scholarly journals Efficient Uncertainty Tracking for Complex Queries with Attribute-level Bounds

2021 ◽  
Author(s):  
Su Feng ◽  
Boris Glavic ◽  
Aaron Huber ◽  
Oliver A. Kennedy
Keyword(s):  
Attribute Level ◽  
Complex Queries

scholarly journals GeoIRIS: Geospatial Information Retrieval and Indexing System—Content Mining, Semantics Modeling, and Complex Queries

2007 ◽  
Vol 45 (4) ◽  
pp. 839-852 ◽  
Author(s):  
Chi-Ren Shyu ◽  
Matt Klaric ◽  
Grant J. Scott ◽  
Adrian S. Barb ◽  
Curt H. Davis ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Geospatial Information ◽  
Complex Queries ◽  
Indexing System ◽  
Content Mining

scholarly journals A Residual-Based Approach to Validate Q-Matrix Specifications

2017 ◽  
Vol 41 (4) ◽  
pp. 277-293 ◽  
Author(s):  
Jinsong Chen
Keyword(s):  
Real Life ◽  
Attribute Level ◽  
Simulation Studies ◽  
Cognitive Diagnosis Models ◽  
Modeling Process ◽  
Response Formats ◽  
Item Level ◽  
Q Matrix ◽  
Matrix Construction ◽  
Test Level

Q-matrix validation is of increasing concern due to the significance and subjective tendency of Q-matrix construction in the modeling process. This research proposes a residual-based approach to empirically validate Q-matrix specification based on a combination of fit measures. The approach separates Q-matrix validation into four logical steps, including the test-level evaluation, possible distinction between attribute-level and item-level misspecifications, identification of the hit item, and fit information to aid in item adjustment. Through simulation studies and real-life examples, it is shown that the misspecified items can be detected as the hit item and adjusted sequentially when the misspecification occurs at the item level or at random. Adjustment can be based on the maximum reduction of the test-level measures. When adjustment of individual items tends to be useless, attribute-level misspecification is of concern. The approach can accommodate a variety of cognitive diagnosis models (CDMs) and be extended to cover other response formats.

scholarly journals Composing Questions through Conceptual Authoring

2007 ◽  
Vol 33 (1) ◽  
pp. 105-133 ◽  
Author(s):  
Catalina Hallett ◽  
Donia Scott ◽  
Richard Power
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Free Text ◽  
Risk Averse ◽  
Proof Of Concept ◽  
Concept System ◽  
Complex Queries ◽  
Extensive Training ◽  
Question Answering Systems ◽  
Medical Histories

This article describes a method for composing fluent and complex natural language questions, while avoiding the standard pitfalls of free text queries. The method, based on Conceptual Authoring, is targeted at question-answering systems where reliability and transparency are critical, and where users cannot be expected to undergo extensive training in question composition. This scenario is found in most corporate domains, especially in applications that are risk-averse. We present a proof-of-concept system we have developed: a question-answering interface to a large repository of medical histories in the area of cancer. We show that the method allows users to successfully and reliably compose complex queries with minimal training.

scholarly journals Design principles of the Metadata Querying Language (MQL) implemented in the ATLAS Metadata Interface (AMI) ecosystem

2020 ◽  
Vol 245 ◽  
pp. 04044
Author(s):  
Jérôme Fulachier ◽  
Jérôme Odier ◽  
Fabian Lambert
Keyword(s):  
Query Language ◽  
Design Principles ◽  
Domain Specific Language ◽  
Specific Language ◽  
Domain Specific ◽  
Complex Queries ◽  
Structured Query Language

This document describes the design principles of the Metadata Querying Language (MQL) implemented in ATLAS Metadata Interface (AMI), a metadata-oriented domain-specific language allowing to query databases without knowing the relation between tables. With this simplified yet generic grammar, MQL permits writing complex queries more simply than with Structured Query Language (SQL).

Fauce

2021 ◽  
Vol 14 (11) ◽  
pp. 1950-1963
Author(s):  
Jie Liu ◽  
Wenqian Dong ◽  
Qingqing Zhou ◽  
Dong Li
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
The State ◽  
Light Weight ◽  
Critical Problem ◽  
Estimation Errors ◽  
Cardinality Estimation ◽  
Complex Queries ◽  
Uncertainty Information ◽  
Deep Learning Model

Cardinality estimation is a fundamental and critical problem in databases. Recently, many estimators based on deep learning have been proposed to solve this problem and they have achieved promising results. However, these estimators struggle to provide accurate results for complex queries, due to not capturing real inter-column and inter-table correlations. Furthermore, none of these estimators contain the uncertainty information about their estimations. In this paper, we present a join cardinality estimator called Fauce. Fauce learns the correlations across all columns and all tables in the database. It also contains the uncertainty information of each estimation. Among all studied learned estimators, our results are promising: (1) Fauce is a light-weight estimator, it has 10× faster inference speed than the state of the art estimator; (2) Fauce is robust to the complex queries, it provides 1.3×--6.7× smaller estimation errors for complex queries compared with the state of the art estimator; (3) To the best of our knowledge, Fauce is the first estimator that incorporates uncertainty information for cardinality estimation into a deep learning model.

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