On Languages for the Specification of Integrity Constraints in Spatial Conceptual Models

1
On Languages for the Specification of Integrity
Constraints in Spatial Conceptual Models
Mehrdad Salehi Yvan Bédard Mir Abolfazl
Mostafavi Jean Brodeur Center for Research in
Geomatics (CRG) Department of Geomatics
Sciences Laval University Canada
ER 2007 Workshop on Semantics and Conceptual
Issues in GIS (SeCoGIS) Auckland, Newzeland
2
Presentation Plan

• The role of spatial integrity constraints in
  spatial data quality
• Definition of spatial integrity constraints
• Classification of languages for the specification
  of spatial integrity constraints at the
  conceptual level Examples
• Natural language
• Visual language
• First-order logic language
• Hybrid language
• Comparison of languages
• Conclusion and on-going/future work

3
The Role of SIC in Spatial Data Quality
Spatial Data Quality

• Internal Data Quality
• Completeness
• Positional Accuracy
• Temporal Accuracy
• Thematic Accuracy
• Logical Consistency

External Data Quality Deals with fitness for use
of the data
SIC carry semantic information of a database
application and are used to preserve logical
consistency in spatial databases
4
Spatial Integrity Constraints

• Integrity constraints (IC) are assertions that
  restrict the data values that may appear in the
  database to prevent insertions of incorrect data.

• A spatial IC (SIC) defines mandatory, allowed,
  and unacceptable spatial relationships and
  values, sometimes in relation to other specific
  attribute values, geometric features shapes,
  specific relationships, or for given areas of
  validity.
• Simple examples of SIC
• Topological IC Based on topological properties
  and relationships
• Each building must be represented by a closed
  area.
• Two buildings do not overlap.
• Metric IC Based on metric properties and
  relationships
• The area of a house must be more than 100
  square meters.
• Distance between a school and a gas station must
  be more than 30 meters.

5
Database Design Process
Traditional Database Design Process
OMGs MDA Approach
Conceptuel Model
CIM
PIM
Implementation Model
CIM (Computation Independent Model) An
end-users view of the data which is independent
of the implementation PIM (Platform Independent
Model) A developers view of the data on a
family of platforms (e.g., OLAP servers) PSM
(Platform Specific Model) A developers view of
the data for a specific software package (e.g.,
Oracle)
6
Definition of SIC

• SIC convey essential semantic information of
  database applications
• It is necessary to define SIC at all levels of a
  spatial database design process
• Each database design level requires its specific
  SIC specification language (spatial ICSL)
• Conceptual level SIC must be first defined with
  a language understandable to a database users
• Implementation level SIC are then translated to
  a DDL or a programming language to be
  understandable to a computer

SIC at the Implementation Model
SIC at the Conceptual Model
Disjoint (Road.geometry, Building.geometry)
Road disjoint Building

• This presentation focuses on the spatial ICSL at
  the conceptual level.

7
Classification of the Spatial ICSL at the
Conceptual Level

• We categorize the existing spatial ICSL at
  conceptual level into
• Natural languages
• Free natural languages
• Controlled natural languages
• Visual languages
• First-order logic languages
• Hybrid languages
• Visual hybrid languages
• Natural hybrid languages

8
Natural Languages

• People use natural languages for their daily
  communications.
• They are the easiest languages for a database
  client to express SIC.
• 1. Free Natural Languages
• are natural languages without additional limit to
  their syntax and semantics
• support a rich vocabulary
• are sometimes ambiguous or used too loosely
• Several words may bear the same semantics
• Words may have several meanings depending on the
  context
• Loose usage of restrictive terms (and, or, must,
  can, )
• 2. Controlled Natural Languages
• are sub-sets of natural languages whose syntax
  and semantics are restricted
• are proposed to overcome the ambiguity of free
  natural languages

9
Natural Languages

• Examples for controlled natural languages
• Ubeda and Egenhofer approach (1997)
• (Entity Class1, Topological Relation,
  Entity Class2, Quantifier)

• forbidden
• at least n times
• at most n times
• exactly n times

extended 9I model topological relationships
(e.g., inside, cross)
Example (Road, Cross, Building, Forbidden)

• Vallieres et. al approach (2006)
• Objects Class1 Topological Relation
  Objects Class2 -,-

8 topological relations extended by three
notions tangent, border, strict
Cardinality
Example Road Segment Touch-Tangent Road Segment
1,2
10
Visual Languages

• Employs graphical and image notations
• Database end-user must
• learn the semantics of every visual construct
• understand very well the very specific context of
  its usage
• Several ambiguities and unintended meanings can
  emerge
• Example for visual spatial ICSL Pizano et al.
  (1989)
• In this language pictures show unacceptable
  database states terms constraint pictures

Cars and people cannot be inside a crosswalk
simultaneously
11
First-Order Logic Language

• Supports precise semantics and syntax
• However, using and understanding this language
  requires a mathematical background
• Database end-users do not necessarily have a
  mathematical background
• Example of FOL for expressing SIC Hadzilacos and
  Tryfona (1992)
• The syntax of this language is structured as
• Atomic topological formulae consisting of
• Binary topological relations between objects
• Geometric operator over objects
• Comparison between attributes of objects
• Negation, conjunction, disjunction, and universal
  and existential quantifications
• Example of SIC A Road and a Building are
  disjoint

12
Hybrid Languages

• Are not purely natural, visual, or logical,
  instead are the combination of them
• Depending on the dominant part of a language,
  they are
• 1. Visual hybrid languages
• The main part includes visual symbols
• Visual constructs are enriched by a limited
  number of natural language descriptions
• 2. Natural hybrid languages
• The dominant part is a natural language
• Complementary components are visual pictograms
  (e.g., ) or symbols

13
Hybrid Languages

• 1. Example for a visual hybrid language
• There is no visual hybrid spatial ICSL
• However, spatio-temporal conceptual modeling
  languages (e.g., Perceptory)
• specify a number of SIC in the conceptual schema
• contradicts the conciseness rule of conceptual
  schemas
• are mostly limited to constraints on spatial
  relations
• leave the remaining SIC to be defined by a
  specific spatial ICSL
• Example A Roundabout is crossed by at least one
  Route

14
Hybrid Languages

• 2. Natural hybrid languages
• 2.1. Example for a natural hybrid language with
  pictograms
• Normand (1999)
• A language for defining SIC in the data
  dictionary and includes
• three pictograms for point, line, and
  polygon
• topological relations based on ISO 9I model
• Defines topological and metric IC on the
  relationship between objects
• Supports multiple geometries
• Express a complex SIC for an object in a tabular
  form

15
Hybrid Languages

• 2. Natural hybrid languages
• 2.2. Example for natural hybrid language with
  symbols
• Spatial OCL (Kang et al. 2004)
• extends OCL, i.e., an ICSL along with UML, by
  adding
• basic geometric primitives (e.g., point) to OCL
  meta-model
• 9I topological relations (e.g., overlap) to OCL
  operators
• specifies topological IC
• Example A building is disjoint from a Road

context Building inv Road.allInstances()-gtforAll(
RR.geometry-gtDisjoint self.geometry))
Is it really a Hybrid Natural Language ?
16
Comparing Spatial ICSL

• Why comparing spatial ICSL?
• We are not aiming at finding the best spatial
  ICSL (if such a thing is possible!).
• We are revisiting our past practices (i.e. is
  Hybrid natural language still the best ICSL for
  the natural level?)
• Our goal is to summarize the potential avenues
  for developing ICSLs for spatio-temporal
  databases AND spatial datacubes.
• Comparison Criteria
• 1. Expressiveness
• Semantic quality Correspondence between ICs
  meaning and concepts supported by a spatial ICSL
  
• Syntactic quality Degree to which the rules of
  spatial ICSL govern the structure of expressions
• Richness Capability to express the needed
  elements of SIC
• Inherence Precision of an ICSL to be straight to
  the point and focuses on the essential aspects of
  SIC
• 2. Pragmatics
• Usability of the spatial ICSL by database
  end-users
• Facility to translate spatial IC into technical
  languages
• In our context the former pragmatic quality
  has priority over the latter.

17
Comparing Spatial ICSL

• Three values Good, Medium, and Weak are
  used to rank the languages.

??
 Natural  is the way to go for the conceptual
level

• The values represent our opinion from a
  literature study and 20 years of experience in
  spatial database modeling and development.

18
Conclusions

• Spatial IC convey important semantic information
  of applications
• They must be first defined at the conceptual
  level for database end-users
• We presented a classification of spatial ICSL at
  the conceptual level
• According to our opinion controlled natural
  languages and natural hybrid languages with
  pictograms are good candidates

19
On-Going and Future Work

• We are currently working on a classification of
  IC in spatio-temporal database applications
• This classification provides the basic constructs
  to build an ICSL for spatio-temporal databases
  and spatial datacubes
• We will build an ICSL for spatial datacubes based
  on
• The results of the classification of ICs
• Spatial datacubes vocabulary (e.g., Dimension and
  Measure)
• The candidate languages resulted from the current
  research work