BEXIS Tech Talk Series
#2: The Conceptual Model
Javad Chamanara
January 2016
Recall from the first talk
• Requirements
– Data Lifecycle Management
– Generic
– Extensible
– Portable
– Scalable
2BEXIS ...
Requirements -> DLM
• Flexible Data Structures
• Data Submission
• Validation
• Preserving
• Metadata Management
• Version...
Conceptual Overview
BEXIS Tech Talk #1: The Big Picture 4
DataMetadata
Data StructureMetadata Structure Semantics Geo
Admi...
Extension Method
BEXIS Tech Talk #1: The Big Picture 5
SearchPublishing
CMLand Use
Reservation
Data
Submission
Data Package
BEXIS Tech Talk #1: The Big Picture 6
DataMetadata
Data StructureMetadata Structure Semantics Geo
Administrat...
Datasets
• A set of tuples
• Data container for
observations,
measurements,
simulations, and other
supported forms of data...
Versions
• Each dataset can have
multiple versions.
• processing and
citations, independent
of the following
changes.
• Ch...
Tuples
• Tuple as a collection of Data
Cells containing the Data Items
• The data tuples belong to the
versions.
• Edits, ...
Data Cells
• The value of an
observation, simulation,
etc
• Single vs Multiple Value
Cell
• Auxilliary Infomation
– sampli...
Metadata
• Each version has its own
metadata
• Dataset’s metadata is
the metadata of its
latest version
BEXIS Tech Talk #1...
Satging
• Stages indicate quality,
status, state in
workflows, etc.
• Versions have stages
• The stage of the latest
versi...
Extensions: Amendments
• Special kinds of data
cells which can be
attached to specific
tuples
• Capturing exceptional/
occ...
Extensions: Extended Properties
• User defined, dataset
specific attribute whose
value applies to a single
column in a sin...
Extensions: Views
• Subset of a dataset
obtained by selection or
projection
• Purpose
– Further processing, sharing
or sam...
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 7...
Data Package All Together
BEXIS Tech Talk #1: The Big Picture 17
Dataset
Dataset Version
Dataset Stage
Tuple
DataValue
Dat...
Data Structure Package
BEXIS Tech Talk #1: The Big Picture 18
DataMetadata
Data StructureMetadata Structure Semantics Geo
...
Data Structure
• Defines the organization
& meaning of the data
•
BEXIS Tech Talk #1: The Big Picture 19
Data Structure
Types of Data Structures
• Structured data has a
header information and
is in tabular form
• Unstructured data can
be of a...
Tabular data headers
• Variables act as table
headers
• There are parameters
too, auxiliary data
about a variable
• Data a...
Dataset and Data Structure
BEXIS Tech Talk #1: The Big Picture 22
Dataset
Data Structure
Structured DataUnstructured Data
...
Data Container
• Factors out reusable
elements of variables (such
as UoM, data types, and
data validation rules)
• Re-use ...
Data Container in Detial
BEXIS Tech Talk #1: The Big Picture 24
Data Container
Parameter VariableMetadata Attribute
{No Ex...
Data Cells to Hold Data
BEXIS Tech Talk #1: The Big Picture 25
DataValue
Data Cell
Dataset
Dataset Version Amendment
Tuple...
Data Structure Package All
Together
BEXIS Tech Talk #1: The Big Picture 26
Data Attribute
Data Container
Aggregate Functio...
Metadata Package
BEXIS Tech Talk #1: The Big Picture 27
DataMetadata
Data StructureMetadata Structure Semantics Geo
Admini...
The Metadata Entity
• Metadata is all the data
gathered about a
dataset
• Belongs to a version
• Is checked-out/in with
th...
Metadata Values
• Metadata consists of
values, which have
definitions.
• Definitions are captured
using the same
technique...
Metadata has structure, too
• What attributes should
be present in a
metadata and how they
are arranaged
BEXIS Tech Talk #...
Metadata Package All Together
Dataset
MetadataMetadata Attribute
Value
Dataset Version
Metadata Simple
Atribute
MetadataSt...
Metadata Structure Package
BEXIS Tech Talk #1: The Big Picture 32
DataMetadata
Data StructureMetadata Structure Semantics ...
Metadata Structure
• Metadata structure
determines what should
be captured by the
metadata
• Different datasets may
choose...
Metadata Structure
• Structures are
hierarchical making it
possible to have any sub
tree as a structure
• Structures at an...
Simple & Compound Attributes
• Simple attributes are
metadata value
specifiers
• Compound attributes
are collection of sim...
Metadata Structure Composition
BEXIS Tech Talk #1: The Big Picture 36
MetadataStructureMetadata PackageMetadata Attribute
...
How a package is used in a
structure
• Packages may have
their
– Roles, e.g., an EML Party
can be the author, the
owner, e...
MetadataStructureMetadata PackageMetadata Attribute
Metadata::MetadataMetadata::Metadata
Attribute Value
Metadata Compound...
MetadataStructureMetadata PackageMetadata Attribute
Metadata::MetadataMetadata::Metadata
Attribute Value
Metadata Compound...
Mapping to external metadata
formats
• Proper for metadata
format conversion
• Import/export from/to
standard metadata
sch...
Metadata Structure Package All
Together
BEXIS Tech Talk #1: The Big Picture 41
MetadataStructureMetadata PackageMetadata A...
Administration Package
BEXIS Tech Talk #1: The Big Picture 42
DataMetadata
Data StructureMetadata Structure Semantics Geo
...
The Party Entity
• Represents individuals,
institutes, projects,
consortiums, etc.
Party
Person Organization
Parties & Datasets
Party
Person Organization
Data::Dataset
**
Parties have various contacts
Person Organization
Party Locator
Parties have statuses
Person Organization
Party Status
1
+CurrentStatus
1
1
+History
*
Statuses are limited
Person Organization
Party Status
StatusType
1
+CurrentStatus
1
1
+History
*
*
1
Statuses are Specific to a Party
Type
Person Organization
Party Status
StatusTypePartyType
1
+CurrentStatus
1
1
+History
*...
Parties are Party Type specific,
too.
Person Organization
Party Status
StatusTypePartyType
1
+CurrentStatus
1
1
+History
*...
Party subclasses may not be
needed
Party
PartyType
Status
StatusType
1 1..*
1
+CurrentStatus
1
*
1
1
+History
*
+type 1
*
Parties have profiles
Party
PartyType
CustomAttribute
CustomAttributeValue
+type 1
*
1
*
*
1
1
*
Parties can be in relationships
PartyPartyRelationship
PartyType
+1st
+2nd
+type 1
*
Relationship types are known,
too
PartyPartyRelationship
PartyTypePartyRelationshipType
+1st
+2nd
+type 1
*
+type 1
*
Pairing is controlled
Party
PartyRelationship
PartyRelationshipType PartyTypePair PartyType
+1st
+2nd
+type 1
*
1
*
+Allow...
Time!
• Parties have lifetime (start/end)
• Relationships have lifetime
• Relationship’s lifetime can’t exceed neither
par...
Party Sub-package All Together
PartyPartyRelationship
PartyRelationshipType PartyTypePartyTypePair
Status
StatusType
Locat...
Data Plan
• Data Plan
– Enforces policies
– Determines chosen data
structures available to
the data plan users
• Each part...
Semantics Package
BEXIS Tech Talk #1: The Big Picture 58
DataMetadata
Data StructureMetadata Structure Semantics Geo
Admin...
Basic Definitions
• An ontology:
– is a formal specification of a shared
conceptualization (Tom Gruber)
– is the study of ...
The Purpose
• Semantic Annotation of Variables, Attributes,
metadata, and data to enhance:
– Data discovery
– Data integra...
Ontology
• Ontology is a collection
of relationships
between some terms
• Can be hierarchical to
build sub ontologies
BEXI...
• Relationships follow this
pattern:
Subject -> Predicate-> Object
• Terms can be anything,
but controlled by the
“TermTyp...
Data Containers Get Annotated
• Data Containers may
get annotated as a unit,
an entity, or a
characteristic, but can
be re...
Semantic Package All Together
BEXIS Tech Talk #1: The Big Picture 64
Data Attribute
Data Container
Semantic
Description
On...
Security Package
BEXIS Tech Talk #1: The Big Picture 65
DataMetadata
Data StructureMetadata Structure Semantics Geo
Admini...
Package Responsibilities
• Authentication
– Internal user base
– Single Sign-On
• Authorization
– Access to functionalitie...
Authorization
• Permission indicates that
whether a subject is
granted a right over an
object.
• Grant is effective only
d...
What is a Subject
• Can be user, a group, or
a role
• Various memberships in
place
• Effective subject is
derived from the...
Permission
• A Right limits the
permission to a aspect,
e.g., Read, Create,
Execute, etc.
BEXIS Tech Talk #1: The Big Pict...
What can be an Object
• Actions are system
functionalities
• They can be hierarchical
• Permission on parents
propagates t...
Complex Authorization Rules
• A logical expression of:
– Attributes of designated
data item
– Operators
– Precedence
• Tha...
Security Package All Together
BEXIS Tech Talk #1: The Big Picture 72
Subject
User
Role
Group Permission
Expression
«enumer...
Geographical Information
Package
BEXIS Tech Talk #1: The Big Picture 73
DataMetadata
Data StructureMetadata Structure Sema...
Geo Package
• Outsourced to a third party system that
provides:
– Features
– Geometry
– Feature Attributes
– Security Inte...
References
• Journal paper
• Datasets paper
• Brazil Presentation
• CM Model URL
• Anything more
BEXIS Tech Talk #1: The B...
Outlook
Whats next in the talk series?
• The Overal Architecture
• The Database Design
BEXIS Tech Talk #1: The Big Picture...
7777
Thanks!
Questions?
Contact:
javad.chamanara@uni-jena.de
http://fusion.cs.uni-jena.de/bexis
BEXIS Tech Talk #1: The Bi...
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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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  • Just focus on the DLM
  • Extended properties are custom properties with values specific to a dataset.
    Amendments are sparse, occasional data items associated to a tuple
    View are projected/selected areas of datasets
  • Red classes come from other packages
  • Variables are the usage of data attributes in various data structures. They can have their own labels, value optionality, etc.
  • Data Container is
  • Red classes come from other packages
  • The Locator class provides a way to fins/locate/ contact the party. Address, phone, GIS, etc.
  • Statuses keep change logs!
  • To avoid status jungle! Party Types are introduced.
  • Person and Organization are delete candidates! Customization is managed by the “Custom Fields”
  • Custom Attributes
  • “Organization” can “Employ” “Person”
    “Project” can “Employ” “Person”

  • 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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