Query Method Research Articles

GLP-1 RA Prescribing Errors in a Multidisciplinary Digital Weight-Loss Service: A Retrospective Quantitative Analysis.

Digital weight loss services (DWLSs) that use Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have demonstrated potential in contributing to a shift in global obesity rates. However, reasonable concerns have been raised about the prescribing safety of these services. Prior to this study, electronic prescribing safety had only been investigated in hospital settings and community clinics. This study retrospectively analyzed prescribing errors committed over a 6-month period in 2023 at Australia's largest GLP-1 RA-supported DWLS. The analysis found that 1654 (4.4%) of the 37323 audited GLP-1 RA prescriptions contained an error. Most errors pertained to insufficient safety counselling (49.15%) and inadequate investigations of potential contraindications (30.29%). Although a large portion of prescribing errors were detected via the automated query method (64.9%), the other three auditing methods all detected a significant number of true errors (>100). Patients from the highest body mass index category (40+ kg/m2) were overrepresented in the service's prescribing error data. These findings lay a vital foundation in the emerging literature on GLP-1 RA-supported DWLSs.

New Media Advertising Information Search Method Based on XML Technology

The sheer volume and complexity of new media advertising information pose significant challenges for users in searching and filtering advertisements. To enhance the match between search content and keywords, this paper proposes a multifunctional search method for new media advertising information based on eXtensible Markup Language (XML) technology. The collected user attribute data are organized into a set, and a membership function for user attributes is established. An information certainty index and a query certainty index are constructed to obtain the integrated attributes of new media advertisements and classify their attributes. The average value of the constant threshold element is calculated, and an attraction model of new media advertisements for random Internet users is constructed. The similarity between text information and query methods is determined, along with the calculation of browsing duration and frequency in any given area. According to the search model for new media advertising information, the sorted new media advertising information documents are merged to obtain search results. Experimental results demonstrate that the introduction of interactive elements enhances attraction, and dynamic new media advertisements exhibit stronger attraction compared to static advertisements. The proposed method shows significant advantages in search efficiency, achieving over 90% search efficiency, with the match between search content and keywords consistently remaining above 98%, the user satisfaction is higher, the system response time is shorter, the results can be obtained faster, and the search efficiency is improved.

The Sharing Model of Kindergarten Art Enlightenment Education Curriculum Resources Based on Deep Learning Algorithm

In order to meet the autonomous needs of children’s art learning, this paper studies the sharing model of kindergarten art enlightenment education curriculum resources under the deep learning algorithm. Build a kindergarten art enlightenment education curriculum resource-sharing model that includes the classification and storage layer of education curriculum resources, the metadata annotation layer of education curriculum resources and the shared business service layer. The deep learning algorithm is used in the classification and storage layer of education curriculum resources. The education curriculum resources are divided into courseware resources, case resources and media materials resources through unsupervised restricted Boltzmann machine (RBM) coding and supervised training process. The ontology model of various educational curriculum resources is constructed, and the semantic information of educational curriculum resources is described using ontology. The metadata annotation layer of education curriculum resources annotates the metadata semantics of education curriculum resources through the constructed ontology, which improves the consistency and sharing of education curriculum resources management. The implementation of resource sharing business service is based on semantic analysis and query of educational curriculum resources. Therefore, the shared business service layer adopts a semantic query method based on improved similarity algorithm, which combines semantic distance, node density, node depth and relationship type to build a semantic similarity calculation method to achieve semantic analysis and query of educational curriculum resources. The experimental results show that the model is suitable for multi-classification of shared resources in the kindergarten art enlightenment education curriculum, and the semantic query accuracy is high in the process of sharing. When the threshold is [Formula: see text] 0.5, the query effect of the sharing model in this paper is ideal, which can meet the needs of users’ autonomous learning.

Enhancing Medication Safety with ML-Enhanced Decision Support Systems: A Comparative Analysis of Prescription Error Detection

Background: Medication prescription errors are a global issue, leading to significant morbidity and mortality. Traditional rule-based Medical Decision Support Systems (MDSS) are often ineffective, generating numerous false alerts and failing to detect all potential errors. This study assesses a new anomaly detection system integrated with Electronic Health Records (EHR) to improve the accuracy and utility of medication error warnings. Methods: Anomalous prescription detection was implemented alongside an existing MDSS in a real-world inpatient setting over 18 months. The new system utilized Machine Learning (ML) combined with a rule-based MDSS to analyze historical EHR data. It aimed to identify and flag high-risk prescriptions through real-time anomaly detection. The performance of this hybrid system was compared against traditional MDSS and multicriteria query (MQ) methods. A clinical pharmacist reviewed 415 patients (3401 prescriptions) to validate the effectiveness of the system, assessing notifications for accuracy, clinical relevance, and practicality. Results: The ML-enhanced MDSS demonstrated superior performance compared to traditional systems. It achieved a 76% interception rate for prescriptions needing pharmacist review and a precision rate of 75%. The hybrid system outperformed traditional MDSS and MQ methods, with areas under the ROC and PRC curves of 0.84 and 0.79, respectively, compared to 0.66 and 0.57 for MDSS and 0.7 and 0.58 for MQ approaches. Conclusion: Integrating ML with rule-based MDSS significantly improves the detection of high-risk medication prescriptions, reducing false alerts and enhancing accuracy. This hybrid approach offers a more effective tool for identifying potential medication errors and improving patient safety in inpatient settings.

Semantic community query in a large‐scale attributed graph based on an attribute cohesiveness optimization strategy

AbstractThe task of a semantic community query is to obtain a subgraph based on a given query vertex (or vertex set) and other query parameters in an attributed graph such that belongs to , contains and satisfies a predefined community cohesiveness model. In most cases, existing community query models based on the network structure for traditional attributed networks usually lack community semantics. However, the features of vertex attributes, especially the attributes of the query vertices, which are closely related to the community semantics, are rarely considered in an attributed graph. Existing community query algorithms based on both structure cohesiveness and attribute cohesiveness usually do not take the attributes of the query vertex as an important factor of the community cohesiveness model, which leads to weak semantics of the communities. This paper proposes a semantic community query method named in a large‐scale attributed graph. First, the k‐core structure model is adopted as the structure cohesiveness of our community query model to obtain a subgraph of the original graph. Second, we define attribute cohesiveness based on the average distance between the query vertices and other vertices in terms of attributes in the community to prune the subgraph and obtain the semantic community. In order to improve the community query efficiency in large‐scale attributed graphs, applies two heuristic pruning strategies. The experimental results show that our method outperforms the existing community query methods in multiple evaluation metrics and is ideal for querying semantic communities in large‐scale attributed graphs.

Dynamic Range Query Privacy-Preserving Scheme for Blockchain-Enhanced Smart Grid Based on Lattice

Blockchain-enhanced Smart Grid has attracted a lot of attention from scholars in recent years, due to its excellent decentralization, anti-collusion and immutability. With the wide deployment of blockchain and the popularity of more accurate intelligent applications, its role in Smart Grid is becoming more and more irreplaceable. However, because of the threat of quantum computer, it still confronts the risk of privacy disclosure. In this paper, we propose a novel and dynamic range query privacy-preserving scheme for blockchain-enhanced Smart Grid based on lattice. In this scheme, lattice-based homomorphic encryption algorithm is designed to resist the attack of quantum computer and realizes data aggregation to improve efficiency. In particular, this article designs a dynamic range query method with the aid of consortium blockchain by using proxy re-encryption. This method avoids the communication pressure caused by repeated data collection and improves the query efficiency and user experience. In addition, dynamic ciphertext and users update are considered, which further improves the flexibility and feasibility of the scheme. Last but not least, security analysis, exhaustive performance analysis and experiments show that our proposed scheme meets the requirements of dynamic, privacy, security and low computational cost.

A secure and efficient log storage and query framework based on blockchain

Log data is crucial for security threat detection and audit analysis. However, traditional log systems are susceptible to tampering, posing a significant security risk to information systems. Although blockchain technology has been introduced to enhance tamper resistance, existing blockchain-based log systems still suffer from storage and query efficiency issues. In this paper, we propose a novel secure and efficient log storage and query framework that combines on-chain and off-chain collaboration. An inverted index table is constructed by extracting keywords from logs, which are stored on the blockchain as on-chain data, while the logs themselves are maintained as off-chain data. This approach facilitates the rapid retrieval of specific keywords and ensures the immutability of the logs. Furthermore, we propose a secure and efficient log query method featuring a smart contract designed to automatically handle requests from legitimate log queriers. We also design a data structure based on merkle adaptive radix tree (MART) and merkle B+ tree (MBT) to store index entries, thereby achieving efficient log retrieval. We provide formal security proofs and comprehensively evaluate the proposed framework’s performance experimentally. Results demonstrate that MBT and MART reduce average query times by 20.09% and 51% respectively, compared to the state-of-the-art schemes.

Optimization study of pruning strategy based on KNN trajectory similarity query

With the development of GPS positioning technology, a large amount of spatiotemporal trajectory data has been generated. Due to the complex structure of trajectory data, which has irregular spatial shapes and continuous temporal sequence attributes, querying massive trajectory data poses certain challenges. The pruning strategy of existing trajectory similarity query methods is not very effective. Even after pruning operations, there are still a large number of trajectories that need to undergo distance calculations to confirm whether they are similar trajectories. This paper proposes multiple local pruning optimization schemes, which maximally reduce the number of trajectories in the candidate set. Specifically, it starts with region pruning in the index space, eliminating index spaces with distances greater than the maximum similarity distance from the query trajectory. Then, it performs distance pruning between trajectories, removing trajectories that do not meet the distance conditions. Finally, it adds a termination condition: when the distance between the current index space and the query trajectory is greater than the maximum similarity distance and the number of trajectories in the result set is K, the loop is exited, and the query process ends. Tests on real datasets demonstrate that the KTSS method outperforms current algorithms of the same type.

On Querying Historical Connectivity in Temporal Graphs

We study the historical connectivity query in temporal graphs where edges continuously arrive. Given an arbitrary time window, and two query vertices, the problem aims to identify if two vertices are connected by a path in the snapshot of the window. The state-of-the-art method designs an index based on the two-hop cover, and updating the index is costly when new edges arrive. In this paper, we propose a new framework and design a novel forest-based index for historical connectivity queries. The index enables us to answer queries by searching if two vertices are connected in the forest. We update the index by modifying a forest structure. Our techniques also work for connectivity query processing in a sliding window of temporal graphs. Extensive experiments have been conducted to show the considerable advantages of our approach compared with the state-of-the-art methods in both historical connectivity queries and sliding-window connectivity queries.

Development and validation of the caregiver Burden from infant home NGT care instrument

ObjectiveTo design and validate an age and condition-specific health status instrument to best reflect the parental experience caring for these children with complex needs and home Nasogastric Tube (NGT) placement. Study designCombined Qualitative and Quantitative design, testing and implementation for item production and reduction, followed by formal validation by evaluating validity, reliability, and establishing a clinically meaningful change score. SettingsTertiary care, multi-disciplinary aerodigestive center. ParticipantsAll caregivers whose infant met criteria for eligibility for discharge home from the NICU or Special Care Nursery (SCN) with NGT in place were offered inclusion in this group.Intervention/Exposure: Structured qualitative interviews of these caregivers to explore and define these concepts and domains, to item generate and then reduce, and then psychometric analyses. MethodsStructured, moderated qualitative interviews with parents/caregivers of children who have undergone home NGT care of their children for item creation, design, and then reduction. Reliability was assessed by Cronbach alpha analysis. Construct validity and clinically meaningful change score was assessed using various query methods. Main outcome measuresCronbach's alpha to assess reliability, a priori hypotheses validity analyses, and minimally important clinical difference calculation. ResultsScaled scores of this condition specific instrument ranged from 14 to 74 where higher scores indicate better QOL related to managing the NGT. Cronbach's alpha with all 14 items was 0.93. Validity was assessed by a self-assessment question to discriminate between change (95% CI: 8.5–14.1; p < 0.0001) as well as by other comparators to identify the instrument's ability to discriminate among populations where parents felt a difference in experience. The minimally important difference was calculated at 18 points. ConclusionThis represents the initial validation of the first condition and age-specific health status instrument to assess parent experience of caring for infants requiring a home NGT for dysphagia.

Skyline query under multidimensional incomplete data based on classification tree

A method for skyline query of multidimensional incomplete data based on a classification tree has been proposed to address the problem of a large amount of useless data in existing skyline queries with multidimensional incomplete data, which leads to low query efficiency and algorithm performance. This method consists of two main parts. The first part is the proposed incomplete data weighted classification tree algorithm. In the first part, an incomplete data weighted classification tree is proposed, and the incomplete data set is classified using this tree. The data classified in the first part serves as the basis for the second step of the query. The second part proposes a skyline query algorithm for multidimensional incomplete data. The concept of optimal virtual points has been recently introduced, effectively reducing the number of comparisons of a large amount of data, thereby improving the query efficiency for incomplete data. Theoretical research and experimental analysis have shown that the proposed method can perform skyline queries for multidimensional incomplete data well, with high query efficiency and accuracy of the algorithm.

Interactive visual query of density maps on latent space via flow‐based models

AbstractVisual querying of spatiotemporal data has become a dominant mode in the field of visual analytics. Previous studies have utilized well‐designed data structures to speed up the querying of spatiotemporal data. However, reducing storage overhead while improving the querying efficiency of data distribution remains a significant challenge. We propose a flow‐based neural representation method for efficient visual querying. First, we transform spatiotemporal data into density maps through kernel density estimation. Then, we leverage the data‐driven modeling capabilities of a flow‐based neural network to achieve a highly latent representation of the data. Various computations and queries can be performed on the latent representation to improve querying efficiency. Our experiments demonstrate that our approach achieves competitive results in visually querying spatiotemporal data in terms of storage overhead and real‐time interaction efficiency.

A nearest neighbor query method for searching objects with time and location informations based on spatiotemporal similarity

A nearest neighbor query method for searching objects with time and location informations based on spatiotemporal similarity

Batch Active Learning of Reward Functions from Human Preferences

Data generation and labeling are often expensive in robot learning. Preference-based learning is a concept that enables reliable labeling by querying users with preference questions. Active querying methods are commonly employed in preference-based learning to generate more informative data at the expense of parallelization and computation time. In this paper, we develop a set of novel algorithms, batch active preference-based learning methods, that enable efficient learning of reward functions using as few data samples as possible while still having short query generation times and also retaining parallelizability. We introduce a method based on determinantal point processes (DPP) for active batch generation and several heuristic-based alternatives. Finally, we present our experimental results for a variety of robotics tasks in simulation. Our results suggest that our batch active learning algorithm requires only a few queries that are computed in a short amount of time. We showcase one of our algorithms in a study to learn human users’ preferences.

A Study on Performance Improvement of Prompt Engineering for Generative AI with a Large Language Model

In the realm of Generative AI, where various models are introduced, prompt engineering emerges as a significant technique within natural language processing-based Generative AI. Its primary function lies in effectively enhancing the results of sentence generation by large language models (LLMs). Notably, prompt engineering has gained attention as a method capable of improving LLM performance by modifying the structure of input prompts alone. In this study, we apply prompt engineering to Korean-based LLMs, presenting an efficient approach for generating specific conversational responses with less data. We achieve this through the utilization of the query transformation module (QTM). Our proposed QTM transforms input prompt sentences into three distinct query methods, breaking them down into objectives and key points, making them more comprehensible for LLMs. For performance validation, we employ Korean versions of LLMs, specifically SKT GPT-2 and Kakaobrain KoGPT-3. We compare four different query methods, including the original unmodified query, using Google SSA to assess the naturalness and specificity of generated sentences. The results demonstrate an average improvement of 11.46% when compared to the unmodified query, underscoring the efficacy of the proposed QTM in achieving enhanced performance.

Matrix Factorization Recommendation Algorithm Based on Attention Interaction

Recommender systems are widely used in e-commerce, movies, music, social media, and other fields because of their personalized recommendation functions. The recommendation algorithm is used to capture user preferences, item characteristics, and the items that users are interested in are recommended to users. Matrix factorization is widely used in collaborative filtering algorithms because of its simplicity and efficiency. However, the simple dot-product method cannot establish a nonlinear relationship between user latent features and item latent features or make full use of their personalized information. The model of a neural network combined with an attention mechanism can effectively establish a nonlinear relationship between the potential features of users and items and improve the recommendation accuracy of the model. However, it is difficult for the general attention mechanism algorithm to solve the problem of attention interaction when the number of features between the users and items is not the same. To solve the above problems, this paper proposes an attention interaction matrix factorization (AIMF) model. The AIMF model adopts a symmetric structure using MLP calculation. This structure can simultaneously extract the nonlinear features of user latent features and item latent features, thus reducing the computation time of the model. In addition, an improved attention algorithm named slide-attention is included in the model. The algorithm uses the sliding query method to obtain the user’s attention to the latent features of the item and solves the interaction problem among different dimensions of the user, and the latent features of the item.

Deep learning-based glioma grading and feature visualization analysis

With the rapid development of geolocation information services, which involves large-scale location data querying, including map data and user location data. Traditional data storage and query methods face challenges of storage space and query efficiency, and the ability to process real-time geolocation data becomes critical in Geographic Information Services positioning systems that require servers to achieve millisecond-level response speeds. In addition to this, in geolocation services, for a given latitude and longitude information, it is necessary to quickly determine whether it exists or matches some specific location in the map service. The common traditional information finding algorithm in geographic location service is B-tree, and this paper proposes a geographic location information finding algorithm based on the Bloom filter, which mainly solves two problems. One is the storage problem of large amounts of latitude and longitude information; the other is how to convert the data stored in bytes level to bits level, which greatly reduces the space complexity and the inefficient information finding. The Bloom filter reduces the query time to linear time complexity O(n). The fastest query time for 5 million items of latitude and longitude information can reach 3.12 seconds. This article will illustrate the efficiency of the proposed method through a real-time traffic navigation scenario.

Research on the fast geolocation algorithm based on Bloom filter

With the rapid development of geolocation information services, which involves large-scale location data querying, including map data and user location data. Traditional data storage and query methods face challenges of storage space and query efficiency, and the ability to process real-time geolocation data becomes critical in Geographic Information Services positioning systems that require servers to achieve millisecond-level response speeds. In addition to this, in geolocation services, for a given latitude and longitude information, it is necessary to quickly determine whether it exists or matches some specific location in the map service. The common traditional information finding algorithm in geographic location service is B-tree, and this paper proposes a geographic location information finding algorithm based on the Bloom filter, which mainly solves two problems. One is the storage problem of large amounts of latitude and longitude information; the other is how to convert the data stored in bytes level to bits level, which greatly reduces the space complexity and the inefficient information finding. The Bloom filter reduces the query time to linear time complexity O(n). The fastest query time for 5 million items of latitude and longitude information can reach 3.12 seconds. This article will illustrate the efficiency of the proposed method through a real-time traffic navigation scenario.

Navigating the Maps: Euclidean vs. Road Network Distances in Spatial Queries

Using spatial data in mobile applications has grown significantly, thereby empowering users to explore locations, navigate unfamiliar areas, find transportation routes, employ geomarketing strategies, and model environmental factors. Spatial databases are pivotal in efficiently storing, retrieving, and manipulating spatial data to fulfill users’ needs. Two fundamental spatial query types, k-nearest neighbors (kNN) and range search, enable users to access specific points of interest (POIs) based on their location, which are measured by actual road distance. However, retrieving the nearest POIs using actual road distance can be computationally intensive due to the need to find the shortest distance. Using straight-line measurements could expedite the process but might compromise accuracy. Consequently, this study aims to evaluate the accuracy of the Euclidean distance method in POIs retrieval by comparing it with the road network distance method. The primary focus is determining whether the trade-off between computational time and accuracy is justified, thus employing the Open Source Routing Machine (OSRM) for distance extraction. The assessment encompasses diverse scenarios and analyses factors influencing the accuracy of the Euclidean distance method. The methodology employs a quantitative approach, thereby categorizing query points based on density and analyzing them using kNN and range query methods. Accuracy in the Euclidean distance method is evaluated against the road network distance method. The results demonstrate peak accuracy for kNN queries at k=1, thus exceeding 85% across classes but declining as k increases. Range queries show varied accuracy based on POI density, with higher-density classes exhibiting earlier accuracy increases. Notably, datasets with fewer POIs exhibit unexpectedly higher accuracy, thereby providing valuable insights into spatial query processing.

Hierarchical pattern-based complex query of temporal knowledge graph

The complex query is an essential type of knowledge graph query, which aims to deal with the complex relationships among multiple triples involved in query problems. However, the existing query methods often directly search the target entities in large knowledge graphs, resulting in unsatisfactory query results. In addition, most of the methods focus on static knowledge graphs, ignoring the critical semantic information contained in time elements in query questions. Therefore, this paper proposes a complex query model for temporal knowledge graphs based on the hierarchical pattern. Firstly, aiming at the multi-level semantic relations in complex query problems, we use the entity mapping method to generate the corresponding query graphs. This method can accurately express the semantic relations by learning the constraints, such as time and quantity, in the query problem by manual rules. In addition, to reduce the impact of noisy data on query results, a complex query algorithm based on a hierarchical pattern is proposed. By decomposing the large-scale temporal knowledge graph into smaller knowledge sub-graphs, the search range of target results is effectively reduced. Finally, extensive experiments demonstrate that our model has certain advantages in executing complex queries.