Evaluating Topk Queries over WebAccessible Databases

1
Evaluating Top-k Queries over Web-Accessible
Databases

• Nicolas Bruno
• Luis Gravano
• Amélie Marian
• Columbia University

2
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
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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.

4
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.

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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

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Data Model

• Top-k Query assignment of weights and target
  values to attributes

close to address
preferred price
preferred rating
weights
Combined in scoring function
price most important attribute
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Sorted Access Source S

• Return objects sorted by scores for a given
  query.
• Example Zagat

GetNextS interface
S-Source Access Time tS(S)
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Random Access Source R

• Return the score of a given object for a given
  query.
• Example MapQuest

GetScoreR interface
R-Source Access Time tR(R)
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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.)

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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).

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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

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TA Algorithm for SR-Sources
Fagin, Lotem, and Naor (PODS 2001)

• 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).

Does NOT handle R-Sources
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Our Adaptation of TA Algorithm for R-Sources
TA-Adapt

• Perform sorted access to S-Source S.
• Probe every R-Source Ri 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).

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An Example Execution of TA-Adapt
Threshold 1
Total Execution Time 9
tS(S)tR(R1)tR(R2)1, w, k1 Final
Score (3.scoreZagat 2.scoreMQ 1.scoreNYT)/6
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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.

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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.
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An Example Execution of Upper

Threshold 1
Total Execution Time 6
tS(S)tR(R1)tR(R2)1, w, k1 Final
Score (3.scoreZagat 2.scoreMQ 1.scoreNYT)/6
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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 Ri 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
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Selecting the Best Source

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

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Experimental Setting Synthetic Data

• Attribute scores randomly generated (three data
  sets uniform, gaussian and correlated).
• tR(Ri) integer between 1 and 10.
• tS(S) ? 0.1, 0.2,,1.0.
• Query execution time ttotal
• Default k50, 10000 objects, uniform data.
• Results average ttotal of 100 queries.
• Optimal assumes complete knowledge
• (unrealistic, but useful performance bound)

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Experiments Varying Number of Objects Requested k
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Experiments Varying Number of Database Objects N
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Experimental Setting Real Web Data

• S-Source Verizon Yellow Pages
• (sorted by distance)
• R-Sources

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Experiments Real-Web Data
of Random Accesses
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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.

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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

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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

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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

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Real-web Experiments
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Real-web Experiments with Adaptive Time
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Relaxing the Source Model
TA-EP
Upper
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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

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Optimality

• TA instance optimal over
• Algorithms that do not make wild guesses.
• Databases that satisfy the distinctness property.
• TAZ instance optimal over
• Algorithms that do not make wild guesses.
• No complexity analysis of our algorithms, but
  experimental evaluation instead