The document discusses reasoning of database consistency through description logics. It begins with an introduction and overview before covering data models and description logics, description logics and database querying, data integration, and concluding. It describes how entity relationship models are used to describe database structure and how they can be transformed into description logics knowledge bases. This allows reasoning about database consistency, satisfiability, and other properties to identify issues like redundancy. Description logics are also discussed as a way to perform querying and classify queries.
Steps in Database Design Process
ER Concepts (Entities, Attributes, Associations, etc)
ER Notations
Class Hierarchies
ER concepts, notations with appropriate examples. how to model databases using ER techniques.
Entity type
Entity sets
Attributes and keys
Relationship model
Mapping Constraints
The ER Model
Cardinality Constraints
Generalization, Specialization and Aggregation
ER Diagram & Database design with the ER Model
Introduction
Relational Model
Concepts
Characteristics
1. What is Entity Relationship Model
2. Entity and Entity Set
3. Relationship and Relationship Set
4. Attributes and it's kinds
5. Participation Constraints and Mapping Cardinality
6. Aggregation, Specialization, and Generalization
7. Some Sample ERD models
The importance of data models, Basic building blocks, Business rules, The evolution of data models, Degrees of data abstraction
Database design and Introduction to UML
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An Entity–relationship model (ER model) describes the structure of a database with the help of a diagram, which is known as Entity Relationship Diagram (ER Diagram). An ER model is a design or blueprint of a database that can later be implemented as a database. The main components of E-R model are: entity set and relationship set
The Puerto Rico Aqueducts and Sewer Authority (PRASA) commissioned the Hydrologic & Hydraulic Unit of the CSA Group/CH2MHILL Joint Venture, to study and evaluate the water availability of the Dos Bocas and Caonillas Reservoir in series systems, and to develop an Optimization Plan to maximize their utilization for multiple functions such as generation of electrical power, production of potable water, navigation/transportation, and recreation, without impacting the aquatic ecosystem of the Rio Grande de Arecibo where the system are located. As Part of this study, the feasibility of several possible modifications to the operation of the reservoirs were evaluated based on operational costs, reliability of the water supply, and the generation of hydroelectric energy from the Dos Bocas and Caonillas I hydroelectric plants.
Steps in Database Design Process
ER Concepts (Entities, Attributes, Associations, etc)
ER Notations
Class Hierarchies
ER concepts, notations with appropriate examples. how to model databases using ER techniques.
Entity type
Entity sets
Attributes and keys
Relationship model
Mapping Constraints
The ER Model
Cardinality Constraints
Generalization, Specialization and Aggregation
ER Diagram & Database design with the ER Model
Introduction
Relational Model
Concepts
Characteristics
1. What is Entity Relationship Model
2. Entity and Entity Set
3. Relationship and Relationship Set
4. Attributes and it's kinds
5. Participation Constraints and Mapping Cardinality
6. Aggregation, Specialization, and Generalization
7. Some Sample ERD models
The importance of data models, Basic building blocks, Business rules, The evolution of data models, Degrees of data abstraction
Database design and Introduction to UML
Mit202 data base management system(dbms)smumbahelp
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
Bca3020– data base management system(dbms)smumbahelp
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
An Entity–relationship model (ER model) describes the structure of a database with the help of a diagram, which is known as Entity Relationship Diagram (ER Diagram). An ER model is a design or blueprint of a database that can later be implemented as a database. The main components of E-R model are: entity set and relationship set
The Puerto Rico Aqueducts and Sewer Authority (PRASA) commissioned the Hydrologic & Hydraulic Unit of the CSA Group/CH2MHILL Joint Venture, to study and evaluate the water availability of the Dos Bocas and Caonillas Reservoir in series systems, and to develop an Optimization Plan to maximize their utilization for multiple functions such as generation of electrical power, production of potable water, navigation/transportation, and recreation, without impacting the aquatic ecosystem of the Rio Grande de Arecibo where the system are located. As Part of this study, the feasibility of several possible modifications to the operation of the reservoirs were evaluated based on operational costs, reliability of the water supply, and the generation of hydroelectric energy from the Dos Bocas and Caonillas I hydroelectric plants.
Default Logics for Plausible Reasoning with Controversial AxiomsRommel Carvalho
Presentation given by Thomas Scharrenbach at the 6th Uncertainty Reasoning for the Semantic Web Workshop at the 9th International Semantic Web Conference in 2010.
Paper: Default Logics for Plausible Reasoning with Controversial Axioms
Abstract: Using a variant of Lehmann's Default Logics and Probabilistic Description Logics we recently presented a framework that invalidates those unwanted inferences that cause concept unsatisfiability without the need to remove explicitly stated axioms. The solutions of this methods were shown to outperform classical ontology repair w.r.t. the number of inferences invalidated. However, conflicts may still exist in the knowledge base and can make reasoning ambiguous. Furthermore, solutions with a minimal number of inferences invalidated do not necessarily minimize the number of conflicts. In this paper we provide an overview over finding solutions that have a minimal number of conflicts while invalidating as few inferences as possible. Specifically, we propose to evaluate solutions w.r.t. the quantity of information they convey by recurring to the notion of entropy and discuss a possible approach towards computing the entropy w.r.t. an ABox.
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The ppt will give the information about the ER-model.
and also here I added one ER-diagram of Placement Management System which will help anyone to understand the components of ER-diagram.
A relational database management system (RDBMS) is a database management system (DBMS) based on the relational model invented by Edgar F. Codd at IBM's San Jose Research Laboratory. Most databases in widespread use today are based on his relational database model.[1]
The relational database model derived from the mathematical concept of relation and set theory. It was proposed as a technique to data modeling by Dr Edgar F. Codd of IBM Analysis in 1970 in his document entitled “A Relational Technique of Information for Huge Shared Data Banks.” This document marked the start of the field of a relational database.
https://www.ducatindia.com/datascienceusingpython
RDBMS - Relational Database Management System It is database management system based on relational model , which is used to manage relational database. Relational model is organization of data in tables which are interrelated. Relational Database It is organized collection of tables. Data is stored in tables. Tables are related to each other using one or more fields.
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3. Reasoning of database consistency through Description Logics
Definitions :
The object of knowledge representation is to express the problem in
computer-understandable form
Description Logics (DL) are a family of knowledge representation
languages called description languages.
Data model is essentially a language or set of concepts for describing
a class of certain kinds of databases.
Entity Relationship (ER) model used to describe the structure of data
stored in the database.
Introduction - Data models & DL - DL & database querying - Data integration - conclusion
Ahmad Karawash
4. Reasoning of database consistency through Description Logics
what is ER?
ER is the most widespread semantic data model, and it has become
a standard, extensively used in the design phase of commercial
applications.
Introduction - Data models & DL - DL & database querying - Data integration - conclusion
Ahmad Karawash
5. Reasoning of database consistency through Description Logics
ER elements:
- Entities set: set of objects that have common properties.(ex: object person have
name, phone, age).
- Relationships: set of tuples (instances), each of which represents an association among a
different combination of instances of the entities that participate in the relationship.
- Attributes: express the Elementary properties whose values belong to one of several
predefined domains, such as Integer, String, or Boolean.
- An IS-A relation between two entities is denoted by an arrow from the more specific to
the more general entity
-ER-role: is introduced since each entity can participate in a relationship more than once
,The arity of a relationship is the number of its ER-roles.
- Cardinality constraints can be attached to an ER-role in order to restrict the number of
times each instance of an entity is allowed to participate.
Introduction - Data models & DL - DL & database querying - Data integration - conclusion
Ahmad Karawash
6. Reasoning of database consistency through Description Logics
Introduction - Data models & DL - DL & database querying - Data integration - conclusion
ER symbols:
- Domain symbol (D) has predefined domain DB
D
- Entity symbol (E) -> set of attribute symbol (A) each has a unique domain
- Relationship symbol (R) -> N ER-role symbols
- Cardinality constraint -> - cminS from ER-role -> nonnegative integer
- cmaxS from ER-role -> all positve integer (+infinity)
String, integer,
…
E
AA
R E2E1
ER-roleER-role
Ahmad Karawash
7. Reasoning of database consistency through Description Logics
Database (B) correspond to ER (S)
- Expressed by nonempty finite set ΔB & function .B (as in algebra f : E-> R)
-.B : D -> DB
D (maps to predefined domain string, integer, …)
- .B : E -> EB (maps from E to instance of E)
- .B : A -> AB (maps to instance of attribute that connects entity to domain)
.B : R -> RB (maps to instance of relationship that connect ER-roles to entities
T : ER-roles -> ΔB
<u1:o1,..,un:on>->T[ui]=oi )
Introduction - Data models & DL - DL & database querying - Data integration - conclusion
R1
R2
E
U1
U2
O1
O2
EB AB RB are instances of E, A, R
Ahmad Karawash
8. Reasoning of database consistency through Description Logics
Database (B) is legal to ER (S) if it satisfy :
- For each pair E1,E2 with E1 Is-a E2 => E1B C E2B
(all individuals that satisfies E1 also satisfies E2)
- For each pair R1,R2 with R1 Is-a R2 => R1B C R2B
- For each entity E : e belong EB there is only one a(e,d) belong to AB such that e
connect entity E to domain D.
- For each relation R of arity N : all instance has the form <U1:O1,…,Un:On>
- For each ER-role U of R with E : cmin(U) <= |{r belong R / r[U]=e }|<= cmax(U)
Introduction - Data models & DL - DL & database querying - Data integration - conclusion
Ahmad Karawash
9. Reasoning of database consistency through Description Logics
How to transform from ER to DLR knowledge?
DLR is an expressive Description Logic (DL) with n-ary relations,
particularly suited for modeling database schemas and queries.
To transform from ER to DLR, a mapping function (Ø) should be
introduced .
Introduction - Data models & DL - DL & database querying - Data integration - conclusion
Ahmad Karawash
10. Reasoning of database consistency through Description Logics
Introduction - Data models & DL - DL & database querying - Data integration - conclusion
Ø(S) -> gives the knowledge base of ER S
- Set of atomic concept of Ø(S)={set of entity & domain symbol of S}
- Set of relation concept of Ø(S)={
- R in S -> PR in Ø(S) [relation symbol between E and R]
- A in S -> PA in Ø(S) [attribute symbol between E and Domain] }
- Set of axiom : - E1 Is-a E2 => E1 C E2
- R1 Is-a R2 => PR1 C PR2
- For each attribute A with domain D of an entity E,
E C (forall[$1](PA ^ ($2:D))) ^ =1 [$1]PA
- For each relationship R of arity n with ER-roles
PR C ($μ R(U1):E1) ^ …••• ^($μ R(Un):En)
Ahmad Karawash
11. Reasoning of database consistency through Description Logics
Introduction - Data models & DL - DL & database querying - Data integration - conclusion
ER
DLR
Ahmad Karawash
12. Reasoning of database consistency through Description Logics
Introduction - Data models & DL - DL & database querying - Data integration - conclusion
What benefits can be derived from having established
relationships?
Reasoning:
- Entity satisfiability, i.e., whether for every concept C, S admits a model in
which it has a nonempty extension. If C must always have an empty
extension then there is an inconsistency
- Relation satisfiability, i.e., whether S admits a model in which a certain
relation has a nonempty extension.(similar to above)
- Consistency of the ER schema, i.e., whether S admits a finite model.
Without this, there is no database that satisfies the schema, so
inconsistent if infinite model.
Ahmad Karawash
13. Reasoning of database consistency through Description Logics
Introduction - Data models & DL - DL & database querying - Data integration - conclusion
Reasoning (continue):
Redundancy of the ER schema. Various forms of redundancy in the ER
schema can be detected: e.g., if A, B are entities and both A v B and B v A
hold, we can conclude that one of the entities is redundant.
- Stronger constraints on relationship roles.
- Entity subsumption, i.e., whether the extension of one concept B is a
subset of the extension of another concept A in every model of S. This
property suggests that the designer check for the possible omission of an
explicit IS-A relationship between B and A.
- Relation subsumption, i.e., whether the extension of one relation is a
subset of the extension of another relation in every model of S. (Similar to
the above.)
Ahmad Karawash
14. Reasoning of database consistency through Description Logics
Introduction - Data models & DL - DL & database querying - Data integration - conclusion
Description Logics as query languages:
- the query description can be compared to the inconsistent description. If
they are equivalent, then there is surely a mistake
- The query can be classified with respect to the concepts in the schema.
This can be used to help users pose queries in an unfamiliar domain.
- Queries can also be classified with respect to each other into a
subsumption hierarchy. In an environment where several people are
asking exploratory questions about the data over a long period of time
(e.g., data mining by humans), it is very useful to have the questions
organized
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15. Data Integration
Integrating different data sources is one of the fundamental problems
faced by the database community.
The goal of a data integration system is to provide a uniform interface to
various data sources [Levy,2000].
The design of a data integration system is a very complex task, which
comprises several different aspects.
Reasoning of database consistency through Description Logics
Introduction - Data models & DL - DL & database querying - Data integration - conclusion
Ahmad Karawash 6
16. Conclusion
The greatest advantage of DL models is not representing
information model only but reasoning with the model.
The subsumption relationship can be used for semantic query
optimization.
DLs are useful in heterogeneous or federated databases.
The meaning of the DL model is unambiguous and precise and is
capable to check the consistency of any entire model.
Reasoning of database consistency through Description Logics
Introduction - Data models & DL - DL & database querying - Data integration - conclusion
Ahmad Karawash 7
18. References
Krötzsch, M., Simacikˇ, F., Horrocks, I.: A Description Logic Primer. CoRR.
abs/1201.4, 1–16 (2012).
Lutz, C.,Toman, D.: Conjunctive Query Answering in the Description Logic EL using a
Relational Database System. International Joint Conferences on Artificial Intelligence.
pp. 2070–2075 (2009).
Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., Rosati, R.: Data complexity
of query answering in description logics. Artif. Intell. 195, 335–360 (2013).
Motik, B., Horrocks, I., Sattler, U.: Integrating Description Logics and Relational
Databases. Science (80-. ). 1–44 (2006).
Bertossi, L.: Consistent query answering in databases, (2006).
Borgida, A., Lenzerini, M., Rosati: Description Logics for Data Bases. Description
Logic Handbook. pp. 472–494 (2002).
…..
Ahmad Karawash