1. Lecture 1: Overview of CSCI 585
Prof. Shahram Ghandeharizadeh
Director of USC Database Lab (
Computer Science Department
University of Southern California

2. Logistics Collection of technical papers: Pre-req for the course:
ACM/IEEE/Springer digital libraries.
URLs work from USC machines.
Pre-req for the course:
CSCI 485: Introduction to File and Database Management, and
Knowledge C++ programming language.
Extensive use of Blackboard for homework and project submissions. Make sure to have access to:
Power-point of presentations also available from

3. Pre-Req 585 assumes you know the following:
Transactions and their ACID properties.
Concurrency control protocols such as locking and time-stamp based protocols.
Crash recovery techniques such as logging and shadow paging.
Physical characteristics of magnetic disks.
SQL
Relational algebra operators
ER data modeling
Alternative normal forms.
Visit for an overview of this material.

4. Instructor Details Dr. Shahram Ghandeharizadeh Office: SAL 208
Phone:
Office Hours:
Tuesday: 12:30 to 2 pm
Thursday: 4:30 to 5:30 pm
Class URL:

5. TA Shahin Shayandeh Office: SAL 200C E-mail: shayande@usc.edu
Office Hours:
Mondays: 3:30 to 5 pm
Thursday: 12:30 to 2 pm

6. Outline Motivation for DBMS An outline for the course material
Grading: Assignments and projects

7. Database Management Systems (DBMS)
Used almost on a daily basis for either individual or business use.
Relational database vendors were one of the fastest growing sectors during the .COM boom!

8. DATABASE & DBMS
Database: An integrated collection of data, usually stored on secondary storage, typically describing the activities of one or more related organizations.
Database management system (DBMS): A collection of software/programs designed to assist in maintaining and utilizing large collections of data.

9. BEFORE DBMS User 1 Data Data User 2 Application programs

10. AFTER DBMS User 1 DBMS User 2 Application programs
Data managed by DBMS
DBMS
User 2
Application programs

11. WHY A DBMS? Reduced application development time
Data independence: Application programs not dependent on data representation and storage details
Data sharing: data is better utilized (discovered and reused), redundancy of data is minimized
Data integrity and consistency: one may enforce consistency constraints on data, e.g., number of seats sold ≤ number of seats on the plane × 1.1
Centralized control: DBA tunes the database to balance user's needs
Security: mechanisms to prevent unauthorized access. These mechanisms are based on content instead of file-oriented approach.
Concurrency control: avoids undesirable race conditions that arise with simultaneous access/updates to data
Crash recovery: ensures the integrity of data in the presence of failures

12. DBMS ARCHITECTURE … User 1 DBMS DB User n Physical data
Conceptual schema

13. An Emerging Phenomena User 1 DBMS User 2 Application programs
Data managed by DBMS
DBMS
User 2
Application programs

14. Example
F. Chang et. al. Bigtable: A Distributed Storage System for Structured Data. In OSDI Last paragraph of the paper:
“Finally, we have found that there are significant advantages to building our own storage solution at Google. We have gotten substantial amount of flexibility from designing our own data model for Bigtable. In addition, our control over Bigtable’s implementation, and the other Google infrastructure upon which Bigtable depends, means that we can remove bottlenecks and inefficiencies as they arise.”

15. WHAT HAS CHANGED?
Relational database technology is now more than a quarter of century old.
While concepts such as concurrency control are extremely valuable, the performance loss attributed to their use is not justified for some non-banking applications.
E.g., A social networking site is not a banking application.
RDBMS vendors increased functionality for their own niche, increasing complexity. Each application used a decreasing fraction of the provided features.
A deployment requires a specialist, trained in database administration, for maintainence.
Availability of data is paramount.
Cost of downtime is estimated at thousands of dollars per minute.
SQL is too general and cumbersome to use with some applications.
Storage has become larger and more economical.
10 cents per Gigabyte of magnetic disk storage.
Flash as a new layer in the storage hierarchy: DRAM, Flash, Disk.
7 to 8 dollars per Gigabyte of DRAM.
A bank’s data (TPC benchmark) becomes main memory resident!

16. Cross-roads
Since 1998, database researchers have been aware of the limitations:
More modular architecture based on simple, component-based building blocks.
One architecture will not satisfy all applications.

17. 585 Syllabus Storage and Storage Management: 2-3 weeks.
M. Seltzer. Beyond Relational Databases. Communications of the ACM, July 2008, Vol. 51, No. 7.
D. A. Patterson, G. Gibson, and R. H. Katz. A Case for Redundant Arrays of Inexpensive Disks (RAID). ACM SIGMOD, 1988.
G. Graefe. The five-minute rule twenty-years later, and how flash memory changes the rules. Proceedings of the Third International Workshop on Data Management on New Hardware (DaMoN), 2007.
Flash as a new storage medium.
2-3 weeks.
Start homework 1 using Berkeley DB.

18. 585 Syllabus (Cont…) Parallel DBMS: 2 Weeks.
D. DeWitt et al. The Gamma Database Machine Project. IEEE Transactions on Knowledge and Data Engineering, Vol. 2, 1990.
F. Chang et al. Bigtable: A Distributed Storage System for Structured Data. In OSDI 2006.
J. Dean and S. Ghemawat. MapReduce: Simplified Data Processing on Large Clusters. In Communications of the ACM, Vol. 51, No. 1, 2008.
Data intensive applications can be parallelized effectively.
2 Weeks.

19. 585 Syllabus (Cont…) Spatial Index Structures: 2 Weeks.
A. Guttman. R-Trees: A Dynamic Index Structure for Spatial Searching. In ACM SIGMOD 1984.
P. E. O’Neil, and D. Quass. Improved Query Performance with Variant Indexes. In ACM SIGMOD 1997.
No substitute for smart data indexing techniques!
Brute-force approaches are not acceptable.
2 Weeks.
Initiate your project to build a relational query processing software using Berkeley DB.

20. 585 Syllabus (Cont…) Query optimizations: 2 Weeks.
P. G. Selinger, M. M. Astrahan, D. D. Chamberlin, R. A. Lorie, T. G. Price. Access Path Selection in Relational Database Management System. In ACM SIGMOD 1979.
S. Chaudhuri. An Overview of Query Optimization in Relational Systems. PODS 1998.
Techniques to select index structures. Focus is on your project.
2 Weeks.

21. 585 Syllabus (Cont…) Decision Support: 2-3 Weeks.
R. Agrawal and R. Srikant. Fast Algorithms for Mining Association Rules in Large Databases. In VLDB 1994.
J. Gray et al. Data Cube: A Relational Aggregation Operator Generalizing Group-by, Cross-Tab and SubTotals. Data Mining and Knowledge Discovery 1(1), 1997.
C. Stolte, D. Tang, and P. Hanrahan. Polaris: A System for Query, Analysis, and Visualization of Multidimensional Databases. Communications of the ACM, Vol. 51, No. 11, November 2008.
Discovery of trends in large data sets and their visualization.
2-3 Weeks.

22. 585 Syllabus (Cont…) Main Memory Databases: 2 Weeks
P. A. Boncz, M. L. Kristen, and S. Manegold. Breaking the Memory Wall in MonetDB. Communications of the ACM, December 2008, Vol. 51, No. 12.
Use L2 cache of a CPU!
2 Weeks

23. 585 Syllabus (Cont…) Cache Management: Time permitting, 1 to 2 weeks.
S. Ghandeharizadeh and S. Shyandeh. Greedy Cache Management Techniques for Mobile Devices. In First International IEEE Workshop on Ambient Intelligence, Media and Sensing. April 2007.
Effective support for variable sized objects.
Time permitting, 1 to 2 weeks.

24. Grading
Midterm 1: 35%
Midterm 2: 35%
Assignments: 10%
Project: 20%

25. For next lecture
Read:
M. Seltzer. Beyond Relational Databases. Communications of the ACM, July 2008, Vol. 51, No. 7.