  Modern big data applications integrate data from various sources. As a
result, these datasets may not satisfy perfect constraints, leading to sparse
schema information and non-optimal query performance. The existing approach of
PatchIndexes enable the definition of approximate constraints and improve query
performance by exploiting the materialized constraint information. As real
world data warehouse workloads are often not limited to read-only queries, we
enhance the PatchIndex structure towards an update-conscious design in this
paper. Therefore, we present a sharded bitmap as the underlying data structure
which offers efficient update operations, and describe approaches to maintain
approximate constraints under updates, avoiding index recomputations and full
table scans. In our evaluation, we prove that PatchIndexes significantly impact
query performance while achieving lightweight update support.
