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33 results for “SQL”
Web-based Editor for Entity-Relationship Modeling with SQL Transformation Algorithm
<p>With the daily growth of data in all kinds of sectors, such as <span>Information Technolo</span><span>gies (IT)</span>, healthcare, education, commerce or telecommunication, it becomes important to use a high-performance system to manage all this data in the best possible way. Indeed, in the absence of good data management, it can be difficult for these sectors to prevent data loss, ensure safe maintenance or guarantee data security. For this reason, an effective data management system is the Entity-Relationship model. Indeed, thanks to this model, all kinds of sectors have the possibility of designing and organizing their data in relational databases, thus improving data security and better internal communication. Furthermore, it is interesting to modernize the classic approach of the Entity-Relationship model and its visual representations of data in the present day. The technologies of Augmented and Virtual Reality respond precisely to this challenge of innovation in the Entity-Relationship model. Therefore, the first objective of this thesis is to implement the Entity-Relationship model in a Meta-Modeling Platform for Augmented and Virtual Reality. The second objective is to implement an algorithm that transforms an Entity-Relationship model into SQL statements to create database tables. The Entity-Relationship model is used to design the logic of a database, and the implementation of the algorithm is used to create the database.</p>
Exploring SQL Quries: A HackerRank Dataset
<p>This dataset was crawled from the HackerRank website and contains data related to SQL programming challenges on the HackerRank platform. Hackerrank supports using different DBMSs ( MySQL, MS SQL Server, and Oracle) to submit queries. The dataset includes information about the challenge problems, and submission statistics, and contains 22 programming problems and millions of submission records. On HackerRank, each problem will provide the table structure to be queried. The tables related to this dataset are the "city" table and the "station" table. Detailed information about these two table structures can be found on the <a href="https://www.hackerrank.com/">HackerRank website</a>. This dataset can be used to analyze the difficulty and popularity of programming challenges, as well as the characteristics of SQL queries. As the dataset is sourced from the HackerRank website, it can be assumed that the samples are randomly selected from programming enthusiasts around the world, making it representative and reliable.</p> <p> </p>
SQL Databases for Students and Educators
<p>Publicly accessible databases often impose query limits or require registration. Even when I maintain public and limit-free APIs, I never wanted to host a public database because I tend to think that the connection strings are a problem for the user.</p> <p>See https://databases.pacha.dev</p>
A Static-Based Approach to Detect SQL Semantic Bugs Dataset
<p>The dataset used for our study: A Static-Based Approach to Detect SQL Semantic Bugs.</p> <p>This dataset contains more than 172,000 queries extracted from StackOverflow posts. It was built for analysing the prevalence of semantic bugs in SQL queries.</p> <p>For more information about our study and tools see our GitHub repository: <a href="https://github.com/SERG-Delft/sql-bug-finder">https://github.com/SERG-Delft/sql-bug-finder</a></p> <p>Description of included files:</p> <ul> <li>sql_db.png: database ER diagram</li> <li>homedb_queries.sql: contains queries extracted from StackOverflow posts</li> <li>homedb_questions.sql: contains SQL related question posts extracted from StackOverflow</li> <li>homedb_answers.sql: contains the answers to SQL related question posts extracted from StackOverflow</li> <li>homedb_bugs.sql: contains queries with semantic bugs extracted from StackOverflow posts</li> <li>homedb_owners.sql: contains data related to the owners (users) of SQL StackOverflow posts</li> <li>homedb_pages.sql: artifact from book-keeping script, tracking the StackOverflow pages from which SQL queries were extracted (SQL tagged pages, ordered by votes in descending order)</li> </ul>
SQL Injection Test (D3)
<p>This dataset has SQL injection attacks as malicious flow data. The attacks carried out are SQL injection for Union Query and Blind SQL injection. To perform the attacks, the SQLmap tool has been used.</p>
Combining dynamic and static analysis for automated grading SQL statements
<p><strong>Introduction</strong></p> <p>Our experiment was conducted in an undergraduate Relational Database course at the Australian National University. The experiment was conducted on August 10th 2018 when students enrolled in the Relational Database course started to learn relational data model and SQL. The experiment was carried out fully online for three weeks and a total of 393 students were enrolled. The students were asked to login in an online assessment platform and complete 15 exercises. This platform provided an SQLite environment in students browsers by compiling the SQLite C code with Emscripten.</p> <p>Students were allowed to submit and execute their answers in the form of SQL statements. If the execution result of the statement submitted by the student is the same as that of the reference statement, the online assessment platforms will return a feedback message indicating that the execution result is correct. During the interaction with the assessment platform, statements submitted by students were recorded and archived. Overall, our experiment had collected 12,899 statements submitted by students. To create a benchmark dataset that can be used to evaluate different grading approaches, we randomly selected 45 SQL statements submitted by students for each exercise, and asked three teaching assistants to grade them manually. Finally, we average the scores provided by the three assistants and take it as the final score of each statement. The dataset collected in this experiment is ready for public release.</p> <p>All experimental data are stored in Submission.sqlite, which is an SQLite database file. It is recommended to use software such as DB browser or SQLite expert to explore the database.</p> <p> </p> <p><strong>Datatable description</strong></p> <p> </p> <p><em><strong>exercises_result</strong></em></p> <p>This datatable stores the statements submitted by students. Based on the execution result of statement, statements were divided into three categories.</p> <ul> <li>noninterpretable: the statement is non-executable.</li> <li>partially correct: the execution result of statement is different from the expected result.</li> <li>correct: the execution result of the SQL statement is the same as the expected result.</li> </ul> <p>After analyzing the correct statements, we found that the correct set contains some statements carefully constructed by students to deceive the examination system.</p> <p>Take exercise 1 as an example, the task is to answer the following questions using SQL statements.</p> <p>Question: Assume persons who were born in the same year are the same age and there is only one youngest person (with no ties/draws) in this database, who is/are the second youngest person(s) in the database? List the id(s) of the person(s).</p> <p>The reference statement to this exercise is:</p> <pre><code class="language-sql">SELECT p.id FROM person p WHERE p.year_born = (SELECT MAX(year_born) FROM person WHERE year_born < (SELECT MAX(year_born) FROM person)); </code></pre> <p>By exploring the database or trying to execute different statements, some students found that the ID of the person who met the conditions was '00000842', so the following statement was submitted.</p> <pre><code class="language-sql">select id from person where id ='00000842'; </code></pre> <p>The execution result of the above code was correct, but it was obviously not what the tutor expected. Therefore, we identified such statements as 'cheating'.</p> <p>Table 1 Description of exercises_result table.</p> <table> <thead> <tr> <th> <p><strong>field</strong></p> </th> <th> <p><strong>desc</strong></p> </th> <th> <p><strong>datatype</strong></p> </th> </tr> </thead> <tbody> <tr> <td> <p>submission_id</p> </td> <td> <p>Submission ID</p> </td> <td> <p>INT</p> </td> </tr> <tr> <td> <p>submitted_answer</p> </td> <td> <p>statement submitted by student</p> </td> <td> <p>TEXT</p> </td> </tr> <tr> <td> <p>submission_time</p> </td> <td> <p>Submission time</p> </td> <td> <p>NUM</p> </td> </tr> <tr> <td> <p>exercise_id</p> </td> <td> <p>Exercise ID</p> </td> <td> <p>INT</p> </td> </tr> <tr> <td> <p>is_correct</p> </td> <td> <p>Mark whether the statement is correct</p> </td> <td> <p>INT</p> </td> </tr> <tr> <td> <p>student_id</p> </td> <td> <p>Student ID</p> </td> <td> <p>INT</p> </td> </tr> <tr> <td> <p>category</p> </td> <td> <p>categories of statement</p> </td> <td> <p>TEXT</p> </td> </tr> </tbody> </table> <p> </p> <p><em><strong>exercises_benchmark</strong></em></p> <p>This datatable stores the scores provided by different assistants. We randomly selected 45 SQL statements submitted by students for each exercise, and asked three teaching assistants to grade them manually. Finally, we averaged the scores provided by the three assistants as the final score of each statement.</p> <p>Table 2 Description of exercises_benchmark table.</p> <table> <thead> <tr> <th> <p><strong>Field</strong></p> </th> <th> <p><strong>comment</strong></p> </th> <th> <p><strong>datatype</strong></p> </th> </tr> </thead> <tbody> <tr> <td> <p>Submission_id</p> </td> <td> <p>Submission ID</p> </td> <td> <p>INT</p> </td> </tr> <tr> <td> <p>grade</p> </td> <td> <p>grade provided by tutor</p> </td> <td> <p>REAL</p> </td> </tr> <tr> <td> <p>tutor</p> </td> <td> <p>tutor</p> </td> <td> <p>TEXT</p> </td> </tr> </tbody> </table> <p> </p> <p><em><strong>exercises_exercise</strong></em></p> <p>This datatable stores the exercises provided by tutor.</p> <p>Table 3 Description of exercises_exercise table.</p> <table> <thead> <tr> <th> <p><strong>Field</strong></p> </th> <th> <p><strong>comment</strong></p> </th> <th> <p><strong>datatype</strong></p> </th> </tr> </thead> <tbody> <tr> <td> <p>id</p> </td> <td> <p>Exercise ID</p> </td> <td> <p>INT</p> </td> </tr> <tr> <td> <p>title</p> </td> <td> <p>Title of exercise</p> </td> <td> <p>TEXT</p> </td> </tr> <tr> <td> <p>preamble</p> </td> <td> <p>Description of exercise</p> </td> <td> <p>TEXT</p> </td> </tr> <tr> <td> <p>difficulty</p> </td> <td> <p>Coefficient of difficulty</p> </td> <td> <p>integer</p> </td> </tr> <tr> <td> <p>ref</p> </td> <td> <p>Reference statement</p> </td> <td> <p>integer</p> </td> </tr> </tbody> </table> <p> </p> <p><em><strong>database schema</strong></em></p> <p>Please refer to db_schema.pdf for the database schema used in the experiment.</p> <p> </p> <p><strong>BibTex</strong></p> <p>if you want to cite our paper:</p> <p> </p> <blockquote> <pre>@article{wang2020combining, title={Combining dynamic and static analysis for automated grading SQL statements}, author={Wang, Jinshui and Zhao, Yunpeng and Tang, Zhengyi and Xing, Zhenchang}, journal={J Netw Intell}, volume={5}, number={4}, pages={179--190}, year={2020} }</pre> </blockquote>
SQL Injection Attack Netflow
<p><strong>Introduction</strong></p> <p>This datasets have SQL injection attacks (SLQIA) as malicious Netflow data. The attacks carried out are SQL injection for Union Query and Blind SQL injection. To perform the attacks, the SQLMAP tool has been used.</p> <p>NetFlow traffic has generated using DOROTHEA (DOcker-based fRamework fOr gaTHering nEtflow trAffic). NetFlow is a network protocol developed by Cisco for the collection and monitoring of network traffic flow data generated. A flow is defined as a unidirectional sequence of packets with some common properties that pass through a network device.</p> <p><strong>Datasets</strong></p> <p>The firts dataset was colleted to train the detection models (<strong>D1</strong>) and other collected using different attacks than those used in training to test the models and ensure their generalization (<strong>D2</strong>).</p> <p>The datasets contain both benign and malicious traffic. All collected datasets are balanced.</p> <p>The version of NetFlow used to build the datasets is 5.</p> <table> <thead> <tr> <th scope="col">Dataset</th> <th scope="col">Aim</th> <th scope="col">Samples</th> <th scope="col">Benign-malicious<br> traffic ratio</th> </tr> </thead> <tbody> <tr> <td>D1</td> <td>Training</td> <td>400,003</td> <td>50%</td> </tr> <tr> <td>D2</td> <td>Test</td> <td>57,239</td> <td>50%</td> </tr> </tbody> </table> <p> </p> <p><strong>Infrastructure and implementation</strong></p> <p>Two sets of flow data were collected with <a href="https://niebla.unileon.es/cybersecurity/dorothea">DOROTHEA</a>. DOROTHEA is a Docker-based framework for NetFlow data collection. It allows you to build interconnected virtual networks to generate and collect flow data using the NetFlow protocol. In DOROTHEA, network traffic packets are sent to a NetFlow generator that has a sensor<a href="https://github.com/aabc/ipt-netflow"><em> ipt_netflow</em></a> installed. The sensor consists of a module for the Linux kernel using Iptables, which processes the packets and converts them to NetFlow flows.</p> <p>DOROTHEA is configured to use Netflow V5 and export the flow after it is inactive for 15 seconds or after the flow is active for 1800 seconds (30 minutes)</p> <p>Benign traffic generation nodes simulate network traffic generated by real users, performing tasks such as searching in web browsers, sending emails, or establishing Secure Shell (SSH) connections. Such tasks run as Python scripts. Users may customize them or even incorporate their own. The network traffic is managed by a gateway that performs two main tasks. On the one hand, it routes packets to the Internet. On the other hand, it sends it to a NetFlow data generation node (this process is carried out similarly to packets received from the Internet).</p> <p>The malicious traffic collected (SQLI attacks) was performed using <a href="https://sqlmap.org/">SQLMAP</a>. SQLMAP is a penetration tool used to automate the process of detecting and exploiting SQL injection vulnerabilities.</p> <p>The attacks were executed on 16 nodes and launch SQLMAP with the parameters of the following table.</p> <table> <thead> <tr> <th scope="col">Parameters</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>'--banner','--current-user','--current-db','--hostname','--is-dba','--users','--passwords','--privileges','--roles','--dbs','--tables','--columns','--schema','--count','--dump','--comments', --schema'</td> <td>Enumerate users, password hashes, privileges, roles, databases, tables and columns</td> </tr> <tr> <td>--level=5</td> <td>Increase the probability of a false positive identification</td> </tr> <tr> <td>--risk=3</td> <td>Increase the probability of extracting data</td> </tr> <tr> <td>--random-agent</td> <td>Select the User-Agent randomly</td> </tr> <tr> <td>--batch</td> <td>Never ask for user input, use the default behavior</td> </tr> <tr> <td>--answers="follow=Y"</td> <td>Predefined answers to yes</td> </tr> </tbody> </table> <p>Every node executed SQLIA on 200 victim nodes. The victim nodes had deployed a web form vulnerable to Union-type injection attacks, which was connected to the <a href="https://www.mysql.com/">MYSQL </a>or <a href="https://www.microsoft.com/es-es/sql-server/sql-server-2019">SQLServer </a>database engines (50% of the victim nodes deployed MySQL and the other 50% deployed SQLServer).</p> <p>The web service was accessible from ports 443 and 80, which are the ports typically used to deploy web services. The IP address space was 182.168.1.1/24 for the benign and malicious traffic-generating nodes. For victim nodes, the address space was 126.52.30.0/24.<br> The malicious traffic in the test sets was collected under different conditions. For <strong>D1</strong>, SQLIA was performed using Union attacks on the MySQL and SQLServer databases.</p> <p>However, for <strong>D2</strong>, BlindSQL SQLIAs were performed against the web form connected to a <a href="https://www.postgresql.org/">PostgreSQL </a>database. The IP address spaces of the networks were also different from those of <strong>D1</strong>. In <strong>D2</strong>, the IP address space was 152.148.48.1/24 for benign and malicious traffic generating nodes and 140.30.20.1/24 for victim nodes.</p> <p>To run the MySQL server we ran <a href="https://mariadb.org/">MariaDB</a> version 10.4.12.<br> Microsoft SQL Server 2017 Express and PostgreSQL version 13 were used.</p>
Integration of Skyline Queries into Spark SQL - Experiment Artifacts
<p>This repository contains the artifacts for the paper "Integration of Skyline Queries into Spark SQL" published at the 2023 EDBT conference. Here, we publish our binaries, benchmark data, queries, and additional scripts used in the experimental evaluation.</p>
METHODS FOR PREVENTING SQL INJECTION IN IDENTITY AND ACCESS MANAGEMENT (IAM) SYSTEMS
<p>This paper discusses methods for preventing SQL (Structured Query Language) injections in identity and access control (IAM) systems. SQL injections represent one of the most serious threats to web security, allowing attackers to gain unauthorized access to and modify data. The main security methods include filtering input data, using prepared statements and parameterization, implementing stored procedures, restricting access rights, and regularly updating software. Effective privilege management and database activity monitoring also play a key role in preventing attacks. The introduction of these measures helps protect confidential information, ensures reliable authentication and authorization, and maintains data integrity. The paper highlights the importance of an integrated approach to database security in the face of growing cyber threats.</p>
Spider Realistic Dataset In Structure-Grounded Pretraining for Text-to-SQL
<p>This folder contains the Spider-Realistic dataset used for evaluation in the paper "Structure-Grounded Pretraining for Text-to-SQL". The dataset is created based on the dev split of the Spider dataset (2020-06-07 version from https://yale-lily.github.io/spider). We manually modified the original questions to remove the explicit mention of column names while keeping the SQL queries unchanged to better evaluate the model's capability in aligning the NL utterance and the DB schema. For more details, please check our paper at https://arxiv.org/abs/2010.12773. </p> <p>It contains the following files:</p> <p>- spider-realistic.json<br> # The spider-realistic evaluation set<br> # Examples: 508<br> # Databases: 19<br> - dev.json<br> # The original dev split of Spider<br> # Examples: 1034<br> # Databases: 20<br> - tables.json<br> # The original DB schemas from Spider<br> # Databases: 166<br> - README.txt<br> - license</p> <p>The Spider-Realistic dataset is created based on the dev split of the Spider dataset realsed by Yu, Tao, et al. "Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task." It is a subset of the original dataset with explicit mention of the column names removed. The sql queries and databases are kept unchanged.<br> For the format of each json file, please refer to the github page of Spider https://github.com/taoyds/spider.<br> For the database files please refer to the official Spider release https://yale-lily.github.io/spider.</p> <p>This dataset is distributed under the CC BY-SA 4.0 license.</p> <p>If you use the dataset, please cite the following papers including the original Spider datasets, Finegan-Dollak et al., 2018 and the original datasets for Restaurants, GeoQuery, Scholar, Academic, IMDB, and Yelp.</p> <p>@article{deng2020structure,<br> title={Structure-Grounded Pretraining for Text-to-SQL},<br> author={Deng, Xiang and Awadallah, Ahmed Hassan and Meek, Christopher and Polozov, Oleksandr and Sun, Huan and Richardson, Matthew},<br> journal={arXiv preprint arXiv:2010.12773},<br> year={2020}<br> }</p> <p>@inproceedings{Yu&al.18c,<br> year = 2018,<br> title = {Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task},<br> booktitle = {EMNLP},<br> author = {Tao Yu and Rui Zhang and Kai Yang and Michihiro Yasunaga and Dongxu Wang and Zifan Li and James Ma and Irene Li and Qingning Yao and Shanelle Roman and Zilin Zhang and Dragomir Radev }<br> }</p> <p>@InProceedings{P18-1033,<br> author = "Finegan-Dollak, Catherine<br> and Kummerfeld, Jonathan K.<br> and Zhang, Li<br> and Ramanathan, Karthik<br> and Sadasivam, Sesh<br> and Zhang, Rui<br> and Radev, Dragomir",<br> title = "Improving Text-to-SQL Evaluation Methodology",<br> booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",<br> year = "2018",<br> publisher = "Association for Computational Linguistics",<br> pages = "351--360",<br> location = "Melbourne, Australia",<br> url = "http://aclweb.org/anthology/P18-1033"<br> }</p> <p>@InProceedings{data-sql-imdb-yelp,<br> dataset = {IMDB and Yelp},<br> author = {Navid Yaghmazadeh, Yuepeng Wang, Isil Dillig, and Thomas Dillig},<br> title = {SQLizer: Query Synthesis from Natural Language},<br> booktitle = {International Conference on Object-Oriented Programming, Systems, Languages, and Applications, ACM},<br> month = {October},<br> year = {2017},<br> pages = {63:1--63:26},<br> url = {http://doi.org/10.1145/3133887},<br> }</p> <p>@article{data-academic,<br> dataset = {Academic},<br> author = {Fei Li and H. V. Jagadish},<br> title = {Constructing an Interactive Natural Language Interface for Relational Databases},<br> journal = {Proceedings of the VLDB Endowment},<br> volume = {8},<br> number = {1},<br> month = {September},<br> year = {2014},<br> pages = {73--84},<br> url = {http://dx.doi.org/10.14778/2735461.2735468},<br> } </p> <p>@InProceedings{data-atis-geography-scholar,<br> dataset = {Scholar, and Updated ATIS and Geography},<br> author = {Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Jayant Krishnamurthy, and Luke Zettlemoyer},<br> title = {Learning a Neural Semantic Parser from User Feedback},<br> booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},<br> year = {2017},<br> pages = {963--973},<br> location = {Vancouver, Canada},<br> url = {http://www.aclweb.org/anthology/P17-1089},<br> }</p> <p>@inproceedings{data-geography-original<br> dataset = {Geography, original},<br> author = {John M. Zelle and Raymond J. Mooney},<br> title = {Learning to Parse Database Queries Using Inductive Logic Programming},<br> booktitle = {Proceedings of the Thirteenth National Conference on Artificial Intelligence - Volume 2},<br> year = {1996},<br> pages = {1050--1055},<br> location = {Portland, Oregon},<br> url = {http://dl.acm.org/citation.cfm?id=1864519.1864543},<br> }</p> <p>@inproceedings{data-restaurants-logic,<br> author = {Lappoon R. Tang and Raymond J. Mooney},<br> title = {Automated Construction of Database Interfaces: Intergrating Statistical and Relational Learning for Semantic Parsing},<br> booktitle = {2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora},<br> year = {2000},<br> pages = {133--141},<br> location = {Hong Kong, China},<br> url = {http://www.aclweb.org/anthology/W00-1317},<br> }</p> <p>@inproceedings{data-restaurants-original,<br> author = {Ana-Maria Popescu, Oren Etzioni, and Henry Kautz},<br> title = {Towards a Theory of Natural Language Interfaces to Databases},<br> booktitle = {Proceedings of the 8th International Conference on Intelligent User Interfaces},<br> year = {2003},<br> location = {Miami, Florida, USA},<br> pages = {149--157},<br> url = {http://doi.acm.org/10.1145/604045.604070},<br> }</p> <p>@inproceedings{data-restaurants,<br> author = {Alessandra Giordani and Alessandro Moschitti},<br> title = {Automatic Generation and Reranking of SQL-derived Answers to NL Questions},<br> booktitle = {Proceedings of the Second International Conference on Trustworthy Eternal Systems via Evolving Software, Data and Knowledge},<br> year = {2012},<br> location = {Montpellier, France},<br> pages = {59--76},<br> url = {https://doi.org/10.1007/978-3-642-45260-4_5},<br> }</p>
Understanding SQL Dialects and Testing Their Implementations
<p>The artifact contains:</p> <ul> <li>The SQL test suite, which is the main contribution of our paper.</li> <li>A DuckDB database that contains all the raw data based on which we made our conclusions.</li> <li>The scripts used to generate the results as well as the tool to extract SQL statements from Java projects in the folder Artifact Files.</li> </ul>
NeuroimaGene SQL Database
<p>This is the NeuroimaGene database containing association data between genetically regulated gene expression measures (GReX) and neuroimaging derived phenotypes (NIDPs). For full explanation of the NeuroimaGene project, pleasse see our paper, "A Transcriptomic Atlas of the Human Brain Reveals Genetically Determined Aspects of Neuropsychiatric Health." This resource is meant to be used in conjunction with the commandline tools described in our corresponding Github (https://github.com/xbledsoe/NeuroimaGene). </p>
Data set in the form a relational database (sql) to denote a network of service providers, service clients and recommenders
<p>This data-set pertains to a network (i.e. graph) represented in the form of a relational data-base of service providers (nodes), service clients (nodes), service recommenders (nodes) and relationaships between then (i.e. a client used a provider, a recommender recommended a service to another client), along with some initial values of the QoS level perceived by any client whi have used a service and the reputation of a recommender. The data-set can be used for developing a reputation-based trust system. </p>
Search-Based Test Data Generation for SQL Queries: Appendix
<p>The appendix of our ICSE 2018 paper "Search-Based Test Data Generation for SQL Queries: Appendix".</p> <p>The appendix contains:</p> <ul> <li>The queries from the three open source systems we used in the evaluation of our tool (the industry software system is not part of this appendix, due to privacy reasons)</li> <li>The results of our evaluation.</li> <li>The source code of the tool. Most recent version can be found at https://github.com/SERG-Delft/evosql.</li> <li>The results of the tuning procedure we conducted before running the final evaluation.</li> </ul>
Supplementary data for "Enhancing Resource-based Test Case Generation For RESTful APIs with SQL Handling"
<p>Supplement to: <em>Enhancing Resource-based Test Case Generation For RESTful APIs with SQL Handling</em></p> <p>In this repository, we provide <em>tests</em> and their <em>coverage reports</em> (conducted by Intellij coverage reports) that are used in the Discussion section of the paper.</p>
Open Context Database SQL Dump: Legacy Schema Tables and New Schema Tables
<p>Open Context (<a href="https://opencontext.org">https://opencontext.org</a>) publishes free and open access research data for archaeology and related disciplines. An open source (but bespoke) Django (Python) application supports these data publishing services. The software repository is here: <a href="https://github.com/ekansa/open-context-py">https://github.com/ekansa/open-context-py</a></p> <p>The Open Context team runs ETL (extract, transform, load) workflows to import data contributed by researchers from various source relational databases and spreadsheets. Open Context uses PostgreSQL (<a href="https://www.postgresql.org">https://www.postgresql.org</a>) relational database to manage these imported data in a graph style schema. The Open Context Python application interacts with the PostgreSQL database via the Django Object-Relational-Model (ORM).</p> <p>In 2023, the Open Context team finished migration of from a legacy database schema to a revised and refactored database schema with stricter referential integrity and better consistency across tables. During this process, the Open Context team de-duplicated records, cleaned some metadata, and redacted attribute data left over from records that had been incompletely deleted in the legacy schema.</p> <p>This database dump includes all Open Context data organized with the legacy schema (table names that start with the 'oc_' or 'link_' prefixes) along with all Open Context data after cleanup and migration to the new database schema (table names that start with 'oc_all_'). The binary media files referenced by these structured data records are stored elsewhere. Binary media files for some projects, still in preparation, are not yet archived with long term digital repositories.</p> <p>These data comprehensively reflect the structured data currently published and publicly available on Open Context. Other data (such as user and group information) used to run the Website are not included. </p> <p> </p> <p><strong>IMPORTANT</strong></p> <p>This database dump contains data from roughly 180 different projects. Each project dataset has its own metadata and citation expectations. If you use these data, you must cite each data contributor appropriately, not just this Zenodo archived database dump.</p> <p> </p> <p> </p>
Automating pharmacovigilance evidence generation: Using large language models to produce context-aware SQL
Open the record for dataset details and reuse information.
Appendix to Combinatorial Methods for Dynamic Gray-Box SQL Injection Testing
<p>This appendix contains the detailed results of our case study performed in the context of our article "Combinatorial Methods for Dynamic Gray-Box SQL Injection Testing", published in the Journal of Software Testing, Verification and Reliability.</p> <p><br> For each web application investigated, one table exists, provided as a separate PDF file.<br> In each of these tables, the results are grouped by endpoint. For each investigated approach, we list the total number of submitted vectors, the number of injected vectors and the injection rate, the amount of executed vectors and the execution rate, and the total time required to submit the attack vectors and evaluate the responses.</p> <p>Please note that no false positive endpoints are listed for WAVSEP. Furthermore, some entries for the execution rate may be displayed as 0.00 despite some vectors having been executed; this is merely a consequence of rounding.</p>
Library Carpentry SQL Lesson - DOAJ Article Sample Database
<p>Sample Library Carpentry SQL lesson database created from the Directory of Open Access Journals (DOAJ) data. The sample SQL database contains tables: articles, journals, languages, licences, and publishers. Previous version of the sample SQL database: Staiger, Christine (2016): LC-articles. figshare. Dataset. https://doi.org/10.6084/m9.figshare.3409471.v3</p>
SQL-database
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.