Data Query Research Articles

Optimized Application of the Decision Tree ID3 Algorithm Based on Big Data in Sports Performance Management

In the rapidly evolving landscape of sports performance management, the integration of Big Data analytics has become a game-changer. This paper established a sports performance management system on big data decision tree ID3 algorithm and analyzed the accuracy and time of the improved decision tree ID3 algorithm in sports performance management. The research results show that the data query and data display functions are widely used in the system, and the utilization rate of each function of big data in the system can be clearly seen. In the accuracy of sports performance management, the improved ID3 algorithm is obviously higher than the ID3 algorithm, and the accuracy is improved by 4.16%. In the average time of sports performance management, the improved ID3 algorithm takes less time than the ID3 algorithm, and the time is reduced by 31.50%. The big data technology used has greatly changed the performance information management system of colleges or sports events and provides a research reference for the management of sports performance.

Spatio-Temporal Big Data Collaborative Storage Mechanism Based on Incremental Aggregation Subvector Commitment in On-Chain and Off-Chain Systems

As mobile internet and Internet of Things technologies rapidly advance, the amount of spatio-temporal big data have surged, and efficient and secure management solutions are urgently needed. Although cloud storage provides convenience, it also brings significant data security challenges. Blockchain technology is an ideal choice for processing large-scale spatio-temporal big data due to its unique security features, but its storage scalability is limited because the data need to be replicated throughout the network. To solve this problem, a common approach is to combine blockchain with off-chain storage to form a hybrid storage blockchain. However, these solutions cannot guarantee the authenticity, integrity, and consistency of on-chain and off-chain data storage, and preprocessing is required in the setup phase to generate public parameters proportional to the data length, which increases the computational burden and reduces transmission efficiency. Therefore, this paper proposes a collaborative storage mechanism for spatio-temporal big data based on incremental aggregation sub-vector commitments, which uses vector commitment binding technology to ensure the secure storage of on-chain and off-chain data. By generating public parameters of fixed length, the computational complexity is reduced and the communication efficiency is improved while improving the security of the system. In addition, we design an aggregation proof protocol that integrates aggregation algorithms and smart contracts to improve the efficiency of data query and verification and ensure the consistency and integrity of spatio-temporal big data storage. Finally, simulation experiments verify the correctness and security of the proposed protocol, providing a solid foundation for the blockchain-based spatio-temporal big data storage system.

Integrating query data for enhanced traffic forecasting: A Spatio-Temporal Graph Attention Convolution Network approach with delay modeling

Accurate prediction of road traffic conditions is essential for the effectiveness of Intelligent Transportation Systems (ITS) and the advancement of smart cities. While existing methodologies mainly focus on the complex structures of road networks and consider factors like weather, holidays, and accidents, the impact of social events is often overlooked. Queries, representing user requests in navigation applications, offer valuable insights into the popularity of social events and their effects on the road network. In this paper, we introduce the Query-aware Spatio-Temporal Graph Attention Convolution Network (QA-STGACN), which utilizes query data to model the impacts of events and forecast traffic conditions. Initially, spatial and temporal blocks are developed to extract spatial dependencies among road segments and capture traffic dynamics. A dynamic query-aware graph constructor then adaptively learns and retains periodic interactions between road segments and queries. Furthermore, three novel query-aware blocks – convolution, attention, and pattern transformation – are designed to uncover hidden dependencies between traffic conditions and query counts during events, as well as to model the delayed impacts of these counts on traffic. Ultimately, a gated fusion mechanism integrates the spatio-temporal correlations in road networks with the representations derived from queries. Comprehensive experiments on six datasets validate the superiority of QA-STGACN over robust baselines.

A Performance Analysis of Hybrid and Columnar Cloud Databases for Efficient Schema Design in Distributed Data Warehouse as a Service

A Data Warehouse (DW) is a centralized database that stores large volumes of historical data for analysis and reporting. In a world where enterprise data grows exponentially, new architectures are being investigated to overcome the deficiencies of traditional Database Management Systems (DBMSs), driving a shift towards more modern, cloud-based solutions that provide resources such as distributed processing, columnar storage, and horizontal scalability without the overhead of physical hardware management, i.e., a Database as a Service (DBaaS). Choosing the appropriate class of DBMS is a critical decision for organizations, and there are important differences that impact data volume and query performance (e.g., architecture, data models, and storage) to support analytics in a distributed cloud environment efficiently. In this sense, we carry out an experimental evaluation to analyze the performance of several DBaaS and the impact of data modeling, specifically the usage of a partially normalized Star Schema and a fully denormalized Flat Table Schema, to further comprehend their behavior in different configurations and designs in terms of data schema, storage form, memory availability, and cluster size. The analysis is done in two volumes of data generated by a well-established benchmark, comparing the performance of the DW in terms of average execution time, memory usage, data volume, and loading time. Our results provide guidelines for efficient DW design, showing, for example, that the denormalization of the schema does not guarantee improved performance, as solutions performed differently depending on its architecture. We also show that a Hybrid Processing (HTAP) NewSQL solution can outperform solutions that support only Online Analytical Processing (OLAP) in terms of overall execution time, but that the performance of each query is deeply influenced by its selectivity and by the number of join functions.

Psychometric analysis and the implications for the use of the scoliosis research society questionnaire (SRS-22r English) for individuals with adolescent idiopathic scoliosis

Despite widespread usage of the SRS-22r questionnaire (Scoliosis Research Society Questionnaire-22r), the English version has only sparingly been subjected to analysis using modern psychometric techniques for patients with adolescent idiopathic scoliosis (AIS). The study purpose was to improve interpretation and clinical utility of the SRS-22r for adolescents with AIS by generating additional robust evidence, using modern statistical techniques. Questions about (1) Structure and (2) Item and Scale Functioning are addressed and interpreted for clinicians and researchers. This retrospective case review analyzed SRS-22r data collected from 1823 patients (mean age 14.9±2.2years) with a primary diagnosis of AIS who clinically completed an SRS-22r questionnaire.Individual SRS-22r questions and domain scores were retrieved through data queries. Patient information collected through chart review included diagnosis, age at assessment, sex, race and radiographic parameters. From 6044 SRS-22r assessments, 1 assessment per patient was randomly selected. Exploratory structural equation modeling (ESEM) and item response theory (IRT) techniques were used for data modeling, item calibration, and reliability assessment. ESEM demonstrated acceptable fit to the data: χ2 (130)=343.73, p<.001; RMSEA=0.035; CFI=0.98; TLI=0.96; SRMR=0.02. Several items failed to adequately load onto their assigned factor. Item fit was adequate for all items except SRSq10 (Self-Image), SRSq16 (Mental Health), and SRSq20 (Mental Health). IRT models found item discriminations are within normal levels for items in psychological measures, except items SRSq1 (pain), SRSq2 (pain), and SRSq16 (mental health). Estimated reliability of the Function domain (ρ=0.69) was low, however, Pain, Self-Image and Mental Health domains exhibited high (ρ>0.80) reliability. Modern psychometric assessment of the SRS-22r, in adolescent patients with AIS, are presented and interpreted to assist clinicians and researchers in understanding its strengths and limitations. Overall, the SRS-22r demonstrated good psychometric properties in all domains except function. Cautious interpretation of the total score is suggested, as it does not reflect a single HRQoL construct.

Design and optimization of multidimensional data models for enhanced OLAP query performance and data analysis

This paper explores the design and optimization of multidimensional data models to enhance the query performance and data analysis capabilities of OLAP (Online Analytical Processing) systems. It delves into three prominent dimensional modeling techniques: Star Schema, Snowflake Schema, and Galaxy Schema, analyzing their impact on query complexity, data redundancy, storage requirements, and ease of maintenance. Additionally, it examines three aggregation strategiesPre-Aggregation, Dynamic Aggregation, and Hybrid Aggregationfocusing on their effectiveness in balancing query response time, storage efficiency, flexibility, and computational cost. The study further investigates performance optimization techniques, including query optimization, partitioning, and materialized views, providing case studies and experimental data to illustrate their benefits and challenges. The findings underscore the importance of tailored optimization strategies in OLAP systems to meet varying business needs and query patterns, highlighting the trade-offs between performance gains, storage requirements, and implementation complexity

Optimizing Database Performance in Complex Event Processing through Indexing Strategies

Complex event processing (CEP) systems have gained significant importance in various domains, such as finance, logistics, and security, where the real-time analysis of event streams is crucial. However, as the volume and complexity of event data continue to grow, optimizing the performance of CEP systems becomes a critical challenge. This paper investigates the impact of indexing strategies on the performance of databases handling complex event processing. We propose a novel indexing technique, called Hierarchical Temporal Indexing (HTI), specifically designed for the efficient processing of complex event queries. HTI leverages the temporal nature of event data and employs a multi-level indexing approach to optimize query execution. By combining temporal indexing with spatial- and attribute-based indexing, HTI aims to accelerate the retrieval and processing of relevant events, thereby improving overall query performance. In this study, we evaluate the effectiveness of HTI by implementing complex event queries on various CEP systems with different indexing strategies. We conduct a comprehensive performance analysis, measuring the query execution times and resource utilization (CPU, memory, etc.), and analyzing the execution plans and query optimization techniques employed by each system. Our experimental results demonstrate that the proposed HTI indexing strategy outperforms traditional indexing approaches, particularly for complex event queries involving temporal constraints and multi-dimensional event attributes. We provide insights into the strengths and weaknesses of each indexing strategy, identifying the factors that influence performance, such as data volume, query complexity, and event characteristics. Furthermore, we discuss the implications of our findings for the design and optimization of CEP systems, offering recommendations for indexing strategy selection based on the specific requirements and workload characteristics. Finally, we outline the potential limitations of our study and suggest future research directions in this domain.

Revisiting Database Indexing for Parallel and Accelerated Computing: A Comprehensive Study and Novel Approaches

While the importance of indexing strategies for optimizing query performance in database systems is widely acknowledged, the impact of rapidly evolving hardware architectures on indexing techniques has been an underexplored area. As modern computing systems increasingly leverage parallel processing capabilities, multi-core CPUs, and specialized hardware accelerators, traditional indexing approaches may not fully capitalize on these advancements. This comprehensive experimental study investigates the effects of hardware-conscious indexing strategies tailored for contemporary and emerging hardware platforms. Through rigorous experimentation on a real-world database environment using the industry-standard TPC-H benchmark, this research evaluates the performance implications of indexing techniques specifically designed to exploit parallelism, vectorization, and hardware-accelerated operations. By examining approaches such as cache-conscious B-Tree variants, SIMD-optimized hash indexes, and GPU-accelerated spatial indexing, the study provides valuable insights into the potential performance gains and trade-offs associated with these hardware-aware indexing methods. The findings reveal that hardware-conscious indexing strategies can significantly outperform their traditional counterparts, particularly in data-intensive workloads and large-scale database deployments. Our experiments show improvements ranging from 32.4% to 48.6% in query execution time, depending on the specific technique and hardware configuration. However, the study also highlights the complexity of implementing and tuning these techniques, as they often require intricate code optimizations and a deep understanding of the underlying hardware architecture. Additionally, this research explores the potential of machine learning-based indexing approaches, including reinforcement learning for index selection and neural network-based index advisors. While these techniques show promise, with performance improvements of up to 48.6% in certain scenarios, their effectiveness varies across different query types and data distributions. By offering a comprehensive analysis and practical recommendations, this research contributes to the ongoing pursuit of database performance optimization in the era of heterogeneous computing. The findings inform database administrators, developers, and system architects on effective indexing practices tailored for modern hardware, while also paving the way for future research into adaptive indexing techniques that can dynamically leverage hardware capabilities based on workload characteristics and resource availability.

BCDA: A blockchain-based dynamic auditing scheme for intelligent IoT

Intelligent Internet of Things (IoT) provides services for machine learning applications by collecting real-time data, where data correctness crucially impacts the training accuracy of models. Data owners host incremental real-time data in the cloud and use blockchain as a public platform for data query, usage, and certification. Malicious attackers may manipulate data through methods like data poisoning, compromising model parameters. Blockchain-based data integrity verification (auditing) schemes can effectively detect and monitor such activities. However, managing the storage demands for massive on-chain data certificates poses significant challenges. Existing compressed certificate storage strategies, such as the Merkle-Hash tree (MHT), incur significant on-chain storage costs because the auxiliary paths and the entire tree structure must be uploaded during verification, resulting in an O(n) storage overhead. Polynomial commitments offer a promising alternative by reducing storage costs to hundreds of bytes. However, current polynomial commitment methods lack effective support for incremental data updates, which are essential for real-time applications. In this paper, we propose a novel polynomial commitment-based dynamic integrity verification scheme that implements cumulative commitment updates for the first time. Our scheme includes a new data block insertion and deletion strategy that maintains the original order of data blocks within polynomial commitments. Theoretical security analysis confirms the robustness of the proposed scheme, while simulation results demonstrate that it meets the efficiency requirements for blockchain-based distributed data integrity verification mechanisms.

Anonymization: The imperfect science of using data while preserving privacy.

Information about us, our actions, and our preferences is created at scale through surveys or scientific studies or as a result of our interaction with digital devices such as smartphones and fitness trackers. The ability to safely share and analyze such data is key for scientific and societal progress. Anonymization is considered by scientists and policy-makers as one of the main ways to share data while minimizing privacy risks. In this review, we offer a pragmatic perspective on the modern literature on privacy attacks and anonymization techniques. We discuss traditional de-identification techniques and their strong limitations in the age of big data. We then turn our attention to modern approaches to share anonymous aggregate data, such as data query systems, synthetic data, and differential privacy. We find that, although no perfect solution exists, applying modern techniques while auditing their guarantees against attacks is the best approach to safely use and share data today.

Enhancing Database Query Performance: Analysis of Indexing Techniques

The need to enhance query performance in databases within the contemporary information environment is crucial for ensuring efficient operations across various domains. This paper is dedicated to an analysis of database indexing techniques aimed at understanding their impact on query performance and efficiency. It meticulously examines various types of indexes, including B-trees, hash tables, and textual indexes, analyzing their advantages and limitations. The investigation into the influence of these techniques on query execution time reveals that performance depends on various factors such as query complexity and data volume. Special attention is given to selecting the optimal index type based on specific database needs and characteristics. The research underscores the importance of considering data volume and database structure in choosing effective indexing methods. Additionally, the advantages and disadvantages of different indexing techniques are scrutinized, taking into account their impact on query execution speed and efficiency. Of the study indicate that the correct selection and utilization of appropriate indexing strategies can significantly enhance the performance of database management systems, providing swift and efficient access to information for users. Ultimately, this work makes a significant contribution to understanding and practically applying indexing techniques to improve query performance in databases.

Cross-Blockchain Communication Using Oracles With an Off-Chain Aggregation Mechanism Based on zk-SNARKs

The closed architecture of prevailing blockchain systems renders the usage of this technology mostly infeasible for a wide range of real-world problems. Most blockchains trap users and applications in their isolated space without the possibility of cooperating or switching to other blockchains. Therefore, blockchains need additional mechanisms for seamless communication and arbitrary data exchange between each other and external systems. Unfortunately, current approaches for cross-blockchain communication are resource-intensive or require additional blockchains or tailored solutions depending on the applied consensus mechanisms of the connected blockchains. Therefore, we propose an oracle with an off-chain aggregation mechanism based on Zero-Knowledge Succinct Non-interactive Arguments of Knowledge (zk-SNARKs) to facilitate cross-blockchain communication. The oracle queries data from another blockchain and applies a rollup-like mechanism to move state and computation off-chain. The zkOracle contract only expects the transferred data, an updated state root, and proof of the correct execution of the aggregation mechanism. The proposed solution only requires constant 378 kgas to submit data on the Ethereum blockchain and is primarily independent of the underlying technology of the queried blockchains.

FedExLSA

Every government in the world has multiple departments that must function and operate to address the various inquiries raised by the population. The government's diverse range of websites offers citizens a platform to submit inquiries, thereby facilitating the fulfilling of their requirements. Comprehending the subjects addressed in People Query is essential for government services. Unstructured query data is analyzed using extracting information from text techniques such as allocation of Latent diffuser (LDA) and analysis of hidden semantics (LSA). LSA outperforms other methods in terms of performance because of its minimal complexity and quick installation process. Research on decentralized learning techniques for natural language processing (NLP) is necessary due to concerns about limited data availability and privacy. Federated learning (FL) employs methods that enable different users to collectively train an integrated broad model while maintaining their information regionally stored and accessible. Nevertheless, the current body of literature lacks a thorough examination and evaluation of FL techniques. Data federation is an approach to data integration that allows the government to access and query data from multiple diverse sources as if they were a single, unified repository. Functioning as a form of data virtualization, it facilitates the creation of a comprehensive representation of data, thereby enhancing operational efficiency and the accuracy of decision-making. FedEx utilizes Federated Learning to apply topic modelling techniques to common NLP tasks. The proposed structure integrates the FL Methodology with Latent Semantic Analysis to deliver outcomes for intelligent data analysis and management.

Public and scholarly interest in social robots: An investigation through Google Trends, bibliometric analysis, and systematic literature review

The COVID-19 pandemic has expedited the integration of social robots within the healthcare sector. This research employs a tripartite methodology, combining Google Trends analysis, bibliometric analysis, and a systematic literature review, to gauge both public and research interest in social robots within the healthcare domain. In the Google Trends analysis, search query data for “Social Robots” was retrieved, encompassing both “all categories” and the specific “health” category. Seasonal effects on relative search volumes (RSV) were assessed through the cosinor model. The analysis confirmed statistically significant seasonal patterns in RSV for “social robots” within the “health” category. Conversely, for the broader “all categories,” only the intercept showed significance, while sinw and cosw were deemed insignificant. For bibliometric analysis, the global literature on “robotics” and “healthcare” was examined in the SCOPUS database. From the extensive pool of publications, 144 relevant studies were identified out of 4037 publications. These studies were further analyzed using VOSviewer, providing insights into recent trends and hot topics concerning social robots in healthcare. The systematic literature review focused on 46 articles published from 2019 to the end of 2023. The findings revealed a lack of consensus on the drivers, barriers, and outcomes associated with social robot acceptance and human-robot interaction (HRI). The study systematically maps the existing research on these aspects, introducing a novel categorization and presenting the concept of a “robot user's ecosystem.” This concept emphasizes the imperative involvement of all stakeholders in the development and understanding of social robots. Ultimately, this methodological approach not only identifies nine research gaps in the current literature but also formulates numerous research questions to guide future researchers in this domain.

Data quality and timeliness analysis for post-vaccination adverse event cases reported through healthcare data exchange to FDA BEST pilot platform.

This study is part of the U.S. Food and Drug Administration (FDA)'s Biologics Effectiveness and Safety (BEST) initiative, which aims to improve the FDA's postmarket surveillance capabilities by using real-world data (RWD). In the United States, using RWD for postmarket surveillance has been hindered by the inability to exchange clinical data between healthcare providers and public health organizations in an interoperable format. However, the Office of the National Coordinator for Health Information Technology (ONC) has recently enacted regulation requiring all healthcare providers to support seamless access, exchange, and use of electronic health information through the interoperable HL7 Fast Healthcare Interoperability Resources (FHIR) standard. To leverage the recent ONC changes, BEST designed a pilot platform to query and receive the clinical information necessary to analyze suspected AEs. This study assessed the feasibility of using the RWD received through the data exchange of FHIR resources to study post-vaccination AE cases by evaluating the data volume, query response time, and data quality. The study used RWD from 283 post-vaccination AE cases, which were received through the platform. We used descriptive statistics to report results and apply 322 data quality tests based on a data quality framework for EHR. The volume analysis indicated the average clinical resources for a post-vaccination AE case was 983.9 for the median partner. The query response time analysis indicated that cases could be received by the platform at a median of 3 min and 30 s. The quality analysis indicated that most of the data elements and conformance requirements useful for postmarket surveillance were met. This study describes the platform's data volume, data query response time, and data quality results from the queried postvaccination adverse event cases and identified updates to current standards to close data quality gaps.

Integration of chromosome locations and functional aspects of enhancers and topologically associating domains in knowledge graphs enables versatile queries about gene regulation.

Knowledge about transcription factor binding and regulation, target genes, cis-regulatory modules and topologically associating domains is not only defined by functional associations like biological processes or diseases but also has a determinative genome location aspect. Here, we exploit these location and functional aspects together to develop new strategies to enable advanced data querying. Many databases have been developed to provide information about enhancers, but a schema that allows the standardized representation of data, securing interoperability between resources, has been lacking. In this work, we use knowledge graphs for the standardized representation of enhancers and topologically associating domains, together with data about their target genes, transcription factors, location on the human genome, and functional data about diseases and gene ontology annotations. We used this schema to integrate twenty-five enhancer datasets and two domain datasets, creating the most powerful integrative resource in this field to date. The knowledge graphs have been implemented using the Resource Description Framework and integrated within the open-access BioGateway knowledge network, generating a resource that contains an interoperable set of knowledge graphs (enhancers, TADs, genes, proteins, diseases, GO terms, and interactions between domains). We show how advanced queries, which combine functional and location restrictions, can be used to develop new hypotheses about functional aspects of gene expression regulation.

On Efficiently Processing MIT Queries in Trajectory Data

On Efficiently Processing MIT Queries in Trajectory Data

Characteristics, Treatment, and Outcomes of Invasive Group A Streptococcal Infections

Background: Group A Streptococcus (GAS; Streptococcus pyogenes) is an important human pathogen that can cause life-threatening invasive disease, ranging from skin/soft tissue infections to infective endocarditis. In the fall of 2022, the Center for Disease Control &amp; Prevention (CDC) issued an alert due to a global increase in invasive GAS infections, particularly among children and adults with co-morbidities. An increase in invasive disease was observed at our five-hospital healthcare system in Southeast Michigan. The objective of this study was to describe characteristics of patients with invasive GAS and characterize treatment and outcomes of disease. Methods: This was a retrospective cross-sectional study of patients from June 2013 to August 2023 with positive blood cultures for GAS. Patients were identified using a data query for positive blood cultures for GAS through Microsoft SQL Server. Patients with age &lt; 1 8 years, polymicrobial bacteremia, incomplete data, or who were enrolled in hospice and/or died within 48-hours of admission were excluded. Collected variables included: demographics, infection characteristics (syndrome, duration of bacteremia), microbiological characteristics (antimicrobial susceptibility testing; AST), antimicrobial treatment (empiric and final, antitoxin therapy), and clinical outcomes (length of hospital stay [LOS], treatment-associated adverse events, 30-day mortality and infection-related readmission). Results: 250 patients were included (Table 1). More than half were male with median age of 57.5 years. Diabetes mellitus (38%) and chronic kidney disease (23%) were common comorbidities [Table 1]. Persons experiencing homelessness and persons who use injection drugs accounted for 9% and 13% of the cases, respectively. The most common infective syndrome accompanying bacteremia was cellulitis (57%). The majority of patients received vancomycin for empiric therapy (81%) and penicillin (38%) or cephalosporin (36%) for final regimen [Table 2]. A total of 79 GAS isolates (32%) were clindamycin resistant. Clindamycin was included in the empiric regimen of 20 (8%) patients, the final regimen in 44 (18%) of patients, and as antitoxin adjunct therapy in 135 (54%) of patients. A third (33%) of patients received no antitoxin. The average duration of antitoxin therapy was 3.6 days and antimicrobial therapy 19.9 days. The mean LOS was 11.4 days (Table 3). Thirty nine (16%) patients had treatment failure and 8 (3%) experienced C. difficile infection within 30 days of antimicrobial treatment. Thirty-day mortality was 11%; of these, 9% had in-hospital mortality. Conclusions: Invasive GAS infection confers significant morbidity and mortality, and ongoing research is needed to determine the best treatment regimens in the era of increasing clindamycin resistance.

A spin on active learning analysis for health monitoring.

Decisions on the operation and maintenance of high-valued structures are critical, collecting and analysing data that can inform these decisions motivates the development of structural health monitoring (SHM) and tool monitoring (TM) systems. Descriptive health-state information labels for measured data points are crucial for successful monitoring systems. However, the labels can be expensive to collect and are often unavailable. Due to this problem, fully-supervised machine learning is limited in it’s application to monitoring systems which gives rise to unsupervised and semi-supervised approaches, one of these approaches is active learning. Active learning aims to highlight which unlabelled data points have the highest value of information and would be most informative to the model if they were labelled. For risk-based active learning in SHM, the model is used to advise some maintenance decision process where querying a data point means an expert inspecting the structure to determine its health-state. The current paper applies an active learning regression technology to a machining tool health case study. The algorithm queries data points that are most important in helping determine the health-state of the tool. Querying the most informative datapoints can drastically reduce the costs of a monitoring system. The regression analysis results are used to estimate tool wear and inform a risk-based maintenance decision process. This addresses the problem of when to replace a tool based on its health status, which has applications for many monitoring system problems.

The Effect of Palatoplasty on Growth.

To assess the effect of palatoplasty on growth velocity in undernourished and nourished orally fed children with cleft palate. A retrospective chart review was conducted using a data query with cleft-associated ICD-10 and ICD-9 codes to identify children who underwent cleft palate repair between 2006 and 2022 at a tertiary pediatric hospital. Data gathered included demographics, admission variables, weight gain, and weight percentile as per the WHO growth chart data. Inclusion was limited to patients with complete growth data and without parenteral feeding support. Nutritional status was defined as undernourished (≤3rd percentile) and nourished (>3rd percentile). Between and within-group comparisons were made using nonparametric tests. A total of 192 patients met the study criteria. Among undernourished patients, the median pre- and postsurgical weekly weight gain was 0.09 kg and 0.05 kg (p = <0.0001), and the median growth percentiles were 0.67% and 1.1%, respectively (p = 0.03). Among nourished patients, the median pre- and postsurgical weekly weight gain was 0.12 kg and 0.07 kg (p = <0.0001), and the median growth percentiles were 25.4% and 29.5%, respectively (p = <0.0001). The postsurgical reduction in weight gain per week was similar across nutritional status (p = 0.43), however, the nourished group demonstrated a larger increase in median growth percentile (3.3% vs. 0.23%; p = 0.03). This study demonstrates that palatoplasty does not independently improve growth velocity in children who were able to maintain an oral preoperative diet. In the case of undernourished children, there was no clinically significant improvement in postoperative growth percentiles. 3 Laryngoscope, 2024.