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Popular Ranking AlgorithmsPowerPoint Presentation

Popular Ranking Algorithms

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Popular Ranking Algorithms

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Popular Ranking Algorithms

Prepared by

-Ranjan Dash

- Efficient ways of Ranking
- Algorithms for ranking
- Sort Algorithm
- Scan Algorithm
- FA Algorithm
- TA Algorithm

- Besides choosing a proper ranking function, efficient way to execute also decides the performance.
- So given a ranking function the execution of this following a particular ranking algorithm plays a key role in the efficiency.

- Prominent Algorithms to get top K results are
- Sort Algorithm
- Scan Algorithm
- FA Algorithm
- TA Algorithm

- Most simple way to decide the top K results of a ranking function like
Score (ObjectId) = Linear combinations of attributes

is to sort the result and take the top K.

- This will take nlogn time.
- Very slow for very large relations where n is quite large.

- Keep K tuples in a buffer.
- Scan this buffer for every tuple in the relation.
- Replace the lowest one in the buffer if the input tuple is more than that.
- Takes O(n.K) time.
- Still low for a large n.

- Fagin’s Algorithm known as FA Algorithm. Developed by Ron Fagin.
- Takes the help of data structures prepared offline.
- Though there is a cost associated with these data structures, yet the amortized cost is very low.
- Sorted access to the attributes. Supports GetNext() operation and is sequential. One sorted table per attribute.
- Random access through the ObjectId. Supports Get(ObjId) operation.
- The pre processing requires the preparation of above two types of data structures which will be used again and again during the processing.

- Step1
- Example of determining top 1 restaurant based on the given ranking function
Score(RestId) = 2.Cusine + Location

Sorted for Cusine

Sorted for Location

Original relation

- Step1
- Do the GetNext from both sorted tables in round robin.
- Stop when K objects have been seen in common from all lists – 1 in our example

RestId 4 is winner in our case

Sorted for Location

Sorted for Cusine

- Step2
- Random access to calculate the score for all visited tuples in step 1.
- Take the top K after evaluation
- This algorithm is applicable if the problem shows monotonic property.
- The worst case will be same as scan algorithm.
- The worst case memory requirement is unbounded.

- Known as Threshold Algorithm
- Similar to FA but sorted access and random access are interleaved.
- Step 1
- Do sorted access (and corresponding random accesses) until you have seen the top K answers.
- Step 2
- Determine threshold value (Hypothetical tuple) based on objects currently seen under sorted access.
- K objects with overall score ≥ threshold value ? Stop.
- Else go to next entry position in sorted list and repeat step 1
- Faster than FA.
- Requires less memory.