Ontology-Based Multidimensional Contexts withApplications to Quality Data Specification and
Extraction
Mostafa Milani Leopoldo Bertossi
Carleton UniversitySchool of Computer Science
Ottawa, Canada
(Carleton University) Ontology-Based Multidimensional Contexts 1 / 23
Problem Statement Introduction
Multidimensional Contexts and Data Quality
Measurements tablecontains thetemperatures of patientsat a hospital
MeasurementsTime Patient Value
Sep/5-12:10 Tom Waits 38.2Sep/6-11:50 Tom Waits 37.1Sep/7-12:15 Tom Waits 37.7Sep/9-12:00 Tom Waits 37.0Sep/6-11:05 Lou Reed 37.5Sep/5-12:05 Lou Reed 38.0
A doctor suppose/expects the table to contain:
”The body temperatures of Tom Waits for September 5
taken around noon with a thermometer of brand B1”
But Measurements does not contain the information to make thisassessment
(Carleton University) Ontology-Based Multidimensional Contexts 2 / 23
Problem Statement Introduction
Multidimensional Contexts and Data Quality
Measurements tablecontains thetemperatures of patientsat a hospital
MeasurementsTime Patient Value
Sep/5-12:10 Tom Waits 38.2Sep/6-11:50 Tom Waits 37.1Sep/7-12:15 Tom Waits 37.7Sep/9-12:00 Tom Waits 37.0Sep/6-11:05 Lou Reed 37.5Sep/5-12:05 Lou Reed 38.0
A doctor suppose/expects the table to contain:
”The body temperatures of Tom Waits for September 5
taken around noon with a thermometer of brand B1”
But Measurements does not contain the information to make thisassessment
(Carleton University) Ontology-Based Multidimensional Contexts 2 / 23
Problem Statement Introduction
Multidimensional Contexts and Data Quality
Measurements tablecontains thetemperatures of patientsat a hospital
MeasurementsTime Patient Value
Sep/5-12:10 Tom Waits 38.2Sep/6-11:50 Tom Waits 37.1Sep/7-12:15 Tom Waits 37.7Sep/9-12:00 Tom Waits 37.0Sep/6-11:05 Lou Reed 37.5Sep/5-12:05 Lou Reed 38.0
A doctor suppose/expects the table to contain:
”The body temperatures of Tom Waits for September 5
taken around noon with a thermometer of brand B1”
But Measurements does not contain the information to make thisassessment
(Carleton University) Ontology-Based Multidimensional Contexts 2 / 23
Problem Statement Introduction
Multidimensional Contexts and Data Quality
Measurements tablecontains thetemperatures of patientsat a hospital
MeasurementsTime Patient Value
Sep/5-12:10 Tom Waits 38.2Sep/6-11:50 Tom Waits 37.1Sep/7-12:15 Tom Waits 37.7Sep/9-12:00 Tom Waits 37.0Sep/6-11:05 Lou Reed 37.5Sep/5-12:05 Lou Reed 38.0
A doctor suppose/expects the table to contain:
”The body temperatures of Tom Waits for September 5
taken around noon with a thermometer of brand B1”
But Measurements does not contain the information to make thisassessment
(Carleton University) Ontology-Based Multidimensional Contexts 2 / 23
Problem Statement Introduction
Multidimensional Contexts and Data Quality
An external context can provide that information, making it possibleto assess the given data
Contex is modeled as relational databases (Bertossi et al., BIRTE 2010)
The database under assessment is mapped into the contextualdatabase for further data quality analysis and cleaning
Context is commonly of a multi-dimensional nature
The dimensional aspects of context are not considered in(Bertossi et al., BIRTE 2010)
(Carleton University) Ontology-Based Multidimensional Contexts 3 / 23
Problem Statement Introduction
Multidimensional Contexts and Data Quality
An external context can provide that information, making it possibleto assess the given data
Contex is modeled as relational databases (Bertossi et al., BIRTE 2010)
The database under assessment is mapped into the contextualdatabase for further data quality analysis and cleaning
Context is commonly of a multi-dimensional nature
The dimensional aspects of context are not considered in(Bertossi et al., BIRTE 2010)
(Carleton University) Ontology-Based Multidimensional Contexts 3 / 23
Problem Statement Introduction
Multidimensional Contexts and Data Quality
An external context can provide that information, making it possibleto assess the given data
Contex is modeled as relational databases (Bertossi et al., BIRTE 2010)
The database under assessment is mapped into the contextualdatabase for further data quality analysis and cleaning
Context is commonly of a multi-dimensional nature
The dimensional aspects of context are not considered in(Bertossi et al., BIRTE 2010)
(Carleton University) Ontology-Based Multidimensional Contexts 3 / 23
Problem Statement Introduction
Multidimensional Contexts and Data Quality
An external context can provide that information, making it possibleto assess the given data
Contex is modeled as relational databases (Bertossi et al., BIRTE 2010)
The database under assessment is mapped into the contextualdatabase for further data quality analysis and cleaning
Context is commonly of a multi-dimensional nature
The dimensional aspects of context are not considered in(Bertossi et al., BIRTE 2010)
(Carleton University) Ontology-Based Multidimensional Contexts 3 / 23
Problem Statement Introduction
Multidimensional Contexts and Data Quality
An external context can provide that information, making it possibleto assess the given data
Contex is modeled as relational databases (Bertossi et al., BIRTE 2010)
The database under assessment is mapped into the contextualdatabase for further data quality analysis and cleaning
Context is commonly of a multi-dimensional nature
The dimensional aspects of context are not considered in(Bertossi et al., BIRTE 2010)
(Carleton University) Ontology-Based Multidimensional Contexts 3 / 23
Multidimensional Context Extended HM Data Model
Extending Context with Multidimensional Data
We can see the context as an ontology, containing:
A MD data model/instance:
PatientWard: A table containing the location of patients
Hospital dimension: Represents the hierarchy of locations
Information such as a hospital guideline:
”Temperature measurement for patients in standard care unithave to be taken with thermometers of Brand B1”
Basis data model: HM model (Hurtado and Mendelzon, 2005)
We extend the HM model (Maleki et al., AMW 2012)
(Carleton University) Ontology-Based Multidimensional Contexts 4 / 23
Multidimensional Context Extended HM Data Model
Extending Context with Multidimensional Data
We can see the context as an ontology, containing:
A MD data model/instance:
PatientWard: A table containing the location of patients
Hospital dimension: Represents the hierarchy of locations
Information such as a hospital guideline:
”Temperature measurement for patients in standard care unithave to be taken with thermometers of Brand B1”
Basis data model: HM model (Hurtado and Mendelzon, 2005)
We extend the HM model (Maleki et al., AMW 2012)
(Carleton University) Ontology-Based Multidimensional Contexts 4 / 23
Multidimensional Context Extended HM Data Model
Extending Context with Multidimensional Data
We can see the context as an ontology, containing:
A MD data model/instance:
PatientWard: A table containing the location of patients
Hospital dimension: Represents the hierarchy of locations
Information such as a hospital guideline:
”Temperature measurement for patients in standard care unithave to be taken with thermometers of Brand B1”
Basis data model: HM model (Hurtado and Mendelzon, 2005)
We extend the HM model (Maleki et al., AMW 2012)
(Carleton University) Ontology-Based Multidimensional Contexts 4 / 23
Multidimensional Context Extended HM Data Model
Extending Context with Multidimensional Data
We can see the context as an ontology, containing:
A MD data model/instance:
PatientWard: A table containing the location of patients
Hospital dimension: Represents the hierarchy of locations
Information such as a hospital guideline:
”Temperature measurement for patients in standard care unithave to be taken with thermometers of Brand B1”
Basis data model: HM model (Hurtado and Mendelzon, 2005)
We extend the HM model (Maleki et al., AMW 2012)
(Carleton University) Ontology-Based Multidimensional Contexts 4 / 23
Multidimensional Context Extended HM Data Model
Extending Context with Multidimensional Data
We can see the context as an ontology, containing:
A MD data model/instance:
PatientWard: A table containing the location of patients
Hospital dimension: Represents the hierarchy of locations
Information such as a hospital guideline:
”Temperature measurement for patients in standard care unithave to be taken with thermometers of Brand B1”
Basis data model: HM model (Hurtado and Mendelzon, 2005)
We extend the HM model (Maleki et al., AMW 2012)
(Carleton University) Ontology-Based Multidimensional Contexts 4 / 23
Multidimensional Context Extended HM Data Model
Extending Context with Multidimensional Data
We can see the context as an ontology, containing:
A MD data model/instance:
PatientWard: A table containing the location of patients
Hospital dimension: Represents the hierarchy of locations
Information such as a hospital guideline:
”Temperature measurement for patients in standard care unithave to be taken with thermometers of Brand B1”
Basis data model: HM model (Hurtado and Mendelzon, 2005)
We extend the HM model (Maleki et al., AMW 2012)
(Carleton University) Ontology-Based Multidimensional Contexts 4 / 23
Multidimensional Context Extended HM Data Model
Extending Context with Multidimensional Data
We can see the context as an ontology, containing:
A MD data model/instance:
PatientWard: A table containing the location of patients
Hospital dimension: Represents the hierarchy of locations
Information such as a hospital guideline:
”Temperature measurement for patients in standard care unithave to be taken with thermometers of Brand B1”
Basis data model: HM model (Hurtado and Mendelzon, 2005)
We extend the HM model (Maleki et al., AMW 2012)
(Carleton University) Ontology-Based Multidimensional Contexts 4 / 23
Multidimensional Context Extended HM Data Model
Extending Context with Multidimensional Data
Informally, some of the new ingredients in MD contexts:
Dimensions as in the HM
Categorical relations: Generalize fact tables, not necessarily numericalvalues, linked to different levels of dimensions, possibly incomplete
Dimensional rules: Generate data where missing
Dimensional constraints: Constraints on (combinations of) categoricalrelations, involve values from dimension categories)
Dimensional rules and constraints can support and restrictupward/downard navigation
(Carleton University) Ontology-Based Multidimensional Contexts 5 / 23
Multidimensional Context Extended HM Data Model
Extending Context with Multidimensional Data
Informally, some of the new ingredients in MD contexts:
Dimensions as in the HM
Categorical relations: Generalize fact tables, not necessarily numericalvalues, linked to different levels of dimensions, possibly incomplete
Dimensional rules: Generate data where missing
Dimensional constraints: Constraints on (combinations of) categoricalrelations, involve values from dimension categories)
Dimensional rules and constraints can support and restrictupward/downard navigation
(Carleton University) Ontology-Based Multidimensional Contexts 5 / 23
Multidimensional Context Extended HM Data Model
Extending Context with Multidimensional Data
Informally, some of the new ingredients in MD contexts:
Dimensions as in the HM
Categorical relations: Generalize fact tables, not necessarily numericalvalues, linked to different levels of dimensions, possibly incomplete
Dimensional rules: Generate data where missing
Dimensional constraints: Constraints on (combinations of) categoricalrelations, involve values from dimension categories)
Dimensional rules and constraints can support and restrictupward/downard navigation
(Carleton University) Ontology-Based Multidimensional Contexts 5 / 23
Multidimensional Context Extended HM Data Model
Extending Context with Multidimensional Data
Informally, some of the new ingredients in MD contexts:
Dimensions as in the HM
Categorical relations: Generalize fact tables, not necessarily numericalvalues, linked to different levels of dimensions, possibly incomplete
Dimensional rules: Generate data where missing
Dimensional constraints: Constraints on (combinations of) categoricalrelations, involve values from dimension categories)
Dimensional rules and constraints can support and restrictupward/downard navigation
(Carleton University) Ontology-Based Multidimensional Contexts 5 / 23
Multidimensional Context Extended HM Data Model
Extending Context with Multidimensional Data
Informally, some of the new ingredients in MD contexts:
Dimensions as in the HM
Categorical relations: Generalize fact tables, not necessarily numericalvalues, linked to different levels of dimensions, possibly incomplete
Dimensional rules: Generate data where missing
Dimensional constraints: Constraints on (combinations of) categoricalrelations, involve values from dimension categories)
Dimensional rules and constraints can support and restrictupward/downard navigation
(Carleton University) Ontology-Based Multidimensional Contexts 5 / 23
Multidimensional Context Extended HM Data Model
Extending Context with Multidimensional Data
Informally, some of the new ingredients in MD contexts:
Dimensions as in the HM
Categorical relations: Generalize fact tables, not necessarily numericalvalues, linked to different levels of dimensions, possibly incomplete
Dimensional rules: Generate data where missing
Dimensional constraints: Constraints on (combinations of) categoricalrelations, involve values from dimension categories)
Dimensional rules and constraints can support and restrictupward/downard navigation
(Carleton University) Ontology-Based Multidimensional Contexts 5 / 23
Multidimensional Context Extended HM Data Model
Extending Context with Multidimensional Data
Example
Ward and Unit:
categories of Hospital
dimension
UnitWard(unit,ward): a
parent/child relation
PatientUnit
id Unit Day Patient
1 Standard Sep/5 Tom Waits
2 Standard Sep/6 Tom Waits
3 Intensive Sep/7 Tom Waits
4 Intensive Sep/6 Lou Reed
5 Standard Sep/5 Lou Reed
PatientWard
id Ward Day Patient
1 W1 Sep/5 Tom Waits
2 W1 Sep/6 Tom Waits
3 W3 Sep/7 Tom Waits
4 W3 Sep/6 Lou Reed
5 W2 Sep/5 Lou Reed
Ward
AllHospital
Institution
Unit
Ward
Standard Intensive Terminal
W1 W2 W3 W4
H1 H2
allHospital
AllTime
Year
Month
Day
Time
PatientWard: categorical relation with Ward and Day categoricalattributes taking values from dimension categories
(Carleton University) Ontology-Based Multidimensional Contexts 6 / 23
Multidimensional Context Extended HM Data Model
Extending Context with Multidimensional Data
Example
Ward and Unit:
categories of Hospital
dimension
UnitWard(unit,ward): a
parent/child relation
PatientUnit
id Unit Day Patient
1 Standard Sep/5 Tom Waits
2 Standard Sep/6 Tom Waits
3 Intensive Sep/7 Tom Waits
4 Intensive Sep/6 Lou Reed
5 Standard Sep/5 Lou Reed
PatientWard
id Ward Day Patient
1 W1 Sep/5 Tom Waits
2 W1 Sep/6 Tom Waits
3 W3 Sep/7 Tom Waits
4 W3 Sep/6 Lou Reed
5 W2 Sep/5 Lou Reed
Ward
AllHospital
Institution
Unit
Ward
Standard Intensive Terminal
W1 W2 W3 W4
H1 H2
allHospital
AllTime
Year
Month
Day
Time
PatientWard: categorical relation with Ward and Day categoricalattributes taking values from dimension categories
(Carleton University) Ontology-Based Multidimensional Contexts 6 / 23
Multidimensional Context Extended HM Data Model
Extending Context with Multidimensional Data
Example
Ward and Unit:
categories of Hospital
dimension
UnitWard(unit,ward): a
parent/child relation
PatientUnit
id Unit Day Patient
1 Standard Sep/5 Tom Waits
2 Standard Sep/6 Tom Waits
3 Intensive Sep/7 Tom Waits
4 Intensive Sep/6 Lou Reed
5 Standard Sep/5 Lou Reed
PatientWard
id Ward Day Patient
1 W1 Sep/5 Tom Waits
2 W1 Sep/6 Tom Waits
3 W3 Sep/7 Tom Waits
4 W3 Sep/6 Lou Reed
5 W2 Sep/5 Lou Reed
Ward
AllHospital
Institution
Unit
Ward
Standard Intensive Terminal
W1 W2 W3 W4
H1 H2
allHospital
AllTime
Year
Month
Day
Time
PatientWard: categorical relation with Ward and Day categoricalattributes taking values from dimension categories
(Carleton University) Ontology-Based Multidimensional Contexts 6 / 23
Multidimensional Context Extended HM Data Model
Extending Context with Multidimensional Data
Example
Ward and Unit:
categories of Hospital
dimension
UnitWard(unit,ward): a
parent/child relation
PatientUnit
id Unit Day Patient
1 Standard Sep/5 Tom Waits
2 Standard Sep/6 Tom Waits
3 Intensive Sep/7 Tom Waits
4 Intensive Sep/6 Lou Reed
5 Standard Sep/5 Lou Reed
PatientWard
id Ward Day Patient
1 W1 Sep/5 Tom Waits
2 W1 Sep/6 Tom Waits
3 W3 Sep/7 Tom Waits
4 W3 Sep/6 Lou Reed
5 W2 Sep/5 Lou Reed
Ward
AllHospital
Institution
Unit
Ward
Standard Intensive Terminal
W1 W2 W3 W4
H1 H2
allHospital
AllTime
Year
Month
Day
Time
PatientWard: categorical relation with Ward and Day categoricalattributes taking values from dimension categories
(Carleton University) Ontology-Based Multidimensional Contexts 6 / 23
Multidimensional Context Extended HM Data Model
Dimensional Constraints
Example
Categorical relations are subject to dimensional constraints:
A referential constraint restricting units in PatientUnitto elements in the Unit category, as a negative constraint:
⊥ ← PatientUnit(u,d ; p),¬Unit(u)
“All thermometers used in a unit are of the same type”:
t = t ′ ← Thermometer(w , t; n),Thermometer(w ′, t′; n′),
UnitWard(u,w),UnitWard(u,w ′) An EGD
“No patient in intensive care unit on August /2005”:
⊥ ← PatientWard(w ,d ; p),UnitWard(Intensive,w),
MonthDay(August/2005, d)
(Carleton University) Ontology-Based Multidimensional Contexts 7 / 23
Multidimensional Context Extended HM Data Model
Dimensional Constraints
Example
Categorical relations are subject to dimensional constraints:
A referential constraint restricting units in PatientUnitto elements in the Unit category, as a negative constraint:
⊥ ← PatientUnit(u,d ; p),¬Unit(u)
“All thermometers used in a unit are of the same type”:
t = t ′ ← Thermometer(w , t; n),Thermometer(w ′, t′; n′),
UnitWard(u,w),UnitWard(u,w ′) An EGD
“No patient in intensive care unit on August /2005”:
⊥ ← PatientWard(w ,d ; p),UnitWard(Intensive,w),
MonthDay(August/2005, d)
(Carleton University) Ontology-Based Multidimensional Contexts 7 / 23
Multidimensional Context Extended HM Data Model
Dimensional Constraints
Example
Categorical relations are subject to dimensional constraints:
A referential constraint restricting units in PatientUnitto elements in the Unit category, as a negative constraint:
⊥ ← PatientUnit(u,d ; p),¬Unit(u)
“All thermometers used in a unit are of the same type”:
t = t ′ ← Thermometer(w , t; n),Thermometer(w ′, t′; n′),
UnitWard(u,w),UnitWard(u,w ′) An EGD
“No patient in intensive care unit on August /2005”:
⊥ ← PatientWard(w ,d ; p),UnitWard(Intensive,w),
MonthDay(August/2005, d)
(Carleton University) Ontology-Based Multidimensional Contexts 7 / 23
Multidimensional Context Extended HM Data Model
Dimensional Constraints
Example
Categorical relations are subject to dimensional constraints:
A referential constraint restricting units in PatientUnitto elements in the Unit category, as a negative constraint:
⊥ ← PatientUnit(u,d ; p),¬Unit(u)
“All thermometers used in a unit are of the same type”:
t = t ′ ← Thermometer(w , t; n),Thermometer(w ′, t′; n′),
UnitWard(u,w),UnitWard(u,w ′) An EGD
“No patient in intensive care unit on August /2005”:
⊥ ← PatientWard(w ,d ; p),UnitWard(Intensive,w),
MonthDay(August/2005, d)
(Carleton University) Ontology-Based Multidimensional Contexts 7 / 23
Multidimensional Context Extended HM Data Model
Dimensional Constraints
Example
Categorical relations are subject to dimensional constraints:
A referential constraint restricting units in PatientUnitto elements in the Unit category, as a negative constraint:
⊥ ← PatientUnit(u,d ; p),¬Unit(u)
“All thermometers used in a unit are of the same type”:
t = t ′ ← Thermometer(w , t; n),Thermometer(w ′, t′; n′),
UnitWard(u,w),UnitWard(u,w ′) An EGD
“No patient in intensive care unit on August /2005”:
⊥ ← PatientWard(w ,d ; p),UnitWard(Intensive,w),
MonthDay(August/2005, d)
(Carleton University) Ontology-Based Multidimensional Contexts 7 / 23
Multidimensional Context Extended HM Data Model
Dimensional Constraints
Example
Categorical relations are subject to dimensional constraints:
A referential constraint restricting units in PatientUnitto elements in the Unit category, as a negative constraint:
⊥ ← PatientUnit(u,d ; p),¬Unit(u)
“All thermometers used in a unit are of the same type”:
t = t ′ ← Thermometer(w , t; n),Thermometer(w ′, t′; n′),
UnitWard(u,w),UnitWard(u,w ′) An EGD
“No patient in intensive care unit on August /2005”:
⊥ ← PatientWard(w ,d ; p),UnitWard(Intensive,w),
MonthDay(August/2005, d)
(Carleton University) Ontology-Based Multidimensional Contexts 7 / 23
Multidimensional Context Extended HM Data Model
Dimensional Constraints
Example
Categorical relations are subject to dimensional constraints:
A referential constraint restricting units in PatientUnitto elements in the Unit category, as a negative constraint:
⊥ ← PatientUnit(u,d ; p),¬Unit(u)
“All thermometers used in a unit are of the same type”:
t = t ′ ← Thermometer(w , t; n),Thermometer(w ′, t′; n′),
UnitWard(u,w),UnitWard(u,w ′) An EGD
“No patient in intensive care unit on August /2005”:
⊥ ← PatientWard(w ,d ; p),UnitWard(Intensive,w),
MonthDay(August/2005, d)
(Carleton University) Ontology-Based Multidimensional Contexts 7 / 23
Multidimensional Context Extended HM Data Model
Dimensional Rules
Example
Data in PatientWard generate data about patients forhigher-level categorical relation PatientUnit:
PatientUnit(u,d ; p) ← PatientWard(w ,d ; p),
UnitWard(u,w)
Since relation schemas ”match”, ∃-variable in the head is not needed
Rule is used to navigate from PatientWard.Ward upwards toPatientUnit.Unit via UnitWard
Once at the level of Unit, it is possible to take advantage of aguideline -in the form of a rule- stating that:
“Temperatures of patients in a standard care unit are taken with oralthermometers”
(Carleton University) Ontology-Based Multidimensional Contexts 8 / 23
Multidimensional Context Extended HM Data Model
Dimensional Rules
Example
Data in PatientWard generate data about patients forhigher-level categorical relation PatientUnit:
PatientUnit(u,d ; p) ← PatientWard(w ,d ; p),
UnitWard(u,w)
Since relation schemas ”match”, ∃-variable in the head is not needed
Rule is used to navigate from PatientWard.Ward upwards toPatientUnit.Unit via UnitWard
Once at the level of Unit, it is possible to take advantage of aguideline -in the form of a rule- stating that:
“Temperatures of patients in a standard care unit are taken with oralthermometers”
(Carleton University) Ontology-Based Multidimensional Contexts 8 / 23
Multidimensional Context Extended HM Data Model
Dimensional Rules
Example
Data in PatientWard generate data about patients forhigher-level categorical relation PatientUnit:
PatientUnit(u,d ; p) ← PatientWard(w ,d ; p),
UnitWard(u,w)
Since relation schemas ”match”, ∃-variable in the head is not needed
Rule is used to navigate from PatientWard.Ward upwards toPatientUnit.Unit via UnitWard
Once at the level of Unit, it is possible to take advantage of aguideline -in the form of a rule- stating that:
“Temperatures of patients in a standard care unit are taken with oralthermometers”
(Carleton University) Ontology-Based Multidimensional Contexts 8 / 23
Multidimensional Context Extended HM Data Model
Dimensional Rules
Example
Data in PatientWard generate data about patients forhigher-level categorical relation PatientUnit:
PatientUnit(u,d ; p) ← PatientWard(w ,d ; p),
UnitWard(u,w)
Since relation schemas ”match”, ∃-variable in the head is not needed
Rule is used to navigate from PatientWard.Ward upwards toPatientUnit.Unit via UnitWard
Once at the level of Unit, it is possible to take advantage of aguideline -in the form of a rule- stating that:
“Temperatures of patients in a standard care unit are taken with oralthermometers”
(Carleton University) Ontology-Based Multidimensional Contexts 8 / 23
Multidimensional Context Extended HM Data Model
Dimensional Rules
Example
Data in PatientWard generate data about patients forhigher-level categorical relation PatientUnit:
PatientUnit(u,d ; p) ← PatientWard(w ,d ; p),
UnitWard(u,w)
Since relation schemas ”match”, ∃-variable in the head is not needed
Rule is used to navigate from PatientWard.Ward upwards toPatientUnit.Unit via UnitWard
Once at the level of Unit, it is possible to take advantage of aguideline -in the form of a rule- stating that:
“Temperatures of patients in a standard care unit are taken with oralthermometers”
(Carleton University) Ontology-Based Multidimensional Contexts 8 / 23
Multidimensional Context Extended HM Data Model
Dimensional Rules
Example
Data in PatientWard generate data about patients forhigher-level categorical relation PatientUnit:
PatientUnit(u,d ; p) ← PatientWard(w ,d ; p),
UnitWard(u,w)
Since relation schemas ”match”, ∃-variable in the head is not needed
Rule is used to navigate from PatientWard.Ward upwards toPatientUnit.Unit via UnitWard
Once at the level of Unit, it is possible to take advantage of aguideline -in the form of a rule- stating that:
“Temperatures of patients in a standard care unit are taken with oralthermometers”
(Carleton University) Ontology-Based Multidimensional Contexts 8 / 23
Multidimensional Context Extended HM Data Model
Dimensional Rules
Example
WorkingSchedules id Unit Day Nurse Type
1 Intensive Sep/5 Cathy cert.
2 Standard Sep/5 Helen cert.
3 Standard Sep/6 Helen cert.
4 Terminal Sep/5 Susan non-cert.
5 Standard Sep/9 Mark non-cert.
Shifts id Ward Day Nurse Shift
1 W4 Sep/5 Cathy night
2 W1 Sep/6 Helen morning
3 W4 Sep/5 Susan evening
Ward
Ward
Unit
Institution
W1 W2 W3 W4
AllHospital
Ward
Standard Intensive Terminal
H1 H2
allHospital
AllTime
Year
Day
Time
Month
Data in categorical relation WorkingSchedules generates data incategorical relation Shifts
(Carleton University) Ontology-Based Multidimensional Contexts 9 / 23
Multidimensional Context Extended HM Data Model
Dimensional Rules
Example
WorkingSchedules id Unit Day Nurse Type
1 Intensive Sep/5 Cathy cert.
2 Standard Sep/5 Helen cert.
3 Standard Sep/6 Helen cert.
4 Terminal Sep/5 Susan non-cert.
5 Standard Sep/9 Mark non-cert.
Shifts id Ward Day Nurse Shift
1 W4 Sep/5 Cathy night
2 W1 Sep/6 Helen morning
3 W4 Sep/5 Susan evening
Ward
Ward
Unit
Institution
W1 W2 W3 W4
AllHospital
Ward
Standard Intensive Terminal
H1 H2
allHospital
AllTime
Year
Day
Time
Month
Data in categorical relation WorkingSchedules generates data incategorical relation Shifts
(Carleton University) Ontology-Based Multidimensional Contexts 9 / 23
Multidimensional Context Extended HM Data Model
Dimensional Rules
Example
∃z Shifts(w ,d ; n, z) ← WorkingSchedules(u,d ; n, t),
UnitWard(u,w)
Captures a guideline stating that: “If a nurse works in a unit on a
specific day, he/she has shifts in every ward of that unit on the same day”
Head has existential variable z for missing values for shift attribute
Rule can be used for downward navigation
(Carleton University) Ontology-Based Multidimensional Contexts 10 / 23
Multidimensional Context Extended HM Data Model
Dimensional Rules
Example
∃z Shifts(w ,d ; n, z) ← WorkingSchedules(u,d ; n, t),
UnitWard(u,w)
Captures a guideline stating that: “If a nurse works in a unit on a
specific day, he/she has shifts in every ward of that unit on the same day”
Head has existential variable z for missing values for shift attribute
Rule can be used for downward navigation
(Carleton University) Ontology-Based Multidimensional Contexts 10 / 23
Multidimensional Context Extended HM Data Model
Dimensional Rules
Example
∃z Shifts(w ,d ; n, z) ← WorkingSchedules(u,d ; n, t),
UnitWard(u,w)
Captures a guideline stating that: “If a nurse works in a unit on a
specific day, he/she has shifts in every ward of that unit on the same day”
Head has existential variable z for missing values for shift attribute
Rule can be used for downward navigation
(Carleton University) Ontology-Based Multidimensional Contexts 10 / 23
Multidimensional Context Extended HM Data Model
Dimensional Rules
Example
∃z Shifts(w ,d ; n, z) ← WorkingSchedules(u,d ; n, t),
UnitWard(u,w)
Captures a guideline stating that: “If a nurse works in a unit on a
specific day, he/she has shifts in every ward of that unit on the same day”
Head has existential variable z for missing values for shift attribute
Rule can be used for downward navigation
(Carleton University) Ontology-Based Multidimensional Contexts 10 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Datalog± as Representation Language
We use Datalog± as our representation language (Cali et al., 2009)
An extension of Datalog for ontology building with efficientaccess to underlying data sources
Our approach to representation of MD contexts is general andsystematic with the following general forms:
Negative constraints capturing referential constraints from categoricalattributes to categories:
⊥ ← Ri (ei ; ai ),¬K (e)
e, ei stand for categorical attributes,
Ri a categorical predicate, and
K a category predicate
(Carleton University) Ontology-Based Multidimensional Contexts 11 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Datalog± as Representation Language
We use Datalog± as our representation language (Cali et al., 2009)
An extension of Datalog for ontology building with efficientaccess to underlying data sources
Our approach to representation of MD contexts is general andsystematic with the following general forms:
Negative constraints capturing referential constraints from categoricalattributes to categories:
⊥ ← Ri (ei ; ai ),¬K (e)
e, ei stand for categorical attributes,
Ri a categorical predicate, and
K a category predicate
(Carleton University) Ontology-Based Multidimensional Contexts 11 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Datalog± as Representation Language
We use Datalog± as our representation language (Cali et al., 2009)
An extension of Datalog for ontology building with efficientaccess to underlying data sources
Our approach to representation of MD contexts is general andsystematic with the following general forms:
Negative constraints capturing referential constraints from categoricalattributes to categories:
⊥ ← Ri (ei ; ai ),¬K (e)
e, ei stand for categorical attributes,
Ri a categorical predicate, and
K a category predicate
(Carleton University) Ontology-Based Multidimensional Contexts 11 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Datalog± as Representation Language
We use Datalog± as our representation language (Cali et al., 2009)
An extension of Datalog for ontology building with efficientaccess to underlying data sources
Our approach to representation of MD contexts is general andsystematic with the following general forms:
Negative constraints capturing referential constraints from categoricalattributes to categories:
⊥ ← Ri (ei ; ai ),¬K (e)
e, ei stand for categorical attributes,
Ri a categorical predicate, and
K a category predicate
(Carleton University) Ontology-Based Multidimensional Contexts 11 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Datalog± as Representation Language
We use Datalog± as our representation language (Cali et al., 2009)
An extension of Datalog for ontology building with efficientaccess to underlying data sources
Our approach to representation of MD contexts is general andsystematic with the following general forms:
Negative constraints capturing referential constraints from categoricalattributes to categories:
⊥ ← Ri (ei ; ai ),¬K (e)
e, ei stand for categorical attributes,
Ri a categorical predicate, and
K a category predicate
(Carleton University) Ontology-Based Multidimensional Contexts 11 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Datalog± as Representation Language
We use Datalog± as our representation language (Cali et al., 2009)
An extension of Datalog for ontology building with efficientaccess to underlying data sources
Our approach to representation of MD contexts is general andsystematic with the following general forms:
Negative constraints capturing referential constraints from categoricalattributes to categories:
⊥ ← Ri (ei ; ai ),¬K (e)
e, ei stand for categorical attributes,
Ri a categorical predicate, and
K a category predicate
(Carleton University) Ontology-Based Multidimensional Contexts 11 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Datalog± as Representation Language
We use Datalog± as our representation language (Cali et al., 2009)
An extension of Datalog for ontology building with efficientaccess to underlying data sources
Our approach to representation of MD contexts is general andsystematic with the following general forms:
Negative constraints capturing referential constraints from categoricalattributes to categories:
⊥ ← Ri (ei ; ai ),¬K (e)
e, ei stand for categorical attributes,
Ri a categorical predicate, and
K a category predicate
(Carleton University) Ontology-Based Multidimensional Contexts 11 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Datalog± as Representation Language
We use Datalog± as our representation language (Cali et al., 2009)
An extension of Datalog for ontology building with efficientaccess to underlying data sources
Our approach to representation of MD contexts is general andsystematic with the following general forms:
Negative constraints capturing referential constraints from categoricalattributes to categories:
⊥ ← Ri (ei ; ai ),¬K (e)
e, ei stand for categorical attributes,
Ri a categorical predicate, and
K a category predicate
(Carleton University) Ontology-Based Multidimensional Contexts 11 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Datalog± as Representation Language
Dimensional constraints as EGDs or negative constraints:
x = x ′ ← Ri (ei ; ai ), ...,Rj(ej ; aj),Dn(en, e′n), ...,Dm(em, e
′m)
⊥ ← Ri (ei ; ai ), ...,Rj(ej ; aj),Dn(en, e′n), ...,Dm(em, e
′m)
Di are parent-child predicates and Ri are categorical predicates
Dimensional rules as TGDs:
∃az Rk(ek ; ak)← Ri (ei ; ai ), ...,Rj(ej ; aj),Dn(en, e′n), ...,Dm(em, e
′m)
Existential quantifiers (possibly not needed) over non-categoricalattributes, which may get labeled nulls as values
Repeated variables in bodies of TGDs only for categorical attributes
”Upward or downward navigation captured by joins betweencategorical predicates and parent-child predicates in bodies”
(Carleton University) Ontology-Based Multidimensional Contexts 12 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Datalog± as Representation Language
Dimensional constraints as EGDs or negative constraints:
x = x ′ ← Ri (ei ; ai ), ...,Rj(ej ; aj),Dn(en, e′n), ...,Dm(em, e
′m)
⊥ ← Ri (ei ; ai ), ...,Rj(ej ; aj),Dn(en, e′n), ...,Dm(em, e
′m)
Di are parent-child predicates and Ri are categorical predicates
Dimensional rules as TGDs:
∃az Rk(ek ; ak)← Ri (ei ; ai ), ...,Rj(ej ; aj),Dn(en, e′n), ...,Dm(em, e
′m)
Existential quantifiers (possibly not needed) over non-categoricalattributes, which may get labeled nulls as values
Repeated variables in bodies of TGDs only for categorical attributes
”Upward or downward navigation captured by joins betweencategorical predicates and parent-child predicates in bodies”
(Carleton University) Ontology-Based Multidimensional Contexts 12 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Datalog± as Representation Language
Dimensional constraints as EGDs or negative constraints:
x = x ′ ← Ri (ei ; ai ), ...,Rj(ej ; aj),Dn(en, e′n), ...,Dm(em, e
′m)
⊥ ← Ri (ei ; ai ), ...,Rj(ej ; aj),Dn(en, e′n), ...,Dm(em, e
′m)
Di are parent-child predicates and Ri are categorical predicates
Dimensional rules as TGDs:
∃az Rk(ek ; ak)← Ri (ei ; ai ), ...,Rj(ej ; aj),Dn(en, e′n), ...,Dm(em, e
′m)
Existential quantifiers (possibly not needed) over non-categoricalattributes, which may get labeled nulls as values
Repeated variables in bodies of TGDs only for categorical attributes
”Upward or downward navigation captured by joins betweencategorical predicates and parent-child predicates in bodies”
(Carleton University) Ontology-Based Multidimensional Contexts 12 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Datalog± as Representation Language
Dimensional constraints as EGDs or negative constraints:
x = x ′ ← Ri (ei ; ai ), ...,Rj(ej ; aj),Dn(en, e′n), ...,Dm(em, e
′m)
⊥ ← Ri (ei ; ai ), ...,Rj(ej ; aj),Dn(en, e′n), ...,Dm(em, e
′m)
Di are parent-child predicates and Ri are categorical predicates
Dimensional rules as TGDs:
∃az Rk(ek ; ak)← Ri (ei ; ai ), ...,Rj(ej ; aj),Dn(en, e′n), ...,Dm(em, e
′m)
Existential quantifiers (possibly not needed) over non-categoricalattributes, which may get labeled nulls as values
Repeated variables in bodies of TGDs only for categorical attributes
”Upward or downward navigation captured by joins betweencategorical predicates and parent-child predicates in bodies”
(Carleton University) Ontology-Based Multidimensional Contexts 12 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Datalog± as Representation Language
Dimensional constraints as EGDs or negative constraints:
x = x ′ ← Ri (ei ; ai ), ...,Rj(ej ; aj),Dn(en, e′n), ...,Dm(em, e
′m)
⊥ ← Ri (ei ; ai ), ...,Rj(ej ; aj),Dn(en, e′n), ...,Dm(em, e
′m)
Di are parent-child predicates and Ri are categorical predicates
Dimensional rules as TGDs:
∃az Rk(ek ; ak)← Ri (ei ; ai ), ...,Rj(ej ; aj),Dn(en, e′n), ...,Dm(em, e
′m)
Existential quantifiers (possibly not needed) over non-categoricalattributes, which may get labeled nulls as values
Repeated variables in bodies of TGDs only for categorical attributes
”Upward or downward navigation captured by joins betweencategorical predicates and parent-child predicates in bodies”
(Carleton University) Ontology-Based Multidimensional Contexts 12 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Datalog± as Representation Language
Dimensional constraints as EGDs or negative constraints:
x = x ′ ← Ri (ei ; ai ), ...,Rj(ej ; aj),Dn(en, e′n), ...,Dm(em, e
′m)
⊥ ← Ri (ei ; ai ), ...,Rj(ej ; aj),Dn(en, e′n), ...,Dm(em, e
′m)
Di are parent-child predicates and Ri are categorical predicates
Dimensional rules as TGDs:
∃az Rk(ek ; ak)← Ri (ei ; ai ), ...,Rj(ej ; aj),Dn(en, e′n), ...,Dm(em, e
′m)
Existential quantifiers (possibly not needed) over non-categoricalattributes, which may get labeled nulls as values
Repeated variables in bodies of TGDs only for categorical attributes
”Upward or downward navigation captured by joins betweencategorical predicates and parent-child predicates in bodies”
(Carleton University) Ontology-Based Multidimensional Contexts 12 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Datalog± as Representation Language
Dimensional constraints as EGDs or negative constraints:
x = x ′ ← Ri (ei ; ai ), ...,Rj(ej ; aj),Dn(en, e′n), ...,Dm(em, e
′m)
⊥ ← Ri (ei ; ai ), ...,Rj(ej ; aj),Dn(en, e′n), ...,Dm(em, e
′m)
Di are parent-child predicates and Ri are categorical predicates
Dimensional rules as TGDs:
∃az Rk(ek ; ak)← Ri (ei ; ai ), ...,Rj(ej ; aj),Dn(en, e′n), ...,Dm(em, e
′m)
Existential quantifiers (possibly not needed) over non-categoricalattributes, which may get labeled nulls as values
Repeated variables in bodies of TGDs only for categorical attributes
”Upward or downward navigation captured by joins betweencategorical predicates and parent-child predicates in bodies”
(Carleton University) Ontology-Based Multidimensional Contexts 12 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Datalog± as Representation Language
Dimensional constraints as EGDs or negative constraints:
x = x ′ ← Ri (ei ; ai ), ...,Rj(ej ; aj),Dn(en, e′n), ...,Dm(em, e
′m)
⊥ ← Ri (ei ; ai ), ...,Rj(ej ; aj),Dn(en, e′n), ...,Dm(em, e
′m)
Di are parent-child predicates and Ri are categorical predicates
Dimensional rules as TGDs:
∃az Rk(ek ; ak)← Ri (ei ; ai ), ...,Rj(ej ; aj),Dn(en, e′n), ...,Dm(em, e
′m)
Existential quantifiers (possibly not needed) over non-categoricalattributes, which may get labeled nulls as values
Repeated variables in bodies of TGDs only for categorical attributes
”Upward or downward navigation captured by joins betweencategorical predicates and parent-child predicates in bodies”
(Carleton University) Ontology-Based Multidimensional Contexts 12 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Datalog± as Representation Language
Dimensional constraints as EGDs or negative constraints:
x = x ′ ← Ri (ei ; ai ), ...,Rj(ej ; aj),Dn(en, e′n), ...,Dm(em, e
′m)
⊥ ← Ri (ei ; ai ), ...,Rj(ej ; aj),Dn(en, e′n), ...,Dm(em, e
′m)
Di are parent-child predicates and Ri are categorical predicates
Dimensional rules as TGDs:
∃az Rk(ek ; ak)← Ri (ei ; ai ), ...,Rj(ej ; aj),Dn(en, e′n), ...,Dm(em, e
′m)
Existential quantifiers (possibly not needed) over non-categoricalattributes, which may get labeled nulls as values
Repeated variables in bodies of TGDs only for categorical attributes
”Upward or downward navigation captured by joins betweencategorical predicates and parent-child predicates in bodies”
(Carleton University) Ontology-Based Multidimensional Contexts 12 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Properties of MD Ontologies
Datalog± is a family of languages with different syntactic restrictionson rules and their interaction to guarantee tractability
Our Datalog± MD ontologies become weakly-sticky Datalog±programs (Cali et al., 2012)
It is crucial that repeated variables in TGDs are for categoricalattributes (a finite number of values can be taken by them, thecategory members)
The chase (that forwards propagates data through rules) may notterminate
Weak-stickiness guarantees tractability of conjunctive queryanswering (QA): only an initial portion of the chase has to beinspected
(Carleton University) Ontology-Based Multidimensional Contexts 13 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Properties of MD Ontologies
Datalog± is a family of languages with different syntactic restrictionson rules and their interaction to guarantee tractability
Our Datalog± MD ontologies become weakly-sticky Datalog±programs (Cali et al., 2012)
It is crucial that repeated variables in TGDs are for categoricalattributes (a finite number of values can be taken by them, thecategory members)
The chase (that forwards propagates data through rules) may notterminate
Weak-stickiness guarantees tractability of conjunctive queryanswering (QA): only an initial portion of the chase has to beinspected
(Carleton University) Ontology-Based Multidimensional Contexts 13 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Properties of MD Ontologies
Datalog± is a family of languages with different syntactic restrictionson rules and their interaction to guarantee tractability
Our Datalog± MD ontologies become weakly-sticky Datalog±programs (Cali et al., 2012)
It is crucial that repeated variables in TGDs are for categoricalattributes (a finite number of values can be taken by them, thecategory members)
The chase (that forwards propagates data through rules) may notterminate
Weak-stickiness guarantees tractability of conjunctive queryanswering (QA): only an initial portion of the chase has to beinspected
(Carleton University) Ontology-Based Multidimensional Contexts 13 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Properties of MD Ontologies
Datalog± is a family of languages with different syntactic restrictionson rules and their interaction to guarantee tractability
Our Datalog± MD ontologies become weakly-sticky Datalog±programs (Cali et al., 2012)
It is crucial that repeated variables in TGDs are for categoricalattributes (a finite number of values can be taken by them, thecategory members)
The chase (that forwards propagates data through rules) may notterminate
Weak-stickiness guarantees tractability of conjunctive queryanswering (QA): only an initial portion of the chase has to beinspected
(Carleton University) Ontology-Based Multidimensional Contexts 13 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Properties of MD Ontologies
Datalog± is a family of languages with different syntactic restrictionson rules and their interaction to guarantee tractability
Our Datalog± MD ontologies become weakly-sticky Datalog±programs (Cali et al., 2012)
It is crucial that repeated variables in TGDs are for categoricalattributes (a finite number of values can be taken by them, thecategory members)
The chase (that forwards propagates data through rules) may notterminate
Weak-stickiness guarantees tractability of conjunctive queryanswering (QA): only an initial portion of the chase has to beinspected
(Carleton University) Ontology-Based Multidimensional Contexts 13 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Properties of MD Ontologies
The separability condition on the (good) interaction between TGDsand EGDs becomes application dependent (Cali et al., 2011)
However, if EGDs have categorical head variables, separability holds
Separability implies decidability of conjunctive query answering
Boolean conjunctive QA is tractable for weakly-sticky Datalog±ontologies (the same applies to open conjunctive QA)
As opposed to sticky Datalog±, for weakly-sticky Datalog± there isno general first-order query rewriting methodology
That is, rewriting of conjunctive queries into FO queries in terms ofunderlying DB predicates
(Carleton University) Ontology-Based Multidimensional Contexts 14 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Properties of MD Ontologies
The separability condition on the (good) interaction between TGDsand EGDs becomes application dependent (Cali et al., 2011)
However, if EGDs have categorical head variables, separability holds
Separability implies decidability of conjunctive query answering
Boolean conjunctive QA is tractable for weakly-sticky Datalog±ontologies (the same applies to open conjunctive QA)
As opposed to sticky Datalog±, for weakly-sticky Datalog± there isno general first-order query rewriting methodology
That is, rewriting of conjunctive queries into FO queries in terms ofunderlying DB predicates
(Carleton University) Ontology-Based Multidimensional Contexts 14 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Properties of MD Ontologies
The separability condition on the (good) interaction between TGDsand EGDs becomes application dependent (Cali et al., 2011)
However, if EGDs have categorical head variables, separability holds
Separability implies decidability of conjunctive query answering
Boolean conjunctive QA is tractable for weakly-sticky Datalog±ontologies (the same applies to open conjunctive QA)
As opposed to sticky Datalog±, for weakly-sticky Datalog± there isno general first-order query rewriting methodology
That is, rewriting of conjunctive queries into FO queries in terms ofunderlying DB predicates
(Carleton University) Ontology-Based Multidimensional Contexts 14 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Properties of MD Ontologies
The separability condition on the (good) interaction between TGDsand EGDs becomes application dependent (Cali et al., 2011)
However, if EGDs have categorical head variables, separability holds
Separability implies decidability of conjunctive query answering
Boolean conjunctive QA is tractable for weakly-sticky Datalog±ontologies (the same applies to open conjunctive QA)
As opposed to sticky Datalog±, for weakly-sticky Datalog± there isno general first-order query rewriting methodology
That is, rewriting of conjunctive queries into FO queries in terms ofunderlying DB predicates
(Carleton University) Ontology-Based Multidimensional Contexts 14 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Properties of MD Ontologies
The separability condition on the (good) interaction between TGDsand EGDs becomes application dependent (Cali et al., 2011)
However, if EGDs have categorical head variables, separability holds
Separability implies decidability of conjunctive query answering
Boolean conjunctive QA is tractable for weakly-sticky Datalog±ontologies (the same applies to open conjunctive QA)
As opposed to sticky Datalog±, for weakly-sticky Datalog± there isno general first-order query rewriting methodology
That is, rewriting of conjunctive queries into FO queries in terms ofunderlying DB predicates
(Carleton University) Ontology-Based Multidimensional Contexts 14 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Properties of MD Ontologies
The separability condition on the (good) interaction between TGDsand EGDs becomes application dependent (Cali et al., 2011)
However, if EGDs have categorical head variables, separability holds
Separability implies decidability of conjunctive query answering
Boolean conjunctive QA is tractable for weakly-sticky Datalog±ontologies (the same applies to open conjunctive QA)
As opposed to sticky Datalog±, for weakly-sticky Datalog± there isno general first-order query rewriting methodology
That is, rewriting of conjunctive queries into FO queries in terms ofunderlying DB predicates
(Carleton University) Ontology-Based Multidimensional Contexts 14 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Query Answering on MD Ontology
A non-deterministic algorithm WeaklySticky-QAns for weakly-stickyDatalog± (Cali et al., 2012)
WeaklyStickyQAns builds an accepting resolution proof schema, atree-like structure
It shows how query atoms are entailed from extensional data
The algorithm runs in polynomial time in the size of the extensionaldatabase
We proposed a deterministic version of the algorithm forweakly-sticky programs (Milani and Bertossi, AMW 2015)
(Carleton University) Ontology-Based Multidimensional Contexts 15 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Query Answering on MD Ontology
A non-deterministic algorithm WeaklySticky-QAns for weakly-stickyDatalog± (Cali et al., 2012)
WeaklyStickyQAns builds an accepting resolution proof schema, atree-like structure
It shows how query atoms are entailed from extensional data
The algorithm runs in polynomial time in the size of the extensionaldatabase
We proposed a deterministic version of the algorithm forweakly-sticky programs (Milani and Bertossi, AMW 2015)
(Carleton University) Ontology-Based Multidimensional Contexts 15 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Query Answering on MD Ontology
A non-deterministic algorithm WeaklySticky-QAns for weakly-stickyDatalog± (Cali et al., 2012)
WeaklyStickyQAns builds an accepting resolution proof schema, atree-like structure
It shows how query atoms are entailed from extensional data
The algorithm runs in polynomial time in the size of the extensionaldatabase
We proposed a deterministic version of the algorithm forweakly-sticky programs (Milani and Bertossi, AMW 2015)
(Carleton University) Ontology-Based Multidimensional Contexts 15 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Query Answering on MD Ontology
A non-deterministic algorithm WeaklySticky-QAns for weakly-stickyDatalog± (Cali et al., 2012)
WeaklyStickyQAns builds an accepting resolution proof schema, atree-like structure
It shows how query atoms are entailed from extensional data
The algorithm runs in polynomial time in the size of the extensionaldatabase
We proposed a deterministic version of the algorithm forweakly-sticky programs (Milani and Bertossi, AMW 2015)
(Carleton University) Ontology-Based Multidimensional Contexts 15 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Query Answering on MD Ontology
A non-deterministic algorithm WeaklySticky-QAns for weakly-stickyDatalog± (Cali et al., 2012)
WeaklyStickyQAns builds an accepting resolution proof schema, atree-like structure
It shows how query atoms are entailed from extensional data
The algorithm runs in polynomial time in the size of the extensionaldatabase
We proposed a deterministic version of the algorithm forweakly-sticky programs (Milani and Bertossi, AMW 2015)
(Carleton University) Ontology-Based Multidimensional Contexts 15 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Query Answering on MD Ontology
The new algorithm uses a modified parsimonious chase procedure(parsimonious chase for shy programs) (Leone et al., KR 2012)
It takes advantage of information about the positions with finite ranks(Fagin et al., 2005)
The algorithm explores only a sufficiently large initial portion of thechase with respect to the query
We also studied the magic-sets rewriting technique in combinationwith this QA algorithm (Milani and Bertossi, AMW 2015)
(Carleton University) Ontology-Based Multidimensional Contexts 16 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Query Answering on MD Ontology
The new algorithm uses a modified parsimonious chase procedure(parsimonious chase for shy programs) (Leone et al., KR 2012)
It takes advantage of information about the positions with finite ranks(Fagin et al., 2005)
The algorithm explores only a sufficiently large initial portion of thechase with respect to the query
We also studied the magic-sets rewriting technique in combinationwith this QA algorithm (Milani and Bertossi, AMW 2015)
(Carleton University) Ontology-Based Multidimensional Contexts 16 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Query Answering on MD Ontology
The new algorithm uses a modified parsimonious chase procedure(parsimonious chase for shy programs) (Leone et al., KR 2012)
It takes advantage of information about the positions with finite ranks(Fagin et al., 2005)
The algorithm explores only a sufficiently large initial portion of thechase with respect to the query
We also studied the magic-sets rewriting technique in combinationwith this QA algorithm (Milani and Bertossi, AMW 2015)
(Carleton University) Ontology-Based Multidimensional Contexts 16 / 23
Multidimensional Context Ontological Representation of the Extended MD Model
Query Answering on MD Ontology
The new algorithm uses a modified parsimonious chase procedure(parsimonious chase for shy programs) (Leone et al., KR 2012)
It takes advantage of information about the positions with finite ranks(Fagin et al., 2005)
The algorithm explores only a sufficiently large initial portion of thechase with respect to the query
We also studied the magic-sets rewriting technique in combinationwith this QA algorithm (Milani and Bertossi, AMW 2015)
(Carleton University) Ontology-Based Multidimensional Contexts 16 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
The MD ontology M becomes part of the context for data qualityassessment
The original instance D of schema S is to be assessed or cleanedthrough the context
By mapping D into the contextual schema/instance C
In the context:
• Nickname predicates R ′i ∈ S ′
• The core MD ontology M• A set of quality predicates P
I
C
aiq
schema C
quality predicates
P
categorical
relations
dimensions
M
ai
S’
nicknames
Ri’
Sq
quality version
R1q
Rnq
Dq
S
under asessment
R1
D
Rn
…
(Carleton University) Ontology-Based Multidimensional Contexts 17 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
The MD ontology M becomes part of the context for data qualityassessment
The original instance D of schema S is to be assessed or cleanedthrough the context
By mapping D into the contextual schema/instance C
In the context:
• Nickname predicates R ′i ∈ S ′
• The core MD ontology M• A set of quality predicates P
I
C
aiq
schema C
quality predicates
P
categorical
relations
dimensions
M
ai
S’
nicknames
Ri’
Sq
quality version
R1q
Rnq
Dq
S
under asessment
R1
D
Rn
…
(Carleton University) Ontology-Based Multidimensional Contexts 17 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
The MD ontology M becomes part of the context for data qualityassessment
The original instance D of schema S is to be assessed or cleanedthrough the context
By mapping D into the contextual schema/instance C
In the context:
• Nickname predicates R ′i ∈ S ′
• The core MD ontology M• A set of quality predicates P
I
C
aiq
schema C
quality predicates
P
categorical
relations
dimensions
M
ai
S’
nicknames
Ri’
Sq
quality version
R1q
Rnq
Dq
S
under asessment
R1
D
Rn
…
(Carleton University) Ontology-Based Multidimensional Contexts 17 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
The MD ontology M becomes part of the context for data qualityassessment
The original instance D of schema S is to be assessed or cleanedthrough the context
By mapping D into the contextual schema/instance C
In the context:
• Nickname predicates R ′i ∈ S ′
• The core MD ontology M• A set of quality predicates P
I
C
aiq
schema C
quality predicates
P
categorical
relations
dimensions
M
ai
S’
nicknames
Ri’
Sq
quality version
R1q
Rnq
Dq
S
under asessment
R1
D
Rn
…
(Carleton University) Ontology-Based Multidimensional Contexts 17 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
The MD ontology M becomes part of the context for data qualityassessment
The original instance D of schema S is to be assessed or cleanedthrough the context
By mapping D into the contextual schema/instance C
In the context:
• Nickname predicates R ′i ∈ S ′
• The core MD ontology M• A set of quality predicates P
I
C
aiq
schema C
quality predicates
P
categorical
relations
dimensions
M
ai
S’
nicknames
Ri’
Sq
quality version
R1q
Rnq
Dq
S
under asessment
R1
D
Rn
…
(Carleton University) Ontology-Based Multidimensional Contexts 17 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
The MD ontology M becomes part of the context for data qualityassessment
The original instance D of schema S is to be assessed or cleanedthrough the context
By mapping D into the contextual schema/instance C
In the context:
• Nickname predicates R ′i ∈ S ′
• The core MD ontology M
• A set of quality predicates P
I
C
aiq
schema C
quality predicates
P
categorical
relations
dimensions
M
ai
S’
nicknames
Ri’
Sq
quality version
R1q
Rnq
Dq
S
under asessment
R1
D
Rn
…
(Carleton University) Ontology-Based Multidimensional Contexts 17 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
The MD ontology M becomes part of the context for data qualityassessment
The original instance D of schema S is to be assessed or cleanedthrough the context
By mapping D into the contextual schema/instance C
In the context:
• Nickname predicates R ′i ∈ S ′
• The core MD ontology M• A set of quality predicates P
I
C
aiq
schema C
quality predicates
P
categorical
relations
dimensions
M
ai
S’
nicknames
Ri’
Sq
quality version
R1q
Rnq
Dq
S
under asessment
R1
D
Rn
…
(Carleton University) Ontology-Based Multidimensional Contexts 17 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Quality predicates are defined with non-recursive Datalog rules interms of categorical predicates and built-ins
Outside context there are Rq1 , ...,R
qn as quality versions
They are defined by quality data extraction rules written innon-recursive Datalog in terms of S ′, P, and built-ins
Quality query answering for Q imposed on S:
1 Replace predicates in Q with their quality versions obtaining Qq
2 Rewrite Qq into QC by applying the quality data extraction rules
3 Unfold the definition of quality predicates P, that results into QM interms of categorical relations and nicknames
4 Answer QM by QA on M
(Carleton University) Ontology-Based Multidimensional Contexts 18 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Quality predicates are defined with non-recursive Datalog rules interms of categorical predicates and built-ins
Outside context there are Rq1 , ...,R
qn as quality versions
They are defined by quality data extraction rules written innon-recursive Datalog in terms of S ′, P, and built-ins
Quality query answering for Q imposed on S:
1 Replace predicates in Q with their quality versions obtaining Qq
2 Rewrite Qq into QC by applying the quality data extraction rules
3 Unfold the definition of quality predicates P, that results into QM interms of categorical relations and nicknames
4 Answer QM by QA on M
(Carleton University) Ontology-Based Multidimensional Contexts 18 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Quality predicates are defined with non-recursive Datalog rules interms of categorical predicates and built-ins
Outside context there are Rq1 , ...,R
qn as quality versions
They are defined by quality data extraction rules written innon-recursive Datalog in terms of S ′, P, and built-ins
Quality query answering for Q imposed on S:
1 Replace predicates in Q with their quality versions obtaining Qq
2 Rewrite Qq into QC by applying the quality data extraction rules
3 Unfold the definition of quality predicates P, that results into QM interms of categorical relations and nicknames
4 Answer QM by QA on M
(Carleton University) Ontology-Based Multidimensional Contexts 18 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Quality predicates are defined with non-recursive Datalog rules interms of categorical predicates and built-ins
Outside context there are Rq1 , ...,R
qn as quality versions
They are defined by quality data extraction rules written innon-recursive Datalog in terms of S ′, P, and built-ins
Quality query answering for Q imposed on S:
1 Replace predicates in Q with their quality versions obtaining Qq
2 Rewrite Qq into QC by applying the quality data extraction rules
3 Unfold the definition of quality predicates P, that results into QM interms of categorical relations and nicknames
4 Answer QM by QA on M
(Carleton University) Ontology-Based Multidimensional Contexts 18 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Quality predicates are defined with non-recursive Datalog rules interms of categorical predicates and built-ins
Outside context there are Rq1 , ...,R
qn as quality versions
They are defined by quality data extraction rules written innon-recursive Datalog in terms of S ′, P, and built-ins
Quality query answering for Q imposed on S:
1 Replace predicates in Q with their quality versions obtaining Qq
2 Rewrite Qq into QC by applying the quality data extraction rules
3 Unfold the definition of quality predicates P, that results into QM interms of categorical relations and nicknames
4 Answer QM by QA on M
(Carleton University) Ontology-Based Multidimensional Contexts 18 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Quality predicates are defined with non-recursive Datalog rules interms of categorical predicates and built-ins
Outside context there are Rq1 , ...,R
qn as quality versions
They are defined by quality data extraction rules written innon-recursive Datalog in terms of S ′, P, and built-ins
Quality query answering for Q imposed on S:
1 Replace predicates in Q with their quality versions obtaining Qq
2 Rewrite Qq into QC by applying the quality data extraction rules
3 Unfold the definition of quality predicates P, that results into QM interms of categorical relations and nicknames
4 Answer QM by QA on M
(Carleton University) Ontology-Based Multidimensional Contexts 18 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Quality predicates are defined with non-recursive Datalog rules interms of categorical predicates and built-ins
Outside context there are Rq1 , ...,R
qn as quality versions
They are defined by quality data extraction rules written innon-recursive Datalog in terms of S ′, P, and built-ins
Quality query answering for Q imposed on S:
1 Replace predicates in Q with their quality versions obtaining Qq
2 Rewrite Qq into QC by applying the quality data extraction rules
3 Unfold the definition of quality predicates P, that results into QM interms of categorical relations and nicknames
4 Answer QM by QA on M
(Carleton University) Ontology-Based Multidimensional Contexts 18 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Quality predicates are defined with non-recursive Datalog rules interms of categorical predicates and built-ins
Outside context there are Rq1 , ...,R
qn as quality versions
They are defined by quality data extraction rules written innon-recursive Datalog in terms of S ′, P, and built-ins
Quality query answering for Q imposed on S:
1 Replace predicates in Q with their quality versions obtaining Qq
2 Rewrite Qq into QC by applying the quality data extraction rules
3 Unfold the definition of quality predicates P, that results into QM interms of categorical relations and nicknames
4 Answer QM by QA on M
(Carleton University) Ontology-Based Multidimensional Contexts 18 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Example
Dimensional rules in M:
WorkingTimes(u, t; n, y)←WorkingSchedules(u, d ; n, y),DayTime(d , t)
PatientUnit(u, t; p)← PatientWard(w , d ; p),DayTime(d , t),
UnitWard(u,w)
Quality predicates in P:
TakenByNurse(t, p, n, y)←WorkingTimes(u, t; n, y),PatientUnit(u, t; p)
TakenWithTherm(t, p, b)← PatientUnit(u, t; p), u = Standard, b = B1
(Carleton University) Ontology-Based Multidimensional Contexts 19 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Example
Dimensional rules in M:
WorkingTimes(u, t; n, y)←WorkingSchedules(u, d ; n, y),DayTime(d , t)
PatientUnit(u, t; p)← PatientWard(w , d ; p),DayTime(d , t),
UnitWard(u,w)
Quality predicates in P:
TakenByNurse(t, p, n, y)←WorkingTimes(u, t; n, y),PatientUnit(u, t; p)
TakenWithTherm(t, p, b)← PatientUnit(u, t; p), u = Standard, b = B1
(Carleton University) Ontology-Based Multidimensional Contexts 19 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Example
Dimensional rules in M:
WorkingTimes(u, t; n, y)←WorkingSchedules(u, d ; n, y),DayTime(d , t)
PatientUnit(u, t; p)← PatientWard(w , d ; p),DayTime(d , t),
UnitWard(u,w)
Quality predicates in P:
TakenByNurse(t, p, n, y)←WorkingTimes(u, t; n, y),PatientUnit(u, t; p)
TakenWithTherm(t, p, b)← PatientUnit(u, t; p), u = Standard, b = B1
(Carleton University) Ontology-Based Multidimensional Contexts 19 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Example
Dimensional rules in M:
WorkingTimes(u, t; n, y)←WorkingSchedules(u, d ; n, y),DayTime(d , t)
PatientUnit(u, t; p)← PatientWard(w , d ; p),DayTime(d , t),
UnitWard(u,w)
Quality predicates in P:
TakenByNurse(t, p, n, y)←WorkingTimes(u, t; n, y),PatientUnit(u, t; p)
TakenWithTherm(t, p, b)← PatientUnit(u, t; p), u = Standard, b = B1
(Carleton University) Ontology-Based Multidimensional Contexts 19 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Example
Dimensional rules in M:
WorkingTimes(u, t; n, y)←WorkingSchedules(u, d ; n, y),DayTime(d , t)
PatientUnit(u, t; p)← PatientWard(w , d ; p),DayTime(d , t),
UnitWard(u,w)
Quality predicates in P:
TakenByNurse(t, p, n, y)←WorkingTimes(u, t; n, y),PatientUnit(u, t; p)
TakenWithTherm(t, p, b)← PatientUnit(u, t; p), u = Standard, b = B1
(Carleton University) Ontology-Based Multidimensional Contexts 19 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Example
Dimensional rules in M:
WorkingTimes(u, t; n, y)←WorkingSchedules(u, d ; n, y),DayTime(d , t)
PatientUnit(u, t; p)← PatientWard(w , d ; p),DayTime(d , t),
UnitWard(u,w)
Quality predicates in P:
TakenByNurse(t, p, n, y)←WorkingTimes(u, t; n, y),PatientUnit(u, t; p)
TakenWithTherm(t, p, b)← PatientUnit(u, t; p), u = Standard, b = B1
(Carleton University) Ontology-Based Multidimensional Contexts 19 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Example
Quality version Measurementsq:
Measurementsq(t, p, v)← Measurements ′(t, p, v),TakenByNurse(t, p, n, y),
TakenWithTherm(t, p, b), b = B1, y = certified
A doctor asks the body temperatures of Tom Waits for September 5taken around noon:
Q(t, v) : Measurements(t, Tom Waits, v) ∧ Sep5-11:45 ≤ t ≤ Sep5-12:15
He expects that the measurements are taken by a certified nurse andwith a thermometer of brand B1
(Carleton University) Ontology-Based Multidimensional Contexts 20 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Example
Quality version Measurementsq:
Measurementsq(t, p, v)← Measurements ′(t, p, v),TakenByNurse(t, p, n, y),
TakenWithTherm(t, p, b), b = B1, y = certified
A doctor asks the body temperatures of Tom Waits for September 5taken around noon:
Q(t, v) : Measurements(t, Tom Waits, v) ∧ Sep5-11:45 ≤ t ≤ Sep5-12:15
He expects that the measurements are taken by a certified nurse andwith a thermometer of brand B1
(Carleton University) Ontology-Based Multidimensional Contexts 20 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Example
Quality version Measurementsq:
Measurementsq(t, p, v)← Measurements ′(t, p, v),TakenByNurse(t, p, n, y),
TakenWithTherm(t, p, b), b = B1, y = certified
A doctor asks the body temperatures of Tom Waits for September 5taken around noon:
Q(t, v) : Measurements(t, Tom Waits, v) ∧ Sep5-11:45 ≤ t ≤ Sep5-12:15
He expects that the measurements are taken by a certified nurse andwith a thermometer of brand B1
(Carleton University) Ontology-Based Multidimensional Contexts 20 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Example
Quality version Measurementsq:
Measurementsq(t, p, v)← Measurements ′(t, p, v),TakenByNurse(t, p, n, y),
TakenWithTherm(t, p, b), b = B1, y = certified
A doctor asks the body temperatures of Tom Waits for September 5taken around noon:
Q(t, v) : Measurements(t, Tom Waits, v) ∧ Sep5-11:45 ≤ t ≤ Sep5-12:15
He expects that the measurements are taken by a certified nurse andwith a thermometer of brand B1
(Carleton University) Ontology-Based Multidimensional Contexts 20 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Example
Quality version Measurementsq:
Measurementsq(t, p, v)← Measurements ′(t, p, v),TakenByNurse(t, p, n, y),
TakenWithTherm(t, p, b), b = B1, y = certified
A doctor asks the body temperatures of Tom Waits for September 5taken around noon:
Q(t, v) : Measurements(t, Tom Waits, v) ∧ Sep5-11:45 ≤ t ≤ Sep5-12:15
He expects that the measurements are taken by a certified nurse andwith a thermometer of brand B1
(Carleton University) Ontology-Based Multidimensional Contexts 20 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Example
Replacing predicates of S in Q with their quality versions in Sq:
Qq(t, v) :Measurementsq(t, Tom Waits, v)∧Sep5-11:45 ≤ t ≤ Sep5-12:15
Applying the definition of quality versions:
QC(t, v) : Measurements ′(t, p, v) ∧ TakenByNurse(t, p, n, certified) ∧TakenWithTherm(t, p, B1) ∧ p = Tom Waits ∧Sep/5-11:45 ≤ t ≤ Sep/5-12:15
Unfolding the definition of quality predicates in P:
QM(t, v) :Measurements ′(t, p, v) ∧WorkingTimes(u, t; n, y) ∧PatientUnit(u, t; p) ∧ u=Standard ∧ y =certified ∧p = Tom Waits ∧ Sep/5-11:45 ≤ t ≤ Sep/5-12:15
(Carleton University) Ontology-Based Multidimensional Contexts 21 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Example
Replacing predicates of S in Q with their quality versions in Sq:
Qq(t, v) :Measurementsq(t, Tom Waits, v)∧Sep5-11:45 ≤ t ≤ Sep5-12:15
Applying the definition of quality versions:
QC(t, v) : Measurements ′(t, p, v) ∧ TakenByNurse(t, p, n, certified) ∧TakenWithTherm(t, p, B1) ∧ p = Tom Waits ∧Sep/5-11:45 ≤ t ≤ Sep/5-12:15
Unfolding the definition of quality predicates in P:
QM(t, v) :Measurements ′(t, p, v) ∧WorkingTimes(u, t; n, y) ∧PatientUnit(u, t; p) ∧ u=Standard ∧ y =certified ∧p = Tom Waits ∧ Sep/5-11:45 ≤ t ≤ Sep/5-12:15
(Carleton University) Ontology-Based Multidimensional Contexts 21 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Example
Replacing predicates of S in Q with their quality versions in Sq:
Qq(t, v) :Measurementsq(t, Tom Waits, v)∧Sep5-11:45 ≤ t ≤ Sep5-12:15
Applying the definition of quality versions:
QC(t, v) : Measurements ′(t, p, v) ∧ TakenByNurse(t, p, n, certified) ∧TakenWithTherm(t, p, B1) ∧ p = Tom Waits ∧Sep/5-11:45 ≤ t ≤ Sep/5-12:15
Unfolding the definition of quality predicates in P:
QM(t, v) :Measurements ′(t, p, v) ∧WorkingTimes(u, t; n, y) ∧PatientUnit(u, t; p) ∧ u=Standard ∧ y =certified ∧p = Tom Waits ∧ Sep/5-11:45 ≤ t ≤ Sep/5-12:15
(Carleton University) Ontology-Based Multidimensional Contexts 21 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Example
Replacing predicates of S in Q with their quality versions in Sq:
Qq(t, v) :Measurementsq(t, Tom Waits, v)∧Sep5-11:45 ≤ t ≤ Sep5-12:15
Applying the definition of quality versions:
QC(t, v) : Measurements ′(t, p, v) ∧ TakenByNurse(t, p, n, certified) ∧TakenWithTherm(t, p, B1) ∧ p = Tom Waits ∧Sep/5-11:45 ≤ t ≤ Sep/5-12:15
Unfolding the definition of quality predicates in P:
QM(t, v) :Measurements ′(t, p, v) ∧WorkingTimes(u, t; n, y) ∧PatientUnit(u, t; p) ∧ u=Standard ∧ y =certified ∧p = Tom Waits ∧ Sep/5-11:45 ≤ t ≤ Sep/5-12:15
(Carleton University) Ontology-Based Multidimensional Contexts 21 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Example
Replacing predicates of S in Q with their quality versions in Sq:
Qq(t, v) :Measurementsq(t, Tom Waits, v)∧Sep5-11:45 ≤ t ≤ Sep5-12:15
Applying the definition of quality versions:
QC(t, v) : Measurements ′(t, p, v) ∧ TakenByNurse(t, p, n, certified) ∧TakenWithTherm(t, p, B1) ∧ p = Tom Waits ∧Sep/5-11:45 ≤ t ≤ Sep/5-12:15
Unfolding the definition of quality predicates in P:
QM(t, v) :Measurements ′(t, p, v) ∧WorkingTimes(u, t; n, y) ∧PatientUnit(u, t; p) ∧ u=Standard ∧ y =certified ∧p = Tom Waits ∧ Sep/5-11:45 ≤ t ≤ Sep/5-12:15
(Carleton University) Ontology-Based Multidimensional Contexts 21 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Example
Replacing predicates of S in Q with their quality versions in Sq:
Qq(t, v) :Measurementsq(t, Tom Waits, v)∧Sep5-11:45 ≤ t ≤ Sep5-12:15
Applying the definition of quality versions:
QC(t, v) : Measurements ′(t, p, v) ∧ TakenByNurse(t, p, n, certified) ∧TakenWithTherm(t, p, B1) ∧ p = Tom Waits ∧Sep/5-11:45 ≤ t ≤ Sep/5-12:15
Unfolding the definition of quality predicates in P:
QM(t, v) :Measurements ′(t, p, v) ∧WorkingTimes(u, t; n, y) ∧PatientUnit(u, t; p) ∧ u=Standard ∧ y =certified ∧p = Tom Waits ∧ Sep/5-11:45 ≤ t ≤ Sep/5-12:15
(Carleton University) Ontology-Based Multidimensional Contexts 21 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Example
Measurements ′ has the same extension of Measurements
WorkingTimes and PatientUnit are computed by QA on M
The first second and lastmeasurements have theexpected quality
The first measurement is aclean answer to Q:t = Sep/5-12:10 and v=38.2
MeasurementsTime Patient Value
Sep/5-12:10 Tom Waits 38.2Sep/6-11:50 Tom Waits 37.1Sep/7-12:15 Tom Waits 37.7Sep/9-12:00 Tom Waits 37.0Sep/6-11:05 Lou Reed 37.5Sep/5-12:05 Lou Reed 38.0
(Carleton University) Ontology-Based Multidimensional Contexts 22 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Example
Measurements ′ has the same extension of Measurements
WorkingTimes and PatientUnit are computed by QA on M
The first second and lastmeasurements have theexpected quality
The first measurement is aclean answer to Q:t = Sep/5-12:10 and v=38.2
MeasurementsTime Patient Value
Sep/5-12:10 Tom Waits 38.2Sep/6-11:50 Tom Waits 37.1Sep/7-12:15 Tom Waits 37.7Sep/9-12:00 Tom Waits 37.0Sep/6-11:05 Lou Reed 37.5Sep/5-12:05 Lou Reed 38.0
(Carleton University) Ontology-Based Multidimensional Contexts 22 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Example
Measurements ′ has the same extension of Measurements
WorkingTimes and PatientUnit are computed by QA on M
The first second and lastmeasurements have theexpected quality
The first measurement is aclean answer to Q:t = Sep/5-12:10 and v=38.2
MeasurementsTime Patient Value
Sep/5-12:10 Tom Waits 38.2Sep/6-11:50 Tom Waits 37.1Sep/7-12:15 Tom Waits 37.7Sep/9-12:00 Tom Waits 37.0Sep/6-11:05 Lou Reed 37.5Sep/5-12:05 Lou Reed 38.0
(Carleton University) Ontology-Based Multidimensional Contexts 22 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Example
Measurements ′ has the same extension of Measurements
WorkingTimes and PatientUnit are computed by QA on M
The first second and lastmeasurements have theexpected quality
The first measurement is aclean answer to Q:t = Sep/5-12:10 and v=38.2
MeasurementsTime Patient Value
Sep/5-12:10 Tom Waits 38.2Sep/6-11:50 Tom Waits 37.1Sep/7-12:15 Tom Waits 37.7Sep/9-12:00 Tom Waits 37.0Sep/6-11:05 Lou Reed 37.5Sep/5-12:05 Lou Reed 38.0
(Carleton University) Ontology-Based Multidimensional Contexts 22 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Example
Measurements ′ has the same extension of Measurements
WorkingTimes and PatientUnit are computed by QA on M
The first second and lastmeasurements have theexpected quality
The first measurement is aclean answer to Q:t = Sep/5-12:10 and v=38.2
MeasurementsTime Patient Value
Sep/5-12:10 Tom Waits 38.2Sep/6-11:50 Tom Waits 37.1Sep/7-12:15 Tom Waits 37.7Sep/9-12:00 Tom Waits 37.0Sep/6-11:05 Lou Reed 37.5Sep/5-12:05 Lou Reed 38.0
(Carleton University) Ontology-Based Multidimensional Contexts 22 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Example
Measurements ′ has the same extension of Measurements
WorkingTimes and PatientUnit are computed by QA on M
The first second and lastmeasurements have theexpected quality
The first measurement is aclean answer to Q:t = Sep/5-12:10 and v=38.2
MeasurementsTime Patient Value
Sep/5-12:10 Tom Waits 38.2Sep/6-11:50 Tom Waits 37.1Sep/7-12:15 Tom Waits 37.7Sep/9-12:00 Tom Waits 37.0Sep/6-11:05 Lou Reed 37.5Sep/5-12:05 Lou Reed 38.0
(Carleton University) Ontology-Based Multidimensional Contexts 22 / 23
Multidimensional Context MD Context for Quality Data Assessment
MD Contexts and Quality Query Answering: The Gist
Example
Measurements ′ has the same extension of Measurements
WorkingTimes and PatientUnit are computed by QA on M
The first second and lastmeasurements have theexpected quality
The first measurement is aclean answer to Q:t = Sep/5-12:10 and v=38.2
MeasurementsTime Patient Value
Sep/5-12:10 Tom Waits 38.2Sep/6-11:50 Tom Waits 37.1Sep/7-12:15 Tom Waits 37.7Sep/9-12:00 Tom Waits 37.0Sep/6-11:05 Lou Reed 37.5Sep/5-12:05 Lou Reed 38.0
(Carleton University) Ontology-Based Multidimensional Contexts 22 / 23
Conclusions
Conclusions
Multidimensional contexts are represented as Datalog± ontologies
They allow us to specify data quality conditions, and to retrievequality data
Development, implementation of the query answering algorithms isongoing work
Several extensions:
Uncertain downward-navigation in dimensional rules
Checking dimensional constraints not only on the result of the chasebut while data generation
Relaxing the assumption of complete categorical data, and studying itseffect on dimensions
(Carleton University) Ontology-Based Multidimensional Contexts 23 / 23
Conclusions
Conclusions
Multidimensional contexts are represented as Datalog± ontologies
They allow us to specify data quality conditions, and to retrievequality data
Development, implementation of the query answering algorithms isongoing work
Several extensions:
Uncertain downward-navigation in dimensional rules
Checking dimensional constraints not only on the result of the chasebut while data generation
Relaxing the assumption of complete categorical data, and studying itseffect on dimensions
(Carleton University) Ontology-Based Multidimensional Contexts 23 / 23
Conclusions
Conclusions
Multidimensional contexts are represented as Datalog± ontologies
They allow us to specify data quality conditions, and to retrievequality data
Development, implementation of the query answering algorithms isongoing work
Several extensions:
Uncertain downward-navigation in dimensional rules
Checking dimensional constraints not only on the result of the chasebut while data generation
Relaxing the assumption of complete categorical data, and studying itseffect on dimensions
(Carleton University) Ontology-Based Multidimensional Contexts 23 / 23
Conclusions
Conclusions
Multidimensional contexts are represented as Datalog± ontologies
They allow us to specify data quality conditions, and to retrievequality data
Development, implementation of the query answering algorithms isongoing work
Several extensions:
Uncertain downward-navigation in dimensional rules
Checking dimensional constraints not only on the result of the chasebut while data generation
Relaxing the assumption of complete categorical data, and studying itseffect on dimensions
(Carleton University) Ontology-Based Multidimensional Contexts 23 / 23
Conclusions
Conclusions
Multidimensional contexts are represented as Datalog± ontologies
They allow us to specify data quality conditions, and to retrievequality data
Development, implementation of the query answering algorithms isongoing work
Several extensions:
Uncertain downward-navigation in dimensional rules
Checking dimensional constraints not only on the result of the chasebut while data generation
Relaxing the assumption of complete categorical data, and studying itseffect on dimensions
(Carleton University) Ontology-Based Multidimensional Contexts 23 / 23
Conclusions
Conclusions
Multidimensional contexts are represented as Datalog± ontologies
They allow us to specify data quality conditions, and to retrievequality data
Development, implementation of the query answering algorithms isongoing work
Several extensions:
Uncertain downward-navigation in dimensional rules
Checking dimensional constraints not only on the result of the chasebut while data generation
Relaxing the assumption of complete categorical data, and studying itseffect on dimensions
(Carleton University) Ontology-Based Multidimensional Contexts 23 / 23
Conclusions
Conclusions
Multidimensional contexts are represented as Datalog± ontologies
They allow us to specify data quality conditions, and to retrievequality data
Development, implementation of the query answering algorithms isongoing work
Several extensions:
Uncertain downward-navigation in dimensional rules
Checking dimensional constraints not only on the result of the chasebut while data generation
Relaxing the assumption of complete categorical data, and studying itseffect on dimensions
(Carleton University) Ontology-Based Multidimensional Contexts 23 / 23