1. Advance Database Systems and Applications COMP 6521
Professor: Dr. Gosta Grahne Lab Instructor: ashkan azarnik Group 15 Aditya Dewal Mohammad Iftekharul Hoque Saleh Ahmed

2. PROJECT 1
Develop a program which sort numbers in ascending order using 2 Phase Multiway Merge Sort(2PMMS) with limitation of 5MB of virtual memory.
External sorting is required when the data being sorted do not fit into the main memory of a computing device and instead they must reside in slower external memory (usually hard drive).

3. Our approached to solve the problem
External sorting typically uses a sort-merge technique.
In the sorting phase, chunks of data small enough to fit in main memory are read, sorted in ascending order using quick sort algorithm and written out to a temporary file.
In the merge phase, the sorted temporary files are combined using 2 phase multiway merge sort into a single larger file.

4. Challenges Which algorithm to choose ?
Quicksort is one of the fastest and simplest sorting algorithm because its inner loop can be efficiently implemented on most architectures.
Efficient average case compared to other sort algorithms.
The complexity of quick sort in the average case is O(n log(n)

5. List of Data Structures
Primitive Types:
Boolean, Integer, Long
Abstract Types:
Array, String
Arrays (Linear Data Structure)
Integer Array, Boolean Array, Long Array
I/O:
Scanner, PrintWriter

6. Buffer Size Experiments

7. Conclusion
After our buffer size experiments we concluded that for number of data which occupying 2.5mb of memory gives best execution time for us.

8. Results from Demo
The execution time to run our program during the demo was 3 minutes.
The reason for taking too much time
was the way we were taking our input and writing output in our program.

9. Project 2
Mining Frequent Itemsets from Secondary Memory Build an application that will compute the frequent itemsets of all sizes (Pairs, Triples, Quadruples, etc.) from a set of transactions based on input support threshold percentage.

10. Algorithms Considered
Apriori
Eclat
Horizontal Data Layout
Vertical Data Layout

11. Algorithms Considered
Apriori
Eclat
Breadth-First Traversal
Depth-First Traversal

12. ECLAT Better Execution Time Memory Efficient Explore the unexplored
Execution time is better than Apriori
Memory Efficient
Require less amount of memory compare to Apriori if itemsets are small in number
Depth-First Search
Explore the unexplored

13. ECLAT Algorithm For each item, store a list of transaction ids (tids)
TID-list

14.   ECLAT Algorithm Determine support of any k-itemset by intersecting
tid-lists of two of its (k-1) subsets.
3 traversal approaches:
top-down
bottom-up
hybrid  

15. ECLAT Algorithm

16. List of Data Structures
ECLAT Implementation
List of Data Structures
Primitive Types
Arrays (Linear Data Struc.)
Boolean, Integer, Double
Hash Map (Hash Table)
Abstract Types
Hash Set (Hash Map)
Map, Set, List, Array,
Array List (Dynamic Array)
String
Bit Set (Bit Array)
String Array
Trees
Search Tree

17. ECLAT Implementation
Our implementation denotes the set of transactions as a bit set.
Intersects rows to determine the support of item sets.
The search follows a depth first traversal of a prefix tree as it is shown in Figure 1.

18. Divide and Conquer Phase
ECLAT Implementation
Divide and Conquer Phase
Divide the file in N partitions.
If an item is frequent in one partition we don’t check it again.
Merge Phase
Suppose an item is not frequent in any partition but it is frequent globally, it is going to come when we would merge.
In the merge part we would run the algorithm again with the infrequent items.

19. ECLAT Implementation File size = 10000, Threshold = 2%
An item is frequent if it occurs >= 200 times
We would get intermediate results by checking all the partitions.
Merge part we would work with the infrequent items for each partition, and then merge the results to get the final output list of frequent items

20. Eclat Execution Time
Execution time of Eclat for Small and Medium datasets:

21. Eclat VS Apriori
We have compared the execution time for Apriori and Eclat for Small and Medium datasets and found the following:

22. Benefits of Divide and Conquer
Program executes for Large files.
Gives better performance.

23. Results from Demo
Execution time was 35 seconds.

24. REFERENCES Project 1 Project 2
Database Systems, the complete book by Hector Gracia-Molina, Jeff Ullman, and Jennifer widom
Project 2