Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 0705.0450

VOODB: A Generic Discrete-Event Random Simulation Model to Evaluate the Performances of OODBs

Jérôme Darmont·Michel Schneider
View original source ↗Read source document ↗Export metadata ↓

Abstract

Performance of object-oriented database systems (OODBs) is still an issue to both designers and users nowadays. The aim of this paper is to propose a generic discrete-event random simulation model, called VOODB, in order to evaluate the performances of OODBs in general, and the performances of optimization methods like clustering in particular. Such optimization methods undoubtedly improve the performances of OODBs. Yet, they also always induce some kind of overhead for the system. Therefore, it is important to evaluate their exact impact on the overall performances. VOODB has been designed as a generic discrete-event random simulation model by putting to use a modelling approach, and has been validated by simulating the behavior of the O2 OODB and the Texas persistent object store. Since our final objective is to compare object clustering algorithms, some experiments have also been conducted on the DSTC clustering technique, which is implemented in Texas. To validate VOODB, performance results obtained by simulation for a given experiment have been compared to the results obtained by benchmarking the real systems in the same conditions. Benchmarking and simulation performance evaluations have been observed to be consistent, so it appears that simulation can be a reliable approach to evaluate the performances of OODBs.

Explore related subjects

cs.DB

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jérôme Darmont, Michel Schneider. 2007-05-03. VOODB: A Generic Discrete-Event Random Simulation Model to Evaluate the Performances of OODBs. https://arxiv.org/abs/0705.0450

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

IBLTs Measure Before They Decode: Self-Sizing Set Reconciliation for Database Consistency Verification

Cross-system data replication pipelines cannot confirm end-to-end consistency from the local guarantees of each hop, so the two endpoints must be compared directly on a periodic basis. Once the rows of a fixed snapshot are normalized into fingerprints, the task reduces to finding the symmetric difference of the two sets. An Invertible Bloom Lookup Table (IBLT) reconciles the sets with communication that grows only with the difference cardinality $d$, independent of table size, but its capacity must be fixed while $d$ is still unknown. Across 41,603 production reconciliations over 90 days, nonzero $d$ spans about seven orders of magnitude, and no reliable empirical constant exists. We show that the count array of an IBLT has already measured $d$ before decoding. The measurement is in-band: it is carried by the recovery sketch itself and adds no bytes dedicated to estimation. A mapping-aware theorem extends the construction to Irregular, Rateless, and MET IBLTs. The protocol reads the estimate only after a decoding failure; we prove that the failure-conditioned lower quantile bounds the risk of underestimation, which gives the second-round capacity a configurable success-probability guarantee. The resulting self-sizing protocol attempts recovery with a small first-round sketch and stops on success; on failure it reads $d$, sizes the second round, and completes reconciliation in at most two rounds. Against a controlled oracle, communication is 1.29-1.47 times that of a scheme given $d$ in advance. Production workload characterization, relational-database replay, and a cross-city KV deployment confirm the end-to-end mechanism. In production on an Oracle-MySQL link, all completed runs succeeded within two rounds, over 90% on the 1-RTT fast path with a single 16 KB sketch.

cs.DB↗
arXiv2026

Transformations for Evolving Property Graph Schemas

Property graph databases are widely used to represent complex and evolving data, yet systematic support for property graph schema evolution remains limited. In practice, schema transformations are typically defined manually, coupled to specific application contexts, and difficult to reuse across schemas or evolution scenarios. We present GRAFT, a logic-based framework that models property graph schema evolution as reusable, order-constrained meta-transformations derived from atomic edits. Schema evolution is formulated as the exploration of a finite meta-graph with schemas as nodes and grounded meta-transformations as edges. To ensure tractability, GRAFT combines similarity-guided search and pruning, guaranteeing duplication-freeness, termination, and correctness. An experimental evaluation on four benchmark and real-world property graph schema evolution scenarios shows that GRAFT efficiently computes high-quality schema transformation sequences. Using greedy exploration, GRAFT reaches the exact target schema on most datasets, producing stable transformation sequences while keeping runtimes low. A qualitative study on both real-world and synthetic large-scale datasets further shows the quality and robustness of the obtained reusable meta-transformations.

cs.DB↗
arXiv2026

VADER: Filtered Vector Search with Declarative Recall

Approximate filtered vector search (FVS), a core operation in many data management tasks that combine structured data with vector embeddings, exhibits increased complexity due to the characteristics of filtering predicates. Each predicate is defined by selectivity (i.e., the fraction of vectors that satisfy the predicate) and correlation (i.e., the relationship between the filter and the vector space), which can significantly affect search difficulty even for the same query vector. This poses a key challenge for users aiming to integrate vector search with structured data, as efficient execution often requires extensive manual tuning of algorithm parameters. In this paper, we present VADER, the first approach that eliminates hyperparameter tuning by introducing declarative recall for approximate filtered vector search. With declarative recall, users specify a desired recall target, and VADER executes FVS queries to meet this target without requiring manual configuration. VADER achieves this by employing a filter-aware recall predictor that generalizes across varying selectivities and correlations without explicit tuning, and by performing early termination once the predicted recall reaches the user-defined target. Through extensive experimental evaluation, we show that VADER achieves near-optimal early termination, while providing significant speedups of up to 53% faster and improved result quality of 28% compared to the best-performing baseline.

cs.DB↗