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Evaluating top-k Queries over Web-Accessible Databases

Paper By: Amelie Marian, Nicolas Bruno, Luis Gravano

Presented By

Bhushan Chaudhari

University of Texas at Arlington

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Overview

More importance to top-k results Fagin’s algorithm talks about effective differentiation

between top-results by various ways e.g. FA, TA Here we discuss about more larger scenario in terms of web-

accessible databases Assumption: Mapping of keywords typed from search text

box to appropriate related modules (Web-accessible databases)

Larger query response times for probing web sources Tries to exploit the parallel access offered by web

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Introduction

We never expect exact answers from search engine but the most nearest possible tuples

Difference between querying a general search engine and dedicated search engine e.g. Google vs Amazon

The paper tries to define the problem using example of restaurants

“ problem of finding nearest available restaurants given the current place, rating and price”

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Approach

Thinking beyond relational databases Web accessible sources storing information about rating of

restaurants, map provider system etc.Rating => Zagat-Review websitePrice => New York Times’s NYT-Review websiteAddress => MapQuest website

Scenario where databases are geographically and functionally different but are related “in some way”

Assumption: 1. The interface required for accessing web sources is in place the dependency can be handled 2. The dependency constraints are handled

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Approach (continued ..)

Can be compared with a similar scenario with several multimedia systems which are more closely connected

Here we try to use the intrinsic parallel nature of web We issue probes to various sources in parallel and try to

improve upon the final query processing time Assumption: Mapping of keywords typed in search text box

to routing it to appropriate related modules (Web-accessible databases)

Larger query response times for probing web sources Tries to exploit the parallel access offered by web

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Data and Query models

The ordering is bases upon how closely the tuple matches with given query

Assignment of different weight to different attribute

Sources S-Source: Provides list of objects in order of their scores

e.g. Rating provider website Zagat-Review R-Source: Provides score of random object e.g. Map-Quest

for providing distance SR-Source: Source that provides both kind of access

U(t) : Upper bound score for t Uunseen : Score upper bound of any object not yet retrieved E(t) : Expected score for t

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Query Model (continued ..)

Getting all k scores with S sources can be expensive Therefore availability of SR sources is important for this

approach Initially we assume that all object know about all other

object If any score is not possible to get then that can be replaced

with some default valuee.g. Opening of any new restaurant, it might not be ranked by other referencing websites

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Sequential Query ProcessingStrategy

This strategy returns sorted unseen objects that might not be probed by other source

Or it can return already seen object with source that needs to be probed randomly for getting the corresponding score

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TA strategy

Processes top-k queries over SR sources Algorithm retrieves the next “best” object via sorted access Probes all its unknown scores via random access Computes the final score for object At any given time keeps track of top-k tuples available When no unretrived object can have a score higher than

current top k tuples, the solution is reached

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Improvements upon TA

The assumption for bounded buffer is removed and none of the object is discarded until algorithm returns

Because same objects might be referenced again by different SR source

For selection queries of nature,p1^p2^…^pn The calculation of each predicate pi can be expensive to

calculate Key idea is to order the evaluation to minimize expected

execution time The order is decided by,

Rank(pi) = 1-selectivity(pi)/cost-per-object(pi)

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Improvements upon TA (Continued..)

Let w1, w2, …w2 be the weights of sources D1,D2,..,Dn Let e(Ri) be the expected score of randomly picked object Ri Then the expected decrease in U(t) after probing Ri for

object t is,di = wi * (1-e(Ri))

We sort the sources in decreasing order of their rank, where rank for a source Di is defined as,Rank(Ri) = di/tR(Ri)

Thus we favor fast sources that might have large impact on final score of object

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Upper Strategy

Upper allows more flexible probes in which sorted and random accesses can be interleaved even when some objects have been partially probed

When a probe completes the Upper decides whether- to perform sorted-access probe on source to get new

objects to perform “most promising” random access probes on

some objects

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Upper Strategy (Continued..)

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Upper Strategy (Continued..)

Selection of further probes will again depend upon the weight for that source and our ranking function

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Parallel Query Processing Strategy

The query processing is bound to take long processing time Web databases exhibit high and variable latency Attempt to maximize the source-access parallelism to

minimize query processing time

Source Access Constraints Possibility of access restrictions, variance in loads and

network capabilities The number of parallel probes for source Di can be

controlled

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Parallel Query Processing Strategy

Adapting the TA strategy When a source Di becomes available pTA chooses which

object to probe for that source It can be optimized by not probing objects whose final

score cannot exceed that of the top-k objects already seen

The object is put on the “discarded” objects list pUpper Strategy

If t is expected to be one of the top-k objects all random accesses on sources for which t’s attribute score is missing will be considered

Otherwise only fastest probes expected to discard t are considered

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Evaluation Settings

Local sources Real Web Accessible sources

Mix of SR and R sources

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Evaluation Results

Sequential Algorithms – Local Database

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Evaluation Results

Sequential Algorithms –Web Database

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Evaluation Results

Parallel algorithms - Local Database

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Evaluation Results

Parallel algorithms - Web Database pUpper is faster than pTA pUpper carefully selects the probs for each

object It considers probing time and source

congestion to make probing choices per object-level

Results in better use of parallelism and faster query processing

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Conclusion

Probe interleaving greatly improves query execution time

Upper is desirable when source shows moderate to high random access time

The approach in this paper exploits the source access constraint of web very well

Extension of this model to capture more expressive web interfaces is possible

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References

Optimal Aggregation Algorithms for Middleware. PODS 2001

Ronald Fagin, Amnon Lotem, Moni Naor Evaluating Top-k Queries over Web-Accessible

Databases. ICDE 2002 (Compact Version) Nicolas Bruno, Luis Gravano, Amelie Marian

Evaluating Top-k Queries over Web-Accessible Databases. ACM 2004 (Full Version)

Nicolas Bruno, Luis Gravano, Amelie Marian