Search arXiv⌕ Search

arXiv · 2305.06226

RiverBench: an Open RDF Streaming Benchmark Suite

Abstract

RDF streaming has been explored by the Semantic Web community from many angles, resulting in multiple task formulations and streaming methods. However, for many existing formulations of the problem, reliably benchmarking streaming solutions has been challenging due to the lack of well-described and appropriately diverse benchmark datasets. Existing datasets and evaluations, except a few notable cases, suffer from unclear streaming task scopes, underspecified benchmarks, and errors in the data. To address these issues, we propose RiverBench, an open and collaborative RDF streaming benchmark suite. RiverBench leverages continuous, community-driven processes, established best practices (e.g., FAIR), and built-in quality guarantees. The suite distributes datasets in a common, accessible format, with clear documentation, licensing, and machine-readable metadata. The current release includes a diverse collection of non-synthetic datasets generated by the Semantic Web community, representing many applications of RDF data streaming, all major task formulations, and emerging RDF features (RDF-star). Finally, we present a list of research applications for the suite, demonstrating its versatility and value even beyond the realm of RDF streaming.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Piotr Sowinski, Maria Ganzha, Marcin Paprzycki. 2023-11-27. RiverBench: an Open RDF Streaming Benchmark Suite. https://arxiv.org/abs/2305.06226

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

KEEP EXPLORING

Related papers

MLSkip: Data Skipping for ML Filters via Lightweight Metadata

Database vendors recently released AI functions that can be used in filter predicates. As such functions often rely on costly, black-box ML models, they unveil new data management challenges. Concretely, traditional data skipping techniques for integer and string data fail to be applicable to the new filter type. Indeed, there is no known mechanism for pruning non-qualifying row groups, e.g., when reading files from blob storage. In this work, we initiate the study of data skipping techniques for ML filters. We make the case that Parquet's default min-max metadata is enough to enable pruning. To this end, we draw connections to two lines of research: (i) the recently proposed query language for ML models and (ii) neural network verification. Our preliminary results on ReLU architectures show that on tables from TPC-H and TPC-DS, the average pruning effectiveness for filters of selectivity below 0.1% amounts to 27.4%. Finally, inspired by research on spatial joins, we propose an enhanced metadata structure: a size-bounded 2D convex hull that verification tools can make better use of, increasing the pruning effectiveness to 38.31%, while occupying at most 45 bytes per row group and column pair. We observe an end-to-end speedup of 1.07$\times$ over PyTorch in DuckDB.

cs.DB↗

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, 90.7% on the 1-RTT fast path with a single 16 KB sketch.

cs.DB↗

KathDB-FAO: Synthesized Query Plans in a Multimodal DBMS

We design, implement, and evaluate KathDB-FAO, a new query evaluation subsystem for our KathDB multimodal DBMS. KathDB-FAO takes as input a query in natural language (NL) and converts it into a query execution plan where each operator is a function whose body is synthesized during query evaluation, which allows powerful query-specific optimizations. To generate accurate and efficient plans from NL, KathDB-FAO first extracts fine-grained atomic actions for correctness, then establishes contracts on the inputs and outputs of those actions and groups them for efficiency, and finally synthesizes the function for each group on the fly. On SemBench, KathDB-FAO cuts execution cost by 58.8% on average across scenarios compared with the next best system, at comparable or better quality.

cs.DB↗