Deductive Databases Presentation
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Deductive databases allow for more complex queries of data through the use of Datalog, an extension of SQL that allows for recursion. Deductive databases combine traditional databases with logical rules to make inferences from the data. This allows applications to have more intelligent capabilities by reasoning over the stored data. The presentation provides an overview of deductive databases and how they can power applications through the use of logical rules and reasoning over the stored data.
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- The network model extends the hierarchical model by allowing nodes to have more than one parent, representing complex relationships. However, it is more complex to implement and manage.
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Introduction to Databases
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Database System
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Evolution of Databases
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The Database Approach
Advantages of Database Approach
Disadvantages of Database Approach
Database Management Systems
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Primary Key
Cardinality and Degree
Relationships
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Data Integrity
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SQL ALTER TABLE
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SQL WHERE
SQL AND & OR
SQL ORDER BY
SQL UPDATE
SQL DELETE
SQL LIKE
SQL IN
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SQL INNER JOIN
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SQL UNION
SQL AS
SQL Aggregate Functions
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SQL GROUP BY
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Data & Information, Drawbacks of File system, What is Database Management Systems, What is the need of DBMS, Examples of DBMS, Database Types, Applications of DBMS, Advantage of DBMS over file system, Disadvantages of DBMS, DBMS vs. File System
Dbms logical dependance
Logical data independence refers to the ability to change the logical schema without affecting external schemas or application programs. Physical data independence means changing the physical schema without changing the logical schema.
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3. Secondary indexes provide an alternative means of accessing records in a data file through a secondary field, and include the field value and a pointer to the record.
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ppt spatial data
This document discusses two types of spatial data used in GIS - vector data and raster data. Vector data represents geographic features as points, lines, and polygons using vertices with x, y, and z coordinates. It allows for accurate representation of shapes and storing of attribute data but requires more processing and storage. Raster data represents geographic information through a grid of cells (pixels), with each cell storing attribute values like elevation or temperature. It is better for continuously changing data but cannot represent linear features well and has increased storage needs at higher resolutions. Both data types have advantages and disadvantages depending on the use case.
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The document discusses the deductive approach to language instruction. It begins by defining the deductive approach as a more teacher-centered style where the teacher first presents grammatical rules and structures to students before having them apply it in examples. It then lists some advantages, such as being more effective for higher-level students and time-saving, and disadvantages, such as lack of student involvement and difficulty for younger learners. Finally, it provides an example of how a deductive approach could be used to teach procedures, asking for directions, and listening skills.
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Deductive Databases Presentation
- 1. Deductive databases Making smart applications Maroun Baydoun Inf 1312
- 4. Data: The backbone of applications
- 5. Large storage space Performance issues Administrative nightmares
- 10. Databases: The prime medium for data storage
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- 14. Recursive SQL
- 15. Datalog: Asking questions and getting answers
- 16. Datalog rules The first atom/subgoal Negation p :- q, not r. The second atom/subgoal The head ‘Is true if’ AND
- 17. Datalog rules ‘p is true if q is true AND not r is true’
- 18. Datalog rules
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- 20. Datalog facts predicate name (list of constants). A fact can only operate on The name of the rule/relation constants. No variables are allowed to which the fact belongs
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- 22. Datalog queries ?- predicate name (list of constants/variable). A query can have either constants or The name of the rule/relation variables or both depending on its to which the query is related type
- 23. Example queries
- 24. Deductive databases: When databases meet Datalog
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- 30. IRIS Reasoner
- 31. Sesame .
- 32. The proposed solutions for the problems
- 33. The proposed solutions for the problems
- 34. Overview of the application
- 35. Overview of the application
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