Categories Of Queries Research Articles

Indexing for Fine Arts Monographs: A Comparison Between User Queries and Controlled Vocabularies

ABSTRACTPrior research suggested the empirical approach to indexing for fine arts based on users' search queries. This study examined the characteristics of user queries submitted to the library catalog of a fine arts academy, and compared users' search terms with controlled vocabularies to assess their levels of matching. Based on search‐log analysis, this study identified eight categories of user queries and 10 subcategories, indicating that the information needs of the fine arts community are diverse in language and culture and focus on named entities, disciplines, and various types and levels of fine arts. The mapping between user queries and controlled vocabularies found that around 20% of user search terms were non‐matching. Based on the findings, this study provided suggestions for improving the search functions of library catalogs and enhancing controlled vocabularies to support the retrieval of fine arts monographs.

Demonstration of MaskSearch: Efficiently Querying Image Masks for Machine Learning Workflows

We demonstrate MaskSearch, a system designed to accelerate queries over databases of image masks generated by machine learning models. MaskSearch formalizes and accelerates a new category of queries for retrieving images and their corresponding masks based on mask properties, which support various applications, from identifying spurious correlations learned by models to exploring discrepancies between model saliency and human attention. This demonstration makes the following contributions: (1) the introduction of MaskSearch's graphical user interface (GUI), which enables interactive exploration of image databases through mask properties, (2) hands-on opportunities for users to explore MaskSearch's capabilities and constraints within machine learning workflows, and (3) an opportunity for conference attendees to understand how MaskSearch accelerates queries over image masks.

Exploratory and directed search strategies at a social science data archive

Researchers need to be able to find, access, and use data to participate in open science. To understand how users search for research data, we analyzed textual queries issued at a large social science data archive, the Inter-university Consortium for Political and Social Research (ICPSR). We collected unique user queries from 988,475 user search sessions over four years (2012-16). Overall, we found that only 30% of site visitors entered search terms into the ICPSR website. We analyzed search strategies within these sessions by extending existing dataset search taxonomies to classify a subset of the 1,554 most popular queries. We identified five categories of commonly-issued queries: keyword-based (e.g., date, place, topic); name (e.g., study, series); identifier (e.g., study, series); author (e.g., institutional, individual); and type (e.g., file, format). While the dominant search strategy used short keywords to explore topics, directed searches for known items using study and series names were also common. We further distinguished exploratory browsing from directed search queries based on their page views, refinements, search depth, duration, and length. Directed queries were longer (i.e., they had more words), while sessions with exploratory queries had more refinements and associated page views. By comparing search interactions at ICPSR to other natural language interactions in similar web search contexts, we conclude that dataset search at ICPSR is underutilized. We envision how alternative search paradigms, such as those enabled by recommender systems, can enhance dataset search.

Re-abstraction and perturbing support pair network for few-shot fine-grained image classification

The goal of few-shot fine-grained image classification (FSFGIC) is to distinguish subordinate-level categories with subtle visual differences such as the species of bird and models of car with only a few samples. In this work, we argue that a designed network that has the ability to better distinguish feature descriptors of different categories will effectively improve the performance of FSFGIC. We propose a re-abstraction and perturbing support pair network (RaPSPNet) for FSFGIC. Specifically, we first design a feature re-abstraction embedding (FRaE) module which can not only effectively amplify the difference between the feature information from different categories but also better extract the feature information from images. Furthermore, a novel perturbing support pair (PSP) based similarity measure module is designed which evaluates the relationships of feature information among a query image and two different categories of support images (a support pair) at the same time for guiding the designed FRaE module to find salient feature information from the same category of query and support images and find distinguishable feature information from the different categories of query and support images. Extensive experiments on FSFGIC tasks demonstrate the superiority of the proposed methods over state-of-the-art benchmarks.

A Retrospective Review of COVID-19 Medicines Information Queries in a Quaternary Hospital with Unique COVID-19 Border Controls

Background: Medicines information (MI) is a specialist area of pharmacy that provides evidence-based answers to often complex medication queries, utilising resources such as textbooks and databases. With the advent of the COVID-19 pandemic, there was a need to change the way COVID-19-related queries were answered due to the rapid evolution of information on vaccination, treatment and prevention. Methods: Medicines information queries were retrospectively reviewed utilising the centre’s medicines information database from January 2020 through December 2022 using the COVID-19 keyword to retrieve relevant queries. Information was collected on the enquirer’s role, query category, time taken to complete the query, relevant keywords and references accessed. Keywords and references were analysed further to determine the types of queries asked and which references were helpful. Results: The centre received 214 COVID-19-related queries, predominantly in 2022. Most queries were from pharmacy staff (95.8%) and related to vaccination (n = 95, 44.4%) or treatment (n = 87, 40.7%). Government and specialist organisation websites were used most commonly as reference sources (24.6% and 16.5%, respectively) for their currency with COVID-19-specific resources (such as national guidelines, COVID-19 treatment interaction checkers) and textbooks/databases used less commonly. Conclusions: MI pharmacists have demonstrated their ability to obtain reliable COVID-19-related information, utilising and interpreting information from less traditional sources.

Google Trends for health research: Its advantages, application, methodological considerations, and limitations in psychiatric and mental health infodemiology.

The high utilization of infodemiological tools for psychiatric and mental health topics signals the emergence of a new discipline. Drawing on the definition of infodemiology by Eysenbach, this emerging field can be termed "psychiatric and mental health infodemiology," defined as the science of distribution and determinants of information in an electronic medium, including the internet, or in a population to inform mental health services and policies. Since Google Trends is one of its popular tools, this minireview describes its advantages, application, methodological considerations, and limitations in psychiatric and mental health research. The advantage of Google Trends is the nature of its data, which may represent the actual behavior rather than their users' stated preferences in real-time through automatic anonymization. As such, it can provide readily available data about sensitive health topics like mental disorders. Therefore, Google Trends has been used to explore public concerns, interests, and behaviors about psychiatric and mental health phenomena, service providers, and specific disciplines. In this regard, several methodological can be considered by studies using Google Trends, including documenting their exact keywords, query category, time range, location, and date of retrieval. Likewise, its limitations should be accounted for in its interpretation, including restricted representation of people who use the Google search engine, limited validity in areas with low internet penetration or freedom of speech, does not provide absolute search volumes, unknown sampled queries, and limited transparency in its algorithm, especially the terms and idioms it subsumes under its "topic" keywords.

A Brief Survey of Methods for Analytics over RDF Knowledge Graphs

There are several Knowledge Graphs expressed in RDF (Resource Description Framework) that aggregate/integrate data from various sources for providing unified access services and enabling insightful analytics. We observe this trend in almost every domain of our life. However, the provision of effective, efficient, and user-friendly analytic services and systems is quite challenging. In this paper we survey the approaches, systems and tools that enable the formulation of analytic queries over KGs expressed in RDF. We identify the main challenges, we distinguish two main categories of analytic queries (domain specific and quality-related), and five kinds of approaches for analytics over RDF. Then, we describe in brief the works of each category and related aspects, like efficiency and visualization. We hope this collection to be useful for researchers and engineers for advancing the capabilities and user-friendliness of methods for analytics over knowledge graphs.

Improving a Library FAQ: Assessment and Reflection of the First Year’s Use

ABSTRACT In 2020, the Leonard Lief Library created a searchable online knowledge base (FAQs) as a complement to virtual reference during the library’s pandemic-related closure. One year of search query data was used to assess the online knowledge base. This paper discusses the assessment’s findings and planned improvements to the FAQs. A content analysis of user queries revealed what users are seeking in the knowledge base. The study examined the actions taken by users after conducting a search to determine the knowledge base’s success rate. The knowledge base was successful in answering user questions almost half of the time. The top three query categories were access, non-library, and instructional. The frequency of access-related queries was expected, due to COVID-19 library building closure. The prevalence of questions about other campus units was unexpected. This finding suggests a perception of the academic library as a source of campus information and supports including this type of information in the library FAQs. This study adds to the body of assessment research within reference services, an area not well represented in the LIS literature. The methodology employed provides a model for assessment of online FAQs that can be adopted by other libraries.

PP95 Health Professionals’ Participation On Health Technology Assessment (HTA) Public Consultation: A Distribution Analysis Of Brazilian HTA In 2021

IntroductionThe Brazilian HTA process includes calls for public consultations, in which society can give its opinion on reports emitted by the National Committee for Health Technology Incorporation (CONITEC). Open and closed queries for public consultation are performed by official formularies and are available online at CONITEC webpage. There are two categories of queries: clinical protocols and guidelines, and incorporation/exclusion demands. Incorporation/exclusion queries are subdivided in two additional categories: opinion and experience, or technical. In this study we analyze health professionals’ technical contributions and their opinion (pro or con) on the inclusion or exclusion of health technologies.MethodsOn November 26th, 2021, formularies concerning concluded public consultations on health technology incorporation/exclusion reports were extracted from CONITEC website for the period, January 1, 2021 to November 26, 2021. Entries on the technical contributions formularies included a close-ended question about the opinion of participants on health technology incorporation/exclusion reports (“favorable”/“against”/“neither”).ResultsA total of 63 health technology incorporation/exclusion queries were carried out during the study period, of which only 4 were exclusions. A total of 7783 contributions were registered. “Patients”, “Family or caregivers”, “Interest on the theme”, and “Health professionals”, accounted for 96.4% (10.9, 15.2, 17.1 and 53% respectively). Health professionals’ participation alone accounted for 4130 entries. Concerning the category “health professionals”, the total number of favorable opinions on the presented documents was 2740 (66.3%), 1306 (31.6%) disagreed, and 84 (2%) had no opinion.ConclusionsHealth professionals can be considered one of the main stakeholders considering HTA for technology incorporation in public health systems. Brazilian HTA reports are submitted to public consultation through queries, which are available open access at the Brazilian National Committee for Health Technology Incorporation website.

An audit of institutional ethics committee queries raised after initial project submission by a single research department at a tertiary referral center in India.

The institutional ethics committees (IECs) raise queries following protocol reviews. The quality of these queries would be a useful metric to assess how well the IEC executes its fundamental role of protecting participants. Queries received after the initial review and replies sent by a single research department were evaluated. A content analysis was done to identify the domains and categories of queries. We categorized these queries as administrative, ethics related, and scientific. The impact of each query in improving the science or safeguarding the rights and safety of research participants (ethics) was evaluated by two authors of this manuscript: one affiliated and the other nonaffiliated to the institute. Kappa statistics were used to evaluate for agreement between the two. A total of 13 studies (investigator-initiated studies [IISs]: 7 and pharmaceutical industry-sponsored studies [PSSs]: 6) formed the final sample size for analysis. The total number of queries was 364 (IIS: 106 and PSS: 258; P < 0.001). With regard to the categories, we found n = 42 (11.54%) to be irrelevant at that stage of the review process; n = 51 (14.01%) were about information already available which the IEC had missed; n = 67 (18.41%) queries where the IEC needed paraphrasing; n = 50 (13.74%) were entirely relevant with the need for further clarification; and n = 154 (42.31%) had been missed by the investigator during the initial submission. The overall agreement between the affiliated and unaffiliated investigators was just 12.9% (P < 0.001). We found that approximately 25% of the queries raised by the IEC were redundant. It is our opinion that this redundancy could have been channeled into greater focus on scientific and ethical aspects of the protocol. Ongoing dialog between investigators and ethics committees may help address this. Perspectives between the affiliated and the unaffiliated investigators with regard to the relevance of queries were grossly different.

Clinical-pharmacological drug information center of Hannover Medical School: experiences and analysis from a tertiary care university hospital

Drug information centers (DICs) are institutions dedicated to provide objective, independent, and up-to-date information on drugs and their rational use. To overcome the lack of recent DIC reports from central Europe, we analyzed all queries (n = 594) submitted to the DIC run by the Institute for Clinical Pharmacology of Hannover Medical School between October 2018 and April 2022. Approximately one in three queries (31.1%; 185/594) was submitted by internists. 82.8% (492/594) of the queries were patient-specific, while the remaining 17.2% (102/594) were general queries. Adverse drug reactions (ADRs), indications/contraindications, and pharmacodynamic interactions (PDIs) represented the three most frequently addressed query categories, being involved in 44.8% (266/594), 43.3% (257/594), and 34.3% (204/594) of all queries, respectively (assignment of more than one category per query was possible). As compared to general queries, patient-specific queries were statistically significantly more often related to ADRs, PDIs, and pharmacokinetic interactions (PKIs) (ADRs: 35.3% vs. 46.7%, P = 0.034; PDIs: 14.7% vs. 38.4%, P < 0.001; PKIs: 20.6% vs. 31.5%, P = 0.028). To demonstrate the complexity of queries submitted to the clinical-pharmacological DIC, we present and comment on an illustrative selection of queries.

Rateworkspace: BIM integrated post-occupancy evaluation system for office buildings

The feedback obtained from occupants regarding their comfort needs and performance of buildings is critical for assessing occupant satisfaction, identifying the operation and maintenance (O&amp;M) issues in time and for improving resource efficiency in buildings. Current facility management (FM) systems and occupant feedback collection practices, however, have limitations in supporting effective decision-making in FM, as they lack the necessary contextual data related to the occupant feedback (e.g., building geometry, systems, elements). Building Information Modeling (BIM)-enabled FM systems are used for combining different types of FM information with building models; however, occupant feedback is still not effectively utilized in FM since it is not integrated with BIM. In this study, a BIM integrated post-occupancy evaluation system prototype is developed for: (1) collecting occupant feedback along with the contextual information related to the feedback items in a structured way, and (2) presenting this information as integrated with BIM to the facility managers. This enables conducting spatio-temporal queries and supports effective decision-making by visualizing the collected feedback. The prototype was designed by using qualitative shadowing with FM teams to identify information needs and use case analysis to determine how contextual data integrated with BIM could be collected from office occupants who are non-technical persons with limited information on building models. This paper identifies the FM query categories that are required to process the occupant feedback and describes the RateWorkSpace prototype developed for office buildings. The deployment of the prototype in a real-world office demonstrates that the proposed system is applicable, practical, usable, and that real-time building performance data can be both collected and analysed with the developed system. This has the potential to increase the effectiveness of the FM and O&amp;M processes, and help to create office spaces with optimized energy use and occupant comfort that also supports occupant well-being and productivity.

Query Processing of Geosocial Data in Location-Based Social Networks

The increasing use of social media and the recent advances in geo-positioning technologies have produced a great amount of geosocial data, consisting of spatial, textual, and social information, to be managed and queried. In this paper, we focus on the issue of query processing by providing a systematic literature review of geosocial data representations, query processing methods, and evaluation approaches published over the last two decades (2000–2020). The result of our analysis shows the categories of geosocial queries proposed by the surveyed studies, the query primitives and the kind of access method used to retrieve the result of the queries, the common evaluation metrics and datasets used to evaluate the performance of the query processing methods, and the main open challenges that should be faced in the near future. Due to the ongoing interest in this research topic, the results of this survey are valuable to many researchers and practitioners by gaining an in-depth understanding of the geosocial querying process and its applications and possible future perspectives.

A Prototypical Network-Based Approach for Low-Resource Font Typeface Feature Extraction and Utilization

This paper introduces a framework for retrieving low-resource font typeface databases by handwritten input. A new deep learning model structure based on metric learning is proposed to extract the features of a character typeface and predict the category of handwrittten input queries. Rather than using sufficient training data, we aim to utilize ancient character font typefaces with only one sample per category. Our research aims to achieve decent retrieval performances over more than 600 categories of handwritten characters automatically. We consider utilizing generic handcrafted features to train a model to help the voting classifier make the final prediction. The proposed method is implemented on the ‘Shirakawa font oracle bone script’ dataset as an isolated ancient-character-recognition system based on free ordering and connective strokes. We evaluate the proposed model on several standard character and symbol datasets. The experimental results showed that the proposed method provides good performance in extracting the features of symbols or characters’ font images necessary to perform further retrieval tasks. The demo system has been released, and it requires only one sample for each character to predict the user input. The extracted features have a better effect in finding the highest-ranked relevant item in retrieval tasks and can also be utilized in various technical frameworks for ancient character recognition and can be applied to educational application development.

Personalized, Sequential, Attentive, Metric-Aware Product Search

The task of personalized product search aims at retrieving a ranked list of products given a user’s input query and his/her purchase history. To address this task, we propose the PSAM model, a Personalized, Sequential, Attentive and Metric-aware (PSAM) model, that learns the semantic representations of three different categories of entities, i.e., users, queries, and products, based on user sequential purchase historical data and the corresponding sequential queries. Specifically, a query-based attentive LSTM (QA-LSTM) model and an attention mechanism are designed to infer users dynamic embeddings, which is able to capture their short-term and long-term preferences. To obtain more fine-grained embeddings of the three categories of entities, a metric-aware objective is deployed in our model to force the inferred embeddings subject to the triangle inequality, which is a more realistic distance measurement for product search. Experiments conducted on four benchmark datasets show that our PSAM model significantly outperforms the state-of-the-art product search baselines in terms of effectiveness by up to 50.9% improvement under NDCG@20. Our visualization experiments further illustrate that the learned product embeddings are able to distinguish different types of products.

Combining Fog and Cloud Computing to Support Spatial Analytics in Smart Cities

Spatial data generated by an Internet of Things (IoT) network is important to assist the spatial analytics process in issues related to smart cities. In these cities, IoT devices generate spatial data constantly. Thus, data can get increasingly voluminous very fast. In this paper, we investigate the challenge of managing these data through the use of a spatial data warehouse designed over a parallel and distributed data processing framework extended with a spatial analytics system. We propose an architecture aimed to assist a smart cities manager in the decision-making process. This architecture integrates a cloud layer where these technologies are located with a fog computing layer for extracting, transforming and loading the data into the spatial data warehouse. Furthermore, we introduce a set of guidelines to aid smart cities managers to implement the proposed architecture. These guidelines describe and discuss important issues that should be faced by the managers. We validate our architecture with a case study that uses real data collected by IoT devices in a smart city. This case study encompasses the execution of three different categories of spatial queries, demonstrating the architecture's efficacy and effectiveness to support spatial analytics in the context of smart cities.

Erectile dysfunction treatment with Phosphodiesterase-5 Inhibitors: Google trends analysis of last 10 years and COVID-19 pandemic.

We aimed to analyze the trend change of the most popular Phosphodiesterase-5 Inhibitors (PDE5i) over time and geography by using Google Trends (GT) data in 10 years period and COVID-19 pandemic. GT is able to generate a "line-graph", showing how interest has increased or decreased over a period within specific territories. The search values for specific terms are indexed as relative search volume (RSV), which is presented on a scale from 0-100. Avarage annual percentage change (AAPC) and RSV were analyzed to evaluate gain or loss of interest in trends. Search terms were generated for Food and Drug Administration (FDA)-approved PDE5i; tadalafil, sildenafil, vardenafil, avanafil, and their most-used brand names. The data was within "worldwide" from 1 January 2010, to 31 December 2020, using the ''global'' query category. The overall interest in PDE5i has doubled. Sildenafil has become the most trend PDE5i of today with a regular increase (AAPC: 0.016, p < 0.01). Although the search trend of tadalafil remained almost constant until 2014, the rate of increase in the last 6 years raised and tadalafil has become the 2nd most popular PDE5i recently (AAPC: 0.007, p < 0.01). For vardenafil there has been a decreased interest (AAPC: -0.009, p < 0.01). There is no significant change in avanafil trend (AAPC: 0.000, p: 0.5). All PDE5i interest on GT decreased notably from February to June 2020. But after June, search trends reached the level before the COVID-19 period in a month. These findings show us, with its increasing prevalence, erectile dysfunction (ED) has become a major public health problem. People from different geographies search the internet for ED treatment options. Patients should be informed that ED may be the first sign of many comorbid diseases, and patients with ED should be referred to a health institution for diagnosis and treatment.

Density-Based Top-K Spatial Textual Clusters Retrieval

So-called spatial web queries retrieve web content representing points of interest, such that the points of interest have descriptions that are relevant to query keywords and are located close to a query location. Two broad categories of such queries exist. The first encompasses queries that retrieve single spatial web objects that each satisfy the query arguments. Most proposals belong to this category. The second category, to which this paper's proposal belongs, encompasses queries that support exploratory user behavior and retrieve sets of objects that represent regions of space that may be of interest to the user. Specifically, the paper proposes a new type of query, the top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> spatial textual cluster retrieval ( <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -STC) query that returns the top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> clusters that (i) are located close to a query location, (ii) contain objects that are relevant with regard to given query keywords, and (iii) have an object density that exceeds a given threshold. To compute this query, we propose a DBSCAN-based approach and an OPTICS-based approach that rely on on-line density-based clustering and that exploit early stop conditions. Empirical studies on real data sets offer evidence that the paper's proposals can find good quality clusters and are capable of excellent performance.

Googling on Colonoscopy: A Retrospective Analysis of Search Engine Statistics.

PurposeColonoscopy is a gold standard for screening and diagnosis of colorectal cancer (CRC). The data from the search engine may reveal what information on coloscopy gains the attention of Internet users. We aimed to investigate Google searches trends and terms related to colonoscopy.Patients and MethodsWe retrieved statistics searches related to colonoscopy using Google Trends (GT) and Google Ads (GA) for the period from April 2016 to March 2020. The GT data was used for the analysis of time and regional search patterns worldwide. GA data for Australia, Canada, Ireland, New Zealand (NZ), Poland, the United Kingdom (UK), and the United States (US) were used to calculate the search volume of categories of queries related to colonoscopy.ResultsGlobally, the relative search volume on colonoscopy has increased until the COVID-19 outbreak and revealed seasonal variation: the highest interest was observed in March (CRC awareness month), and the lowest during December (Christmas holidays). The highest number of searches per 1000 Google users-years was done in Poland (59.62) and the lowest in the UK (19.46). Most commonly, Google users searched for details on colonoscopy techniques (Australia, Canada, Ireland, NZ), anesthesia during the procedure (Poland), facility performing colonoscopy (UK, US). In all seven countries, less than 2% of queries concerned with bowel preparation before the procedure.ConclusionBefore the COVID-19 pandemic, the interest in colonoscopy has increased among Google users. Google users may underestimate the importance of proper bowel preparation.

A timely encounter and a loss

A timely encounter and a loss