Dontopedia

databases

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-12.)

databases has 79 facts recorded in Dontopedia across 27 references, with 10 live disagreements.

79 facts·32 predicates·27 sources·10 in dispute

Mostly:rdf:type(20), has member(8), contains element(6)

Maturity scale raw canonical shape-checked rule-derived certified

Rdf:typein disputerdf:type

Inbound mentions (52)

Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.

includesIncludes(4)

appliesToApplies to(2)

areHandledByAre Handled by(2)

definesVariableDefines Variable(2)

hasIndexHas Index(2)

iteratedFromIterated From(2)

memberOfMember of(2)

mentionsMentions(2)

providesMcpToolsForProvides Mcp Tools for(2)

checksForChecks for(1)

claimsBulletproofQualityClaims Bulletproof Quality(1)

claimsExpertiseInClaims Expertise in(1)

containsSkillContains Skill(1)

containsVariableContains Variable(1)

enablesIntegrationWithEnables Integration With(1)

exampleIncludesExample Includes(1)

handlesSourcesHandles Sources(1)

hasMemberHas Member(1)

hasVariableHas Variable(1)

indexedByIndexed by(1)

instructsToInvestigateInstructs to Investigate(1)

integratesWithIntegrates With(1)

involvesBestPracticesForInvolves Best Practices for(1)

isCommonlyUsedInIs Commonly Used in(1)

isProvidedByIs Provided by(1)

iteratesOverIterates Over(1)

iteratesOverDatabasesIterates Over Databases(1)

mentionsResourcesMentions Resources(1)

monitorsMonitors(1)

outerDataSourceOuter Data Source(1)

outerIteratorOuter Iterator(1)

outerLoopOuter Loop(1)

potentiallyDisruptsPotentially Disrupts(1)

readsFromReads From(1)

responsibleForResponsible for(1)

scaryForScary for(1)

supportsModelContextProtocolSupports Model Context Protocol(1)

usedByUsed by(1)

usedForUsed for(1)

usesListSyntaxUses List Syntax(1)

usesTechnologyUses Technology(1)

Other facts (52)

The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.

52 facts
PredicateValueRef
Has MemberMilvus 2.3.0[16]
Has MemberFaiss 1.7.3[16]
Has MemberAnnoy 1.18.0[16]
Has MemberHnswlib 0.9.2[16]
Has MemberQdrant 0.8.1[16]
Has MemberWeaviate 1.19.0[16]
Has MemberPostgresql[21]
Has MemberMongodb[21]
Contains ElementMilvus 2.3.0[14]
Contains ElementFaiss 1.7.3[14]
Contains ElementAnnoy 1.18.0[14]
Contains ElementHnswlib 0.9.2[14]
Contains ElementQdrant 0.8.1[14]
Contains ElementWeaviate 1.14.0[14]
Has KeywordDynamodb[2]
Has KeywordSqlite3[2]
Has KeywordPostgresql[2]
Has KeywordMysql[2]
Has KeywordMongodb[2]
ProvidesDatabase Name[12]
ProvidesConnection[12]
Has Missing DataQdrant 0.8.1[14]
Has Missing DataWeaviate 1.14.0[14]
Provides Capabilityefficient querying[20]
Provides Capabilitymanagement of large datasets[20]
Has BenefitPersistent Storage[21]
Has BenefitScalability[21]
Can HandleComplex Relationships[22]
Can HandleTransactions[22]
Have Mereological StructureCollections and Tables[1]
Has LevelAdvanced[2]
Vulnerable to SnapshotsZfs Snapshots[3]
Iteration MethodItems[12]
Iteration PatternKey Value Pairs[12]
Has Element TypeString[14]
Has Total Count6[14]
Has Length6[15]
Is Supported byManaged Services[18]
Providedata_persistence[21]
Ensuredata_safety[21]
Handleapplication_crash[21]
Uses Processing ModelPersistent Storage[21]
EnsuresData Safety During Crash[21]
Protects AgainstApplication Crash[21]
Ensures Data SafetyApplication Crash Scenario[21]
Can Scale HorizontallyMongodb[22]
Can Scale VerticallyPostgresql[22]
OfferRobust Querying Capabilities[22]
Are Optimized forConcurrent Access[22]
Designed forLarge Data Volumes[22]
Is Subject toAt Rest Encryption[25]
Confirm Intuitiontrue[27]

Timeline

Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.

haveMereologicalStructureblah/omega/part-870
ex:collections-and-tables
hasKeywordblah/unturf/part-26
ex:dynamodb
hasKeywordblah/unturf/part-26
ex:sqlite3
hasKeywordblah/unturf/part-26
ex:postgresql
hasKeywordblah/unturf/part-26
ex:mysql
hasKeywordblah/unturf/part-26
ex:mongodb
hasLevelblah/unturf/part-26
Advanced
vulnerableToSnapshotsblah/unturf/part-52
ex:zfs-snapshots
typebeam/1d41af65-75cc-4f7b-99f8-1df77ff73426
ex:System_Component
typebeam/70bfd1bc-86a4-4247-8a58-8a3ab388d827
ex:CloudResource
typebeam/6a7a1fb3-f58d-4bac-afbd-ca5ebe65e50f
ex:CloudService
labelbeam/6a7a1fb3-f58d-4bac-afbd-ca5ebe65e50f
databases
typebeam/d4c6094d-452e-4d82-a02a-3780b702ea60
ex:ServiceType
typebeam/143ce1b7-180e-4da5-9263-37de05238e72
ex:Service
labelbeam/143ce1b7-180e-4da5-9263-37de05238e72
Databases
typebeam/6c11a8ca-86fe-48a1-9e18-48120df12610
ex:ConfigurationDictionary
typebeam/f8f42f6b-a669-4fde-b310-665b40c0f92a
ex:DatabaseCollection
typebeam/3832d2ff-7f9e-4f2f-b174-098cdca2342e
ex:DatabaseSystem
typebeam/cb3641cd-c89b-4b65-a979-2de4bbe7aa55
ex:DatabaseCollection
iterationMethodbeam/cb3641cd-c89b-4b65-a979-2de4bbe7aa55
ex:items
providesbeam/cb3641cd-c89b-4b65-a979-2de4bbe7aa55
ex:database_name
providesbeam/cb3641cd-c89b-4b65-a979-2de4bbe7aa55
ex:connection
iterationPatternbeam/cb3641cd-c89b-4b65-a979-2de4bbe7aa55
ex:key-value-pairs
typebeam/b912e0a3-7996-465b-854f-18d563489c75
ex:Dictionary
labelbeam/b912e0a3-7996-465b-854f-18d563489c75
databases
hasElementTypebeam/7fe8a152-f4b0-4ead-886d-12532ab7dcc3
ex:string
containsElementbeam/7fe8a152-f4b0-4ead-886d-12532ab7dcc3
ex:milvus-2.3.0
containsElementbeam/7fe8a152-f4b0-4ead-886d-12532ab7dcc3
ex:faiss-1.7.3
containsElementbeam/7fe8a152-f4b0-4ead-886d-12532ab7dcc3
ex:annoy-1.18.0
containsElementbeam/7fe8a152-f4b0-4ead-886d-12532ab7dcc3
ex:hnswlib-0.9.2
containsElementbeam/7fe8a152-f4b0-4ead-886d-12532ab7dcc3
ex:qdrant-0.8.1
containsElementbeam/7fe8a152-f4b0-4ead-886d-12532ab7dcc3
ex:weaviate-1.14.0
hasMissingDatabeam/7fe8a152-f4b0-4ead-886d-12532ab7dcc3
ex:qdrant-0.8.1
hasMissingDatabeam/7fe8a152-f4b0-4ead-886d-12532ab7dcc3
ex:weaviate-1.14.0
hasTotalCountbeam/7fe8a152-f4b0-4ead-886d-12532ab7dcc3
6
hasLengthbeam/f046bfd3-c03b-4abb-8935-1462ceeedfa6
6
typebeam/da04535a-2bc8-4334-9bca-f9b43cd01117
ex:List
hasMemberbeam/da04535a-2bc8-4334-9bca-f9b43cd01117
ex:Milvus 2.3.0
hasMemberbeam/da04535a-2bc8-4334-9bca-f9b43cd01117
ex:Faiss 1.7.3
hasMemberbeam/da04535a-2bc8-4334-9bca-f9b43cd01117
ex:Annoy 1.18.0
hasMemberbeam/da04535a-2bc8-4334-9bca-f9b43cd01117
ex:Hnswlib 0.9.2
hasMemberbeam/da04535a-2bc8-4334-9bca-f9b43cd01117
ex:Qdrant 0.8.1
hasMemberbeam/da04535a-2bc8-4334-9bca-f9b43cd01117
ex:Weaviate 1.19.0
typeblah/atlas-ai/2
ex:Topic
labelblah/atlas-ai/2
databases
typebeam/859d2483-79b5-41d7-8d23-dc2a639fa9bb
ex:Component
labelbeam/859d2483-79b5-41d7-8d23-dc2a639fa9bb
Databases
isSupportedBybeam/859d2483-79b5-41d7-8d23-dc2a639fa9bb
ex:managed-services
typebeam/5bcb9ed9-64c5-48c5-9a99-45384d3cb83e
ex:StorageType
labelbeam/5bcb9ed9-64c5-48c5-9a99-45384d3cb83e
Databases
providesCapabilitybeam/abc06278-4d34-4aaa-a9f7-c35d156b37d6
efficient querying
providesCapabilitybeam/abc06278-4d34-4aaa-a9f7-c35d156b37d6
management of large datasets
typebeam/dd064674-37b1-4f57-ad58-28af115a4278
ex:Storage_System
providebeam/dd064674-37b1-4f57-ad58-28af115a4278
data_persistence
ensurebeam/dd064674-37b1-4f57-ad58-28af115a4278
data_safety
handlebeam/dd064674-37b1-4f57-ad58-28af115a4278
application_crash
hasBenefitbeam/dd064674-37b1-4f57-ad58-28af115a4278
ex:persistent_storage
hasBenefitbeam/dd064674-37b1-4f57-ad58-28af115a4278
ex:scalability
hasMemberbeam/dd064674-37b1-4f57-ad58-28af115a4278
ex:postgresql
hasMemberbeam/dd064674-37b1-4f57-ad58-28af115a4278
ex:mongodb
usesProcessingModelbeam/dd064674-37b1-4f57-ad58-28af115a4278
ex:persistent_storage
ensuresbeam/dd064674-37b1-4f57-ad58-28af115a4278
ex:data_safety_during_crash
protectsAgainstbeam/dd064674-37b1-4f57-ad58-28af115a4278
ex:application_crash
ensuresDataSafetybeam/dd064674-37b1-4f57-ad58-28af115a4278
ex:application_crash_scenario
canScaleHorizontallybeam/e39061c2-5736-4349-8e36-a6ca658aad94
ex:mongodb
canScaleVerticallybeam/e39061c2-5736-4349-8e36-a6ca658aad94
ex:postgresql
offerbeam/e39061c2-5736-4349-8e36-a6ca658aad94
ex:robust-querying-capabilities
canHandlebeam/e39061c2-5736-4349-8e36-a6ca658aad94
ex:complex-relationships
canHandlebeam/e39061c2-5736-4349-8e36-a6ca658aad94
ex:transactions
areOptimizedForbeam/e39061c2-5736-4349-8e36-a6ca658aad94
ex:concurrent-access
labelbeam/e39061c2-5736-4349-8e36-a6ca658aad94
Databases
designedForbeam/e39061c2-5736-4349-8e36-a6ca658aad94
ex:large-data-volumes
typebeam/f5a8f724-eae5-404d-abdf-559e2ebf9353
ex:DataSourceType
typebeam/97c16e85-2563-4855-9a67-aec2c81baa34
ex:DataStorageType
typebeam/b5b5ca47-b1d3-46d9-9eea-b112681ea7a1
ex:StorageSystem
isSubjectTobeam/b5b5ca47-b1d3-46d9-9eea-b112681ea7a1
ex:at-rest-encryption
typebeam/407ce4ee-bf98-4ecf-9678-4f46736fd693
ex:system-component
typedeep-time/berezkin-cosmic-hunt-motif
ex:Resource
confirmIntuitiondeep-time/berezkin-cosmic-hunt-motif
true

References (27)

27 references
  1. [1]Part 8701 fact
    ctx:discord/blah/omega/part-870
  2. [2]Part 266 facts
    ctx:discord/blah/unturf/part-26
  3. [3]Part 521 fact
    ctx:discord/blah/unturf/part-52
  4. ctx:claims/beam/1d41af65-75cc-4f7b-99f8-1df77ff73426
    • full textbeam-chunk
      text/plain1 KBdoc:beam/1d41af65-75cc-4f7b-99f8-1df77ff73426
      Show excerpt
      [Turn 1312] User: hmm, what specific alerts should I set up in Alertmanager to ensure 99.85% uptime? [Turn 1313] Assistant: Ensuring 99.85% uptime requires setting up specific alerts in Alertmanager to monitor critical aspects of your syst
  5. ctx:claims/beam/70bfd1bc-86a4-4247-8a58-8a3ab388d827
    • full textbeam-chunk
      text/plain1 KBdoc:beam/70bfd1bc-86a4-4247-8a58-8a3ab388d827
      Show excerpt
      [Turn 1580] User: I'm trying to troubleshoot some integration issues with our cloud provider, and I've identified a few potential areas where the issues might be hiding. However, I'm not sure how to debug these issues. Can you help me come
  6. ctx:claims/beam/6a7a1fb3-f58d-4bac-afbd-ca5ebe65e50f
  7. ctx:claims/beam/d4c6094d-452e-4d82-a02a-3780b702ea60
    • full textbeam-chunk
      text/plain1 KBdoc:beam/d4c6094d-452e-4d82-a02a-3780b702ea60
      Show excerpt
      print(f'The bandwidth cost difference between AWS and Azure is ${bandwidth_cost_difference:.2f}') ``` ### Total Cost Difference Now, let's sum up the total cost differences. ```python # Sum up the total cost differences total_cost_differ
  8. ctx:claims/beam/143ce1b7-180e-4da5-9263-37de05238e72
  9. ctx:claims/beam/6c11a8ca-86fe-48a1-9e18-48120df12610
    • full textbeam-chunk
      text/plain1 KBdoc:beam/6c11a8ca-86fe-48a1-9e18-48120df12610
      Show excerpt
      [Turn 1986] User: I'm working with Patricia on database selection for our project, and we're discussing how to achieve 30% better indexing strategies. We're considering different database options, but I'm not sure which one would be the bes
  10. ctx:claims/beam/f8f42f6b-a669-4fde-b310-665b40c0f92a
    • full textbeam-chunk
      text/plain1 KBdoc:beam/f8f42f6b-a669-4fde-b310-665b40c0f92a
      Show excerpt
      {'id': 2, 'name': 'Jane Doe'}, {'id': 3, 'name': 'Bob Smith'} ] # Define the test queries test_queries = [ {'query': 'SELECT * FROM table WHERE name = "John Doe"'}, {'query': 'SELECT * FROM table WHERE id = 1'} ] # Run the
  11. ctx:claims/beam/3832d2ff-7f9e-4f2f-b174-098cdca2342e
    • full textbeam-chunk
      text/plain1 KBdoc:beam/3832d2ff-7f9e-4f2f-b174-098cdca2342e
      Show excerpt
      [Turn 1987] Assistant: Sure, I can help you build a comparison tool to evaluate the indexing performance of different databases using Python. Below is a more comprehensive implementation that includes the necessary steps to create tables, i
  12. ctx:claims/beam/cb3641cd-c89b-4b65-a979-2de4bbe7aa55
    • full textbeam-chunk
      text/plain1 KBdoc:beam/cb3641cd-c89b-4b65-a979-2de4bbe7aa55
      Show excerpt
      # Run the tests and compare the results for database_name, connection in databases.items(): for strategy in indexing_strategies[database_name]: if database_name == 'mysql': with managed_cursor(connection) as cursor:
  13. ctx:claims/beam/b912e0a3-7996-465b-854f-18d563489c75
  14. ctx:claims/beam/7fe8a152-f4b0-4ead-886d-12532ab7dcc3
    • full textbeam-chunk
      text/plain1 KBdoc:beam/7fe8a152-f4b0-4ead-886d-12532ab7dcc3
      Show excerpt
      8. **Ease of Integration**: How easy it is to integrate the database into your existing system. 9. **Community Support**: The level of community support and documentation available. 10. **Cost**: The financial cost associated with using the
  15. ctx:claims/beam/f046bfd3-c03b-4abb-8935-1462ceeedfa6
    • full textbeam-chunk
      text/plain1 KBdoc:beam/f046bfd3-c03b-4abb-8935-1462ceeedfa6
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      # Define the databases to compare databases = ['Milvus 2.3.0', 'Faiss 1.7.3', 'Annoy 1.18.0', 'Hnswlib 0.9.2', 'Qdrant 0.8.1', 'Weaviate 1.14.0'] # Define the performance metrics to compare metrics = [ 'search_time', 'indexing_time', '
  16. ctx:claims/beam/da04535a-2bc8-4334-9bca-f9b43cd01117
    • full textbeam-chunk
      text/plain1 KBdoc:beam/da04535a-2bc8-4334-9bca-f9b43cd01117
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      'search_time', 'indexing_time', 'memory_usage', 'storage_size', 'recall_rate', 'precision_rate', 'f1_score', 'query_latency', 'scalability', 'concurrency_support', 'throughput', 'uptime', 'ease_of_integration', 'community_su
  17. [17]22 facts
    ctx:discord/blah/atlas-ai/2
    • full textctx:discord/blah/atlas-ai/2
      text/plain3 KBdoc:discord/blah/atlas-ai/2
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      [2025-04-04 05:23] lisamegawatts: I had a polisci professor that worked on this, he used to say theory is fine but no match for data https://correlatesofwar.org/ [2025-04-04 05:23] lisamegawatts: Trying to catalog and predict all factors th
    • full textatlas-ai-2
      text/plain3 KBdoc:agent/atlas-ai-2/3a79ad11-fcb3-4da8-b38e-c15390bfab94
      Show excerpt
      [2025-04-04 05:23] lisamegawatts: I had a polisci professor that worked on this, he used to say theory is fine but no match for data https://correlatesofwar.org/ [2025-04-04 05:23] lisamegawatts: Trying to catalog and predict all factors th
  18. ctx:claims/beam/859d2483-79b5-41d7-8d23-dc2a639fa9bb
    • full textbeam-chunk
      text/plain1 KBdoc:beam/859d2483-79b5-41d7-8d23-dc2a639fa9bb
      Show excerpt
      - **Service Discovery**: Use a service discovery mechanism to manage and route requests to available services. 3. **Cloud Infrastructure**: - **Cloud Providers**: Utilize cloud providers like AWS, Azure, or GCP, which offer scalable
  19. ctx:claims/beam/5bcb9ed9-64c5-48c5-9a99-45384d3cb83e
  20. ctx:claims/beam/abc06278-4d34-4aaa-a9f7-c35d156b37d6
    • full textbeam-chunk
      text/plain1 KBdoc:beam/abc06278-4d34-4aaa-a9f7-c35d156b37d6
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      Your current implementation uses a simple class-based approach with lists and dictionaries. While this is straightforward, it may not scale well for larger teams or more complex dynamics. Here are some improvements and alternative technolog
  21. ctx:claims/beam/dd064674-37b1-4f57-ad58-28af115a4278
    • full textbeam-chunk
      text/plain1 KBdoc:beam/dd064674-37b1-4f57-ad58-28af115a4278
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      [Turn 3295] Assistant: That sounds like a great plan! Using Pandas for now is a practical choice given its ease of use and efficiency for moderate-sized datasets. It will allow you to get started quickly and handle your current needs effect
  22. ctx:claims/beam/e39061c2-5736-4349-8e36-a6ca658aad94
    • full textbeam-chunk
      text/plain1 KBdoc:beam/e39061c2-5736-4349-8e36-a6ca658aad94
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      - Databases are designed to handle large volumes of data and can scale horizontally (MongoDB) or vertically (PostgreSQL). - They offer robust querying capabilities and can handle complex relationships and transactions. 3. **Concurren
  23. ctx:claims/beam/f5a8f724-eae5-404d-abdf-559e2ebf9353
    • full textbeam-chunk
      text/plain1 KBdoc:beam/f5a8f724-eae5-404d-abdf-559e2ebf9353
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      - Responsible for reading documents from the source (e.g., files, databases, APIs). 2. **Processing Modules:** - Various processing steps such as parsing, cleaning, enriching, and validating documents. 3. **Sink Module:** -
  24. ctx:claims/beam/97c16e85-2563-4855-9a67-aec2c81baa34
    • full textbeam-chunk
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      num_batches = 1200000 // batch_size for i in range(num_batches): process_batch(batch_size) if __name__ == "__main__": main() ``` ->-> 3,6 [Turn 4465] Assistant: Certainly! Using Apache NiFi for your ETL workflows can b
  25. ctx:claims/beam/b5b5ca47-b1d3-46d9-9eea-b112681ea7a1
    • full textbeam-chunk
      text/plain1 KBdoc:beam/b5b5ca47-b1d3-46d9-9eea-b112681ea7a1
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      - **At-Rest Encryption:** Encrypt data at rest using strong encryption algorithms like AES-256. This applies to data stored in databases, file systems, and any other storage solutions. 2. **Key Management:** - **Use a Key Management
  26. ctx:claims/beam/407ce4ee-bf98-4ecf-9678-4f46736fd693
    • full textbeam-chunk
      text/plain1 KBdoc:beam/407ce4ee-bf98-4ecf-9678-4f46736fd693
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      [Turn 9440] User: I've been studying secure tuning practices and I'm interested in applying them to our model fine-tuning process, particularly focusing on reducing the risk associated with sensitive enterprise data, and I'd like to discuss
  27. ctx:seven-sisters/deep-time/berezkin-cosmic-hunt-motif
    • full textc03
      text/plain2 KBdoc:agent/c03/7d983cd3-2377-4ac6-bef0-4cce0c40ea2a
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      [Source: Berezkin — Cosmic Hunt Motif and the Pleiades (Analytical Catalogue) — tradition: deep-time; era: pub 2007 (original Cosmic Hunt paper); ~15,000 BP hypothesis for Cosmic Hunt; Paleolithic. Excerpt 3/4. Provenance: https://www.seman
    • full textc04
      text/plain2 KBdoc:agent/c04/b297301f-31a4-4b1c-924c-8aa031c22a91
      Show excerpt
      [Source: Berezkin — Cosmic Hunt Motif and the Pleiades (Analytical Catalogue) — tradition: deep-time; era: pub 2007 (original Cosmic Hunt paper); ~15,000 BP hypothesis for Cosmic Hunt; Paleolithic. Excerpt 4/4. Provenance: https://www.seman
    • full textc02
      text/plain2 KBdoc:agent/c02/c051c93a-ec6a-40b4-8399-84e1c78acf17
      Show excerpt
      [Source: Berezkin — Cosmic Hunt Motif and the Pleiades (Analytical Catalogue) — tradition: deep-time; era: pub 2007 (original Cosmic Hunt paper); ~15,000 BP hypothesis for Cosmic Hunt; Paleolithic. Excerpt 2/4. Provenance: https://www.seman
    • full textc01
      text/plain2 KBdoc:agent/c01/6c9d2424-4119-444d-81ef-16c300c7536a
      Show excerpt
      [Source: Berezkin — Cosmic Hunt Motif and the Pleiades (Analytical Catalogue) — tradition: deep-time; era: pub 2007 (original Cosmic Hunt paper); ~15,000 BP hypothesis for Cosmic Hunt; Paleolithic. Excerpt 1/4. Provenance: https://www.seman

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