  We propose a novel framework for ontology-based access to temporal log data
using a datalog extension datalogMTL of a Horn fragment of the metric temporal
logic MTL. We show that datalogMTL is ExpSpace-complete even with punctual
intervals, in which case full MTL is known to be undecidable. We also prove
that nonrecursive datalogMTL is PSpace-complete for combined complexity and in
AC0 for data complexity. We demonstrate by two real-world use cases that
nonrecursive datalogMTL programs can express complex temporal concepts from
typical user queries and thereby facilitate access to temporal log data. Our
experiments with Siemens turbine data and MesoWest weather data show that
datalogMTL ontology-mediated queries are efficient and scale on large datasets.
