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Data Type

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)

Data Type has 30 facts recorded in Dontopedia across 14 references, with 5 live disagreements.

30 facts·8 predicates·14 sources·5 in dispute

Mostly:rdf:type(13), rdfs:label(7), has value(3)

Maturity scale raw canonical shape-checked rule-derived certified

Rdf:typein disputerdf:type

  • Class[5]all time · 886e5d26 Dd7f 4315 Aed0 E67c69b9eb2f
  • Class[11]sourceall time · 2d01e538 646d 45ad Abfa Ac14c6091f19
  • Class[12]all time · 018071ba Eeb7 46eb 9af2 E3728d58c1d6
  • Data Type Enum[10]all time · Be6814ba Aa07 4fc4 B58d D8d7b642906f
  • Enum[7]all time · 5322bb97 5c91 4db0 Bf82 Cf4a4ac41105
  • Enum[13]all time · 86785515 9f1f 4fdd 887b 9264324ad027
  • Enum[4]sourceall time · 634b378d C567 4d90 Bca9 6ed67f28473b
  • Enum Class[14]all time · 926f1488 328b 43c2 9fba D5492a192351
  • Enumeration[13]all time · 86785515 9f1f 4fdd 887b 9264324ad027
  • Enum Type[2]all time · Bf38e99d 74ad 46c4 A6f9 80d36566aa7b

Rdfs:labelin disputerdfs:label

  • DataType[7]sourceall time · 5322bb97 5c91 4db0 Bf82 Cf4a4ac41105
  • data type[8]all time · 9bef49d0 7623 4f5c 8e00 F769e885a383
  • Data Type[9]all time · 4f3f0e67 2593 4f7f 9625 25393b3512e1
  • DataType enum[7]sourceall time · 5322bb97 5c91 4db0 Bf82 Cf4a4ac41105
  • vector[8]all time · 9bef49d0 7623 4f5c 8e00 F769e885a383
  • Data Type Enum[10]all time · Be6814ba Aa07 4fc4 B58d D8d7b642906f
  • DataType[2]sourceall time · Bf38e99d 74ad 46c4 A6f9 80d36566aa7b

Has Valuein disputehasValue

  • Float Vector[2]sourceall time · Bf38e99d 74ad 46c4 A6f9 80d36566aa7b
  • Int64[2]sourceall time · Bf38e99d 74ad 46c4 A6f9 80d36566aa7b
  • FLOAT_VECTOR[3]all time · 3ec8c303 E081 4923 9f67 5956a4f6bef5

Has Subtypein disputehasSubtype

  • Float Vector[1]sourceall time · 19d581bd 9e09 4819 Ad3a F497c9d8b02d
  • Int64[1]sourceall time · 19d581bd 9e09 4819 Ad3a F497c9d8b02d

Provides Valuein disputeprovidesValue

  • INT64[6]all time · 1e47faff 9001 4475 B47f Aee14dcc46af
  • FLOAT_VECTOR[6]all time · 1e47faff 9001 4475 B47f Aee14dcc46af

Is Imported FromisImportedFrom

  • Pymilvus[5]all time · 886e5d26 Dd7f 4315 Aed0 E67c69b9eb2f

Imported FromimportedFrom

  • Pymilvus[4]sourceall time · 634b378d C567 4d90 Bca9 6ed67f28473b

Used inusedIn

Inbound mentions (100)

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.

rdf:typeRdf:type(83)

importsImports(3)

providesProvides(3)

importsClassImports Class(2)

containsClassContains Class(1)

containsImportContains Import(1)

dataTypesData Types(1)

exportedClassExported Class(1)

exportedFunctionsExported Functions(1)

hasFunctionHas Function(1)

importedModulesImported Modules(1)

importsMultipleImports Multiple(1)

isAIs a(1)

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.

30 undated
—
hasSubtypebeam/19d581bd-9e09-4819-ad3a-f497c9d8b02d
ex:FLOAT_VECTOR
—
hasSubtypebeam/19d581bd-9e09-4819-ad3a-f497c9d8b02d
ex:INT64
—
hasValuebeam/bf38e99d-74ad-46c4-a6f9-80d36566aa7b
ex:FLOAT_VECTOR
—
hasValuebeam/bf38e99d-74ad-46c4-a6f9-80d36566aa7b
ex:INT64
—
hasValuebeam/3ec8c303-e081-4923-9f67-5956a4f6bef5
FLOAT_VECTOR
—
importedFrombeam/634b378d-c567-4d90-bca9-6ed67f28473b
ex:pymilvus
—
isImportedFrombeam/886e5d26-dd7f-4315-aed0-e67c69b9eb2f
ex:pymilvus
—
providesValuebeam/1e47faff-9001-4475-b47f-aee14dcc46af
INT64
—
providesValuebeam/1e47faff-9001-4475-b47f-aee14dcc46af
FLOAT_VECTOR
—
labelbeam/5322bb97-5c91-4db0-bf82-cf4a4ac41105
DataType
—
labelbeam/9bef49d0-7623-4f5c-8e00-f769e885a383
data type
—
labelbeam/4f3f0e67-2593-4f7f-9625-25393b3512e1
Data Type
—
labelbeam/5322bb97-5c91-4db0-bf82-cf4a4ac41105
DataType enum
—
labelbeam/9bef49d0-7623-4f5c-8e00-f769e885a383
vector
—
labelbeam/be6814ba-aa07-4fc4-b58d-d8d7b642906f
Data Type Enum
—
labelbeam/bf38e99d-74ad-46c4-a6f9-80d36566aa7b
DataType
—
typebeam/886e5d26-dd7f-4315-aed0-e67c69b9eb2f
ex:Class
—
typebeam/2d01e538-646d-45ad-abfa-ac14c6091f19
ex:Class
—
typebeam/018071ba-eeb7-46eb-9af2-e3728d58c1d6
ex:Class
—
typebeam/be6814ba-aa07-4fc4-b58d-d8d7b642906f
ex:DataTypeEnum
—
typebeam/5322bb97-5c91-4db0-bf82-cf4a4ac41105
ex:Enum
—
typebeam/86785515-9f1f-4fdd-887b-9264324ad027
ex:Enum
—
typebeam/634b378d-c567-4d90-bca9-6ed67f28473b
ex:Enum
—
typebeam/926f1488-328b-43c2-9fba-d5492a192351
ex:Enum-Class
—
typebeam/86785515-9f1f-4fdd-887b-9264324ad027
ex:Enumeration
—
typebeam/bf38e99d-74ad-46c4-a6f9-80d36566aa7b
ex:EnumType
—
typebeam/5322bb97-5c91-4db0-bf82-cf4a4ac41105
ex:PythonEnum
—
typebeam/3ec8c303-e081-4923-9f67-5956a4f6bef5
ex:PythonEnum
—
typebeam/1e47faff-9001-4475-b47f-aee14dcc46af
ex:PythonEnum
—
usedInbeam/634b378d-c567-4d90-bca9-6ed67f28473b
ex:CollectionSchema

References (14)

14 references
  1. [1]beam-chunk2 facts
    customctx:claims/beam/19d581bd-9e09-4819-ad3a-f497c9d8b02d
    • full textbeam-chunk
      text/plain1 KBdoc:beam/19d581bd-9e09-4819-ad3a-f497c9d8b02d
      Show excerpt
      FieldSchema(name="id", dtype=DataType.INT64, is_primary=True, auto_id=True), FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=128) ] schema = CollectionSchema(fields, "Test Collection") # Create a collection collectio…
  2. [2]beam-chunk4 facts
    customctx:claims/beam/bf38e99d-74ad-46c4-a6f9-80d36566aa7b
    • full textbeam-chunk
      text/plain1 KBdoc:beam/bf38e99d-74ad-46c4-a6f9-80d36566aa7b
      Show excerpt
      - **Disaster Recovery**: Have a disaster recovery plan in place to quickly recover from failures. ### 8. **Security** - **Authentication and Authorization**: Implement authentication and authorization mechanisms to secure access to your Mi…
  3. [3]3ec8c303 E081 4923 9f67 5956a4f6bef52 facts
    customctx:claims/beam/3ec8c303-e081-4923-9f67-5956a4f6bef5
  4. [4]beam-chunk3 facts
    customctx:claims/beam/634b378d-c567-4d90-bca9-6ed67f28473b
    • full textbeam-chunk
      text/plain1 KBdoc:beam/634b378d-c567-4d90-bca9-6ed67f28473b
      Show excerpt
      ``` ->-> 5,12 [Turn 4945] Assistant: Certainly! Designing an efficient vector indexing workflow using Milvus involves several key steps: defining the schema, creating a collection, ingesting data, creating an index, and executing queries. …
  5. [5]beam-chunk2 facts
    customctx:claims/beam/886e5d26-dd7f-4315-aed0-e67c69b9eb2f
    • full textbeam-chunk
      text/plain1 KBdoc:beam/886e5d26-dd7f-4315-aed0-e67c69b9eb2f
      Show excerpt
      Ensure that the index creation process has completed successfully. You can check the status of the index building process using the `describe_index` method. 2. **Rebuild the Index**: If the index is not built, you may need to rebuild…
  6. [6]beam-chunk3 facts
    customctx:claims/beam/1e47faff-9001-4475-b47f-aee14dcc46af
    • full textbeam-chunk
      text/plain1 KBdoc:beam/1e47faff-9001-4475-b47f-aee14dcc46af
      Show excerpt
      Create a Python script named `setup_milvus.py` with the following content: ```python from pymilvus import connections, FieldSchema, CollectionSchema, DataType, Collection # Connect to Milvus connections.connect("default", ho…
  7. [7]beam-chunk4 facts
    customctx:claims/beam/5322bb97-5c91-4db0-bf82-cf4a4ac41105
    • full textbeam-chunk
      text/plain1 KBdoc:beam/5322bb97-5c91-4db0-bf82-cf4a4ac41105
      Show excerpt
      - For larger datasets (millions or more vectors), IVFPQ or HNSW are often better choices due to their efficiency in terms of memory and search speed. 2. **Search Latency Requirements**: - If you need very low search latency (under 20…
  8. [8]9bef49d0 7623 4f5c 8e00 F769e885a3832 facts
    customctx:claims/beam/9bef49d0-7623-4f5c-8e00-f769e885a383
  9. [9]beam-chunk1 fact
    customctx:claims/beam/4f3f0e67-2593-4f7f-9625-25393b3512e1
    • full textbeam-chunk
      text/plain1 KBdoc:beam/4f3f0e67-2593-4f7f-9625-25393b3512e1
      Show excerpt
      # Convert columns to appropriate data types datasets['some_column'] = pd.to_numeric(datasets['some_column'], errors='coerce') # Define secure tuning function def secure_tuning(row): # Implement secure tuning logic here # Example: C…
  10. [10]Be6814ba Aa07 4fc4 B58d D8d7b642906f2 facts
    customctx:claims/beam/be6814ba-aa07-4fc4-b58d-d8d7b642906f
  11. [11]beam-chunk1 fact
    customctx:claims/beam/2d01e538-646d-45ad-abfa-ac14c6091f19
    • full textbeam-chunk
      text/plain1 KBdoc:beam/2d01e538-646d-45ad-abfa-ac14c6091f19
      Show excerpt
      - Redis supports various data types such as strings, hashes, lists, sets, and sorted sets. Depending on your use case, you might want to use a more suitable data type. ### 2. **Configure Redis for Performance** - Tune Redis configura…
  12. [12]018071ba Eeb7 46eb 9af2 E3728d58c1d61 fact
    customctx:claims/beam/018071ba-eeb7-46eb-9af2-e3728d58c1d6
  13. [13]86785515 9f1f 4fdd 887b 9264324ad0272 facts
    customctx:claims/beam/86785515-9f1f-4fdd-887b-9264324ad027
  14. [14]beam-chunk1 fact
    customctx:claims/beam/926f1488-328b-43c2-9fba-d5492a192351
    • full textbeam-chunk
      text/plain1 KBdoc:beam/926f1488-328b-43c2-9fba-d5492a192351
      Show excerpt
      FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=128) ] schema = CollectionSchema(fields, "Document Embeddings") # Create the collection collection = Collection("document_embeddings", schema) ``` #### 3. Insert Vectors …

See also

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