The Experts below are selected from a list of 27 Experts worldwide ranked by ideXlab platform
Olaf Hartig - One of the best experts on this subject based on the ideXlab platform.
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squin a traversal based query execution system for the web of linked Data
International Conference on Management of Data, 2013Co-Authors: Olaf HartigAbstract:The World Wide Web (WWW) currently evolves into a Web of Linked Data where content providers publish and link their Data as they have done with hypertext for the last 20 years. We understand this emerging Dataspace as a huge, distributed Database which is -at best- partially known to query execution systems. To tap the full potential of the Web, such a system must be able to answer a query using Data from initially unknown Data sources. For this purpose, traditional query execution paradigms are unsuitable because those assume a fixed set of Potentially Relevant Data sources beforehand. We demonstrate the query execution system SQUIN which implements a novel query execution approach. The main idea is to integrate the traversal of Data links into the result construction process. This approach allows the execution engine to discover Potentially Relevant Data during the query execution. In our demonstration, attendees can query the Web of Linked Data using SQUIN and, thus, learn about the new query execution approach. Furthermore, attendees can experience the suitability of the approach for Web applications by using a simple, Linked Data based mash-up implemented on top of SQUIN.
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zero knowledge query planning for an iterator implementation of link traversal based query execution
Extended Semantic Web Conference, 2011Co-Authors: Olaf HartigAbstract:Link traversal based query execution is a new query execution paradigm for the Web of Data. This approach allows the execution engine to discover Potentially Relevant Data during the query execution and, thus, enables users to tap the full potential of the Web. In earlier work we propose to implement the idea of link traversal based query execution using a synchronous pipeline of iterators. While this idea allows for an easy and efficient implementation, it introduces restrictions that cause less comprehensive result sets. In this paper we address this limitation. We analyze the restrictions and discuss how the evaluation order of a query may affect result set size and query execution costs. To identify a suitable order, we propose a heuristic for our scenario where no a-priory information about Relevant Data sources is present. We evaluate this heuristic by executing real-world queries over the Web of Data.
Danielle J Navarro - One of the best experts on this subject based on the ideXlab platform.
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selective sampling and inductive inference drawing inferences based on observed and missing evidence
Cognitive Psychology, 2019Co-Authors: Brett K Hayes, Stephanie Banner, Suzy Forrester, Danielle J NavarroAbstract:Abstract We propose and test a Bayesian model of property induction with evidence that has been selectively sampled leading to “censoring” or exclusion of Potentially Relevant Data. A core model prediction is that identical evidence samples can lead to different patterns of inductive inference depending on the censoring mechanisms that cause some instances to be excluded. This prediction was confirmed in four experiments examining property induction following exposure to identical samples that were subject to different sampling frames. Each experiment found narrower generalization of a novel property when the sample instances were selected because they shared a common property (property sampling) than when they were selected because they belonged to the same category (category sampling). In line with model predictions, sampling frame effects were moderated by the addition of explicit negative evidence (Experiment 1), sample size (Experiment 2) and category base rates (Experiments 3–4). These Data show that reasoners are sensitive to constraints on the sampling process when making property inferences; they consider both the observed evidence and the reasons why certain types of evidence has not been observed.
Brett K Hayes - One of the best experts on this subject based on the ideXlab platform.
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selective sampling and inductive inference drawing inferences based on observed and missing evidence
Cognitive Psychology, 2019Co-Authors: Brett K Hayes, Stephanie Banner, Suzy Forrester, Danielle J NavarroAbstract:Abstract We propose and test a Bayesian model of property induction with evidence that has been selectively sampled leading to “censoring” or exclusion of Potentially Relevant Data. A core model prediction is that identical evidence samples can lead to different patterns of inductive inference depending on the censoring mechanisms that cause some instances to be excluded. This prediction was confirmed in four experiments examining property induction following exposure to identical samples that were subject to different sampling frames. Each experiment found narrower generalization of a novel property when the sample instances were selected because they shared a common property (property sampling) than when they were selected because they belonged to the same category (category sampling). In line with model predictions, sampling frame effects were moderated by the addition of explicit negative evidence (Experiment 1), sample size (Experiment 2) and category base rates (Experiments 3–4). These Data show that reasoners are sensitive to constraints on the sampling process when making property inferences; they consider both the observed evidence and the reasons why certain types of evidence has not been observed.
Suzy Forrester - One of the best experts on this subject based on the ideXlab platform.
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selective sampling and inductive inference drawing inferences based on observed and missing evidence
Cognitive Psychology, 2019Co-Authors: Brett K Hayes, Stephanie Banner, Suzy Forrester, Danielle J NavarroAbstract:Abstract We propose and test a Bayesian model of property induction with evidence that has been selectively sampled leading to “censoring” or exclusion of Potentially Relevant Data. A core model prediction is that identical evidence samples can lead to different patterns of inductive inference depending on the censoring mechanisms that cause some instances to be excluded. This prediction was confirmed in four experiments examining property induction following exposure to identical samples that were subject to different sampling frames. Each experiment found narrower generalization of a novel property when the sample instances were selected because they shared a common property (property sampling) than when they were selected because they belonged to the same category (category sampling). In line with model predictions, sampling frame effects were moderated by the addition of explicit negative evidence (Experiment 1), sample size (Experiment 2) and category base rates (Experiments 3–4). These Data show that reasoners are sensitive to constraints on the sampling process when making property inferences; they consider both the observed evidence and the reasons why certain types of evidence has not been observed.
Stephanie Banner - One of the best experts on this subject based on the ideXlab platform.
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selective sampling and inductive inference drawing inferences based on observed and missing evidence
Cognitive Psychology, 2019Co-Authors: Brett K Hayes, Stephanie Banner, Suzy Forrester, Danielle J NavarroAbstract:Abstract We propose and test a Bayesian model of property induction with evidence that has been selectively sampled leading to “censoring” or exclusion of Potentially Relevant Data. A core model prediction is that identical evidence samples can lead to different patterns of inductive inference depending on the censoring mechanisms that cause some instances to be excluded. This prediction was confirmed in four experiments examining property induction following exposure to identical samples that were subject to different sampling frames. Each experiment found narrower generalization of a novel property when the sample instances were selected because they shared a common property (property sampling) than when they were selected because they belonged to the same category (category sampling). In line with model predictions, sampling frame effects were moderated by the addition of explicit negative evidence (Experiment 1), sample size (Experiment 2) and category base rates (Experiments 3–4). These Data show that reasoners are sensitive to constraints on the sampling process when making property inferences; they consider both the observed evidence and the reasons why certain types of evidence has not been observed.