1. What’s Hot and What’s Not: Tracking Most Frequent Items Dynamically By Graham Cormode & S. Muthukrishnan Rutgers University, Piscataway NY Presented by Tal Sterenzy

2. Motivation A basic statistic on database relationship is which items are hot – occur frequently Dynamically maintaining hot items in the presence of delete and insert transactions. Examples: DBMS – keep statistics to improve performance Telecommunication networks - network connections start and end over time

3. Overview Definitions Prior work Algorithm description & analysis Experimental results Summery

4. Formal definition Sequence of n transactions on m items [1…m] - Net occurrence of item i at time t The number of times it has inserted minus the times it has been deleted - current frequency of item at time t - most frequent item at time t The k most frequent items at time t are those with the k largest

5. Finding k hot items k is a parameter Item i is an hot item if Frequent items that appear a significant fraction of the entire dataset There can be at most k hot items, and there can be none Assume basic integrity constraint

6. Our algorithm highly efficient, randomized algorithm for maintaining hot items in a dynamically changing database monitors the changes to the data distribution and maintains O(klogklogm) When queried, we can find all hot items in time O(klogklogm) with probability 1-δ No need to scan the underlying relation

7. Small tail assumption Restriction: are the frequencies of items A set of frequencies has a small tail if If there are k hot items  then small tail probability holds If small tail probability holds  then some top k items might not be hot We shall analyze our solution in the presence and absence of this small tail property (STP)

8. Prior work – why is it not adaptable? All these algorithms hold counters: incremented when the item is observed decremented or reallocated under certain circumstances Can’t directly adapt these algorithms for insertions and deletions: the state of the algorithm is different to that reached without the insertions and deletions of the item. Work on dynamic data is sparse, and provide no guarantees for the fully dynamic case with deletions

9. Our algorithm - idea Do not keep counters of individual items, but rather of subsets of items Ideas from group testing: Design a number of tests, each of which group together a number of m items in order to find up to k items which test positive Here: find k items that are hot Minimize number of tests, where each group consists of a subset of items

10. General procedure For each transaction on item i, determine which subsets it is included in: S(i) Each subset has a counter: For insertion: increment all S(i) counters For deletion: decrement all S(i) counters The test will be: does the counter exceed a threshold Identifying the hot items is done by combining test results from several groups

11. The challenge is choosing the subsets Bounding the number of required subsets Finding concise representation of the groups Giving efficiant way to go from results of tests to the sets of hot items Lets start with a simple case: k=1 (freq>1/2)  Deterministic algorithm for maintaining majority item

12. Finding majority item For insertions only, constant time and space Keep logm+1 counters: 1 counter of items “alive”: The rest are labeled,one per group Each group represents a bit in the binary representation of the item Each group consists of half of the items

13. Finding majority item – cont. bit(i,j) – reports value of jth bit in binary representation of i gt(i, j) – return 1 if i>j, 0 otherwise Scheme: Insertion of item i: Increment each counter such that bit( i, j ) = 1 in time O(logm). Deletion of i: Decrement each counter such that bit(i, j) = 1 in time O(logm). Query: If there is a majority, then it is given by computed in time O(logm).

14. Finding majority item – cont. Theorem: The algorithm finds a majority item if there is one with time O(logm) per operation The state of the data structure is equivalent if there are I insertion and D deletions, or if there are c = I - D insertions In case of insertions only: the majority is found

15. UpdateCounters procedure int c[0…logm] UpdateCounters( i,transtype,c[0…logm] ) c[0]=c[0] + diff for j=1 to logm do If (transtype = ins) c[j] = c[j] + bit(j,i) Else c[j] = c[j] - bit(j,i)

16. FindMajority procedure FindMajority(c[0... log m]) Position = 0, t =1 for j=1 to log m do if (c[j] > c[0]/2) then position = position + t t = 2* t return position

17. Randomized constructions for finding hot items Observation: If we select subsets with one hot item exactly  applying the majority algorithm will identify the hot item Definition:

18. How many subsets do we need? Theorem: Picking O(k logk) subsets by drawing m/k items uniformly from [1…m] means that with constant probability we have included k good subsets S 1 …S k such that Proof: p – pick one item from F O(k logk) subsets will guarantee with constant probability that we have one of each hot item (coupon’s collector problem)

19. Coupon collector problem p is probability that coupon is good X – number of trials required to collect at least one of each type of coupon Epoch i begins with after i-th success and ends with (i+1)-th success X i – number of trials in the i-th epoch X i distributed geometrically and p i = p(k-i)/k

20. Defining the groups with universal hash functions The groups are chosen in a pseudo-random way using universal hash functions: Fix prime P > 2k a, b are drawn uniformly from [0…P-1] Then set: Fact: Over all choices of a and b, for x<>y:

21. Choosing and updating the subsets We will choose T = logk/δ values of a and b, Which creates 2kT= 2klogk/δ subsets of items Processing an item i means: To which T sets i belongs? For each one: update logm counters based on bit representation of i If the set is good, this gives us the hot item

22. Space requirements a and b are O(m): O(logk/δ logm) Number of counters: 2k logk/δ (logm + 1) Total space: O(k logk/δ logm) log(k/ δ) choices of a,b 2k subsets log m + 1 counters

23. Probability of each hot item being in at least one good subset is at least 1-δ Consider one hot item: For each T repetitions we put it in one of 2k groups The expected total frequency of other items: If f<1/(k+1)  majority will be found  success If f>1/(k+1)  majority can’t be found  failure Probability of failure < ½ (by Markov inequality) Probability to fail on each T < Probability of any hot items failing at most δ.

24. Detecting good subsets Given a subset and it’s associated counters, it is possible to detect deterministically whether the subset is a good subset Proof: a subset can fail in two cases: No hot items (assuming STP) : then More than one hot item: there will be j such that:  a good subset is determined

25. ProcessItem procedure Initialize c[0 … 2Tk][0 … log m] Draw a[1 … T], b[1 … T], c=0 ProccessItem(i,transtype,T,k) if (trans = ins) then c = c + 1 else c = c – 1 for x = 1 to T do index =2k(x-1)+(i*a[x]+b[x]modP)mod2k UpdateCounters(i,transtype,c[index])

26. GroupTest procedure GroupTest(T,k,b) for i=1 to 2Tk do if c[i][0] > cb position = 0; t =1 for j = 1 to log m do if (c[i][j] > cb and c[i][0] – c[i][j] > cb) then Skip to next i if c[i][j] > cb position += t t = 2 * t output position

27. Algorithm correctness With probability at least 1-δ, calling the GroupTest(logk/δ,k,1/k+1) procedure finds all hot items. Time processing item is: O(logk/δ logm) Time to get all hot items is O(k logk/δ logm) With or without STP, we are still guarenteed to include all hot items with high probability Without STP, we might output infrequent items

28. Algorithm correctness – cont. When will an infrequent item be output? (no STP) A set with 2 hot items or more will be detected A set with one hot item will never fault. Even if there is a split without the hot item that exceeds the threshold – it will be detected A set with no hot item, and for all logm splits one half will exceed the threshold and the other not  only then the algorithm will fail

29. Algorithm properties The set of counters created with T= log k/ δ can be used to find hot items with parameter k’ for any k’<k with probability of success 1 – δ by calling GroupTest(logk/δ,k,1/(k’+1)) Proof: in the proof of probability for k hot items:

30. Experiments GroupTesting algorithm was compared to Loosy Counting and Frequent algorithms. The authors implemented them so that when an item is deleted we decrement the corresponding counter if such exist. The recall is the proportion of the hot items that are found by the method to the total number of hot items. The precision is the proportion of items identified by the algorithm, which are hot, to number of all output items.

31. Synthetic data (Recall) Zipf for hot items: 0 – distributed uniformly, 3 – highly skewed

32. Synthetic data (Precision) Zipf for hot items: 0 – distributed uniformly, 3 – highly skewed

33. Real data (Recall) Real data was obtained from one of AT&T network for part of a day.

34. Real Data (Percision) Real data has no guarantee of having small tail property

35. Varying frequency at query time The data structure was build for queries at the 0.5% level, but was then tested with queries ranged from 10% to 0.02%

36. Conclusions and extensions New method which can cope with dynamic dataset is proposed. It’s interesting to try to use the algorithm to compare the differences in frequencies between different datasets. Can we find combinatorial design that achieve the same properties but in deterministic construction for maintaining hot items?