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Employers increasingly prioritize candidates who can solve real-world Structured Query Language (SQL) problems, particularly during technical interviews. However, many undergraduate students feel underprepared for these interviews because they have not engaged in the deep learning needed to apply SQL concepts confidently. Additionally, students often fail to recognize the career relevance of SQL skills. This Teaching Tip introduces an immersive SQL lesson designed to bridge the gap between conceptual learning and practical application. The lesson includes a mock SQL technical interview, where students apply their knowledge to solve real-world business problems, class discussions on SQL-related careers, and a post-interview debrief to foster reflection and feedback. Results from pre- and post-lesson surveys indicate significant benefits, including enhanced student confidence in their SQL knowledge, student intention to continue learning and using SQL in the future, and student confidence in their ability to perform well in real SQL interviews. Open-ended survey responses support these findings and further reveal that the SQL lesson positively impacts students by clarifying concepts, reinforcing learned skills, and demonstrating the applicability of SQL in realworld scenarios. This approach demonstrates a practical and scalable framework for integrating immersive professional experiences into technical coursework that may be adapted to different class types (e.g., adopting an abridged version) and different courses (e.g., data analysis).

Relational databases are established and widespread tools for storing and managing information. The efficient collection of information in a database appears to be a promising solution for bridge management (BM), thus facilitating the digital transition. The Italian regulatory framework on infrastructure operation and maintenance (O&M) is complex and is constantly being updated. The current plan for implementing its guidelines envisages that infrastructure managers, also on a regional scale, equip themselves with their own digital database for BM. Within this context, this research proposes an integrated methodology that collects information derived from project documentation, in situ inspections, digital surveys, and monitoring and field tests in a queryable database for digitalising, georeferencing, and creating models of many bridges. Structured query language (SQL) statements are used to efficiently export specific shared information, enabling network cross-analysis. Furthermore, the database represents the source of a geographic information system (GIS) catalogue and the basis for deriving models for building information modelling (BIM). The methodology focuses on the infrastructural context of the Lazio region, Italy, the first beneficiary of the research.

In recent years, Apache Spark has become the de facto standard for big data processing. SparkSQL is a module offering support for relational analysis on Spark with Structured Query Language (SQL). SparkSQL provides convenient data processing interfaces. Despite its efficient optimizer, SparkSQL still suffers from the inefficiency of Spark resulting from Java virtual machine and the unnecessary data serialization and deserialization. Adopting native languages such as C++ could help to avoid such bottlenecks. Benefiting from a bare-metal runtime environment and template usage, systems with C++ interfaces usually achieve superior performance. However, the complexity of native languages also increases the required programming and debugging efforts. In this work, we present LotusSQL, an engine to provide SQL support for dataset abstraction on a native backend Lotus. We employ a convenient SQL processing framework to deal with frontend jobs. Advanced query optimization technologies are added to improve the quality of execution plans. Above the storage design and user interface of the compute engine, LotusSQL implements a set of structured dataset operations with high efficiency and integrates them with the frontend. Evaluation results show that LotusSQL achieves a speedup of up to9×in certain queries and outperforms Spark SQL in a standard query benchmark by more than2×on average.

Importance: The stratification of indeterminate lung nodules is a growing problem, but the burden of lung nodules on healthcare services is not well-described. Manual service evaluation and research cohort curation can be time-consuming and potentially improved by automation. Objective: To automate lung nodule identification in a tertiary cancer centre. Methods: This retrospective cohort study used Electronic Healthcare Records to identify CT reports generated between 31st October 2011 and 24th July 2020. A structured query language/natural language processing tool was developed to classify reports according to lung nodule status. Performance was externally validated. Sentences were used to train machine-learning classifiers to predict concerning nodule features in 2,000 patients. Results: 14,586 patients with lung nodules were identified. The cancer types most commonly associated with lung nodules were lung (39%), neuro-endocrine (38%), skin (35%), colorectal (33%) and sarcoma (33%). Lung nodule patients had a greater proportion of metastatic diagnoses (45 vs. 23%, p < 0.001), a higher mean post-baseline scan number (6.56 vs. 1.93, p < 0.001), and a shorter mean scan interval (4.1 vs. 5.9 months, p < 0.001) than those without nodules. Inter-observer agreement for sentence classification was 0.94 internally and 0.98 externally. Sensitivity and specificity for nodule identification were 93 and 99% internally, and 100 and 100% at external validation, respectively. A linear-support vector machine model predicted concerning sentence features with 94% accuracy. Conclusion: We have developed and validated an accurate tool for automated lung nodule identification that is valuable for service evaluation and research data acquisition.

The SQL-Explore Learning Module detailed in this teaching tip provides an opportunity for students to apply database course knowledge beyond solving traditional pre-determined Structured Query Language (SQL) coding questions. In this unique constructivist activity using the apropos 5E Instructional Model, students explore tables to locate data anomalies, trends, and other key findings in a 100,000-invoice dataset. Detailed instructions and the source code needed to facilitate this innovative learning experience are included. Based on student feedback, 100% of study participants strongly agree or somewhat agree that exploring datasets through the SQL-Explore Activity enhances their knowledge of SQL.

The digital India program encourages Indian citizens to become conversant with e-services which are primarily English language-based services. However, the vast majority of the Indian population is comfortable with vernacular languages like Bengali, Assamese, Hindi, etc. The rural villagers are not able to interact with the Relational Database Management system in their native language. Therefore, create a system that produces SQL queries from natural language queries in Bengali, containing ambiguous words. This paper proposes a Bengali Query Processor named Extended Bengali language Query Processing System (XBLQPS) to handle queries containing ambiguous words posted to a Healthcare Information database in the electronic domain. The Healthcare Information database contains doctor, hospital and department details in the Bengali language. The proposed system provides support for the Bengali-speaking Indian rural population to efficiently fetch required information from the database. The proposed system extracts the Bengali root word by removing the inflectional part and categorizing them to a specific part of speech (POS) using modified Bengali WordNet. The proposed system uses manually annotated parts of speech detection of a word based on Bengali WordNet. Patterns of noun phrases are generated to detect the correct noun phrase as well as entity and attribute(s). Entity and attributes are used to prepare the semantic table which is utilized to create the Structured Query Language (SQL). The simplified LESK method is utilized to resolve ambiguous Bengali phrases in this query processing system. The accuracy, precision, recall and F1 score of the system is measured as 70%, 74%, 73%, and 73% respectively.

SQL query writing is a challenging task for novices, even after considerable training. Query writing is a programming task and a translation task where the writer must translate a user's request for information into code that conforms to the structure, constraints, and syntax of an SQL SELECT statement and that references specific tables and columns from a database. This paper investigates the impact of two instructional interventions on query errors under conditions of low and high query complexity. Data was collected from an experimental study of 63 undergraduate students nearing completion of a 15-week database course. Our analysis reveals specific areas of query writing where each of the interventions helped, and hindered, task performance. We discuss the implications of these findings for improving SQL training and for future research on SQL training effectiveness.

Digital data collection during routine clinical practice is now ubiquitous within hospitals. The data contains valuable information on the care of patients and their response to treatments, offering exciting opportunities for research. Typically, data are stored within archival systems that are not intended to support research. These systems are often inaccessible to researchers and structured for optimal storage, rather than interpretability and analysis. Here we present MIMIC-IV, a publicly available database sourced from the electronic health record of the Beth Israel Deaconess Medical Center. Information available includes patient measurements, orders, diagnoses, procedures, treatments, and deidentified free-text clinical notes. MIMIC-IV is intended to support a wide array of research studies and educational material, helping to reduce barriers to conducting clinical research. Measurement(s) Homo sapiens Technology Type(s) Electronic Health Record Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment hospital Sample Characteristic - Location Commonwealth of Massachusetts

Maintaining enrollments in information systems programs capable of meeting industry demands is an ongoing challenge. While significant research has been conducted examining macro-level strategies (e.g., promoting MIS activities), very few studies have examined micro- level strategies (e.g., promoting Structured Query Language). The purpose of this study is to empirically examine both curriculum-oriented and promotional interventions by introducing SQL into foundation information systems curricula. Based on the Theory of Reasoned Action (TRA), 180 students completed a survey to measure attitude, behavior norms, and plans to enroll in a database class in the future. Additionally, both the hands-on SQL instruction and the promotional SQL intervention played a moderating role on the impact attitude had on plans to take a database course. These results add to our pedagogical understanding of enrollment decisions as well as provide practical solutions educators may use to keep pace with enrollment demands.