Another mode of exploration, applicable to equally to human and machine users, is data exploration via queries. We developed the following set of queries to include a synthesis-based search, a synthesis and measurement-based search, a provenance-based search, and a provenance-based search conditioned on analysis results: 1) Find samples annealed at 350 • C; 2) Find all electrochemistry measurements performed on a sample that contains both Bi and V; 3) Find all provenances wherein a sample was synthesized by inkjet printing and whose first 2 electrochemistry measurements were chronopotentiometry measurements at 0.03 augmented with facile metadata management. Our approach to this challenge is to recognize the experimental events as the data source, resulting in the Event Sourced Architecture for Materials Provenance Management (ESAMP). To facilitate ingestion of a variety of data sources and automate some aspects of data validation, we implemented ESAMP with a Structured Query Language (SQL) database. The sequence of experimental steps is most naturally modelled as a directed graph, and in the present work we demonstrate a graph database that encodes experiment provenance along with a variety of other relationships. The graph approach to modelling experiment sequences has been primarily applied in the field o f c hemical synthesis. The MEKG (pronounced "Mek G") extends this concept to span synthesis, processing, and characterization experiments, while additionally encoding other relationships that facilitate knowledge representation in general, and data exploration in particular.