Query Language Research Articles

Reinforcement Learning with Multi-Policy Movement Strategy for Weakly Supervised Temporal Sentence Grounding

Temporal grounding involves identifying the target moment based on the provided sentence in an untrimmed video. In weakly supervised temporal grounding studies, existing temporal sentence grounding methods face challenges in (1) learning semantic alignment between the candidate window and language query and (2) identifying accurate temporal boundaries during the grounding process. In this work, we propose a reinforcement learning (RL)-based multi-policy movement framework (MMF) for weakly supervised temporal sentence grounding. We imitate the behavior of human beings when grounding specified content in a video, starting from a coarse location and then identifying fine-grained temporal boundaries. The RL-based framework initially sets a series of candidate windows and learns to adjust them step-by-step by maximizing the rewards, indicating the semantic alignment between the current window and the query. To better learn the alignment, we propose a Gaussian-based Dual-Alignment Module (GDAM) which combines the strengths of both scoring-based and reconstruction-based alignment methods, addressing the issues of negative sample bias and language bias. We also employ the multi-policy movement strategy (MMS) which grounds the temporal position in a coarse-to-fine manner. Extensive experiments demonstrate that our proposed method outperforms existing weakly supervised algorithms, achieving state-of-the-art performance on the Charades-STA and ActivityNet Captions datasets.

Analysis of the Spatial Constraints of Urban Underpass Channels Based on BIM

The construction of urban underpasses faces various spatial constraints, which will lead to deformation or failure of the structures if not satisfied. The introduction of BIM (Building Information Modeling) and other modern information technology to achieve rapid and accurate spatial constraint analysis of the design scheme is of great significance to ensure the quality and safety of the underpass and adjacent structures. Firstly, through extensive literature collection and survey, the key spatial constraint indicators of underpasses in dense urban spaces and the evaluation rules of each indicator are determined. Then, an SQL (Structured Query Language)-based database of spatial constraint rules for underpasses in dense urban space areas is constructed, and the spatial constraint analysis process for underpasses in dense urban space areas is proposed. Finally, based on Building Information Modeling, programming support, and a development environment provided by C# and Visual Studio, a spatial constraint analysis system for underpasses in dense urban space areas is developed to realize complex spatial constraint analysis functions. The application practice of the system is carried out by taking a city underpass project as an example. The results show that in the design of urban underpass engineering, the spatial constraint analysis system designed in this paper and the calculation method adopted can simplify the model transmission process, save on labor costs, and reduce the error of the calculation results.

AI chatbot: I want help, not the answer!

In recent years, the integration of artificial intelligence (AI) into educational settings has become a topic of great interest. As technology continues to evolve, educators and students are exploring how AI can enhance learning experiences. University of South-Eastern Norway (USN) and Kristiania University College (HK) are developing a web-based educational tool, DbPersist, where IT students can practice tasks within the database subject and receive automated feedback. The latest version now also offers students suggestions on how to utilize AI for learning while they are solving various database related tasks. In this study, we explore the students' experiences with getting automatically generated “prompts” for further use in their preferred AI tool, whenever they request assistance. Do they see the value in gaining experience in using prompts designed for learning? Do they believe it might help them using AI for learning purposes in other courses in their study? To answer these questions, we conducted surveys and interviews. Participants included bachelor students across various disciplines and multiple campuses at USN. The participants were all enrolled in database courses with learning goals involving the database query language SQL and ER modelling (drawing Entity-Relationship Diagrams). Early findings suggest that students appreciate the assistance provided by DbPersist’s AI prompts. They value the emphasis on AI-assisted learning and find the prompts useful for getting assistance in solving difficult tasks as opposed to getting complete answers. They also see the value in gaining experience in using AI chatbots using prompts designed for learning and understanding. As AI continues to shape education, tools like DbPersist offer exciting possibilities. DbPersist exemplifies how AI may be used to empower students, emphasizing learning and understanding alongside correctness.

Designing Semantic Tutoring Maps for Enhancing Peer Learning through Lesson Preparation

Advancements in Natural Language Processing technology present significant opportunities to enhance student learning through peer tutoring. While there is substantial research on the effectiveness of peer tutoring in learning science, few studies focus on the lesson preparation stage of peer tutoring. This study utilizes Semantic Web technologies to organize tutoring maps, aiming to develop an innovative learning artifact to address this research gap. Adopting a design-based research methodology, this study first provides a literature review regarding the application of semantic technology in education and peer tutoring preparation. Subsequently, a tutoring map application based on a semantic database is developed, encompassing the organization and storage of tutoring lesson plans using Resource Description Framework (RDF) technology, and the querying and feedback on lesson plan quality based on SPARQL Protocol and RDF Query Language (SPARQL) technology. This paper describes the theoretical basis and design rationale by which student tutors’ learning is improved through structured tutoring preparation. This includes enhancing cognitive and meta-cognitive abilities in designing learning activities, raising questions, anticipating answers, and providing guidance responses to incorrect answers. The paper concludes with recommendations for educational researchers and practitioners. Future research will focus on empirical studies of peer tutoring in scientific fields to verify the validity and adaptability of the design, further facilitating peer learning by tutoring and collective knowledge building.

Bridging Data Models in Health Care With a Novel Intermediate Query Format for Feasibility Queries: Mixed Methods Study.

To advance research with clinical data, it is essential to make access to the available data as fast and easy as possible for researchers, which is especially challenging for data from different source systems within and across institutions. Over the years, many research repositories and data standards have been created. One of these is the Fast Healthcare Interoperability Resources (FHIR) standard, used by the German Medical Informatics Initiative (MII) to harmonize and standardize data across university hospitals in Germany. One of the first steps to make these data available is to allow researchers to create feasibility queries to determine the data availability for a specific research question. Given the heterogeneity of different query languages to access different data across and even within standards such as FHIR (eg, CQL and FHIR Search), creating an intermediate query syntax for feasibility queries reduces the complexity of query translation and improves interoperability across different research repositories and query languages. This study describes the creation and implementation of an intermediate query syntax for feasibility queries and how it integrates into the federated German health research portal (Forschungsdatenportal Gesundheit) and the MII. We analyzed the requirements for feasibility queries and the feasibility tools that are currently available in research repositories. Based on this analysis, we developed an intermediate query syntax that can be easily translated into different research repository-specific query languages. The resulting Clinical Cohort Definition Language (CCDL) for feasibility queries combines inclusion criteria in a conjunctive normal form and exclusion criteria in a disjunctive normal form, allowing for additional filters like time or numerical restrictions. The inclusion and exclusion results are combined via an expression to specify feasibility queries. We defined a JSON schema for the CCDL, generated an ontology, and demonstrated the use and translatability of the CCDL across multiple studies and real-world use cases. We developed and evaluated a structured query syntax for feasibility queries and demonstrated its use in a real-world example as part of a research platform across 39 German university hospitals.

A Runtime System for Interruptible Query Processing: When Incremental Computing Meets Fine-Grained Parallelism

Online data services have stringent performance requirement and must tolerate workload fluctuation. This paper introduces PitStop, a new query language runtime design built on the idea of interruptible query processing: the time-consuming task of data inspection for processing each query or update may be interrupted and resumed later at the boundary of fine-grained data partitions. This counter-intuitive idea enables a novel form of fine-grained concurrency while preserving sequential consistency. We build PitStop through modifying the language runtime of Cypher, the query language of a state-of-the-art graph database, Neo4j. Our evaluation on the Google Cloud shows that PitStop can outperform unmodified Neo4j during workload fluctuation, with reduced latency and increased throughput.

HERALD: A domain-specific query language for longitudinal health data analytics

BackgroundLarge-scale health data has significant potential for research and innovation, especially with longitudinal data offering insights into prevention, disease progression, and treatment effects. Yet, analyzing this data type is complex, as data points are repeatedly documented along the timeline. As a consequence, extracting cross-sectional tabular data suitable for statistical analysis and machine learning can be challenging for medical researchers and data scientists alike, with existing tools lacking balance between ease-of-use and comprehensiveness. ObjectiveThis paper introduces HERALD, a novel domain-specific query language designed to support the transformation of longitudinal health data into cross-sectional tables. We describe the basic concepts, the query syntax, a graphical user interface for constructing and executing HERALD queries, as well as an integration into Informatics for Integrating Biology and the Bedside (i2b2). MethodsThe syntax of HERALD mimics natural language and supports different query types for selection, aggregation, analysis of relationships, and searching for data points based on filter expressions and temporal constraints. Using a hierarchical concept model, queries are executed individually for the data of each patient, while constructing tabular output. HERALD is closed, meaning that queries process data points and generate data points. Queries can refer to data points that have been produced by previous queries, providing a simple, but powerful nesting mechanism. ResultsThe open-source implementation consists of a HERALD query parser, an execution engine, as well as a web-based user interface for query construction and statistical analysis. The implementation can be deployed as a standalone component and integrated into self-service data analytics environments like i2b2 as a plugin. HERALD can be valuable tool for data scientists and machine learning experts, as it simplifies the process of transforming longitudinal health data into tables and data matrices. ConclusionThe construction of cross-sectional tables from longitudinal data can be supported through dedicated query languages that strike a reasonable balance between language complexity and transformation capabilities.

8309 Injectable Estradiol Dosing Regimens; A Retrospective Review of Hormone Therapy for Gender-Diverse Adults Assigned Male at Birth

Abstract Disclosure: M. Kanin: None. M. Slack: None. R. Patel: None. K. Chen: None. N. Jackson: None. K. Williams: None. S. Grock: None. Introduction: Many gender incongruent individuals assigned male at birth (AMAB) seek hormone therapy to achieve physical and emotional changes. Hormone therapy typically includes estradiol, with or without an anti-androgen. However, limited data with relatively weak justifications support the currently recommended regimens. In particular, dosing schedules for injectable estradiol can vary considerably. UCSF guidelines recommend initiating estradiol valerate at 5 mg parenterally weekly or estradiol cypionate 2 mg weekly. Endocrine Society Guidelines recommend estradiol valerate or cypionate parenterally 5-30 mg every 2 weeks or 2-10 mg weekly. Our clinical observations led to the concern that current guideline-based dosing of injectable estradiol can result in supratherapeutic estradiol levels. Thus, we designed a retrospective cohort study to evaluate injectable estradiol dosing requirements for gender diverse individuals at our institution. Hypothesis: We hypothesize that low dose estradiol injections can lead to therapeutic estradiol levels (100-200 pg/mL) and suppressed testosterone (<50 ng/dL). Design: We performed a retrospective cohort study of gender diverse individuals AMAB aged 18-89 who were newly prescribed injectable estradiol by a single clinic from January 2017 to March 2023. Exclusion criteria included history of orchiectomy or estradiol use within the previous six months. Patients were identified using structured query language to extract data from our institution’s electronic health record system. A total 33 patients were identified, 29 of whom were eligible for inclusion. Data was collected for 15 months after estradiol initiation. Results: All individuals were prescribed estradiol valerate except for 1 who was prescribed estradiol cypionate. Estradiol dose ranged from 2.5 to 10 mg weekly and an average initial dose of 4.26 mg weekly. Mean initial estradiol level obtained while on treatment for at least 30 days was 349.8 pg/mL, which is supratherapeutic. Mean initial testosterone level was 25.3 ng/dL, which is suppressed. Mean estradiol dose at completion of data collection was 3.68 mg with a mean final estradiol level of 245.2 pg/mL. Testosterone levels reached less than 50 ng/dL for everyone in the sample with a mean final testosterone of 23.3ng/dL despite significant reductions in estradiol dose (change=-0.58 mg per week, p=0.001) Conclusion: Our study demonstrates lower dosages of estradiol can be used to achieve therapeutic estradiol levels in gender incongruent patients seeking estrogen therapy. Using lower dosages can avoid supratherapeutic estradiol levels and the associated risks. Further prospective studies are needed with the aim to reevaluate current guideline dosing recommendations for injectable estradiol. Presentation: 6/2/2024

8309 Injectable Estradiol Dosing Regimens; A Retrospective Review of Hormone Therapy for Gender-Diverse Adults Assigned Male at Birth

Abstract Disclosure: M. Kanin: None. M. Slack: None. R. Patel: None. K. Chen: None. N. Jackson: None. K. Williams: None. S. Grock: None. Introduction: Many gender incongruent individuals assigned male at birth (AMAB) seek hormone therapy to achieve physical and emotional changes. Hormone therapy typically includes estradiol, with or without an anti-androgen. However, limited data with relatively weak justifications support the currently recommended regimens. In particular, dosing schedules for injectable estradiol can vary considerably. UCSF guidelines recommend initiating estradiol valerate at 5 mg parenterally weekly or estradiol cypionate 2 mg weekly. Endocrine Society Guidelines recommend estradiol valerate or cypionate parenterally 5-30 mg every 2 weeks or 2-10 mg weekly. Our clinical observations led to the concern that current guideline-based dosing of injectable estradiol can result in supratherapeutic estradiol levels. Thus, we designed a retrospective cohort study to evaluate injectable estradiol dosing requirements for gender diverse individuals at our institution. Hypothesis: We hypothesize that low dose estradiol injections can lead to therapeutic estradiol levels (100-200 pg/mL) and suppressed testosterone (<50 ng/dL). Design: We performed a retrospective cohort study of gender diverse individuals AMAB aged 18-89 who were newly prescribed injectable estradiol by a single clinic from January 2017 to March 2023. Exclusion criteria included history of orchiectomy or estradiol use within the previous six months. Patients were identified using structured query language to extract data from our institution’s electronic health record system. A total 33 patients were identified, 29 of whom were eligible for inclusion. Data was collected for 15 months after estradiol initiation. Results: All individuals were prescribed estradiol valerate except for 1 who was prescribed estradiol cypionate. Estradiol dose ranged from 2.5 to 10 mg weekly and an average initial dose of 4.26 mg weekly. Mean initial estradiol level obtained while on treatment for at least 30 days was 349.8 pg/mL, which is supratherapeutic. Mean initial testosterone level was 25.3 ng/dL, which is suppressed. Mean estradiol dose at completion of data collection was 3.68 mg with a mean final estradiol level of 245.2 pg/mL. Testosterone levels reached less than 50 ng/dL for everyone in the sample with a mean final testosterone of 23.3ng/dL despite significant reductions in estradiol dose (change=-0.58 mg per week, p=0.001) Conclusion: Our study demonstrates lower dosages of estradiol can be used to achieve therapeutic estradiol levels in gender incongruent patients seeking estrogen therapy. Using lower dosages can avoid supratherapeutic estradiol levels and the associated risks. Further prospective studies are needed with the aim to reevaluate current guideline dosing recommendations for injectable estradiol. Presentation: 6/2/2024

Developing a Geo-Spatial Database and Standard Queries for Election Management by Stakeholders within Jos South, Nigeria

One of the major set-backs of the 2023 general elections in Nigeria as highlighted by YIAGA Africa (2024) was the failure of the body saddled with election management which is Independent Electoral Commission (INEC), to deploy Geo-spatial Database (GSDB) as a repository in the efficient management processes which enable all election stakeholder participate seamlessly and interactively. The modus of oparandy for the geo-spatial database was hierarchical by dis-aggregating GSDB to the lowest geographical unit. The recent court verdict on re-delineation of electoral wards in Rivers State did not go down well with the political pundits prior to the forth coming local government election in the state. It surffices to say that, INEC from the on-set never had a geo-spatial database from which delineation and update of electoral wards, polling units can be carried out This will further build confidence and integrity, then reducing litigations that characterized the 2023 elections. The GSDB used was predicated on file geo-database of ArcGIS, where both spatial and attribute data of the study were stored; and standard query languages that are pertinent to elections were issued at prompt which generated maps and texts for decision making by all stakeholders.

National Experience of Technology-based Eye Care Services (TECS): A Comprehensive Ophthalmology Telemedicine Initiative

Technology-Based Eye Care Services (TECS) is a tele-ophthalmology program operating in the Veterans' Administration since 2015. This study explores characteristics of the national TECS population, evaluates implementation and sustainability of TECS, and analyzes possible associations and effects of demographic characteristics and social determinants of health (SDOH) on being diagnosed with a vision threatening (VT) disease. Implementation and sustainability of TECS from 2015-2022 was examined along with the sociodemographic characteristics of Veterans served through TECS in 2021. Veteran patients seen in TECS nationwide. Characteristics, disease prevalence and diagnoses, implementation success rate, sustainability rate, preliminary analysis of outcomes and disparity. Per quarter from 2015-2022, TECS sites were classified as implementing, active, or sustained. Standard Query Language (SQL) was used to determine sociodemographic data and logistic regression models were used to identify risk factors associated with VT eye diagnosis. 21,712 Veterans, 52.1% rural or highly rural, were served by TECS in 2021. The average age was 64.7 years, with females comprising 10.9% of the population served. From 2015-2022, of the 67 TECS sites initiated, 6 were implementing with 51 of 61 initiated sites still operational in the first quarter (Q1) of 2022 (83.6% success rate).Macular degeneration (AMD) and cataracts were more prevalent in rural and highly rural populations (7.6% and 11.3%, and 48.8% and 55.0%, respectively) vs. urban populations; glaucoma and diabetic retinopathy (DR) had the opposite association. The prevalence of any type of VT eye disease was lowest in the Mountains/Central region (0.54%) and highest in the Southeast region (3.2%) of the US. Rural and highly rural residents were 1.3 and 2.5 times as likely, respectively, to be diagnosed with a VT eye disease than urban residents. Implementation and sustainability of TECS has been promising. The data provides key information that can be utilized to improve the deployment of TECS and similar programs, along with the possible future direction of TECS services. Moreover, experience from one national ocular telehealth program clearly illustrates that telemedicine can address eye care disparities in the Veteran population and may be utilized for other vulnerable groups as well.

Application of a Question Answering System in the Ontology-Based Health Domain and Question Templates to Improve the Quality of Answers

Developing an effective Question Answering System (QAS) to address a wide range of health-related inquiries presents a significant challenge. This study introduces an innovative approach using an ontology-based QAS within the health domain, leveraging the Resource Description Framework (RDF) and SPARQL query language. By utilizing ontology, the system can more accurately map health concepts, improving the relevance and precision of its responses. Additionally, the incorporation of structured Question Templates enhances the system’s ability to understand and respond to diverse user queries. The system's performance was evaluated using Black Box Testing, which demonstrated that it consistently provides accurate and relevant answers. The system achieved a Mean Average Precision (MAP) of 79.6%, indicating its potential to effectively address a broad spectrum of health issues.

Explaining results of path queries on graphs: Single-path results for context-free path queries

Many graph query languages use, at their core, path queries that yield node pairs (m,n) that are connected by a path of interest. For the end-user, such node pairs only give limited insight as to why this result is obtained, as the pair does not directly identify the underlying path of interest.In this paper, we propose the single-path semantics to address this limitation of path queries. Under single-path semantics, path queries evaluate to a single path connecting nodes m and n and that satisfies the conditions of the query. To put our proposal in practice, we provide an efficient algorithm for evaluating context-free path queries using the single-path semantics. Additionally, we perform a short evaluation of our techniques that shows that the single-path semantics is practically feasible, even when query results grow large.In addition, we explore the formal relationship between the single-path semantics we propose the problem of finding the shortest string in the intersection of a regular language (representing a graph) and a context-free language (representing a path query). As our formal results show, there is a distinction between the complexity of the single-path semantics for queries that use a single edge label and queries that use multiple edge labels: for queries that use a single edge label, the length of the shortest path is linearly upper bounded by the number of nodes in the graph; whereas for queries that use multiple edge labels, the length of the shortest path has a worst-case quadratic lower bound.

Empowering Research With the American Heart Association Get With The Guidelines Registries Through Integration of a Database and Research Tools.

The American Heart Association's Get With The Guidelines (GWTG) has emerged as a vital resource in advancing the standards and practices of inpatient care across stroke, heart failure, coronary artery disease, atrial fibrillation, and resuscitation focus areas. The GWTG registry data have also created new opportunities for secondary use of real-world clinical data in biomedical research. Our goal was to implement a scalable database with an integrated user interface (UI) to improve GWTG data management and accessibility. The curation of registry data begins by going through a data processing and quality control pipeline programmed in Python. This pipeline includes data cleaning and record exclusion, variable derivation and unit harmonization, limited data set preparation, and documentation generation of the registry data. The database was built using PostgreSQL, and integrations between the database and the UI were built using the Django Web Framework in Python. Smaller subsets of data were created using SQLite database files for distribution purposes. Use cases of these tools are provided in the article. We implemented an automated data curation pipeline, centralized database, and UI application for the American Heart Association GWTG registry data. The database and the UI are accessible through a Precision Medicine Platform workspace. As of March 2024, the database contains over 13.2 million cleaned GWTG patient records. The SQLite subsets benefit researchers by optimizing data extraction and manipulation using Structured Query Language. The UI improves accessibility for nontechnical researchers by presenting data in a user-friendly tabular format with intuitive filtering options. With the implementation of the GWTG database and UI application, we addressed data management and accessibility concerns despite its growing scale. We have launched tools to provide streamlined access and accessibility of GWTG registry data to all researchers, regardless of familiarity or experience in coding.

Exploring the Efficacy of GenAI in Grading SQL Query Tasks: A Case Study

Abstract Numerous techniques, including problem-solving, seeking clarification, and creating questions, have been employed to utilize generative Artificial Intelligence (AI) in education. This study investigates the possibility of using Generate AI (GenAI) to grade Structured Query Language (SQL) queries automatically. Three models were used which are ChatGPT, Gemini, and Copilot. The study uses an experimental approach to assess how well the models perform in evaluating student responses by comparing the models’ accuracy with those of human experts. The results showed that despite some inconsistencies, GenAI holds great promise for streamlining. Thus, further research is required in light of inconsistent GenAI performance. If these issues were resolved, GenAI can be utilized in education. However, human oversight and ethical issues must always come first.

SQL Injection Attack Detection Based on Similarity Matching Between Vectors Extracted From Design Time and Run-Time Queries

Everyone uses web-based applications to carry out daily business and personal tasks. These programmes are vulnerable to attack by hackers, who may also misuse the data. The most serious attack with the greatest damaging potential on digital platforms is the structured query language injection attack (SQLiA). The backend databases could be corrupted or destroyed by SQLiA if it manages to breach security protections. Using SQLiA tactics, hackers can get unauthorized access, steal important data, and take over the network completely or partially. An automatic SQL injection prevention and detection technique is needed to safeguard web-based applications from SQLiA. This research suggests a novel similarity-matching algorithm of vectors extracted from design time and run-time queries. This technique allocates the weights of different SQL keywords used in design time and run-time queries and further design time and run-time vectors have been created from respective queries. The similarity between the design time and run time vector is determined by calculating the angle between these two vectors. The angle of deviation between the design time vector and run time vector is calculated and if the angle of deviation is zero, then it is concluded as no SQL injection otherwise, it indicates the existence of SQLiA vulnerability. The proposed algorithm is validated against the GitHub dataset. In the first dataset, out of 1300 injected queries, the proposed method identifies 1219 injected queries; out of 300 normal queries, it identifies 290 normal queries with 93.76% and 96.66% detection accuracy, respectively. Similarly, for the second dataset, out of 10489 injected queries, it identifies 10280 injected queries and out of 301 normal queries, it identifies 280 normal queries with 98.01% and 93.02% detection accuracy, respectively.

Open-set 3D semantic instance maps for vision language navigation – O3D-SIM

Humans excel at forming mental maps of their surroundings, equipping them to understand object relationships and navigate based on language queries. Our previous work SI Maps (Nanwani L, Agarwal A, Jain K, et al. Instance-level semantic maps for vision language navigation. In: 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE; 2023 Aug.) showed that having instance-level information and the semantic understanding of an environment helps significantly improve performance for language-guided tasks. We extend this instance-level approach to 3D while increasing the pipeline's robustness and improving quantitative and qualitative results. Our method leverages foundational models for object recognition, image segmentation, and feature extraction. We propose a representation that results in a 3D point cloud map with instance-level embeddings, which bring in the semantic understanding that natural language commands can query. Quantitatively, the work improves upon the success rate of language-guided tasks. At the same time, we qualitatively observe the ability to identify instances more clearly and leverage the foundational models and language and image-aligned embeddings to identify objects that, otherwise, a closed-set approach wouldn't be able to identify. Project Page - https://smart-wheelchair-rrc.github.io/o3d-sim-webpage

Matching and Rewriting Rules in Object-Oriented Databases

Graph query languages such as Cypher are widely adopted to match and retrieve data in a graph representation, due to their ability to retrieve and transform information. Even though the most natural way to match and transform information is through rewriting rules, those are scarcely or partially adopted in graph query languages. Their inability to do so has a major impact on the subsequent way the information is structured, as it might then appear more natural to provide major constraints over the data representation to fix the way the information should be represented. On the other hand, recent works are starting to move towards the opposite direction, as the provision of a truly general semistructured model (GSM) allows to both represent all the available data formats (Network-Based, Relational, and Semistructured) as well as support a holistic query language expressing all major queries in such languages. In this paper, we show that the usage of GSM enables the definition of a general rewriting mechanism which can be expressed in current graph query languages only at the cost of adhering the query to the specificity of the underlying data representation. We formalise the proposed query language in terms declarative graph rewriting mechanisms described as a set of production rules L→R while both providing restriction to the characterisation of L, and extending it to support structural graph nesting operations, useful to aggregate similar information around an entry-point of interest. We further achieve our declarative requirements by determining the order in which the data should be rewritten and multiple rules should be applied while ensuring the application of such updates on the GSM database is persisted in subsequent rewriting calls. We discuss how GSM, by fully supporting index-based data representation, allows for a better physical model implementation leveraging the benefits of columnar database storage. Preliminary benchmarks show the scalability of this proposed implementation in comparison with state-of-the-art implementations.

Structured query language tool to identify untreated individuals with abdominal aortic aneurysms

Structured query language tool to identify untreated individuals with abdominal aortic aneurysms

Comparing methods for creating a national random sample of twitter users

Twitter data has been widely used by researchers across various social and computer science disciplines. A common aim when working with Twitter data is the construction of a random sample of users from a given country. However, while several methods have been proposed in the literature, their comparative performance is mostly unexplored. In this paper, we implement four common methods to create a random sample of Twitter users in the US: 1% Stream, Bounding Box, Location Query, and Language Query. Then, we compare these methods according to their tweet- and user-level metrics as well as their accuracy in estimating the US population. Our results show that users collected by the 1% Stream method tend to have more tweets, tweets per day, followers, and friends, a fewer number of likes, are younger accounts, and include more male users compared to the other three methods. Moreover, it achieves the minimum error in estimating the US population. However, the 1% Stream method is time-consuming, cannot be used for the past time frames, and is not suitable when user engagement is part of the study. In situation where these three drawbacks are important, our results support the Bounding Box method as the second-best method.