Schema Evolution. Schema Evolution. Ref: Alex Borgida & K. E. Williamson. “Accommodating Exceptions in Databases, and Refining the Schema by Hearing From Them”. VLDB, 1985. SCHEMA User Describes the “domain of discoveries” of the database Check the correctness of the data entry DBMS
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Ref: Alex Borgida & K. E. Williamson. “Accommodating Exceptions in Databases, and Refining the Schema by Hearing From Them”. VLDB, 1985.
How To Live With Exceptions
Using Empirical Generalization For Schema Refinement
Supervisor: Accountant -> Supervisor: Employee
Research (age 30, Ph.D.) and Research (age 35, MS)
-> Research (age 30-35, MS-Ph.D.)
Ref: Q. Li and D. McLeod. “Object Flavor Evolution Through Learning in an Object-Oriented Database System”. Proceedings of the 2nd International Conference on Expert Database Systems, 1989, pp. 469-495.
PKM: Personal Knowledge Model
Level 1 Atomic Object - Non-decomposable units of information at a certain stage (e.g., John Smith).
Level 2 Open Atomic Set - A collection of atomic objects, members are not fixed (e.g., P names).
Closed Atomic Set Through Learning in an Object-Oriented Database System”. Proceedings of the 2 - A collection of atomic objects, members are fixed [e.g., sex, age (>= 0, integer)]
Mapping - Mapping from one object to another
- Domain & range mapping (e.g., has sender mapping from letter to person)
Social - Concept describes relationship (e.g., Jon has GPA 3.75)
Procedure - Operation/methods (e.g., method for creating or destroying objects)
Level 3 Open Social Set - classified a collection of objects (e.g., applicants)
Closed Social Set – (e.g., courses)
Composed Mapping - virtual objects derived by composing other objects
Composed Procedure - virtual object derived by grouping a collection of other procedure objects
ICE Intelligent Concept Evolver
PKM Personal Knowledge Manager
LFI Learning From Instruction (passive learning)
LFE Learning From Exceptions (passive learning)
Learning is triggered by:
LFO Learning From Observation (active learning)
1) Find GPA of all students in the objects (e.g., applicants) third world country. Since third world country is not defined in the schema, ICE conducts LFI (learning from instruction)
Acquire the concept of third world country given the member of the country and ask the user to identify if they are a third world country.
ICE learning all the third world countries in the DB introduces a new object “countries-class” with member “third world”, “second world”, and “first world”. Countries evolved to countries class.
LFO (learning from observation) is triggered to find mapping cardinality constraint of “has class”, also determines if it is a many-to-one mapping.
2) CS 10 -> CS 10a, 10b, 10c
CS 10 violation! ICE calls LFE and LFI
LFI learns there are 10a, 10b, 10c (same course name, unit number, instructor, and students)
Students of each section should be proper subset of original student set.Examples
3) Honor student has GPA >= 3.75 objects (e.g., applicants)
Sponsored by the Defense Advanced Research Project Agency (DARPA) Rome Laboratories (RL)
develops automated reasoning capabilities that are relevant to PI
Merging efforts in Tier 1 into a single system
and single operational problem