Archiving raw network traffic is fundamental for network forensics and troubleshooting. In order to efficiently retrieve specific entries from large-scale archives, multi-attribute queries are essential. However, most previous work mainly focus on single-attribute indexing, leading to limited performance for multi-attribute queries and index updates. To this end, we propose BK-Index, a novel multi-attribute index algorithm for network traffic. BK-Index combines multi-dimensional k-ary search trees and bitmap structures to efficiently handle both high-cardinality and low-cardinality attributes. This hybrid approach significantly reduces multi-attribute retrieval response time. Additionally, BK-Index employs a periodic batch construction scheme for real-time index updates in high-speed network links, eliminating the need for costly dynamic index maintenance. A comprehensive suite of experiments conducted on actual campus network traffic data has elucidated the superior performance of the BK-Index in terms of multi-attribute indexing and retrieval compared with other state-of-the-art index methods.

MOTIVATION: Virus integration in the host genome is frequently reported to be closely associated with many human diseases, and the detection of virus integration is a critically challenging task. However, most existing tools show limited specificity and sensitivity. Therefore, the objective of this study is to develop a method for accurate detection of virus integration into host genomes. RESULTS: Herein, we report a novel method termed HIVID2 that is a significant upgrade of HIVID. HIVID2 performs a paired-end combination (PE-combination) for potentially integrated reads. The resulting sequences are then remapped onto the reference genomes, and both split and discordant chimeric reads are used to identify accurate integration breakpoints with high confidence. HIVID2 represents a great improvement in specificity and sensitivity, and predicts breakpoints closer to the real integrations, compared with existing methods. The advantage of our method was demonstrated using both simulated and real datasets. HIVID2 uncovered novel integration breakpoints in well-known cervical cancer-related genes, including FHIT and LRP1B, which was verified using protein expression data. In addition, HIVID2 allows the user to decide whether to automatically perform advanced analysis using the identified virus integrations. By analyzing the simulated data and real data tests, we demonstrated that HIVID2 is not only more accurate than HIVID but also better than other existing programs with respect to both sensitivity and specificity. We believe that HIVID2 will help in enhancing future research associated with virus integration. AVAILABILITYAND IMPLEMENTATION: HIVID2 can be accessed at https://github.com/zengxi-hada/HIVID2/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.