1 / 27

Teaching Aid for Bernstein’s Algorithm

CS4221 Database Design. Teaching Aid for Bernstein’s Algorithm. Group P10. Daphne Tay A0071939U Pan Lu A0070083N Tan Wee Kwan A0074053M Yeo Yai Hui A0069830Y. Objective. T eaching aid with interactive user interface Learn in a simple yet efficient manner.

bisa
Télécharger la présentation

Teaching Aid for Bernstein’s Algorithm

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. CS4221Database Design Teaching Aid for Bernstein’s Algorithm Group P10 Daphne Tay A0071939U Pan Lu A0070083N Tan Wee Kwan A0074053M Yeo YaiHui A0069830Y

  2. Objective • Teaching aid with interactive user interface • Learn in a simple yet efficient manner.

  3. What is Bernstein’s Algorithm • Invented by Philip A. Bernstein in 1976 • 6-steps synthesis algorithm • Obtain 3NF given a relation with FDs

  4. Bernstein’s Algorithm • Eliminate extraneous attributes • Find covering • Partition • Merge equivalent keys • Eliminate transitive dependencies • Construct Relation

  5. Design • Select relation and construct FDs • Step-by-step synthesis and explanation • Additional Features: • 2NF / 3NF / BCNF Checkers • Lossless Checkers • Candidate Key finder • Superfluous Attribute Finder

  6. Implementation

  7. Step 1 • Check the left side of FDs containing more than 1 attribute. • Remove 1 attribute and find closure of remaining attributes. • If right side of FD can be found in closure, it is extraneous. R(A,B,C,D) ABE->CD A->B B found in closure. It is redundant! AB ->CD A+ = ABCD

  8. Step 2 • Split all right sides of FDs into separate FDs. • Remove a FD X->Y. • Find closure with X. • If Y can be found in closure, FD X->Y is redundant and removed.

  9. Step 3 • Merge all FDs with same left sides.

  10. Step 4 • Get closure of all partitions. • Compare closure for properly equivalence. • If present, create J and obtain H’. • Remaining partitions as it is.

  11. Step 5 • Remove a FD X->Y. • Find closure with X. • If Y can be found in closure, FD X->Y is redundant and removed.

  12. Step 6 • Construct relation based on each H. • Obtain keys based on left side of FD.

  13. Shortcomings • Does not guarantee losslessness • Does not find all the keys • Does not remove all superfluous attributes • Set of 3NF relations produced depends on the type of minimal cover found in Step 2 • BCNF relation may contain superfluous attributes

  14. Additional Features • Losslessness Checker • Superfluous Attribute Checker • BCNF Checker • 2NF / 3NF Checkers • Candidate Key Finder

  15. 2NF Checker • Naïve way to verify • Check that a FD is a subset of another FD in both left and right for violation. E.g. AB->CD and A->C

  16. 3NF Checker • Check if the relation already in 2NF. • If not, then also not in 3NF. • If it is, check for transitive dependencies for violations.

  17. Candidate Key Finder • Classify the attributes to Left, Middle, Right. • Left are definitely part of the candidate keys. • Create combinations of all Left and Middle to derive all possible super keys through closure. • Filter obtain set of candidate keys.

  18. BCNF Checker • Obtain closure of attribute(s) appear on the right side of FD • If closure contains part of left side of that FD, violation.

  19. Lossless Checker • Gather attributes in relations resulted in step 6 • Check against initial set of attributes given before Step 1 • If equivalent, then it is lossless

  20. Superfluous Attributes Finder • Naïve way to check for superfluous attributes • Check based on definitions above

  21. Demo

  22. Limitations • Limited Number of Attributes • 9 attributes at the tool’s maximum • To facilitate efficient learning • Could be improve by adding more attributes • Increase the complexity of possible FDs created by the users • Naming the attributes • Fixed naming: A- I • Users could not key in their own desired names • Real life examples set by users could make the tool more vivid for learning

  23. Limitations • Design of the tool • Could be make more vibrant and attractive • Tool for age around 20 and above • Interesting user interface could be implemented: e.g. graphically • Minimum Covering • Usually Bernstein’s Step 2 will output different results if different coverings of the same set of relational schema and FDs were used • Our algorithm only output one covering at any random time • Tool could be enhanced by using random seeds to select which FDs to start with while looping

  24. Limitations • Finding all candidate keys using Bernstein’s • NP-complete problem • Not all candidate keys can be found using Bernstein’s • Time to solve the problem increase quickly when size of the problem increases • Using any currently known algorithm • Could reach into billions or trillions of years as size grows • Good side of keeping our maximum attributes to be 9: • Able to find the candidate keys more efficiently at smaller sizes

  25. Conclusion • Teaching Tool is coded in Java and runs with Jar File • Run on any platforms with Java Runtime Environment • Main purpose is to help users to understand Bernstein’s Algorithm • Addressing the limitations will make our tool becoming better

  26. Thank you

  27. Q & A

More Related