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2 the conceptual model

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In this presentation we deeply explain the conceptual model of BExIS. The model is built around a generic scientific data lifecycle.

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2 the conceptual model

  1. 1. BEXIS Tech Talk Series #2: The Conceptual Model Javad Chamanara January 2016
  2. 2. Recall from the first talk • Requirements – Data Lifecycle Management – Generic – Extensible – Portable – Scalable 2BEXIS Tech Talk #1: The Big Picture
  3. 3. Requirements -> DLM • Flexible Data Structures • Data Submission • Validation • Preserving • Metadata Management • Versioning 3BEXIS Tech Talk #1: The Big Picture
  4. 4. Conceptual Overview BEXIS Tech Talk #1: The Big Picture 4 DataMetadata Data StructureMetadata Structure Semantics Geo Administration Security «use» «use» «use» «use» «use»
  5. 5. Extension Method BEXIS Tech Talk #1: The Big Picture 5 SearchPublishing CMLand Use Reservation Data Submission
  6. 6. Data Package BEXIS Tech Talk #1: The Big Picture 6 DataMetadata Data StructureMetadata Structure Semantics Geo Administration Security «use» «use» «use» «use» «use»
  7. 7. Datasets • A set of tuples • Data container for observations, measurements, simulations, and other supported forms of data • has one Data Structure (later) BEXIS Tech Talk #1: The Big Picture 7 Dataset
  8. 8. Versions • Each dataset can have multiple versions. • processing and citations, independent of the following changes. • Check-Out, Edit, Check- In procedure • Blocking check-outs • Same user check-ins BEXIS Tech Talk #1: The Big Picture 8 Dataset Dataset Version 1
  9. 9. Tuples • Tuple as a collection of Data Cells containing the Data Items • The data tuples belong to the versions. • Edits, deletion, additions are preserved • Previous versions are reproducible • Differential versioning BEXIS Tech Talk #1: The Big Picture 9 Dataset Dataset Version Tuple 1 {Delta Association, Only Structured Data}
  10. 10. Data Cells • The value of an observation, simulation, etc • Single vs Multiple Value Cell • Auxilliary Infomation – sampling time – result time – description BEXIS Tech Talk #1: The Big Picture 10 Dataset Version Tuple DataValue Data Cell +Variable Values
  11. 11. Metadata • Each version has its own metadata • Dataset’s metadata is the metadata of its latest version BEXIS Tech Talk #1: The Big Picture 11 Dataset Version Metadata::Metadata 1 1
  12. 12. Satging • Stages indicate quality, status, state in workflows, etc. • Versions have stages • The stage of the latest version is the dataset’s current stage. BEXIS Tech Talk #1: The Big Picture 12 Dataset Dataset VersionDataset Stage 1 10..1 1 +Current Stage 1
  13. 13. Extensions: Amendments • Special kinds of data cells which can be attached to specific tuples • Capturing exceptional/ occasional observations • Diff. tuples may have diff. amendments BEXIS Tech Talk #1: The Big Picture 13 Dataset Version Tuple Amendment
  14. 14. Extensions: Extended Properties • User defined, dataset specific attribute whose value applies to a single column in a single dataset • Sampe usage : – Storing the error margin of the instrument used to measure the values of a variable BEXIS Tech Talk #1: The Big Picture 14 Dataset Dataset Version Extended Property Value 1
  15. 15. Extensions: Views • Subset of a dataset obtained by selection or projection • Purpose – Further processing, sharing or sampling – Security /Digital rights management • Spanning view – View across multiple dataset using the same Data Structure BEXIS Tech Talk #1: The Big Picture 15 Dataset View Criteria 0..1 1 +Content Selection Criteria 0..1 1 +Variable Selection Criteria 0..1
  16. 16. Example of Extensions BEXIS Tech Talk #1: The Big Picture 16 Views S.N. Tmp Time S.M. Depth Pos. Hu. 14 22 1 12 -10 A 46 78 Green 13 23 2 10 -10 B 45 16 21 3 12 -11 C 30 0.11 16 18 5 15 -10 A 25 18 14 6 17 -9 D 25 Yes 100 EP Variable 1 Variable 2 Amendments ±0.10%Error YesRounded 1 Sec.Interval Tmp Time Hu. 22 1 46 23 2 45 21 3 30 18 5 25 14 6 25 S.N. S.M. Depth 14 12 -10 13 10 -10 16 15 -10 18 17 -9 Extended Properties ‫خاک‬ ‫رطوبت‬Persian BodenfeuchteGerman Soil MoistureEnglish Globalization Info Data Structure Observation (Tuple)
  17. 17. Data Package All Together BEXIS Tech Talk #1: The Big Picture 17 Dataset Dataset Version Dataset Stage Tuple DataValue Data Cell Extended Property Extended Property Value Data Structure Content DescriptorView MetadataAmendment +Variable Values1 1 1 1 10..1 1 +Current Stage 1 {Delta Association, Only Structured Data} 1 1 {ordered} {At least one for Unstructured} 0..* 0..1
  18. 18. Data Structure Package BEXIS Tech Talk #1: The Big Picture 18 DataMetadata Data StructureMetadata Structure Semantics Geo Administration Security «use» «use» «use» «use» «use»
  19. 19. Data Structure • Defines the organization & meaning of the data • BEXIS Tech Talk #1: The Big Picture 19 Data Structure
  20. 20. Types of Data Structures • Structured data has a header information and is in tabular form • Unstructured data can be of any format BEXIS Tech Talk #1: The Big Picture 20 Data Structure Structured DataUnstructured Data
  21. 21. Tabular data headers • Variables act as table headers • There are parameters too, auxiliary data about a variable • Data attributes are the shared/ reusable parts of the variables BEXIS Tech Talk #1: The Big Picture 21 Data Structure Structured DataUnstructured Data Data Container Data Attribute Base Usage Variable +Variables 1..*
  22. 22. Dataset and Data Structure BEXIS Tech Talk #1: The Big Picture 22 Dataset Data Structure Structured DataUnstructured Data Dataset Version 1 1
  23. 23. Data Container • Factors out reusable elements of variables (such as UoM, data types, and data validation rules) • Re-use Data Containers in different data structures used in different datasets • Automatic unit conversion functionality • Benefits – Cross dataset query – Easier data integration – Enhanced data discovery BEXIS Tech Talk #1: The Big Picture 23 Data Attribute Data Container Metadata Attribute {No Extended Property}
  24. 24. Data Container in Detial BEXIS Tech Talk #1: The Big Picture 24 Data Container Parameter VariableMetadata Attribute {No Extended Property} Data Container Data Type Unit 0..1 +Applies To 1 Data Container Data Type Unit 0..1 +Applies To 1 Data Container Methodology Aggregate Function 0..1 Data Container Methodology Aggregate Function 0..1 Data Container Constraint Default Value Domain Value Validator Data Container Extended Property Globalization Info«enumeration» Measurement Scale 0..1 1 {No Duplicate} Data Container Constraint Default Value Domain Value Validator Data Container Extended Property Globalization Info«enumeration» Measurement Scale 0..1 1 {No Duplicate} Data Container Semantic Description Data Container Semantic Description
  25. 25. Data Cells to Hold Data BEXIS Tech Talk #1: The Big Picture 25 DataValue Data Cell Dataset Dataset Version Amendment Tuple Base Usage Variable Data Structure Structured Data Data Container Data Attribute 1 +Variable Values 1 +Variables 1..*
  26. 26. Data Structure Package All Together BEXIS Tech Talk #1: The Big Picture 26 Data Attribute Data Container Aggregate Function Globalization Info Structured Data Semantic Description Data::Dataset Value Type Container Reference Type Container Domain Value «enumeration» Measurement Scale Constraint Extended PropertyUnstructured Data Data Structure View Default Value Validator Methodology Classifier Unit Conversion Method Data Type ParameterVariable Spanning View Common::Base Usage 1 {No Duplicate} 0..1 0..1 {Structure Type} +Indexer 0..1 0..1 1 +Variables 1..* {Duplicate Control} +Parameters +Usage Context 1 {Data Structure Only} 1 +Applies To +To 1 +From 1 0..1 * * 0..1 0..1
  27. 27. Metadata Package BEXIS Tech Talk #1: The Big Picture 27 DataMetadata Data StructureMetadata Structure Semantics Geo Administration Security «use» «use» «use» «use» «use»
  28. 28. The Metadata Entity • Metadata is all the data gathered about a dataset • Belongs to a version • Is checked-out/in with the version Metadata Dataset Version 1 1
  29. 29. Metadata Values • Metadata consists of values, which have definitions. • Definitions are captured using the same technique as Data Cells; “Data Containers” BEXIS Tech Talk #1: The Big Picture 29 Metadata Metadata Attribute Value Dataset Version Metadata Simple Atribute 1 1..* 11 1
  30. 30. Metadata has structure, too • What attributes should be present in a metadata and how they are arranaged BEXIS Tech Talk #1: The Big Picture 30 Metadata Dataset Version MetadataStructure 11 1
  31. 31. Metadata Package All Together Dataset MetadataMetadata Attribute Value Dataset Version Metadata Simple Atribute MetadataStructure 1 11..* 1 1 1 1
  32. 32. Metadata Structure Package BEXIS Tech Talk #1: The Big Picture 32 DataMetadata Data StructureMetadata Structure Semantics Geo Administration Security «use» «use» «use» «use» «use»
  33. 33. Metadata Structure • Metadata structure determines what should be captured by the metadata • Different datasets may choose to have different metadata structures MetadataStructure Metadata Package Metadata Attribute 1..* +Parent +Children
  34. 34. Metadata Structure • Structures are hierarchical making it possible to have any sub tree as a structure • Structures at any level have a collection of packages, which are a bunch of attributes • Attributes can be shared among various packages, the same for the packages MetadataStructure Metadata Package Metadata Attribute 1..* +Parent +Children
  35. 35. Simple & Compound Attributes • Simple attributes are metadata value specifiers • Compound attributes are collection of simple and compound attributes to build coarser grain attributes BEXIS Tech Talk #1: The Big Picture 35 Metadata Attribute Metadata::Metadata Attribute Value Metadata Compound Attribute Metadata Simple Atribute 1 2..*
  36. 36. Metadata Structure Composition BEXIS Tech Talk #1: The Big Picture 36 MetadataStructureMetadata PackageMetadata Attribute Metadata::MetadataMetadata::Metadata Attribute Value Metadata Compound Attribute Metadata Simple Atribute 1 +Parent +Children 11..* 1 2..* 1..*
  37. 37. How a package is used in a structure • Packages may have their – Roles, e.g., an EML Party can be the author, the owner, etc. of a dataset. – Cardinalities: Min/Max occurrences – Optional/Mandatory – … BEXIS Tech Talk #1: The Big Picture 37 MetadataStructureMetadata PackageMetadata Attribute Metadata::MetadataMetadata::Metadata Attribute Value Metadata Compound Attribute Metadata Simple Atribute Metdata Package Usage 1 +Parent +Children 11..* 1 2..* 1..*
  38. 38. MetadataStructureMetadata PackageMetadata Attribute Metadata::MetadataMetadata::Metadata Attribute Value Metadata Compound Attribute Metadata Simple Atribute Metdata Attribute Usage 1 +Parent +Children 11..* 1 2..* 1..* How an attribute is used in a package • Attributes may have their – Roles, e.g., a Date attribute can be the project start date, publication date, etc. – Cardinalities: Min/Max occurrences – Optional/Mandatory – … • In their associated packages BEXIS Tech Talk #1: The Big Picture 38
  39. 39. MetadataStructureMetadata PackageMetadata Attribute Metadata::MetadataMetadata::Metadata Attribute Value Metadata Compound Attribute Metadata Simple Atribute Metdata Compound Usage 1 +Parent +Children 11..* 1 2..* 1..* How an attribute is used in a package • A compound attribute may contain other compound and/or simple attributes. E.g., Person can have name and Address • At least two attributes are needed • Cardinality and Role playing are available. BEXIS Tech Talk #1: The Big Picture 39
  40. 40. Mapping to external metadata formats • Proper for metadata format conversion • Import/export from/to standard metadata schemas BEXIS Tech Talk #1: The Big Picture 40 MetadataStructure Metadata PackageMetadata Attribute Mapping Info 1..* +Parent +Children 1 0..*
  41. 41. Metadata Structure Package All Together BEXIS Tech Talk #1: The Big Picture 41 MetadataStructureMetadata PackageMetadata Attribute MetadataMetadata Attribute Value Mapping Info Dataset Version Dataset Metadata Compound Attribute Metadata Simple Atribute Metdata Package Usage Data Container Metdata Attribute Usage Metdata Compound Usage Base Usage 1 +Parent +Children 11 11..* 1 10..* 1 {No Extended Property} 2..* 1..*
  42. 42. Administration Package BEXIS Tech Talk #1: The Big Picture 42 DataMetadata Data StructureMetadata Structure Semantics Geo Administration Security «use» «use» «use» «use» «use»
  43. 43. The Party Entity • Represents individuals, institutes, projects, consortiums, etc. Party Person Organization
  44. 44. Parties & Datasets Party Person Organization Data::Dataset **
  45. 45. Parties have various contacts Person Organization Party Locator
  46. 46. Parties have statuses Person Organization Party Status 1 +CurrentStatus 1 1 +History *
  47. 47. Statuses are limited Person Organization Party Status StatusType 1 +CurrentStatus 1 1 +History * * 1
  48. 48. Statuses are Specific to a Party Type Person Organization Party Status StatusTypePartyType 1 +CurrentStatus 1 1 +History * * 1 1 1..*
  49. 49. Parties are Party Type specific, too. Person Organization Party Status StatusTypePartyType 1 +CurrentStatus 1 1 +History * * 1 1 1..* +type 1 *
  50. 50. Party subclasses may not be needed Party PartyType Status StatusType 1 1..* 1 +CurrentStatus 1 * 1 1 +History * +type 1 *
  51. 51. Parties have profiles Party PartyType CustomAttribute CustomAttributeValue +type 1 * 1 * * 1 1 *
  52. 52. Parties can be in relationships PartyPartyRelationship PartyType +1st +2nd +type 1 *
  53. 53. Relationship types are known, too PartyPartyRelationship PartyTypePartyRelationshipType +1st +2nd +type 1 * +type 1 *
  54. 54. Pairing is controlled Party PartyRelationship PartyRelationshipType PartyTypePair PartyType +1st +2nd +type 1 * 1 * +AllowedSource +AllowedTarget +type 1 *
  55. 55. Time! • Parties have lifetime (start/end) • Relationships have lifetime • Relationship’s lifetime can’t exceed neither parties’ lifetimes
  56. 56. Party Sub-package All Together PartyPartyRelationship PartyRelationshipType PartyTypePartyTypePair Status StatusType Locator CustomAttribute CustomAttributeValue Dataset Data Plan 1 1..* +type 1 * +type 1 * 1 +History * 1 * +AllowedSource +AllowedTarget +1st +2nd 1 +CurrentStatus 1 1 * 1 * * 1 * * * 1 0..1 * *
  57. 57. Data Plan • Data Plan – Enforces policies – Determines chosen data structures available to the data plan users • Each party may have a set of plans, e.g., Project A uses Plan P1 to enforce open access policy BEXIS Tech Talk #1: The Big Picture 57 Data Structure::Data Structure Data::Dataset Data Plan Accessibility Policy Ownership PolicyPublishing Policy Access Policy Party::Party * +Proposed Data Structures 1..* 1 0..1 * * **
  58. 58. Semantics Package BEXIS Tech Talk #1: The Big Picture 58 DataMetadata Data StructureMetadata Structure Semantics Geo Administration Security «use» «use» «use» «use» «use»
  59. 59. Basic Definitions • An ontology: – is a formal specification of a shared conceptualization (Tom Gruber) – is the study of entities and their relations in an area of concern BEXIS Tech Talk #1: The Big Picture 59
  60. 60. The Purpose • Semantic Annotation of Variables, Attributes, metadata, and data to enhance: – Data discovery – Data integration BEXIS Tech Talk #1: The Big Picture 60
  61. 61. Ontology • Ontology is a collection of relationships between some terms • Can be hierarchical to build sub ontologies BEXIS Tech Talk #1: The Big Picture 61 Ontology TermRelationship +Sub Ontologies
  62. 62. • Relationships follow this pattern: Subject -> Predicate-> Object • Terms can be anything, but controlled by the “TermType” BEXIS Tech Talk #1: The Big Picture 62 Ontology Term TermRelationship «enumeration» Term Type +Sub Ontologies 1 +Root *1 * +predicate 1 * +object 1 * +subject 1
  63. 63. Data Containers Get Annotated • Data Containers may get annotated as a unit, an entity, or a characteristic, but can be relaxed • Data and metadata attributes inherit the annotation feature • Variables, data cells, and metadata values are included, too. BEXIS Tech Talk #1: The Big Picture 63 Data Container Semantic Description Ontology Term TermRelationship +Sub Ontologies +Chracteristic 0..1 +Entity 0..1+Root *1 * +predicate 1 * +object 1 * +subject 1 +Unit 0..1
  64. 64. Semantic Package All Together BEXIS Tech Talk #1: The Big Picture 64 Data Attribute Data Container Semantic Description Ontology Term TermRelationship «enumeration» Term Type Metadata Attribute +Sub Ontologies 1 +Chracteristic 0..1 +Entity 0..1+Root *1 * +predicate 1 * +object 1 * +subject 1 +Unit 0..1 Its just the conceptual model; implementation may differ
  65. 65. Security Package BEXIS Tech Talk #1: The Big Picture 65 DataMetadata Data StructureMetadata Structure Semantics Geo Administration Security «use» «use» «use» «use» «use»
  66. 66. Package Responsibilities • Authentication – Internal user base – Single Sign-On • Authorization – Access to functionalities – Access to data objects – Attribute & Expression based Az • Auditing – Who, what, when, on what, etc. BEXIS Tech Talk #1: The Big Picture 66
  67. 67. Authorization • Permission indicates that whether a subject is granted a right over an object. • Grant is effective only during "From" and "To" date/ times. • No permission means DENY, which can be also determined by a global security policy. • The Az can be completely turned OFF! BEXIS Tech Talk #1: The Big Picture 67 Subject Permission Object 1 0..* 1 0..*
  68. 68. What is a Subject • Can be user, a group, or a role • Various memberships in place • Effective subject is derived from the membership graph, knowing the user BEXIS Tech Talk #1: The Big Picture 68 Subject User Role Group Security Role Security User Membership «trace» Membership «trace»
  69. 69. Permission • A Right limits the permission to a aspect, e.g., Read, Create, Execute, etc. BEXIS Tech Talk #1: The Big Picture 69 Permission «enumeratio... Right 0..* +Operation 1..*
  70. 70. What can be an Object • Actions are system functionalities • They can be hierarchical • Permission on parents propagates to children • Data are the single or partial entities, e.g., dataset, metadata, view, etc. BEXIS Tech Talk #1: The Big Picture 70 Object Data Action * 0..1
  71. 71. Complex Authorization Rules • A logical expression of: – Attributes of designated data item – Operators – Precedence • That is evaluated at runtime to determine whether the “Permission” is granted BEXIS Tech Talk #1: The Big Picture 71 Permission Expression 1 0..*
  72. 72. Security Package All Together BEXIS Tech Talk #1: The Big Picture 72 Subject User Role Group Permission Expression «enumeratio... Right Object «enumeratio... Action Type Data Action {XOR} Security Role Security User 1 1 0..* 1 0..* 0..* +Operation 1..* Membership «trace» Membership * 0..1 «trace» 10..*
  73. 73. Geographical Information Package BEXIS Tech Talk #1: The Big Picture 73 DataMetadata Data StructureMetadata Structure Semantics Geo Administration Security «use» «use» «use» «use» «use»
  74. 74. Geo Package • Outsourced to a third party system that provides: – Features – Geometry – Feature Attributes – Security Integration – API Access • Querying • Visualizing BEXIS Tech Talk #1: The Big Picture 74 Geographic Description Geometry Feature AttributeParty::Party Feature Feature Attribute Value 1 1 0..1 1 1
  75. 75. References • Journal paper • Datasets paper • Brazil Presentation • CM Model URL • Anything more BEXIS Tech Talk #1: The Big Picture 75
  76. 76. Outlook Whats next in the talk series? • The Overal Architecture • The Database Design BEXIS Tech Talk #1: The Big Picture 76
  77. 77. 7777 Thanks! Questions? Contact: javad.chamanara@uni-jena.de http://fusion.cs.uni-jena.de/bexis BEXIS Tech Talk #1: The Big Picture Acknowledgment

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