1. Stream Data Operator Ordering  Query Optimization Query Index

2. Query Optimization Operator Ordering Problem Assumption –A query consists of a set of commutative filters –Filter Drop or Select Overall processing costs can vary widely across different filter order Ex –Filter O 1 drops 1, 3, 5 –Filter O 2 drops 2, 4, 6 –Let an input stream be 2, 4, 6. –The cost of Operator Order O 2,O 1 is cheaper than that of O 1, O 2

3. Operator ordering Operator Ordering –Choose efficient order –The optimal order is changed over time. Eddy[4] –Tuple routing Technique –An operator dropping many tuples has high priority

4. Operator ordering A-Greedy[9] –Query Cost C –d(i|j) denotes the conditional probability that i th operator O f(i) will drop a tuple e, given that e was not dropped by any of operators O f(1), O f(2),..., O f(j). –t i represents the expect time for O f(i) to process one tuple Goal  Minimized C –Greedy heuristic rule which rearrange the operator order satisfying the following formula 

5. Operator ordering A-Greedy –Profiler To obtain conditional selectivity d(i|j), profiling is used. In profiling, a tuple e which is dropped during processing is selected with probability p Then, profiler artificially applies e to all operators and generate a profile tuple whose attribute b i is 1 if O i drops e –Reoptimizer Keeps the operator order Maintains a matrix view Ex) first row: O4 drops most tuples, second row : reports the numbers of tuples which are not dropped by O4 droped by O1,O3, and O2. Profile matrix view

6. Operator ordering Problem of A-Greedy –Profiling overhead –A normal tuple may be dropped by an operator, but a tuple for profiling is applied to all operators. –In other words, when 10% data of input are profiled, the increment of system overheads is greater than 10%.

7. Push-based data source High and unpredictable data rates Problem –Load > Capacity –Load Shedding: eliminate excess load by dropping data Load Shedding[8]

8. Aurora App QoS............ App QoS...... App QoS............        Slide Tumble                       App Tumble App

9. QoS: Aurora QoS Specifies “Utility” Of Imperfect Query Results Delay-Based (specify utility of late results) Delivery-Based, Value-Based (specify utility of partial results) QoS Influences… Scheduling, Storage Management, Load Shedding B A C

10. Load Shedding: Aurora Two Load Shedding Techniques: Random Tuple Drops Add DROP box to network (DROP a special case of FILTER) Position to affect queries w/ tolerant delivery-based QoS reqts Semantic Load Shedding FILTER values with low utility (acc to value-based QoS)

11. Load Coefficient Load Shedding: Aurora

12. Best location of Drop operator –Maximize cycle gain, minimize utility loss –Cycle gain: processor cycles gained fro each percentage of tuples dropped G(x) = R*(x*L-D) R: input rate, L is load coefficient –Loss/Gain ratio  the smaller, the better Load Shedding: Aurora Drop x% RL D cycles/tuple Loss-tolerant graph

13. Load Shedding  where, when, how much. –Where ->[8], How much  [26[ Particularly, in multi-Query Environments Ex) Two Query, Q1 and Q2 Data size = 24, Processing cost per tuple = c Overall cost = 24*2*c = 48c System capability = 30c Goal : Min G =  ((1-r p )/r p )*f p where r p is the fraction to be considered for a query Q p f p is actual frequency of tuples to be result. Assume f a =1, f b =4 Plan 1) Uniform  r a = r b =15  G = 3 Plan 2) Proportional  fb/fa = 4  6:24  r a = 6/24, r b = 24/24  G=3 Plan 3) Optimal  r a = 10/24, r b 20/24  G = 2.2 Load Shedding[26]

14. Estimate f p –Let b i = 1 if a tuple t i is a query result. Otherwise b i =0 –f p =  b i –Each tuple t i is processed with a probability r q and discard with a probability 1-r q –Let X i = b i /r q with a probability rq and X i = 0 with a probability 1-r q –Estimate f p =  X i E(f p ) = E(  X i ) =  b i = f p –Var(f p ) =((1-r q )/r q ) *f p Variance means average error e p Load Shedding[26]

15. –Let S is a set of query, |S|= N –Error vector E = [e 1,…, e N ] –Importance of queries V = [v 1,…,v N ] –Resource Cost C = [c 1,…c N ] –Processing ratio r = [r 1,…, r N ] –Total resource limitation = L –Data Size = W Goal :  Constraint r  C =  r i *c i <= L/W Minimize G = E  V=  e i *v i –Apply e q =((1-r q )/r q ) *f p –G= -  f i *v i + G 1 where G 1 =  (f j *v j )/r j –To minimize G, it suffices to minimize G 1 –  non-linear programming(separable and convex resource allocation) –  Sorting  O(NlogN) –In the paper, suggest O(N) algorithm Load Shedding[26]

16. Query Index Invoke all query whenever data arrives –  Query Index Property of Stream Data –Locality –ex. the temperature in near future will be similar to the current temperature –Some or all queries will be reused in near future

17. Query Index –The number of registered queries is huge –Overhead to find out the proper queries which can evaluate the input stream item. –IBS(Interval Binary Search Tree) –R-Tree Multi-Dimensional data access method Range conditions of Queries are overlaped. Many nodes should be traversed due to a large amount of overlap of query conditions

18. Query Index IBS[10] –Use balanced binary search tree for query indexes –When a data item arrives, balanced binary search trees and hash table are probed with the value of tuples –Not appropriate to general range queries which have two bounded conditions Each condition is indexed in individual binary tree.  unnecessary partial result Query Conditions q1: R.a  1 and R.a < 10 q2: R.a > 5 q3: R.a > 7 q4: R.a = 4 q5: R.a = 6 5 17 q1q2q3 10 q1 1=q1 4=q4 6=q5 Group Filter for R.a < > = !=

19. Query Processing Based on Spatial Join[26] –Query- represented as a region –Data – represented as a point Batch mode Accumulate arriving data elements and process continuous queries  Set of data  represented as a region –Uses Spatial Indexes for data set and queries Query Index

20. A set of data  region Query  region –  compute overlap relationships In [26], Use Corner Transformation –n-dim object  2n-dim point Query Index

21. –BMQ-Index [11] DMR List is a list of DN i –DN i = –DR i is a matching Region (b i-1, b i ) –+DQSet is a set of queries whose lower bound l k = b i-1 –-DQSet is a set of queries whose upper bound u k = b i-1 A stream table keeps the recently accessed DN i Query Conditions q1: R.a  1 and R.a < 10 q2: R.a > 5 q3: R.a > 7 q4: R.a = 4 q5: R.a = 6 1456710inf q1 q2 q3 DN 1 DN 2 DN 3 DN 4 DN 5 DN 6 DN 6 {+q1} {+q2} {+q3} {-q1} {-q2,-q3} stream table

22. Query Index QSet(t) is a set of queries for data v t Let v t be in DN j and v t+1 be in DN h, –e.g., b j-1 <= v t < b j and b h-1 <= v t+1 < b h Then QSet(t+1) is obtained as follows For example v t = 4.5, QSet(t) = {q1} if v t+1 = 12, –U+DQSet = {q2,q3} –U-DQSet = {q1} –Thus QSet(t+1) = {q2,q3} 1456710inf q1 q2 q3 DN 1 DN 2 DN 3 DN 4 DN 5 DN 6 DN 6 {+q1} {+q2} {+q3} {-q1} {-q2,-q3} stream table

23. Query Index Problem of BMQ-Index –If the forthcoming data is quite different from the current data, many DRM nodes should be retrieved like a linear search –Support only (l, u) style condition. q4 and q5 is not registered –does not work correctly on the boundary condition. 1456710inf q1 q2 q3 DN 1 DN 2 DN 3 DN 4 DN 5 DN 6 DN 6 {+q1} {+q2} {+q3} {-q1} {-q2,-q3} stream table Let v t = 5.5 and QSet(t) = {q1,q2} If v t+1 = 5, Then QSet(t+1) is also {q1,q2} But, actual query set of v t+1 is {q1}.

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