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Predicting Query Performance by Query-Drift Estimation
[chapter]
2009
Lecture Notes in Computer Science
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We argue that query-drift can potentially be estimated by measuring the diversity (e.g., standard deviation) of the retrieval scores of these documents. ...
Predicting query performance, that is, the effectiveness of a search performed in response to a query, is a highly important and challenging problem. ...
This paper is based upon work supported in part by Google's and IBM's faculty awards. ...
doi:10.1007/978-3-642-04417-5_30
fatcat:7fkrswosrbhizfifpgwzjkmyl4
Predicting Query Performance by Query-Drift Estimation
2012
ACM Transactions on Information Systems
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We argue that query-drift can potentially be estimated by measuring the diversity (e.g., standard deviation) of the retrieval scores of these documents. ...
Predicting query performance, that is, the effectiveness of a search performed in response to a query, is a highly important and challenging problem. ...
This paper is based upon work supported in part by Google's and IBM's faculty awards. ...
doi:10.1145/2180868.2180873
fatcat:sickq2o4ufhyzhhafoiv5ccqni
Predicting Query Performance via Classification
[chapter]
2010
Lecture Notes in Computer Science
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In an empirical study we compare the performance of class-based statistics to their language-model counterparts for two performance-related tasks: predicting query difficulty and expansion risk. ...
Our findings suggest that using class predictions can offer comparable performance to full language models while reducing computation overhead. ...
Our examination of model drift extends recent studies that find variance to be an important facet of predicting query performance. ...
doi:10.1007/978-3-642-12275-0_15
fatcat:fmupa3v3bng6hf47xftbmuvraa
A Unified Framework for Post-Retrieval Query-Performance Prediction
[chapter]
2011
Lecture Notes in Computer Science
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The query-performance prediction task is estimating the effectiveness of a search performed in response to a query in lack of relevance judgments. ...
The framework is based on using a pseudo effective and/or ineffective ranking as reference comparisons to the ranking at hand, the quality of which we want to predict. ...
This paper is based upon work supported in part by the Israel Science Foundation under grant no. 557/09, and by IBM's SUR award. ...
doi:10.1007/978-3-642-23318-0_4
fatcat:a5sglzqpordxpifpmwsjqvxizy
Query-performance prediction and cluster ranking
2012
Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12
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The first is query-performance prediction; i.e., estimating the effectiveness of a search performed in response to a query in the absence of relevance judgments. ...
Finally, we empirically demonstrate that using insights gained in work on query-performance prediction can help, in many cases, to improve the performance of a previously proposed cluster ranking method ...
The query performance prediction (QPP) task [3] is estimating the effectiveness of D res using some clustering algorithm. ...
doi:10.1145/2396761.2398666
dblp:conf/cikm/KurlandRS12
fatcat:mcoc43qfjnaq5p2sqzrbajnmfe
Aggregate Query Prediction under Dynamic Workloads
2019
2019 IEEE International Conference on Big Data (Big Data)
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The estimations are performed in milliseconds and are inexepensive as the mechanism learns from past analytical-query patterns. ...
Therefore, we introduce an adaptive Machine Learning mechanism which is light-weight, stored client-side, can estimate the answers of a variety of aggregate queries and can avoid the big data backend. ...
Our work contributes with monitoring and detecting real-time query patterns change based on approximating the prediction error, which differentiates with the previous concept drift methods by measuring ...
doi:10.1109/bigdata47090.2019.9006267
dblp:conf/bigdataconf/SavvaAT19
fatcat:aor2vcoddzeodhdz4ogomumwyq
Using Query Performance Predictors to Reduce Spoken Queries
[chapter]
2017
Lecture Notes in Computer Science
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The goal of query performance prediction is to estimate a query's retrieval effectiveness without user feedback. ...
Our results show that we are able to outperform the original spoken query by a small, but significant margin. ...
This work was supported in part by NSF grant IIS-1451668. ...
doi:10.1007/978-3-319-56608-5_3
fatcat:tiwpjztv3zbubms4dhbzf3gqxy
Estimating query performance using class predictions
2009
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '09
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In an empirical study we compare the performance of class-based statistics to their languagemodel counterparts for predicting two measures: query difficulty and expansion risk. ...
Our findings suggest that using class predictions can offer comparable performance to full language models while reducing computation overhead. ...
DISCUSSION & CONCLUSIONS We explored the use of new sources of evidence in estimating two important measures of query performance, query difficulty and expansion risk, by comparing two document representations ...
doi:10.1145/1571941.1572071
dblp:conf/sigir/Collins-ThompsonB09
fatcat:65a27ndzafgklmitrcdisqzrzq
Stream-based Active Learning with Verification Latency in Non-stationary Environments
[article]
2022
arXiv
pre-print
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We propose PRopagate (PR), a latency independent utility estimator which also predicts the requested, but not yet known, labels. ...
Stream-based Active Learning (AL) approaches address this problem by interactively querying a human expert to provide new data labels for the most recent samples, within a limited budget. ...
For readability, we only report the best performing query strategy (pal ) of Fig. 6 , and we compare the proposed PR with FS.B utility estimator. ...
arXiv:2204.06822v1
fatcat:uirw2p7e5zcxvm42vxpsn47b7i
Adaptive learning of aggregate analytics under dynamic workloads
2020
Future generations computer systems
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The estimations are performed in milliseconds are inexpensive and accurate as the mechanism learns from past analytical-query patterns. ...
Therefore, we introduce an adaptive, reciprocity-based Machine Learning mechanism which is light-weight, stored client-side, can estimate the answers of a variety of aggregate queries and can avoid the ...
Then the lower-bound and upper-bound for the given predicates is set and we execute the query over the restricted space given by the bounds over the selected columns by b using dataset d. ...
doi:10.1016/j.future.2020.03.063
fatcat:yiz6yhi4s5d63iv3746lya6cya
Selectivity correction with online machine learning
[article]
2020
arXiv
pre-print
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As an experiment, we teach models to improve the selectivity estimates made by PostgreSQL's cost model. ...
In the database community, many recent proposals have been made to improve selectivity estimation with batch machine learning methods. ...
We then multiplied each predicted correction factor with the selectivity estimate made by PostgreSQL's cost model. ...
arXiv:2009.09884v1
fatcat:fo6ieco7azaclpg6thgh2cglx4
A Bi-Criteria Active Learning Algorithm for Dynamic Data Streams
2018
IEEE Transactions on Neural Networks and Learning Systems
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A learner deliberately queries specific instances to tune the classifier's model using as few labels as possible. ...
Index Terms-Active learning (AL), Bayesian online learning, concept drift, data streams. 2162-237X ...
To sum up, BAL shows the ability to adapt to different types of drift but its performance may be affected by the data distribution. ...
doi:10.1109/tnnls.2016.2614393
pmid:27775910
fatcat:4bj6vywl3fhz7h7lpzg7gw2l6a
Adaptive Learning of Aggregate Analytics under Dynamic Workloads
[article]
2019
arXiv
pre-print
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Other Versions
The estimations are performed in milliseconds are inexpensive and accurate as the mechanism learns from past analytical-query patterns. ...
Therefore, we introduce an adaptive Machine Learning mechanism which is light-weight, stored client-side, can estimate the answers of a variety of aggregate queries and can avoid the big data backend. ...
Our work contributes with monitoring and detecting real-time query patterns change based on approximating the prediction error, which differentiates with the previous concept drift methods by measuring ...
arXiv:1908.04772v1
fatcat:o2jgnqnv7baezodnaxldyyz37a
Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts
2022
Proceedings of the 2022 International Conference on Management of Data
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We also show that faster model adaptation improves query performance by shortening the period for which imperfect query plans are picked by a query optimizer due to incorrect cardinality estimates. ...
Recent learned cardinality estimation (CE) models are vulnerable when query predicates or the underlying datasets drift from what the models were trained upon. ...
By improving the model accuracy, Warper improves the query performance in a sizable way; as the figure on the right shows, adapting to workload drift reduces cardinality estimation errors by up to 3× and ...
doi:10.1145/3514221.3526179
fatcat:dhht7qkthjertjhpeflzuqz3cu
A fuzzy kernel c-means clustering model for handling concept drift in regression
2017
2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
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Experiments show that the FKLL algorithm is better able to respond to drift as soon as the learning sets are updated, and is also suitable for dealing with reoccurring drift, when compared to the original ...
There is, however, a shortage of research on drift adaptation for regression cases in the literature. ...
ACKNOWLEDGMENT The work presented in this paper was supported by the Australian Research Council (ARC) under discovery grant DP150101645. ...
doi:10.1109/fuzz-ieee.2017.8015515
dblp:conf/fuzzIEEE/SongZLL17
fatcat:nfl4thwxdbhqlg6xrojbqjfppm
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