Ensuring order-fairness in distributed data management systems deployed in untrustworthy environments is crucial to prevent adversarial manipulation of transaction ordering, particularly in unpredictable markets where transaction order directly influences financial outcomes. While Byzantine Fault-Tolerant (BFT) consensus protocols guarantee safety and liveness, they inherently lack mechanisms to enforce order-fairness, exposing distributed systems to attacks such as frontrunning and sandwiching. Previous attempts to integrate order-fairness have often introduced substantial performance overhead, largely due to limitations of the underlying consensus protocols. This paper presents DAG of DAGs (DoD), a high-performance order-fairness protocol designed on top of DAG-based BFT consensus protocols. By leveraging the high throughput and resilience of DAG-based protocols, DoD addresses the performance limitations of existing order-fairness solutions. DoD's novel DAG of DAGs architecture enables seamless integration of order fairness with BFT consensus protocols. Through concurrent block proposals and a wave-based leader election mechanism, DoD significantly improves resilience against adversarial manipulation. A prototype implementation and experimental evaluation demonstrate that DoD effectively provides order fairness with minimal performance overhead.

Nearest neighbor searching of large databases in high-dimensional spaces is inherently difficult due to the curse of dimensionality. A flavor of approximation is, therefore, necessary to practically solve the problem of nearest neighbor search. In this paper, we propose a novel yet simple indexing scheme, HD-Index , to solve the problem of approximate k-nearest neighbor queries in massive high-dimensional databases. HD-Index consists of a set of novel hierarchical structures called RDB-trees built on Hilbert keys of database objects. The leaves of the RDB-trees store distances of database objects to reference objects, thereby allowing efficient pruning using distance filters. In addition to triangular inequality, we also use Ptolemaic inequality to produce better lower bounds. Experiments on massive (up to billion scale) high-dimensional (up to 1000+) datasets show that HD-Index is effective, efficient , and scalable.