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datasetSchema.json 4.6 kB

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5 years ago
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  1. {
  2. "about": {"type":"dict", "required":true, "allow_unknown":true, "schema": {
  3. "datasetID":{"type":"string", "required":true, "empty": false},
  4. "datasetName":{"type":"string", "required":true, "empty": false},
  5. "datasetURI":{"type":"string","required":false},
  6. "description":{"type":"string","required":false},
  7. "citation":{"type":"string","required":false},
  8. "publicationDate":{"type":"string","required":false},
  9. "humanSubjectsResearch": {"type":"boolean", "required":false},
  10. "license":{"type":"string", "required":false},
  11. "source":{"type":"string", "required":false},
  12. "sourceURI":{"type":"string", "required":false},
  13. "approximateSize":{"type":"string", "required":false},
  14. "applicationDomain":{"type":"string","required":false},
  15. "datasetVersion":{"type":"string", "required":true},
  16. "datasetSchemaVersion":{"type":"string", "required":true},
  17. "redacted":{"type":"boolean", "required":false},
  18. "digest":{"type":"string", "required":false}
  19. }},
  20. "dataResources":{"type":"list","required":true,"schema":{"type":"dict","required": true,"allow_unknown":true,"schema":{
  21. "resID":{"type":"string","required":true},
  22. "resPath":{"type":"string","required":true},
  23. "resType":{"type":"string","required":true},
  24. "resFormat":{"type":"dict", "required":true, "keysrules": {"type":"string"}, "valuesrules": {"type":"list"}},
  25. "isCollection":{"type":"boolean","required":false,"default":false},
  26. "columnsCount": {"type":"integer","required":false},
  27. "columns": {
  28. "type":"list",
  29. "required": false,
  30. "schema": {"type":"dict", "required": true, "allow_unknown":true, "schema": {
  31. "colIndex":{"type":"integer", "required":true},
  32. "colName":{"type":"string", "required":true, "empty":false},
  33. "colDescription":{"type":"string", "required":false},
  34. "colType":{"required":true, "type":"string"},
  35. "role":{"type":"list","required":true,"schema":{"type":"string"}},
  36. "refersTo":{"type": "dict", "required":false, "allow_unknown":true, "schema":{
  37. "resID":{"type":"string","required":true},
  38. "resObject":{"required":true, "oneof":[
  39. {"type":"string"},
  40. {"type":"dict", "allow_unknown":true, "schema":{
  41. "nodeAttribute":{"type":"string","excludes":["edgeAttribute","columnIndex","columnName"]},
  42. "edgeAttribute":{"type":"string","excludes":["nodeAttribute","columnIndex","columnName"]},
  43. "columnIndex":{"type":"integer","excludes":["nodeAttribute","edgeAttribute","columnName"]},
  44. "columnName":{"type":"string","excludes":["nodeAttribute","edgeAttribute","columnIndex"]}}}
  45. ]}
  46. }},
  47. "timeGranularity":{"type":"dict", "required":false, "allow_unknown":true, "schema":{
  48. "value":{"type":"number", "required":true},
  49. "unit":{"type":"string", "required":true}
  50. }}
  51. }}
  52. }}}},
  53. "qualities":{"type":"list","required":false,"schema":{"type":"dict","required":true,"allow_unknown":true,"schema":{
  54. "qualName":{"type":"string","required":true},
  55. "qualValue":{"required":true},
  56. "qualValueType":{"type":"string","required":true},
  57. "qualValueUnits":{"type":"string","required":false},
  58. "restrictedTo":{"type":"dict","required":false,"allow_unknown":true,"schema":{
  59. "resID":{"type":"string","required":true},
  60. "resComponent":{"oneof":[
  61. {"type":"dict","required":false,"allow_unknown":true,"schema":{
  62. "columnIndex":{"type":"integer","excludes":["columnName","nodeAttribute","edgeAttribute","selector"]},
  63. "columnName":{"type":"string","excludes":["columnIndex","nodeAttribute","edgeAttribute","selector"]},
  64. "nodeAttribute":{"type":"string","excludes":["columnIndex","columnName","edgeAttribute","selector"]},
  65. "edgeAttribute":{"type":"string","excludes":["columnIndex","columnName","nodeAttribute","selector"]},
  66. "selector":{"type":"list","excludes":["columnIndex","columnName","nodeAttribute","edgeAttribute"]}
  67. }},
  68. {"type":"string","required":false}
  69. ]}
  70. }}
  71. }}}
  72. }

全栈的自动化机器学习系统,主要针对多变量时间序列数据的异常检测。TODS提供了详尽的用于构建基于机器学习的异常检测系统的模块,它们包括:数据处理(data processing),时间序列处理( time series processing),特征分析(feature analysis),检测算法(detection algorithms),和强化模块( reinforcement module)。这些模块所提供的功能包括常见的数据预处理、时间序列数据的平滑或变换,从时域或频域中抽取特征、多种多样的检测算