  Data silos create barriers in accessing and utilizing data dispersed over
networks. Directly sharing data easily suffers from the long downloading time,
the single point failure and the untraceable data usage. In this paper, we
present Minerva, a peer-to-peer cross-cluster data query system based on
InterPlanetary File System (IPFS). Minerva makes use of the distributed Hash
table (DHT) lookup to pinpoint the locations that store content chunks. We
theoretically model the DHT query delay and introduce the fat Merkle tree
structure as well as the DHT caching to reduce it. We design the query plan for
read and write operations on top of Apache Drill that enables the collaborative
query with decentralized workers. We conduct comprehensive experiments on
Minerva, and the results show that Minerva achieves up to $2.08 \times$ query
performance acceleration compared to the original IPFS data query, and could
complete data analysis queries on the Internet-like environments within an
average latency of $0.615$ second. With collaborative query, Minerva could
perform up to $1.39 \times$ performance acceleration than centralized query
with raw data shipment.
