Query Language Research Articles

Zero-Shot Prompting Strategies for Table Question Answering with a Low-Resource Language

This work explores the application of zero-shot prompting strategies for table question answering (TQA) in Portuguese, focusing specifically on the Text2SQL task. This task involves translating questions posed in natural language into Structured Query Language (SQL) queries, which can be executed against a database to answer the original question. Given the popularity of relational databases across various domains, advancements in this field can substantially impact the accessibility and democratization of data as simpler and more intuitive interfaces for database interaction are developed. Despite this significant potential, progress in developing Portuguese TQA solutions remains limited. The proposed approach leverages Large Language Models (LLMs)—specifically the GPT-3.5 and GPT-4 models—through zero-shot prompting. The primary objectives are to assess the effectiveness of such LLMs in this task and to identify the most suitable prompt styles. These are evaluated using a Portuguese translation of the popular Spider Text2SQL benchmark. Results reveal that the proposed approach can generate adequate SQL queries to answer Portuguese language questions about various databases, mainly when using GPT-4. The findings suggest that including schema information and database content in the prompts is critical for satisfactory outcomes. Doi: 10.28991/ESJ-2024-08-05-020 Full Text: PDF

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.

The Block Copolymer Phase Behavior Database.

The Block Copolymer Database (BCDB) is a platform that allows users to search, submit, visualize, benchmark, and download experimental phase measurements and their associated characterization information for di- and multiblock copolymers. To the best of our knowledge, there is no widely accepted data model for publishing experimental and simulation data on block copolymer self-assembly. This proposed data schema with traceable information can accommodate any number of blocks and at the time of publication contains over 5400 block copolymer total melt phase measurements mined from the literature and manually curated and simulation data points of the phase diagram generated from self-consistent field theory that can rapidly be augmented. This database can be accessed via the Community Resource for Innovation in Polymer Technology (CRIPT) web application and the Materials Data Facility. The chemical structure of the polymer is encoded in BigSMILES, an extension of the Simplified Molecular-Input Line-Entry System (SMILES) into the macromolecular domain, and the user can search repeat units and functional groups using the SMARTS search syntax (SMILES Arbitrary Target Specification). The user can also query characterization and phase information using Structured Query Language (SQL) and download custom sets of block copolymer data to train machine learning models. Finally, a protocol is presented in which GPT-4, an AI-powered large language model, can be used to rapidly screen and identify block copolymer papers from the literature using only the abstract text and determine whether they have BCDB data, allowing the database to grow as the number of published papers on the World Wide Web increases. The F1 score for this model is 0.74. This platform is an important step in making polymer data more accessible to the broader community.

Towards cross-application model-agnostic federated cohort discovery.

To demonstrate that 2 popular cohort discovery tools, Leaf and the Shared Health Research Information Network (SHRINE), are readily interoperable. Specifically, we adapted Leaf to interoperate and function as a node in a federated data network that uses SHRINE and dynamically generate queries for heterogeneous data models. SHRINE queries are designed to run on the Informatics for Integrating Biology & the Bedside (i2b2) data model. We created functionality in Leaf to interoperate with a SHRINE data network and dynamically translate SHRINE queries to other data models. We randomly selected 500 past queries from the SHRINE-based national Evolve to Next-Gen Accrual to Clinical Trials (ENACT) network for evaluation, and an additional 100 queries to refine and debug Leaf's translation functionality. We created a script for Leaf to convert the terms in the SHRINE queries into equivalent structured query language (SQL) concepts, which were then executed on 2 other data models. 91.1% of the generated queries for non-i2b2 models returned counts within 5% (or ±5 patients for counts under 100) of i2b2, with 91.3% recall. Of the 8.9% of queries that exceeded the 5% margin, 77 of 89 (86.5%) were due to errors introduced by the Python script or the extract-transform-load process, which are easily fixed in a production deployment. The remaining errors were due to Leaf's translation function, which was later fixed. Our results support that cohort discovery applications such as Leaf and SHRINE can interoperate in federated data networks with heterogeneous data models.

Obesity and 1-year all-cause survival of adult intensive care patients with heart failure: data from the MIMIC-IV.

BackgroundHeart failure is a disease that threatens global public safety. In recent years, the obesity paradox has been studied in cardiovascular disease and other fields. With the progress of aging, metabolic changes and regulation of fat function, it also provides many bridges for the dialogue between disease and molecular metabolism. The purpose of this study is to investigate the effect of obesity on the outcome of adult intensive care patients with heart failure combined with age factors.MethodData were derived from the fourth-generation Medical Information Marketplace for Intensive Care (MIMIC-IV version2.1) using structured query language on the Navicat (12.0.11) platform. People were divided into two groups based on the body mass index (BMI), one group with BMI ≥ 30 kg/m² and another group with BMI < 30 kg/m². Afterwards, the patients were divided into two subgroups based on their ages. One group included patients aged<60, and the other included patients aged ≥ 60. The extracted information includes demographic characteristics, laboratory findings, comorbidities, scores. Main results included in-hospital mortality, ICU mortality, and 1-year mortality. Secondary outcomes included hospital interval and ICU interval, use of renal replacement therapy, and rates of noninvasive and invasive ventilation support.ResultIn this cohort study, 3390 people were in the BMI<30 group, 2301 people were in the BMI ≥ 30 group, 960 people were in the age<60 group, and 4731 people were in the age ≥ 60 group, including 3557 patients after propensity score matching in high age group. Among patients aged ≥ 60, BMI ≥ 30 group vs. BMI<30 group showed significantly lower in-hospital mortality (13% vs. 16%) and one-year mortality (41% vs. 55%), respectively. Neither primary nor secondary outcomes were significantly described in the competition among patients aged under 60. Restricted cubic spline reveals a J-shaped nonlinear association between BMI and clinical endpoints within the entire cohort. Kaplan-Meier curves revealed a survival advantage in BMI ≥ 30 group (p < 0.001). Following age stratification, a beneficial effect of BMI categories on one-year mortality risk was observed in heart failure patients aged ≥ 60 (Univariable HR, 0.71, 95% CI, 0.65–0.78, p < 0.001; Multivariable HR, 0.74, 95% CI, 0.67–0.81, p < 0.001), but not in those under 60 years old.OutcomeIn ICU patients with heart failure, obesity offers a survival benefit to those aged ≥ 60. No obesity paradox was observed in patients younger than 60 years old. The obesity paradox applies to patients aged ≥ 60 with heart failure.

Automated SPARQL Template for Flexible Question Answering

The knowledge bases required the query language SPARQL, which consists of subject, property, and object. SPARQL is a structured query language and is difficult to understand. That issue becomes a problem in natural language processing queries. One situation in question answering is how to translate natural language into a structural SPARQL. This work aims to develop an automated SPARQL template algorithm regardless of the pattern structure of the query triples. It provides a more varied SPARQL query for data retrieval named Flexible SPARQL. This approach initially lies in combining elements of RDF with basic techniques of natural language processing to generate a template of SPARQL. In this work, the approach to making automatic templates is proposed without regard to the pattern of the triple structure or the location of the subject and object. Template-based research that exists today still uses rules to determine the position of subjects, objects, and properties in the SPARQL structure. Therefore, this work used the QALD 7 question set and DBpedia dataset. The previous systems utilized the same questions and data sets. Despite the simple proposed approaches that do not use complex, sophisticated techniques, they have shown promising results compared to the previous systems. The accuracy result from 215 questions is 73% and micro-Recall 0.701, micro-Precision 0.664, micro-F-Measure 0.682, macro-Recall 0.711, macro-Precision 0.592, macro-F-Measure 0.646. Overall, the Flexible SPARQL system has higher results on several measurements that define a promising approach. However, it's important to note that Flexible SPARQL generally tends to fail at generating complex SPARQL, which is a limitation of the system.

F-KGQA: A fuzzy question answering system for knowledge graphs

The wide usage of large-scale knowledge graphs (KGs) motivates the development of user-friendly interfaces so that knowledge graphs become more readily accessible to a larger population. Natural language-based question answering (QA) systems are widely investigated and developed in the context of KGs, which can provide users with a natural means to retrieve the information they need from KGs without expecting them to know the query language. It is very common that natural language contains linguistic terms (fuzzy terms), and fuzzy (flexible) query has been widely investigated in the context of databases. This paper contributes a QA system with fuzzy terms over KGs called f-KGQA. f-KGQA can deal with different types of questions, including simple questions, complex questions, and questions with fuzzy terms. More importantly, users are provided with a channel to flexibly define their fuzzy terms based on their understanding. Our experimental results demonstrate the effectiveness and applicability of f-KGQA in handling questions with fuzzy terms.

VQFT: A Visual Query Approach Based on Full-Text Search for Knowledge Graphs

Existing knowledge graph query approaches, whether traditional textual query languages or visual query languages, have steep learning curves that are unfriendly for non-expert users. This demonstration presents a Visual Query approach based on Full-Text search for knowledge graphs, called VQFT, which simplifies the process of querying knowledge graphs for users. Inspired by full-text search techniques, VQFT aims to combine the user-friendliness of visual query with the intuitiveness of full-text search , enabling users to query knowledge graphs as straightforward as using a search engine. Faceted full-text indexes, visual query constructor , and an interactive user interface are designed to achieve this goal. User tests and surveys have demonstrated that VQFT is more user-friendly and easier to learn than existing methods, which simplifies the construction of knowledge graph queries for non-expert users.

Ensemble learning techniques against structured query language injection attacks

Structured query language (SQL) injection threats pose severe risks to web applications, necessitating robust detection measures. This study introduced DSQLIA, employing ensemble learning algorithms-Bagging, Stacking, and AdaBoost classifiers-for SQL injection detection. Results unveiled the bagging classifier's 84% accuracy with perfect precision (100%) but moderate recall (68%). The stacking classifier achieved 85% accuracy, exceptional precision (99%), and balanced memory (72%), yielding an 83% F1-Score. Remarkably, the AdaBoost classifier outperformed, achieving 99% accuracy, high precision (98%), and outstanding recall (99%), leading to a remarkable 99% F1-Score. These findings highlight AdaBoost's superior ability to identify malicious queries with minimal false positives accurately. Overall, this research underscores the potential of ensemble learning in fortifying web application security against SQL injection attacks, emphasizing the AdaBoost classifier's exceptional performance in achieving precise and comprehensive detection.

SQL Has Problems. We Can Fix Them: Pipe Syntax In SQL

SQL has been extremely successful as the de facto standard language for working with data. Virtually all mainstream database-like systems use SQL as their primary query language. But SQL is an old language with significant design problems, making it difficult to learn, difficult to use, and difficult to extend. Many have observed these challenges with SQL, and proposed solutions involving new languages. New language adoption is a significant obstacle for users, and none of the potential replacements have been successful enough to displace SQL. In GoogleSQL, we've taken a different approach - solving SQL's problems by extending SQL. Inspired by a pattern that works well in other modern data languages, we added piped data flow syntax to SQL. The results are transformative - SQL becomes a flexible language that's easier to learn, use and extend, while still leveraging the existing SQL ecosystem and existing userbase. Improving SQL from within allows incrementally adopting new features, without migrations and without learning a new language, making this a more productive approach to improve on standard SQL.

DTGraph: Declarative Transformations of Property Graphs

Current graph query languages, including the standards SQL/PGQ and GQL, define their semantics in terms of sets of tuples. This is largely inadequate for data interoperability tasks such as data migration or data integration which require queries to output new property graphs. This demonstration showcases DTGraph, an open-source declarative rule-based framework for easily specifying and efficiently executing property graph transformations. We describe a novel comprehensive system that allows the declarative specification of property graph transformations, by extending openCypher queries with a new GENERATE clause for creating new property graphs. The system includes several modules: a parser, a compiler for translating the transformation logic into an efficient executable openCypher script, and an interface assisting users in developing their transformations. The demonstration showcases the ability of our framework to scale to large graph data, and its suitability for transforming real-world datasets.

Enhancing multilingual information retrieval: The efficacy of hybrid approaches

Multilingual Information Retrieval (MLIR) plays a crucial role in accessing information across different languages. This paper explores various techniques and tools used in Cross-Language Information Retrieval (CLIR), focusing on query translation, document translation, and hybrid approaches. Query translation employs bilingual dictionaries and machine translation systems to convert user queries from one language to another, whereas document translation involves translating documents into the query language for indexing and retrieval. Hybrid approaches combine these methods to optimize retrieval performance, leveraging the strengths of both to address their individual limitations. Our comparative analysis shows that hybrid systems consistently outperform standalone query or document translation systems, achieving higher precision, recall, and user satisfaction. For instance, hybrid systems in multilingual legal document retrieval tasks achieved precision rates of 88%, recall rates of 82%, and an F1 score of 0.85. These results underscore the effectiveness of hybrid approaches in handling the complexities of MLIR, providing more accurate and comprehensive retrieval outcomes. This study highlights the practical benefits of hybrid CLIR systems and suggests directions for future research in enhancing multilingual access to information.

CUPID: An efficient spatio-temporal data engine

In the IoT era, abundant spatio-temporal data is generated from various devices thanks to the prevalence of positioning techniques. Due to a lack of effective systems to manipulate the data, advanced scalable and efficient data management systems are necessary to support more and more urban applications.We propose Cupid, which is powered by scholars from Chongqing University and Guangzhou Urban Planning and Design Survey Research Institute, an efficient spatio-temporal Data engine. It extends JUST by introducing many functionalities to make it more applicable to deployment, and can efficiently manage large-scale spatio-temporal data. In Cupid, Apache HBase is utilized as the storage, GeoMesa serves as the spatio-temporal data indexing tool, and Apache Spark acts as the execution engine. We introduce many optimizations to ensure usability and reduce computational overhead. To make Cupid easy to use, we design and implement a SQL-like query language. Furthermore, to deploy the system as a PaaS while speeding up the execution, we leverage Apache Livy, a service that enables easy interaction with a Spark cluster over REST interfaces, together with gRPC to effectively collect results. Extensive experiments illustrate that Cupid outperforms other state-of-the-art spatio-temporal big data management systems in terms of efficiency and scalability.