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F-IVM: analytics over relational databases under updates.
VLDB J
November 2023
Department of Informatics, University of Zurich, Zurich, Switzerland.
This article describes F-IVM, a unified approach for maintaining analytics over changing relational data. We exemplify its versatility in four disciplines: processing queries with group-by aggregates and joins; learning linear regression models using the covariance matrix of the input features; building Chow-Liu trees using pairwise mutual information of the input features; and matrix chain multiplication. F-IVM has three main ingredients: higher-order incremental view maintenance; factorized computation; and ring abstraction.
View Article and Find Full Text PDFParsing Clinical Trial Eligibility Criteria for Cohort Query by a Multi-Input Multi-Output Sequence Labeling Model.
Proceedings (IEEE Int Conf Bioinformatics Biomed)
December 2023
School of Information, Florida State University, Tallahassee, USA.
To enable electronic screening of eligible patients for clinical trials, free-text clinical trial eligibility criteria should be translated to a computable format. Natural language processing (NLP) techniques have the potential to automate this process. In this study, we explored a supervised multi-input multi-output (MIMO) sequence labelling model to parse eligibility criteria into combinations of fact and condition tuples.
View Article and Find Full Text PDFA Cardinality Estimator in Complex Database Systems Based on TreeLSTM.
Sensors (Basel)
August 2023
College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
Cardinality estimation is critical for database management systems (DBMSs) to execute query optimization tasks, which can guide the query optimizer in choosing the best execution plan. However, traditional cardinality estimation methods cannot provide accurate estimates because they cannot accurately capture the correlation between multiple tables. Several recent studies have revealed that learning-based cardinality estimation methods can address the shortcomings of traditional methods and provide more accurate estimates.
View Article and Find Full Text PDFInfrastructure tools to support an effective Radiation Oncology Learning Health System.
J Appl Clin Med Phys
October 2023
Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia, USA.
Purpose: Radiation Oncology Learning Health System (RO-LHS) is a promising approach to improve the quality of care by integrating clinical, dosimetry, treatment delivery, research data in real-time. This paper describes a novel set of tools to support the development of a RO-LHS and the current challenges they can address.
Methods: We present a knowledge graph-based approach to map radiotherapy data from clinical databases to an ontology-based data repository using FAIR concepts.
ConSQL: A Database Language for Contex-aware Conversational Cooperative Query Answering.
Res Sq
July 2023
Department of Computer Science, University of Idaho, 875 Perimeter Drive, Moscow, 83844, ID, USA.
In conversational query answering systems, context plays a significant role in accurately and meaningfully carrying it forward. In many chatbots, such as in Expedia, the discussion quickly degenerates into circling back to restarting the conversation or to inviting a live agent to intervene because the bot could not grasp the context. Contexts shorten interactions by way of implied query constraints to narrow search and to not repeat them in subsequent queries.
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