, then utilizes Gaussian
distribution to fit for each class of probability vectors, and finally uses
separation degree on the Gaussian distributions to measure the target-related
knowledge of the materialized model. Moreover, we propose an improved
\textsf{MMQ} (\textsf{I-MMQ}), which significantly reduces the query time while
retaining the query performance of \textsf{MMQ}. Extensive experiments on a
range of practical model reuse workloads demonstrate the effectiveness and
efficiency of \textsf{MMQ}.
