Definition Of Queries Research Articles

Tuning fuzzy SPARQL queries

During the last years, the study of fuzzy database query languages has attracted the attention of many researchers. In this line of research, our group has proposed and developed FSA-SPARQL (Fuzzy Sets and Aggregators based SPARQL), which is a fuzzy extension of the Semantic Web query language SPARQL. FSA-SPARQL works with fuzzy RDF datasets and allows the definition of fuzzy queries involving fuzzy conditions through fuzzy connectives and aggregators. However, there are two main challenges to be solved for the practical applicability of FSA-SPARQL. The first problem is the lack of fuzzy RDF data sources. The second is how to customize fuzzy queries on fuzzy RDF data sources. Our research group has also recently proposed a fuzzy logic programming language called FASILL that offers powerful tuning capabilities that can accept applications in many fields. The purpose of this paper is to show how the FASILL tuning capabilities serve to accomplish in a unified framework both challenges in FSA-SPARQL: data fuzzification and query customization. More concretely, from a FSA-SPARQL to FASILL transformation, data fuzzification and query customization in FSA-SPARQL become FASILL tuning problems. We have validated the approach with queries against datasets from online communities.

On the relevance of query definition in the performance of 3D ligand-based virtual screening

Ligand-based virtual screening (LBVS) methods are widely used to explore the vast chemical space in the search of novel compounds resorting to a variety of properties encoded in 1D, 2D or 3D descriptors. The success of 3D-LBVS is affected by the overlay of molecular pairs, thus making selection of the template compound, search of accessible conformational space and choice of the query conformation to be potential factors that modulate the successful retrieval of actives. This study examines the impact of adopting different choices for the query conformation of the template, paying also attention to the influence exerted by the structural similarity between templates and actives. The analysis is performed using PharmScreen, a 3D LBVS tool that relies on similarity measurements of the hydrophobic/philic pattern of molecules, and Phase Shape, which is based on the alignment of atom triplets followed by refinement of the volume overlap. The study is performed for the original DUD-E+ database and a Morgan Fingerprint filtered version (denoted DUD-E+-Diverse; available in https://github.com/Pharmacelera/Query-models-to-3DLBVS), which was prepared to minimize the 2D resemblance between template and actives. Although in most cases the query conformation exhibits a mild influence on the overall performance, a critical analysis is made to disclose factors, such as the content of structural features between template and actives and the induction of conformational strain in the template, that underlie the drastic impact of the query definition in the recovery of actives for certain targets. The findings of this research also provide valuable guidance for assisting the selection of the query definition in 3D LBVS campaigns.Graphical

Everest: GPU-Accelerated System for Mining Temporal Motifs

Temporal motif mining is the task of finding the occurrences of subgraph patterns within a large input temporal graph that obey the specified structural and temporal constraints. Despite its utility in several critical application domains that demand high performance ( e.g. , detecting fraud in financial transaction graphs), the performance of existing software is limited on commercial hardware platforms, in that it runs for tens of hours. This paper presents Everest---a system that efficiently maps the workload of mining (supports both enumeration and counting) temporal motifs to the highly parallel GPU architecture. In particular, using an input temporal graph and a more expressive user-defined temporal motif query definition compared to prior works, Everest generates an execution plan and runtime primitives that optimize the workload execution by exploiting the high compute throughput of a GPU. Everest generates motif-specific mining code to reduce long-latency memory accesses and frequent thread divergence operations. Everest incorporates novel low-cost runtime mechanisms to enable load balancing to improve GPU hardware utilization. To support large graphs that do not fit on GPU memory, Everest also supports multi-GPU execution by intelligently partitioning the edge list that prevents inter-GPU communication. Everest hides the implementation complexity of presented optimizations away from the targeted system user for better usability. Our evaluation shows that, using proposed optimizations, Everest improves the performance of a baseline GPU implementation by 19X, on average.

Improving speculative query execution support by the use of the hypergraph representation

Speculative support for query execution in Relational Database Management Systems (RDBMSs) based on a graph-analytic approach is the subject of this paper. Optimized speculative queries are defined by analyzing graph representations of a stream of input queries arriving to a RDBMS. The analysis is permanently performed based on a Speculation Window moving on the RDBMS input query queue. The proposed optimization concerns both the definition of the speculative queries to be executed and the methods of speculative query results assignment to incoming input queries. The paper shows how to design RDBMS query execution speculative support using two kinds of the query graph representations: the query multigraphs and the query hypergraphs. In particular, the paper proposes an improvement of the use of the speculative query results defined based on the query multigraph analysis by an additional assignment to queries in an extension of the Speculation Window in the input query queue. The additional assignment is based on the analysis of the speculative and user input queries represented jointly in a common hypergraph. Two approaches to the proposed speculative results improvement are discussed which increase the speculative result life time. The properties of the speculative query execution support based on the mentioned query graph representations and algorithmic approaches have been assessed by the experiments with the simulated execution of the query testbed sets based on the popular TPC-H benchmark.

SAXRegEx: Multivariate time series pattern search with symbolic representation, regular expression, and query expansion

We present SAXRegEx, a method for pattern search in multivariate time series in the presence of various distortions, such as duration variation, warping, and time delay between signals. For example, in the automotive industry, calibration engineers spontaneously search for event-induced patterns in new measurements under time pressure. Current methods do not sufficiently address duration (horizontal along the time axis) scaling and inter-track time delay. One reason is that it can be overwhelmingly complex to consider scaling and warping jointly and analyze temporal dynamics and attribute interrelation simultaneously. SAXRegEx meets this challenge with a novel symbolic representation adapted to handle time series with multiple tracks. We employ methods from text retrieval, i.e., regular expression matching, to perform a pattern retrieval and develop a novel query expansion algorithm to deal flexibly with pattern distortions. Experiments show the effectiveness of our approach, especially in the presence of such distortions, and its efficiency surpassing the benchmarking methods. We have developed a user interface with the emphasis on multivariate query definition with inter-track time shifts and algorithm parameter setting. While we design the method primarily for automotive data, it is well transferable to other domains.

Group Tracking for Video Monitoring Systems: A Spatio-Temporal Query Processing Approach

Recently, many video monitoring systems utilize deep learning technologies to recognize locations and trajectories of people in video data. In video monitoring systems, a fast discovery of human groups is an important task for several applications, for example, crime surveillance, contact tracing, and customer behavior analysis. To tackle the demand, we propose a group tracking method. First, we propose a spatial proximity definition and define a novel query type, a group tracking query that considers characteristics of video data. A group tracking query retrieves the groups that travel for more than a certain amount of video frame within a certain distance. We propose an efficient query processing method that exploits the spatio-temporal characteristics of groups. Through extensive experiments using real-world datasets, we verify the efficiency and effectiveness of our query definition and query processing method.

Ergonomics Evaluation Using Motion Capture Technology—Literature Review

Due to the increasingly high proportion of manual activities in production processes, there is a constant risk of musculoskeletal disorders or work-related injuries. The risk of these problems is exacerbated by the trend towards an ageing working population. European legislation is pressing for improved working conditions to eliminate the risks associated with health problems for workers. For this reason, the application of ergonomics in this field is growing. Musculoskeletal disorders, which are most often caused by inappropriate working postures, are a major problem. There are many methods for evaluating working postures. However, there is a high degree of subjectivity in the risk assessment. Motion capture kinematic suits can ensure the objectivity of the assessment. This article discusses research on ergonomics assessment using motion capture technology. A systematic literature search method was used for the research, beginning with the determination of the research procedure, through the definition of the research queries, to the formulation of the research itself to identify relevant sources. The study presents the most widely used methods for assessing the ergonomics of work positions using motion capture technology, their advantages, and disadvantages. It also follows the trend in the number of publications between 2010 and 2022 in countries where the topic is most frequently addressed and in the industries where motion capture technology is used for ergonomics assessment in general. The research showed that this approach is most often used in industry and logistics, and less frequently in healthcare and sport. The authors agree that the most frequently used ergonomics assessment methods are not complex enough to be used in combination with motion capture and that a combination of the two is needed. At the same time, this technology has become very important in the field of ergonomic evaluation of work positions, offering a higher degree of objectivity, or can be combined with the use of virtual reality, but the evaluation systems are still not error-free and there is a need for continuous improvement.

Data Stream Processing for Packet-Level Analytics.

One of the most challenging tasks for network operators is implementing accurate per-packet monitoring, looking for signs of performance degradation, security threats, and so on. Upon critical event detection, corrective actions must be taken to keep the network running smoothly. Implementing this mechanism requires the analysis of packet streams in a real-time (or close to) fashion. In a softwarized network context, Stream Processing Systems (SPSs) can be adopted for this purpose. Recent solutions based on traditional SPSs, such as Storm and Flink, can support the definition of general complex queries, but they show poor performance at scale. To handle input data rates in the order of gigabits per seconds, programmable switch platforms are typically used, although they offer limited expressiveness. With the proposed approach, we intend to offer high performance and expressive power in a unified framework by solely relying on SPSs for multicores. Captured packets are translated into a proper tuple format, and network monitoring queries are applied to tuple streams. Packet analysis tasks are expressed as streaming pipelines, running on general-purpose programmable network devices, and a second stage of elaboration can process aggregated statistics from different devices. Experiments carried out with an example monitoring application show that the system is able to handle realistic traffic at a 10 Gb/s speed. The same application scales almost up to 20 Gb/s speed thanks to the simple optimizations of the underlying framework. Hence, the approach proves to be viable and calls for the investigation of more extensive optimizations to support more complex elaborations and higher data rates.

CMIID: A comprehensive medical information identifier for clinical search harmonization in Data Safe Havens

Over the last decades clinical research has been driven by informatics changes nourished by distinct research endeavors. Inherent to this evolution, several issues have been the focus of a variety of studies: multi-location patient data access, interoperability between terminological and classification systems and clinical practice and records harmonization. Having these problems in mind, the Data Safe Haven paradigm emerged to promote a newborn architecture, better reasoning and safe and easy access to distinct Clinical Data Repositories. This study aim is to present a novel solution for clinical search harmonization within a safe environment, making use of a hybrid coding taxonomy that enables researchers to collect information from multiple repositories based on a clinical domain query definition. Results show that is possible to query multiple repositories using a single query definition based on clinical domains and the capabilities of the Unified Medical Language System, although it leads to deterioration of the framework response times. Participants of a Focus Group and a System Usability Scale questionnaire rated the framework with a median value of 72.5, indicating the hybrid coding taxonomy could be enriched with additional metadata to further improve the refinement of the results and enable the possibility of using this system as data quality tagging mechanism.

A survey of typical attributed graph queries

Graphs are commonly used for representing complex structures such as social relationships, biological interactions, and knowledge bases. In many scenarios, graphs not only represent topological relationships but also store the attributes that denote the semantics associated with their vertices and edges, known as attributed graphs. Attributed graphs can meet demands for a wide range of applications, and thus a variety of queries on attributed graphs have been proposed. However, these diverse types of attributed graph queries have not been systematically investigated yet. In this paper, we provide an extensive survey of several typical types of attributed graph queries. We propose a taxonomy of attributed graph queries based on query inputs and outputs. We summarize the definitions of queries that fall into each category and present a fine-grained classification of queries within each category by analyzing the semantics and algorithmic motivations behind these queries. Moreover, we discuss the insights of how existing studies address the technical challenges of query processing and outline several promising future research directions.

Interoperable Access to Heterogeneous Test Knowledge

Test processes of complex mechatronic systems became so heterogeneous that a direct knowledge transfer from test cases is necessary in the product lifecycle. In contrast to the necessity, the seamless access, exchange and direct usage of test case related knowledge is not feasible any longer considering the growth heterogeneity and corresponding horizontal and vertical interoperability efforts. Especially, the exchange of test knowledge from test cases related to best practices of stimuli and functional behaviour checks are codified in different test case languages. These test patterns are spread over thousands of test cases and are hidden in proprietary test management tools. The vision of the paper is to achieve interoperability of test case related content for the product lifecycle management. To achieve this vision, this paper presents an approach, how test cases of distributed silos can be aggregated to a test case related Test Knowledge Base. In addition, the paper presents a querying approach to use the test knowledge in an efficient manner. For that purpose, the paper demonstrates the definition of complex search queries without the need of knowing any query language to meet the envisaged user group of tester. The import of test cases and the corresponding usage for the developed pattern search is evaluated based on an aircraft related use case.

Non-Query-Based Pattern Mining and Sentiment Analysis for Massive Microblogging Online Texts

Pattern mining has been widely studied in the last decade given its great interest for research and its numerous applications in the real world. In this paper the definition of query and non-query based systems is proposed, highlighting the needs of non-query based systems in the era of Big Data. For this, we propose a new approach of a non-query based system that combines association rules, generalized rules and sentiment analysis in order to catalogue and discover opinion patterns in the social network Twitter. Association rules have been previously applied for sentiment analysis, but in most cases, they are used once the process of sentiment analysis is finished to see which tokens appear commonly related to a certain sentiment. On the other hand, they have also been used to discover patterns between sentiments. Our work differs from these in that it proposes a non-query based system which combines both techniques, in a mixed proposal of sentiment analysis and association rules to discover patterns and sentiment patterns in microblogging texts. The obtained rules generalize and summarize the sentiments obtained from a group of tweets about any character, brand or product mentioned in them. To study the performance of the proposed system, an initial set of 1.7 million tweets have been employed to analyse the most salient sentiments during the American pre-election campaign. The analysis of the obtained results supports the capability of the system of obtaining association rules and patterns with great descriptive value in this use case. Parallelisms can be established in these patterns that match perfectly with real life events.

Heart failure with insulin degludec versus glargine U100 in patients with type 2 diabetes at high risk of cardiovascular disease: DEVOTE 14

BackgroundHeart failure (HF) is a common cardiovascular complication of type 2 diabetes (T2D). This secondary analysis investigated baseline factors and treatment differences associated with risk of hospitalization for HF (hHF), and the possible association between severe hypoglycemia and hHF.MethodsDEVOTE was a treat-to-target, double-blind cardiovascular outcomes trial in patients (n = 7637) with T2D and high cardiovascular risk randomized to insulin degludec (degludec) or insulin glargine 100 units/mL (glargine U100). The main endpoint of this secondary analysis was time to first hHF (standardized MedDRA Query definition). Severe hypoglycemia was adjudicated (American Diabetes Association definition). The main endpoint and the temporal association between severe hypoglycemia and hHF were analyzed with a Cox proportional hazards regression model. Predictors of time to first hHF were identified using baseline variables.ResultsOverall, 372 (4.9%) patients experienced hHF (550 events). There was no significant difference in the risk of hHF between treatments (hazard ratio [HR] 0.88 [0.72;1.08]95% CI, p = 0.227). Prior HF (HR 4.89 [3.90;6.14]95% CI, p ≤ 0.0001) was the strongest predictor of future hHF events. The risk of hHF significantly increased after (HR 2.2), and within a week after (HR 11.1), experiencing a severe hypoglycemic episode compared with before an episode.ConclusionsIn patients with T2D and high cardiovascular risk there were no treatment differences in terms of hHF. Prior HF was the strongest predictor of future hHF events, and there was an association between severe hypoglycemia and subsequent hHF. Further research should evaluate whether the risk of hHF can be modified by treatments aimed at reducing hypoglycemia.Trial Registration NCT01959529

Tackling Similarity Search for Soccer Match Analysis: Multimodal Distance Measure and Interactive Query Definition.

Analysts and coaches in soccer sports need to investigate large sets of past matches of opposing teams in a short time to prepare their teams for upcoming matches. Thus, they need appropriate methods and systems supporting them in searching for soccer moves for comparison and explanation. For the search of similar soccer moves, established distance and similarity measures typically only take spatiotemporal features like shape and speed of movement into account. However, movement in invasive team sports such as soccer includes much more than just a sequence of spatial locations. We propose an enhanced similarity measure integrating spatial, player, event as well as high level context such as pressure into the process of similarity search. We present a visual search system supporting analysts in interactively identifying similar contextual enhanced soccer moves in a dataset containing more than 60 soccer matches. Our approach is evaluated by several expert studies. The results of the evaluation reveal the large potential of enhanced similarity measures in the future.

Wet Markets and Food Safety: TripAdvisor for Improved Global Digital Surveillance.

BackgroundWet markets are markets selling fresh meat and produce. Wet markets are critical for food security and sustainable development in their respective regions. Due to their cultural significance, they attract numerous visitors and consequently generate tourist-geared information on the Web (ie, on social networks such as TripAdvisor). These data can be used to create a novel, international wet market inventory to support epidemiological surveillance and control in such settings, which are often associated with negative health outcomes.ObjectiveUsing social network data, we aimed to assess the level of wet markets’ touristic importance on the Web, produce the first distribution map of wet markets of touristic interest, and identify common diseases facing visitors in these settings.MethodsA Google search was performed on 31 food market–related keywords, with the first 150 results for each keyword evaluated based on their relevance to tourism. Of all these queries, wet market had the highest number of tourism-related Google Search results; among these, TripAdvisor was the most frequently-occurring travel information aggregator, prompting its selection as the data source for this study. A Web scraping tool (ParseHub) was used to extract wet market names, locations, and reviews from TripAdvisor. The latter were searched for disease-related content, which enabled assignment of GeoSentinel diagnosis codes to each. This syndromic categorization was overlaid onto a mapping of wet market locations. Regional prevalence of the most commonly occurring symptom group - food poisoning - was then determined (ie, by dividing the number of wet markets per continent with more than or equal to 1 review containing this syndrome by the total number of wet markets on that continent with syndromic information).ResultsOf the 1090 hits on TripAdvisor for wet market, 36.06% (393/1090) conformed to the query’s definition; wet markets were heterogeneously distributed: Asia concentrated 62.6% (246/393) of them, Europe 19.3% (76/393), North America 7.9% (31/393), Oceania 5.1% (20/393), Africa 3.1% (12/393), and South America 2.0% (8/393). Syndromic information was available for 14.5% (57/393) of wet markets. The most frequently occurring syndrome among visitors to these wet markets was food poisoning, accounting for 54% (51/95) of diagnoses. Cases of this syndrome were identified in 56% (22/39) of wet markets with syndromic information in Asia, 71% (5/7) in Europe, and 71% (5/7) in North America. All wet markets in South America and Oceania reported food poisoning cases, but the number of reviews with syndromic information was very limited in these regions (n=2).ConclusionsThe map produced illustrates the potential role of touristically relevant social network data to support global epidemiological surveillance. This includes the possibility to approximate the global distribution of wet markets and to identify diseases (ie, food poisoning) that are most prevalent in such settings.

Getting answers from semantic repositories: a keywords-based approach

The Web of Data proposes to publish and connect data by applying the semantic web technologies for the representation of knowledge and data and the definition of queries. The success of the Web of Data requires that both humans and machines are able to extract information from such semantic repositories. For this purpose, query interfaces for humans must make the interaction with the repository as transparent as possible. In this work, we present a generic method for querying semantic repositories based on processing and recognizing the keywords input by the users as entities of the ontology used in the description of the data. A SPARQL query is automatically derived from the query graph extracted from the list of keywords. We also describe the application of the method to three different semantic repositories from different domains.

Efficient interactive search for geo-tagged multimedia data

Due to the advances in mobile computing and multimedia techniques, there are vast amount of multimedia data with geographical information collected in multifarious applications. In this paper, we propose a novel type of image search namedinteractive geo-tagged image search which aims to find out a set of images based on geographical proximity and similarity of visual content, as well as the preference of users. Existing approaches for spatial keyword query and geo-image query cannot address this problem effectively since they do not consider these three type of information together for query. In order to solve this challenge efficiently, we propose the definition of interactive top-k geo-tagged image query and then present a framework including candidate search stage , interaction stage and termination stage. To enhance the searching efficiency in a large-scale database, we propose the candidate search algorithm named GI-SUPER Search based on a new notion called superior relationship and GIR-Tree, a novel index structure. Furthermore, two candidate selection methods are proposed for learning the preferences of the user during the interaction. At last, the termination procedure and estimation procedure are introduced in brief. Experimental evaluation on real multimedia dataset demonstrates that our solution has a really high performance.

Querying archetype-based EHRs by search ontology-based XPath engineering

BackgroundLegacy data and new structured data can be stored in a standardized format as XML-based EHRs on XML databases. Querying documents on these databases is crucial for answering research questions. Instead of using free text searches, that lead to false positive results, the precision can be increased by constraining the search to certain parts of documents.MethodsA search ontology-based specification of queries on XML documents defines search concepts and relates them to parts in the XML document structure. Such query specification method is practically introduced and evaluated by applying concrete research questions formulated in natural language on a data collection for information retrieval purposes. The search is performed by search ontology-based XPath engineering that reuses ontologies and XML-related W3C standards.ResultsThe key result is that the specification of research questions can be supported by the usage of search ontology-based XPath engineering. A deeper recognition of entities and a semantic understanding of the content is necessary for a further improvement of precision and recall. Key limitation is that the application of the introduced process requires skills in ontology and software development. In future, the time consuming ontology development could be overcome by implementing a new clinical role: the clinical ontologist.ConclusionThe introduced Search Ontology XML extension connects Search Terms to certain parts in XML documents and enables an ontology-based definition of queries. Search ontology-based XPath engineering can support research question answering by the specification of complex XPath expressions without deep syntax knowledge about XPaths.

A novel distributed registry approach for efficient and resilient service discovery in megascale distributed systems?

Service discovery is a well-known but important aspect of dynamic servicebased systems, which is rather unsolved for megascale systems with a huge number of dynamically appearing and vanishing service providers. In this paper first requirements for service discovery in megascale systems are identified from three perspectives: provider, client and system architecture side. Existing approaches are evaluated along these lines and it becomes apparent that modern solutions make advances with respect to the distributed system architecture but fail to support many aspects of client side requirements like persistent queries and elaborate query definitions. Based on these shortcomings a novel solution architecture is presented. It is based on the idea that service description data can be subdivided into static and dynamic properties. The first group remains constant over time while the second is valid only for shorter durations and has to be updated. Expressive service queries rely on both, e.g. service location as example for the first and response time for the latter category. In order to deal with this problem, our main idea is to also subdivide the architecture into two interconnected processing levels that work independently on static and dynamic query parts. Both processing levels consist of interconnected peers allowing to auto-scale the registry dynamically according to the current workload. The implementation using the Jadex middleware is explained and the approach is empirically evaluated using an example scenario.

Chronic Pain and Associated Factors in India and Nepal: A Pilot Study of the Vanderbilt Global Pain Survey.

Evaluation and treatment of chronic pain worldwide are limited by the lack of standardized assessment tools incorporating consistent definitions of pain chronicity and specific queries of known social and psychological risk factors for chronic pain. The Vanderbilt Global Pain Survey (VGPS) was developed as a tool to address these concerns, specifically in the low- and middle-income countries where global burden is highest. The VGPS was developed using standardized and cross-culturally validated metrics, including the Brief Pain Inventory and World Health Organization Disability Assessment Scale, as well as the Pain Catastrophizing Scale, the Fibromyalgia Survey Questionnaire along with queries about pain attitudes to assess the prevalence of chronic pain and disability along with its psychosocial and emotional associations. The VGPS was piloted in both Nepal and India over a 1-month period in 2014, allowing for evaluation of this tool in 2 distinctly diverse cultures. Prevalence of chronic pain in Nepal and India was consistent with published data. The Nepali cohort displayed a pain point prevalence of 48%-50% along with some form of disability present in approximately one third of the past 30 days. Additionally, 11% of Nepalis recorded pain in 2 somatic sites and 39% of those surveyed documented a history of a traumatic event. In the Indian cohort, pain point prevalence was approximately 24% to 41% based on the question phrasing, and any form of disability was present in 6 of the last 30 days. Of the Indians surveyed, 11% reported pain in 2 somatic sites, with only 4% reporting a previous traumatic event. Overall, Nepal had significantly higher chronic pain prevalence, symptom severity, widespread pain, and self-reported previous traumatic events, yet lower reported pain severity. Our findings confirm prevalent chronic pain, while revealing pertinent cultural differences and survey limitations that will inform future assessment strategies. Specific areas for improvement identified in this VGPS pilot study included survey translation methodology, redundancy of embedded metrics and cultural limitations in representative sampling and in detecting the prevalence of mental health illness, catastrophizing behavior, and previous traumatic events. International expert consensus is needed.