We contribute a novel, query-driven, function estimation model of analyst-defined data subspace cardinality. The proposed estimation model is highly accurate in ...
We contribute a novel, query-driven, function estimation model of analyst-defined data subspace cardinality. The proposed estimation model is highly accurate in ...
We contribute a novel, query-driven, function estimation model of analyst-defined data subspace cardinality. The proposed estimation model is highly accurate in ...
A novel, query-driven, function estimation model of analyst-defined data subspace cardinality, which is an attractive solution when data-driven statistical ...
Fundamental to many predictive analytics tasks is the ability to predict the number of data items fetched in analytics queries. This is crucial for data ...
This page is a summary of: Query-Driven Learning for Predictive Analytics of Data Subspace Cardinality , ACM Transactions on Knowledge Discovery from Data ...
The aim of this work is to study mechanisms which can democratize analytics, in the sense of making them affordable, while at the same time ensuring high ...
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Abstract—We study a novel solution to executing aggregation. (and specifically COUNT) queries over large-scale data. The proposed solution is generally ...
Abstract—We study a novel solution to executing aggregation. (and specifically COUNT) queries over large-scale data. The proposed solution is generally ...
Sep 19, 2024 · Our method engages the most suitable nodes by predicting their relevant distributed data and learning predictive models per query.