Long-term network packet traces (e.g., pcaps), if available, can enable and inform lots of management tasks. However, storing packet data at scale is very expensive, forcing operators to choose between coarse historical summaries or short retention windows. In this context, deep generative compression (DGC) offers a new hope to store compact model parameters and regenerate structurally accurate traces on demand. We evaluate the suitability of recent deep generative approaches [21, 24] for packet trace modeling and generation. We find that their fidelity metrics are disconnected from the domain-specific queries/use cases, and they have bad cost-fidelity trade-off. We propose DeePCAP, an end-to-end trace storage system to close this gap. DeePCAP introduces a query-driven fidelity framework spanning packet- and flow-level queries to tackle the fidelity disconnection, and proposes a novel dimensionality reduction approach using frequency domain encoding to improve cost-fidelity trade-off. Our preliminary results show that DeePCAP achieves the best fidelity on the 100+ query suite and the strongest cost-fidelity trade-off.
Fenghao Dong, Yucheng Yin, Yajie Zhou et al.· 0 citations
Synthetic tabular data support use cases like data sharing, model development under access restrictions, and rapid prototyping of analytical workflows. Modern generative models are evaluated by their statistical similarity, correlation structure, privacy, and downstream machine-learning utility. However, such evaluations leave a gap: they rarely test the structure that matters for analytical queries. We present TabQueryBench, a query-centric benchmark that uses SQL-shaped analytical queries as structural assessors for synthetic data fidelity. It provides an extensible foundation for query-centric synthetic-data evaluation. From 12 public sources of analytical queries, TabQueryBench taxonomizes recurring cross-domain logic into 44 reusable query templates and grounds them to each dataset via a policy-guided template-to-SQL pipeline. This makes queries schema-aware while preserving comparability across generative models. Across 49 datasets and 11 generative models, it activates 10-12 templates per dataset, producing more than 100 executable SQL queries per dataset. Our systematic experiments show five main patterns. First, current tabular generative models can have good distance-based fidelity, but they still fall short on query-centric fidelity: RealTabFormer achieves the highest query-centric fidelity, but it only reaches 0.75 +/- 0.15 (REAL data score is 1.00). Second, tabular generative models struggle with very high-cardinality discrete support. Third, SOTA generative models preserve good global conditional query-centric fidelity, but fail more on local queries. Fourth, tail fidelity deteriorates as queries move toward the extreme tail; even the best model recovers only about 40.7% of real rare values. Finally, there is a fidelity-cost tradeoff in tabular generation: BayesNet offers the strongest tradeoff, with slightly lower query-centric fidelity but much lower generation cost.
Jialin Zhang, Fenghao Dong, Yajie Zhou et al.· 0 citations