Datasets:

graphguard-anon
/

graphguard-benchmarks

Error code:   DatasetGenerationError
Exception:    ParserError
Message:      Error tokenizing data. C error: Expected 1 fields in line 6, saw 2

Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/csv/csv.py", line 198, in _generate_tables
                  for batch_idx, df in enumerate(csv_file_reader):
                                       ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1843, in __next__
                  return self.get_chunk()
                         ~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1985, in get_chunk
                  return self.read(nrows=size)
                         ~~~~~~~~~^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1923, in read
                  ) = self._engine.read(  # type: ignore[attr-defined]
                      ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                      nrows
                      ^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 234, in read
                  chunks = self._reader.read_low_memory(nrows)
                File "pandas/_libs/parsers.pyx", line 850, in pandas._libs.parsers.TextReader.read_low_memory
                File "pandas/_libs/parsers.pyx", line 905, in pandas._libs.parsers.TextReader._read_rows
                File "pandas/_libs/parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
                File "pandas/_libs/parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
                File "pandas/_libs/parsers.pyx", line 2061, in pandas._libs.parsers.raise_parser_error
              pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 6, saw 2
              
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1879, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

----Source Graph Pattern:---- string
Node:0 Label:DBLPPapers
Node:1 Label:DBLPVenues
Node:2 Label:DBLPVenues
Edge: e1 Label:DBLPpublishedIn Source:0 Target:1
Edge: e2 Label:DBLPpublishedIn Source:0 Target:2
---Source Constraints-----
--------Target Graph Pattern--------
Equality: X_1 = X_2
Node:0 Label:DBLPAuthors
Node:1 Label:DBLPOrganizations
Node:2 Label:DBLPOrganizations
Edge: e1 Label:DBLPFrom Source:0 Target:1
Edge: e2 Label:DBLPFrom Source:0 Target:2
---Source Constraints-----
--------Target Graph Pattern--------
Equality: X_1 = X_2
Node:0 Label:Forum
Node:1 Label:Person
Node:2 Label:Person
Edge: e1 Label:hasModeratorFoPe Source:0 Target:1
Edge: e2 Label:hasModeratorFoPe Source:0 Target:2
---Source Constraints-----
--------Target Graph Pattern--------
Equality: X_1 = X_2
Node:0 Label:Post
Node:1 Label:Person
Node:2 Label:Person
Edge: e1 Label:hasCreatorPoPe Source:0 Target:1
Edge: e2 Label:hasCreatorPoPe Source:0 Target:2
---Source Constraints-----
--------Target Graph Pattern--------
Equality: X_1 = X_2
Node:0 Label:Comment
Node:1 Label:Person
Node:2 Label:Person
Edge: e1 Label:hasCreatorCoPe Source:0 Target:1
Edge: e2 Label:hasCreatorCoPe Source:0 Target:2
---Source Constraints-----
--------Target Graph Pattern--------
Equality: X_1 = X_2
Node:0 Label:Forum
Node:1 Label:Forum
Node:2 Label:Post
Edge: e1 Label:containerOfFoPo Source:0 Target:2
Edge: e2 Label:containerOfFoPo Source:1 Target:2
---Source Constraints-----
--------Target Graph Pattern--------
Equality: X_0 = X_1
Node:0 Label:Person
Node:1 Label:Place
Node:2 Label:Place
Edge: e1 Label:isLocatedInPePl Source:0 Target:1
Edge: e2 Label:isLocatedInPePl Source:0 Target:2
---Source Constraints-----
--------Target Graph Pattern--------
Equality: X_1 = X_2
Node:0 Label:Place
Node:1 Label:Place
Node:2 Label:Place
Edge: e1 Label:isPartOfPlPl Source:0 Target:1
Edge: e2 Label:isPartOfPlPl Source:0 Target:2
---Source Constraints-----
--------Target Graph Pattern--------
Equality: X_1 = X_2
Node:0 Label:Station
Node:1 Label:Station
Property: Node:0 Key:zoneOriginal Value:zo
Property: Node:1 Key:zoneOriginal Value:zo
Property: Node:0 Key:zone Value:z1
Property: Node:1 Key:zone Value:z2
---Source Constraints-----
--------Target Graph Pattern--------
Equality: V_z1 = V_z2
Node:0 Label:Station
Node:1 Label:Station
Property: Node:0 Key:latitude Value:lat
Property: Node:1 Key:latitude Value:lat
Property: Node:0 Key:longitude Value:lon
Property: Node:1 Key:longitude Value:lon
---Source Constraints-----
--------Target Graph Pattern--------
Equality: X_0 = X_1
Node:0 Label:Station
Node:1 Label:Station
Property: Node:0 Key:name Value:nm
Property: Node:1 Key:name Value:nm
---Source Constraints-----
--------Target Graph Pattern--------
Equality: X_0 = X_1
Node:0 Label:Station
Node:1 Label:Station
Node:2 Label:Station
Node:3 Label:Station
Edge: e1 Label:CONNECTED_THROUGH Source:0 Target:1
Edge: e2 Label:CONNECTED_THROUGH Source:2 Target:3
Property: Edge:e1 Key:line Value:l
Property: Edge:e2 Key:line Value:l
Property: Edge:e1 Key:color Value:c1
Property: Edge:e2 Key:color Value:c2
---Source Constraints-----

End of preview.

YAML Metadata Warning:The task_categories "graph-machine-learning" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

YAML Metadata Warning:The task_categories "relational-databases" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

YAML Metadata Warning:The task_categories "knowledge-graphs" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

UpdateGuard Benchmark Suite (Anonymized Submission)

This repository contains the complete cross-paradigm benchmark suites, schemas, constraints, and data instances for UpdateGuard, an automated first-order theorem proving and Craig interpolation framework for database integrity verification.

Repository Contents

The repository is modularly structured into tier-based packages:

1. Constraints Suite (`constraints.zip` ~0.5 MB)

Contains all integrity constraints evaluated across the three paradigms:

Property Graphs: Mined and hand-crafted Graph Generating Dependencies (GGDs) for CORDIS, DBLP, GDELT, and LDBC SNB, alongside property-level constraints: Graph Object Functional Dependencies (GO-FDs) for Northwind, graph functional/uniqueness dependencies (gFDs/gUCs) for Offshore Leaks, and graph functional dependencies and keys for London Public Transport (LPT) and Train Services (TS).
RDF Knowledge Graphs: W3C SHACL shape schemas and OWL ontologies for LUBM and WatDiv.
Relational Schemas: Relational dependencies (foreign keys, uniqueness EGDs, check constraints) for TPC-H and all ChaseBench scenarios (doctors, doctors-fd, LUBM, Ontology-256, STB-128, deep, and correctness suites).

2. Standard Evaluation Benchmarks (`graph_benchmarks.zip`, `rdf_benchmarks.zip`, `relational_benchmarks.zip`)

Property Graphs (graph_benchmarks.zip): Ready-to-evaluate node and edge CSV files for Neo4j and localized r-hop neighborhood extraction (cordis_small, dblp_small, gdelt_small, ldbc_small_*, northwind, offshore_small, lpt, trains).
RDF Knowledge Graphs (rdf_benchmarks.zip): LUBM multi-university instances (1 to 15 universities in OWL/RDF), WatDiv 100k triple sample (watdiv_100k.nt), and UBA synthetic data generator.
Relational Databases (relational_benchmarks.zip): ChaseBench relational scenarios with tables, schemas, queries, and repair tools, alongside TPC-H decision-support tables at scale factors 0.01 to 0.1.

3. Full-Scale Datasets (Optional, for heavy data scaling)

ldbc_scales.zip: LDBC SNB SF 1 and SF 3 instances.
watdiv_10m.zip: WatDiv full 10M triples dataset (watdiv.10M.nt).
dblp_raw.zip: Complete DBLP publication XML database (dblp.xml, dblp.dtd).
tpch_scales.zip: TPC-H tables at SF 0.3, SF 1, SF 3, and SF 10.

Quick Download & Setup

You can download and extract these datasets directly into the codebase using the automated downloader script:

# Download standard evaluation bundle (constraints + benchmark datasets):
python scripts/download_benchmarks.py

# Download only constraints (ultra-lightweight, ~300 KB):
python scripts/download_benchmarks.py --constraints-only

# Download specific datasets:
python scripts/download_benchmarks.py --dataset ldbc cordis

All archives extract into the datasets/ root directory, maintaining the exact folder paths expected by the evaluation pipelines (run_all_benchmarks.py, run_eval.sh, run_chasebench_eval.sh, and run_lubm_scaling.sh).

Double-Blind Anonymity Compliance

This repository and its contents are fully anonymized for double-blind peer review. All local paths, personal metadata, and author identifications have been scrubbed in accordance with SIGMOD review guidelines.

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