Evaluating Top-k Queries over Web-Accessible Databases Nicolas Bruno Luis Gravano Amélie Marian Columbia University

2/27/20022 “Top- k ” Queries Natural in Many Scenarios Example: NYC Restaurant Recommendation Service. Goal: Find best restaurants for a user: Close to address: “2290 Broadway” Price around $25 Good rating Query: Specification of Flexible Preferences Answer: Best k Objects for Distance Function

2/27/20023 Attributes often Handled by External Sources MapQuest returns the distance between two addresses. NYTimes Review gives the price range of a restaurant. Zagat gives a food rating to the restaurant.

2/27/20024 “Top- k ” Query Processing Challenges Attributes handled by external sources (e.g., MapQuest distance). External sources exhibit a variety of interfaces (e.g., NYTimes Review, Zagat ). Existing algorithms do not handle all types of interfaces.

2/27/20025 Processing Top- k Queries over Web-Accessible Data Sources Data and query model Algorithms for sources with different interfaces Our new algorithm: Upper Experimental results

2/27/20026 Data Model Top-k Query: assignment of weights and target values to attributes preferred price close to address preferred rating weights: price: most important attribute Combined in scoring function

2/27/20027 Sorted Access Source S Return objects sorted by scores for a given query. Example: Zagat GetNext S interface S-Source Access Time: tS(S)

2/27/20028 Random Access Source R Return the score of a given object for a given query. Example: MapQuest R-Source Access Time: tR(R) GetScore R interface

2/27/20029 Query Model Attributes scores between 0 and 1. Sequential access to sources. Score Ties broken arbitrarily. No wild guesses. One S-Source (or SR-Source ) and multiple R-sources. (More on this later.)

2/27/ Query Processing Goals Processing top- k queries over R-Sources. Returning exact answer to top- k query q. Minimizing query response time. Naïve solution too expensive (access all sources for all objects).

2/27/ Example: NYC Restaurants S-Source: Zagat : restaurants sorted by food rating. R-Sources: MapQuest: distance between two input addresses. User address: “2290 Broadway” NYTimes Review: price range of the input restaurant. Target Value: $25

2/27/ TA Algorithm for SR-Sources Perform sorted access sequentially to all SR-Sources Completely probe every object found for all attributes using random access. Keep best k objects. Stop when scores of best k objects are no less than maximum possible score of unseen objects (threshold). Fagin, Lotem, and Naor (PODS 2001) Does NOT handle R-Sources

2/27/ Our Adaptation of TA Algorithm for R-Sources: TA-Adapt Perform sorted access to S-Source S. Probe every R-Source R i for newly found object. Keep best k objects. Stop when scores of best k objects are no less than maximum possible score of unseen objects (threshold).

2/27/ An Example Execution of TA-Adapt ObjectS(Zagat)R 1 (MQ)R 2 (NYT)Final Score tS(S)=tR(R 1 )=tR(R 2 )=1, w=, k=1 Final Score = (3. score Zagat + 2. score MQ + 1. score NYT )/6 Threshold = 1 Total Execution Time = 9 o1o1 GetNext S (q) Threshold = GetScore R1 (q,o 1 ) Threshold = GetScore R2 (q,o 1 ) Threshold = GetNext S (q) Threshold = 0.9 o2o2 0.8 GetScore R1 (q,o 2 ) Threshold = GetScore R2 (q,o 2 ) Threshold = GetNext S (q) Threshold = o3o GetScore R1 (q,o 3 ) Threshold = GetScore R2 (q,o 3 ) Threshold =

2/27/ Improvements over TA-Adapt Add a shortcut test after each random- access probe ( TA-Opt ). Exploit techniques for processing selections with expensive predicates ( TA-EP ). Reorder accesses to R-Sources. Best weight/time ratio.

2/27/ The Upper Algorithm Selects a pair (object,source) to probe next. Based on the property: The object with the highest upper bound will be probed before top-k solution is reached. Object is one of top- k objectsObject is not one of top- k objects

2/27/ Threshold = 1 An Example Execution of Upper ObjectUpper BoundS(Zagat)R 1 (MQ)R 2 (NYT)Final Score Total Execution Time = GetNext S (q) Threshold = 0.95 o1o GetScore R1 (q,o 1 ) Threshold = 0.95 o2o GetNext S (q) Threshold = GetScore R1 (q,o 2 ) Threshold = o3o GetNext S (q) Threshold = GetScore R2 (q, o 2 ) Threshold = tS(S)=tR(R 1 )=tR(R 2 )=1, w=, k=1 Final Score = (3. score Zagat + 2. score MQ + 1. score NYT )/6

2/27/ The Upper Algorithm Choose object with highest upper bound. If some unseen object can have higher upper bound: Access S-Source S Else: Access best R-Source R i for chosen object Keep best k objects If top- k objects have final values higher than maximum possible value of any other object, return top- k objects. Interleaves accesses on objects

2/27/ Selecting the Best Source Upper relies on expected values to make its choices. Upper computes “best subset” of sources that is expected to: 1.Compute the final score for k top objects. 2.Discard other objects as fast as possible. Upper chooses best source in “best subset”. Best weight/time ratio.

2/27/ Experimental Setting: Synthetic Data Attribute scores randomly generated (three data sets: uniform, gaussian and correlated). tR(R i ) : integer between 1 and 10. tS(S)  {0.1, 0.2,…,1.0}. Query execution time: t total Default: k =50, objects, uniform data. Results: average t total of 100 queries. Optimal assumes complete knowledge (unrealistic, but useful performance bound)

2/27/ Experiments: Varying Number of Objects Requested k

2/27/ Experiments: Varying Number of Database Objects N

2/27/ Experimental Setting: Real Web Data S-Source: Verizon Yellow Pages (sorted by distance) R-Sources: Subway Navigator Subway time Altavista Popularity MapQuest Driving time NYTimes Review Food and price ratings Zagat Food, Service, Décor and Price ratings

2/27/ Experiments: Real-Web Data # of Random Accesses

2/27/ Evaluation Conclusions TA-EP and TA-Opt much faster than TA-Adapt. Upper significantly better than all versions of TA. Upper close to optimal. Real data experiments: Upper faster than TA adaptations.

2/27/ Conclusion Introduced first algorithm for top- k processing over R-Sources. Adapted TA to this scenario. Presented new algorithms: Upper and Pick (see paper) Evaluated our new algorithms with both real and synthetic data. Upper close to optimal

2/27/ Current and Future Work Relaxation of the Source Model Current source model limited Any number of R-Sources and SR-Sources Upper has good results even with only SR-Sources Parallelism Define a query model for parallel access to sources Adapt our algorithms to this model Approximate Queries

2/27/ References Top-k Queries: Evaluating Top-k Selection Queries, S. Chaudhuri and L. Gravano. VLDB 1999 TA algorithm: Optimal Aggregation Algorithms for Middleware, R. Fagin, A. Lotem, and M. Naor. PODS 2001 Variations of TA: Query Processing Issues on Image (Multimedia) Databases, S. Nepal and V. Ramakrishna. ICDE 1999 Optimizing Multi-Feature Queries for Image Databases, U. Güntzer, W.-T. Balke, and W.Kießling. VLDB 2000 Expensive Predicates Predicate Migration: Optimizing queries with Expensive Predicates, J.M. Hellerstein and M. Stonebraker. SIGMOD 1993

2/27/ Real-web Experiments

2/27/ Real-web Experiments with Adaptive Time

2/27/ Relaxing the Source Model Upper TA-EP

2/27/ Upcoming Journal Paper Variations of Upper Select best source Data Structures Complexity Analysis Relaxing Source Model Adaptation of our Algorithms New Algorithms Variations of Data and Query Model to handle real web data

2/27/ Optimality TA instance optimal over: Algorithms that do not make wild guesses. Databases that satisfy the distinctness property. TA Z instance optimal over: Algorithms that do not make wild guesses. No complexity analysis of our algorithms, but experimental evaluation instead