Individual Queries Research Articles

Managing fatigue after an acquired brain injury: a pilot randomised controlled trial and qualitative investigation

Background/AimsFatigue after brain injury is common and enduring, and rehabilitation improves patient understanding and strategy development. The aim of this study was to determine whether therapy supporting fatigue management can be provided economically in groups to inpatients undertaking rehabilitation.MethodsA mixed-methods study was undertaken, including a pilot randomised control trial and a parallel qualitative investigation. A total of 78 adult inpatients with moderate to severe acquired brain injury were recruited to the study; 40 were allocated to routine usual care and 38 were allocated to the intervention, which consisted of routine usual care plus four group therapy sessions that took place over 2 weeks, run by an experienced occupational therapist. Participants were aged between 18 and 65 years and were considered to be able to manage learning in a group setting. Outcome measures were collected at baseline and at the end of the therapy intervention (Barrow Neurological Institute Fatigue Scale, Quality of Life after Brain Injury questionnaire and a Fatigue Knowledge questionnaire assessing knowledge related to the aims of the education modules). A qualitative study was conducted after the quantitative investigation in the form of patient interviews. A total of 10 patients were interviewed to discuss their fatigue journey and impressions of receiving fatigue management therapy in a group.ResultsA repeated measures analysis of variance with time as a within-subject factor and group as a between-subjects factor showed no interaction effect of group × time for fatigue, quality of life or acquisition of knowledge; however, knowledge improved over time, irrespective of group allocation (P<0.01). Although most participants found the group work satisfactory in terms of overall knowledge development, a number would have preferred to address their specific fatigue issues in an individual session. Participants described a confusing journey understanding fatigue symptoms before therapy, but reported an improved understanding and acquiring management strategies after receiving the educational therapy sessions.ConclusionsEducation regarding fatigue following brain injury can be provided successfully to inpatients receiving rehabilitation in group settings. However, it may be important to build in an additional individual therapy session to address each patient's individual issues and queries.

What on-line searches tell us about public interest and potential impact on behaviour in response to minimum unit pricing of alcohol in Scotland.

To investigate whether the introduction of minimum unit pricing (MUP) in Scotland on 1 May 2018 was reflected in changes in the likelihood of alcohol-related queries submitted to an internet search engine, and in particular whether there was any evidence of increased interest in purchasing of alcohol from outside Scotland. Observational study in which individual queries to the internet Bing search engine for 2018 in Scotland and England were captured and analysed. Fluctuations over time in the likelihood of specific topic searches were examined. The patterns seen in Scotland were contrasted with those in England. Scotland and England. People who used the Bing search engine during 2018. Numbers of daily queries submitted to Bing in 2018 on eight alcohol-related topics expressed as a proportion of queries on that day on any topic. These daily likelihoods were smoothed using a 14-day moving average for Scotland and England separately. There were substantial peaks in queries about MUP itself, cheap sources of alcohol and online alcohol outlets at the time of introduction of MUP in May 2018 in Scotland, but not England. These were relatively short-lived. Queries related to intoxication and alcohol problems did not show a MUP peak, but were appreciably higher in Scotland than in England throughout 2018. Analysis of internet search engine queries appears to show that a fraction of people in Scotland may have considered circumventing minimum unit pricing in 2018 by looking for on-line alcohol retailers. The overall higher levels of queries related to alcohol problems in Scotland compared with England mirrors the corresponding differences in alcohol consumption and harms between the countries.

Knowledge Graph-Based Spatial-Aware User Community Preference Query Algorithm for LBSNs

Abstract User community preference in Location-Based Social Networks (LBSNs) can meet the diversified location demands of group LBSN users. Although individual's location-based service recommendation or personal spatial preference query problem has been well addressed by many studies, user group or user community preference query is still under way and most only consider the spatial distance factor, which causes accuracy cannot satisfy user demands. To solve the user community spatial preference problem and improve its performance, a knowledge graph-based spatial-aware user community preference query algorithm, Type R-tree (tR-tree) Query Algorithm (TRQA) is proposed to effectively discover user's community preference from LBSNs considering both location semantic information and preference weight of users' Points of Interest (POIs). To achieve this goal, this paper first leverages the tR-tree spatial index to improve query efficiency. Then a community satisfaction degree model based on knowledge graphs is introduced to comprehensively evaluate whether the POI can best meet the preference requirements of a user community. The experimental results show that TRQA has outperformed Perceptual Quality Adaptation Algorithm (PQA) in terms of pruning efficiency and query time. The query time of our proposed algorithm is 80% shorter than PQA as the number of users in the user community changes.

Evaluating fluctuations in homicide: crowdsourcing trends and assessing sentiments of change

ABSTRACT Guided by both recent anecdotal speculation of and realized statistical spikes in homicide and violence in several major U.S. cities, this study presents results of an effort to empirically examine the nature and correlates of recent changes in homicide rates (i.e. 2012–2018) with particular attention to a spike in 2014–2016 in select U.S. cities. Data were derived from multiple sources using a unique mixed-methods approach. To measure the magnitude of changes in homicide rates in recent years, quantitative homicide data were collected from the Uniform Crime Reports, media articles, individual police department queries, Project Safe Neighborhood grantee sites, and the Major Cities Chiefs Association, Violent Crime Survey. Qualitative assessments were then captured from both academic experts as well as practitioners in the field relative to the possible correlates of these observed trends. Quantitative findings substantiate that homicide rates in select U.S. cities were increasing but variation was also found with rates in other cities having remained stable or even declining over the period studied. This work delineates the practical contours of these correlates and finds concordance of practitioner beliefs with research findings while providing a glimpse toward future actions in response to both real and perceived fluctuations.

Categorizing Multi-Label Product Questionnaires through SVM Based Click stream

In this paper, Question Categorization (QC) has been studied most primarily in order to understand customers' search intention. In both of these searches, the items in the question list relate to the category label belonging to the taxonomy tree that is being examined. Despite this, search queries about the product usually vary depending on what is vague, and introduce new products over time, seasonal trends and narrow. Traditional supervised approaches to E-Commerce QC are not possible due to the high volume of traffic and high cost for manual annotation in E-Commerce search engines. Here, clickstream data is utilized to determine the effectiveness of a channel's marketplace. So, using the customer's click concept, to collect large-scale question categorization data, this paper uses unsupervised methods that means SVM algorithm is mainly used in this system. Here the data is in the multiclass and multi-label classifier is used to classify them. This paper gets on a large multi-label data set with specific and individual queries from a specific category. In this paper, a comparison of different sophisticated text classifiers is viewed. This paper calculates the micro-F1 scores of top and leaf, which are considered to be a linear SVM-ensemble.

Collaborative query reformulation in tourism information search

Purpose Collaborative information search (CIS) is a growing and significant research area. Query formulation and reformulation is an important search strategy in information search. However, limited research has investigated query behavior during CIS. The purpose of this paper is to characterize collaborative query reformulation (CQR) by exploring the sources of collaborative query (CQ) terms and the types and patterns of CQR in the context of tourism information search. Design/methodology/approach An empirical study was designed to investigate search query reformulation as tourists performed CIS on a devised interface. A total of 36 participants (in 18 pairs) took part in the study; data were documented in pre- and post-search questionnaires, search logs and chat logs. Findings The findings show that participants intermixed individual search and collaborative search during CIS. Participants constructed CQ terms mainly by selecting terms from individual search queries and discussion chat logs. Eight types of CQR were identified, with specialization (82 percent) accounting for the most used search tactics. At most times, participants were found to add terms to the previous query. Findings demonstrated 27 specific CQR patterns; in excess of two-third participants (69 percent) took only one move to reformulate CQ by adding terms, or replacing/using new words. Practical implications The results of this research can be used to inform the design of search systems supporting collaborative querying in CIS. Originality/value This study is highlighting an important research direction of CQ reformulation in collaborative search while previous studies of the topic are limited, comparing to the vast body of work on query reformulation in individual information search using regular search systems.

Interactive faceted query suggestion for exploratory search: Whole‐session effectiveness and interaction engagement

AbstractThe outcome of exploratory information retrieval is not only dependent on the effectiveness of individual responses to a set of queries, but also on relevant information retrieved during the entire exploratory search session. We study the effect of search assistance, operationalized as an interactive faceted query suggestion, for both whole‐session effectiveness and engagement through interactive faceted query suggestion. A user experiment is reported, where users performed exploratory search tasks, comparing interactive faceted query suggestion and a control condition with only conventional typed‐query interaction. Data comprised of interaction and search logs show that the availability of interactive faceted query suggestion substantially improves whole‐session effectiveness by increasing recall without sacrificing precision. The increased engagement with interactive faceted query suggestion is targeted to direct situated navigation around the initial query scope, but is not found to improve individual queries on average. The results imply that research in exploratory search should focus on measuring and designing tools that engage users with directed situated navigation support for improving whole‐session performance.

CongraPlus: Towards Efficient Processing of Concurrent Graph Queries on NUMA Machines

Graph analytics has been routinely used to solve problems in a wide range of real-life applications. Efficiently processing concurrent graph analytics queries in a multiuser environment is highly desirable as we enter a world of edge device oriented services. Existing research, however, primarily focuses on analyzing a single, large graph dataset and leaves the efficient processing of multiple mid-sized graph analytics queries an intriguing yet challenging open problem. In this work, we investigate the scheduling of concurrent graph analytics queries on NUMA machines. We analyze the performance of several graph analytics algorithms and observe that they have diminishing performance returns as the number of processor cores increases. With concurrent graph analytics, such diminishing returns translate to no or even negative performance gains because of increasing contention on shared hardware resources. We also demonstrate the unpredictability of memory bandwidth usage for numerous graph analytics algorithms, which can lead to sub-optimal performance due to its potential to cause severe memory bandwidth contention. Motivated by the above observations, we propose CongraPlus, a NUMA-aware scheduler that intelligently manages concurrent graph analytics queries for better system throughput and memory bandwidth efficiency. CongraPlus collects the memory bandwidth consumption characteristics of graph analytics queries via offline profiling and eliminates memory bandwidth contention by computing the optimal sequence to launch queries. It also avoids computation resource contention by assigning a certain number of processor cores to the individual queries. We implement CongraPlus in C++ on top of the Ligra graph processing framework and test it with judiciously selected graph processing query combinations. Our results reveal that CongraPlus-based schemes improve query throughput by 30 percent compared to the conventional approach. It also exhibits a much better quality of service and scalability.

Envisioning the future of academic cartographic education

Envisioning the future of academic cartographic education

Characterising postgraduate students’ corpus query and usage patterns for disciplinary data-driven learning

AbstractData-driven learning (DDL; Johns, 1991), involving students’ hands-on use of corpora for self-guided language learning, is a methodology now increasingly used in many tertiary contexts to enhance the teaching of disciplinary postgraduate thesis writing. However, there are still few studies tracking students’ actual engagement with corpora for DDL. This mixed-methods study reports on the tracking of students’ corpus use via a purpose-built corpus query and data visualisation platform integrated into a large postgraduate disciplinary thesis writing program at a university in Hong Kong. Data on corpus usage history (e.g. times of access, duration of use), query syntax (e.g. query lexis/phraseology and use of wildcards and part-of-speech tags), query function (e.g. frequency lists/distribution, concordance sorting and collocation) and query filters (e.g. searches by faculty, discipline, or thesis section) were collected from 327 students spanning over 11,000 individual corpus queries. The results show significant interdisciplinary and inter-/intra-user trends and variation in the use of particular corpus functions and query syntax adopted by corpus users. Students varied in the type of knowledge (e.g. domain-specific, language-specific) they were accessing, and frequently went beyond the exemplars of the DDL course materials to generate unique queries under their own initiative. Qualitative case study data from three corpus users’ activity logs also show distinctive individual corpus engagement by query frequency and function. These data provide a clearer insight into what students actually do during DDL and the different directions and trajectories that individual users take as a result of DDL. All accompanying DDL tasks are also included as supplementary materials.

A new approach for query expansion using Wikipedia and WordNet

Query expansion (QE) is a well-known technique used to enhance the effectiveness of information retrieval. QE reformulates the initial query by adding similar terms that help in retrieving more relevant results. Several approaches have been proposed in literature producing quite favorable results, but they are not evenly favorable for all types of queries (individual and phrase queries). One of the main reasons for this is the use of the same kind of data sources and weighting scheme while expanding both the individual and the phrase query terms. As a result, the holistic relationship among the query terms is not well captured or scored. To address this issue, we have presented a new approach for QE using Wikipedia and WordNet as data sources. Specifically, Wikipedia gives rich expansion terms for phrase terms, while WordNet does the same for individual terms. We have also proposed novel weighting schemes for expansion terms: in-link score (for terms extracted from Wikipedia) and a tf-idf based scheme (for terms extracted from WordNet). In the proposed Wikipedia-WordNet-based QE technique (WWQE), we weigh the expansion terms twice: first, they are scored by the weighting scheme individually, and then, the weighting scheme scores the selected expansion terms concerning the entire query using correlation score. The proposed approach gains improvements of 24% on the MAP score and 48% on the GMAP score over unexpanded queries on the FIRE dataset. Experimental results achieve a significant improvement over individual expansion and other related state-of-the-art approaches. We also analyzed the effect on retrieval effectiveness of the proposed technique by varying the number of expansion terms.

Oblivious DNS: Practical Privacy for DNS Queries

Abstract Virtually every Internet communication typically involves a Domain Name System (DNS) lookup for the destination server that the client wants to communicate with. Operators of DNS recursive resolvers—the machines that receive a client’s query for a domain name and resolve it to a corresponding IP address—can learn significant information about client activity. Past work, for example, indicates that DNS queries reveal information ranging from web browsing activity to the types of devices that a user has in their home. Recognizing the privacy vulnerabilities associated with DNS queries, various third parties have created alternate DNS services that obscure a user’s DNS queries from his or her Internet service provider. Yet, these systems merely transfer trust to a different third party. We argue that no single party ought to be able to associate DNS queries with a client IP address that issues those queries. To this end, we present Oblivious DNS (ODNS), which introduces an additional layer of obfuscation between clients and their queries. To do so, ODNS uses its own authoritative namespace; the authoritative servers for the ODNS namespace act as recursive resolvers for the DNS queries that they receive, but they never see the IP addresses for the clients that initiated these queries. We present an initial deployment of ODNS; our experiments show that ODNS introduces minimal performance overhead, both for individual queries and for web page loads. We design ODNS to be compatible with existing DNS protocols and infrastructure, and we are actively working on an open standard with the IETF.

Don't Even Ask

This paper presents a vision and description for query control, which is a paradigm for database access control. In this model, individual queries are examined before being executed and are either allowed or denied by a pre-defined policy. Traditional view-based database access control requires the enforcer to view the query, the records, or both. That may present difficulty when the enforcer is not allowed to view database contents or the query itself. This discussion of query control arises from our experience with privacy-preserving encrypted databases, in which no single entity learns both the query and the database contents. Query control is also a good fit for enforcing rules and regulations that are not well-addressed by view-based access control. With the rise of federated database management systems, we believe that new approaches to access control will be increasingly important.

Performance analysis of relational databases Oracle and MS SQL based on desktop application

Comparison of efficiency is not a trivial phenomenon because of disparities between different database systems. This paper presents a methodology of comparing relational database systems in respect of mean time of execution individual DML queries containing subqueries and conjunction of tables. The presented methodology can be additionally accommodated to studies of efficiency in a range of database system itself (study of queries executed directly in database engine). The described methodology allows to receive statement telling which database system is better in comparison to another in dependency of functionalities fulfilled by external application. In the article the analysis of mean time of execution individual DML queries was performed.Two research hypotheses have been put forward: "Microsoft SQL Server database system needs less time to execute INSERT and UPDATE queries than Oracle database" and "Oracle database system needs less time to execute DML queries with binary data than SQL Server"

Learning to Adaptively Rank Document Retrieval System Configurations

Modern Information Retrieval (IR) systems have become more and more complex, involving a large number of parameters. For example, a system may choose from a set of possible retrieval models (BM25, language model, etc.), or various query expansion parameters, whose values greatly influence the overall retrieval effectiveness. Traditionally, these parameters are set at a system level based on training queries, and the same parameters are then used for different queries. We observe that it may not be easy to set all these parameters separately, since they can be dependent. In addition, a global setting for all queries may not best fit all individual queries with different characteristics. The parameters should be set according to these characteristics. In this article, we propose a novel approach to tackle this problem by dealing with the entire system configurations (i.e., a set of parameters representing an IR system behaviour) instead of selecting a single parameter at a time. The selection of the best configuration is cast as a problem of ranking different possible configurations given a query. We apply learning-to-rank approaches for this task. We exploit both the query features and the system configuration features in the learning-to-rank method so that the selection of configuration is query dependent. The experiments we conducted on four TREC ad hoc collections show that this approach can significantly outperform the traditional method to tune system configuration globally (i.e., grid search) and leads to higher effectiveness than the top performing systems of the TREC tracks. We also perform an ablation analysis on the impact of different features on the model learning capability and show that query expansion features are among the most important for adaptive systems.

Perceptions of multimodal learner-driven feedback in EAP

The ‘Learner-Driven Feedback’ (LDF) procedure is innovative in its combination of approaches to feedback provision in second/foreign language writing instruction which a) respond to learners’ individual queries and b) employ digital modes of delivery ([anonymised ] 2016). In LDF, formative feedback is given by the teacher, but the learners indicate on what aspects of their writing and in what mode they would like to receive feedback. In this study, LDF was used with 36 postgraduate students of an English for Academic Purposes (EAP) class at a university in Germany. The presented analysis of survey data highlights high levels of receptivity to LDF and reveals students’ perceptions of the affordances of different digital delivery modes for improving their writing. While feedback in the form of margin comment bubbles was perceived to be helpful to foster improvement in general language accuracy, students preferred emailed or audio-recorded feedback for the improvement of their writing related academic skills. The potential applicability of LDF for EAP and other writing instruction contexts is discussed based on these findings.

Improved Comorbidity Capture Using a Standardized 1‐Step Quality Improvement Documentation Tool

Objective To assess the impact of implementation of a "1-step" documentation query system on comorbidity capture and quality outcomes within the Department of Otolaryngology-Head and Neck Surgery. Methods Implementation of the 1-step documentation query system was instituted for all otolaryngology-head and neck surgery faculty at a single institution. Individual query responses and impact metrics were analyzed. Departmental case-mix index (CMI), risk of mortality (ROM), and severity of illness (SOI) were collated over a 14-month implementation period and compared to a 12-month preimplementation period. Results A total of 226 documentation queries occurred during the program pilot period, with an 86.7% response rate. Of queries with a response, 91.0% resulted in a significant impact for the hospitalization diagnoses-related group, ROM, or SOI. Departmental CMI increased from 2.73 to 2.91 over the implementation period, and observed/expected mortality ratio decreased from 0.50 to 0.42 pre- to postimplementation. Discussion With increasing emphasis on quality metrics outcomes within the United States health care system, there is a need for institutions to accurately capture the complexity and acuity of the patients they care for. There was a positive change in quality outcomes metrics, including ROM, SOI, and CMI over the first year of deployment of the 1-step documentation query process. Implications for Practice Clinical severity metrics are becoming increasingly important to otolaryngologists, as insurers move to severity-adjusted profiles. The 1-step documentation query process provides a reproducible and effective way for clinical documentation specialists and physicians to collaborate on improving departmental clinical severity metrics.

Impact analysis of data placement strategies on query efforts in distributed RDF stores

In the last years, scalable RDF stores in the cloud have been developed, where graph data is distributed over compute and storage nodes for scaling efforts of query processing and memory needs. One main challenge in these RDF stores is the data placement strategy that can be formalized in terms of graph covers. These graph covers determine whether (a) the triples distribution is well-balanced over all storage nodes (storage balance) (b) different query results may be computed on several compute nodes in parallel (vertical parallelization) and (c) individual query results can be produced only from triples assigned to few – ideally one – storage node (horizontal containment). We analyse the impact of three most commonly used graph cover strategies in these terms and found out that balancing query workload reduces the query execution time more than reducing data transfer over network. To this end, we present our novel benchmark and open source evaluation platform Koral.

Griffin

Interactive information retrieval services, such as enterprise search and document search, must provide relevant results with consistent, low response times in the face of rapidly growing data sets and query loads. These growing demands have led researchers to consider a wide range of optimizations to reduce response latency, including query processing parallelization and acceleration with co-processors such as GPUs. However, previous work runs queries either on GPU or CPU, ignoring the fact that the best processor for a given query depends on the query's characteristics, which may change as the processing proceeds. We present Griffin, an IR systems that dynamically combines GPU- and CPU-based algorithms to process individual queries according to their characteristics. Griffin uses state-of-the-art CPU-based query processing techniques and incorporates a novel approach to GPU-based query evaluation. Our GPU-based approach, as far as we know, achieves the best available GPU search performance by leveraging a new compression scheme and exploiting an advanced merge-based intersection algorithm. We evaluate Griffin with real world queries and datasets, and show that it improves query performance by 10x compared to a highly optimized CPU-only implementation, and 1.5x compared to our GPU-approach running alone. We also find that Griffin helps reduce the 95th-, 99th-, and 99.9th-percentile query response time by 10.4x, 16.1x, and 26.8x, respectively.

Impact Analysis of Data Placement Strategies on Query Efforts in Distributed RDF Stores

In the last years, scalable RDF stores in the cloud have been developed, where graph data is distributed over compute and storage nodes for scaling efforts of query processing and memory needs. One main challenge in these RDF stores is the data placement strategy that can be formalized in terms of graph covers. These graph covers determine whether (a) the triples distribution is well-balanced over all storage nodes (storage balance) (b) different query results may be computed on several compute nodes in parallel (vertical parallelization) and (c) individual query results can be produced only from triples assigned to few — ideally one — storage node (horizontal containment). We analyse the impact of three most commonly used graph cover strategies in these terms and found out that balancing query workload reduces the query execution time more than reducing data transfer over network. To this end, we present our novel benchmark and open source evaluation platform Koral.