MNISAR / Relational-Database-Engine

A extension to Minibase: a relational database in Java. Added Skyline, Joins, GroupBy operators along with indexes (hash & b-tree) and the database catalog with query executor.

Extending a Relational Database ( CSE 510: DBMSI)

The project deals with adding layers on top of the implementation of relational database "minibase". The base implementation only contains bare minimum features for a database to work. So it has File System (Heap files, Index files), Indexing (b-tree index), Buffer Manager and Disk Manager implemented.

####Our goal is to use these modules of MiniBase as building blocks for implementing a user preference sensitive DBMS to:

1. Support e-commerce application for user preference sensitive: Skyline.
2. Add skyline operators. Use different algorithm to compare the working of each algorithm.
3. Implement Clusteres B-tree index, Hash based clustered & un-clustered index, GroupBy, Join Operator (index nested & hash join), top-K join (NRA & hash)
4. Implement a Database Catalog along with interface to perform the database operations.

####Reports on implementation of the above goals in three phase:

• This document provides information on system working and some operation that the base implementation supports.
• This document provides information on implementation of skyline operator and different algorithms used.
• This document provides information about implementation of other features (join, groupby & index) along with database catalog implementation.

References

1. Ramakrishnan, Gehrke, "Database Management Systems"
2. S. Borzsony, D. Kossmann and K. Stocker, ”The Skyline operator,” Proceedings 17th International Conference on Data Engineering, Heidelberg, Germany, 2001, pp. 421-430, doi: 10.1109/ICDE.2001.914855.
3. Chomicki J., Godfrey P., Gryz J., Liang D. (2005) Skyline with Presorting: Theory and Optimizations. In: Kopotek M.A., Wierzcho S.T., Trojanowski K. (eds) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol 31. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32392-9_72
4. K.L. Tan, P.K. Eng and B. C. Ooi. “Efficient Progressive Skyline Computation.” 27th Int. Conference on Very Large Data Bases (VLDB), Roma, Italy, 301-310, September 2001.
5. M. Nagendra, “Efficient Processing of Skyline Queries on Static Data Sources, Data Streams and Incomplete Datasets,” Arizona State University, 2014. https://repository.asu.edu/items/27470
6. Rudenko L., Endres M. (2018) Real-Time Skyline Computation on Data Streams. In: Benczúr A. et al. (eds) New Trends in Databases and Information Systems. ADBIS 2018. Communications in Computer and Information Science, vol 909. Springer, Cham. https://doi.org/10.1007/978-3-030-00063-9_3
7. K Ramamohanarao and R Sacks-Davis. 1984. Recursive linear hashing. ACM Trans. Database Syst. 9, 3 (Sept. 1984), 369–391. DOI:https://doi.org/10.1145/1270.1285
8. Ilyas, Ihab & Aref, Walid & Elmagarmid, Ahmed. (2004). Supporting top-k join queries in relational databases. The VLDB Journal. 13. 10.1007/s00778-004-0128-2.
9. Function Based Index, Oracle 11.2 documentation, https://docs.oracle.com/cd/E11882_01/appdev.112/e41502/adfns_indexes.htm#ADFNS00505
10. Goncalves M., Vidal ME. (2005) Top-k Skyline: A Unified Approach. In: Meersman R., Tari Z., Herrero P. (eds) On the Move to Meaningful Internet Systems 2005: OTM 2005 Workshops. OTM 2005. Lecture Notes in Computer Science, vol 3762. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11575863_99