Structured Query Research Articles

4494 Predictors of Reintubation After Cardiac Surgery

OBJECTIVES/GOALS: We modeled risk of reintubation within 48 hours of cardiac surgery using variables available in the electronic health record (EHR). This model will guide recruitment for a prospective, pragmatic clinical trial entirely embedded within the EHR among those at high risk of reintubation. METHODS/STUDY POPULATION: All adult patients admitted to the cardiac intensive care unit following cardiac surgery involving thoracotomy or sternotomy were eligible for inclusion. Data were obtained from operational and analytical databases integrated into the Epic EHR, as well as institutional and departmental-derived data warehouses, using structured query language. Variables were screened for inclusion in the model based on clinical relevance, availability in the EHR as structured data, and likelihood of timely documentation during routine clinical care, in the hopes of obtaining a maximally-pragmatic model. RESULTS/ANTICIPATED RESULTS: A total of 2325 patients met inclusion criteria between November 2, 2017 and November 2, 2019. Of these patients, 68.4% were male. Median age was 63.0. The primary outcome of reintubation occurred in 112/2325 (4.8%) of patients within 48 hours and 177/2325 (7.6%) at any point in the subsequent hospital encounter. Univariate screening and iterative model development revealed numerous strong candidate predictors (ANOVA plot, figure 1), resulting in a model with acceptable calibration (calibration plot, figure 2), c = 0.666. DISCUSSION/SIGNIFICANCE OF IMPACT: Reintubation is common after cardiac surgery. Risk factors are available in the EHR. We are integrating this model into the EHR to support real-time risk estimation and to recruit and randomize high-risk patients into a clinical trial comparing post-extubation high flow nasal cannula with usual care. CONFLICT OF INTEREST DESCRIPTION: REF has received grant funding and consulting fees from Medtronic for research on inpatient monitoring.

Web Application Reinforcement via Efficient Systematic Analysis and Runtime Validation (ESARV)

Securing the data, a fundamental asset in an organization, against SQL Injection (SQLI), the most frequent attack in web applications, is vital. In SQLI, an attacker alters the structure of the actual query by injecting code via the input, and gaining access to the database. This paper proposes a new method for securing web applications against SQLI Attacks (SQLIAs). It contains two phases based on systematic analysis and runtime validation and uses our new technique for detection and prevention. At the static phase, our method removes user inputs from SQL queries and gathers as much information as possible, from static and dynamic queries in order to minimize the overhead at runtime. On the other hand, at the dynamic phase, the prepared information alongside our technique are used to check the validity of the runtime query. To facilitate the usage of our method and show our expectations in practice, ESARV was implemented. The empirical evaluations demonstrated in this paper, indicate that ESARV is efficient, accurate, effective, and also has no deployment requirements.

Red blood cell distribution width is associated with mortality risk in patients with acute respiratory distress syndrome based on the Berlin definition: A propensity score matched cohort study

BackgroundAcute respiratory distress syndrome (ARDS) is a severe inflammatory disorder of the lungs and is associated with oxidative damage. However, red blood cell distribution width (RDW), as an indicator of body response to inflammation and oxidative stress, has not been studied for its relationship with ARDS as diagnosed by the Berlin definition. ObjectivesTo examine the value of RDW in predicting the prognosis of in patients with ARDS. MethodsThis is a retrospective study based on the Medical Information Mart for Intensive Care III (MIMIC-III) database. Berlin-defined ARDS patients using mechanical ventilation for more than 48 hours were selected using structured query language. The primary statistical methods were propensity score matching and sensitivity analysis, including an inverse probability weighting model to ensure the robustness of our findings. ResultsA total of 529 intensive care unit (ICU) patients with ARDS according to the Berlin definition were enrolled in the study. The adjusted OR showed an adverse effect between the higher RDW group and 30-day mortality [OR 2.33, 95% CI (1.15–4.75), P=0.019]. However, we found that length of ICU stay was not related to RDW (P=0.167), and in the anaemia group, RDW was poorly predictive of 30-day mortality (P=0.307). ConclusionIn unselected ARDS patients, higher RDW was associated with higher 30-day mortality rate. Further investigation is required to validate this relationship with prospectively collected data.

CosmoHub: Interactive exploration and distribution of astronomical data on Hadoop

We present CosmoHub ( https://cosmohub.pic.es ), a web application based on Hadoop to perform interactive exploration and distribution of massive cosmological datasets. Recent Cosmology seeks to unveil the nature of both dark matter and dark energy mapping the large-scale structure of the Universe, through the analysis of massive amounts of astronomical data, progressively increasing during the last (and future) decades with the digitization and automation of the experimental techniques. CosmoHub, hosted and developed at the Port d’Informació Científica (PIC), provides support to a worldwide community of scientists, without requiring the end user to know any Structured Query Language (SQL). It is serving data of several large international collaborations such as the Euclid space mission, the Dark Energy Survey (DES), the Physics of the Accelerating Universe Survey (PAUS) and the Marenostrum Institut de Ciències de l’Espai (MICE) numerical simulations. While originally developed as a PostgreSQL relational database web frontend, this work describes the current version of CosmoHub, built on top of Apache Hive, which facilitates scalable reading, writing and managing huge datasets. As CosmoHub’s datasets are seldomly modified, Hive it is a better fit. Over 60 TiB of cataloged information and 50 × 1 0 9 astronomical objects can be interactively explored using an integrated visualization tool which includes 1D histogram and 2D heatmap plots. In our current implementation, online exploration of datasets of 1 0 9 objects can be done in a timescale of tens of seconds. Users can also download customized subsets of data in standard formats generated in few minutes.

Creating Collections with Embedded Documents for Document Databases Taking into Account the Queries

In this article, we describe a new formalized method for constructing the NoSQL document database of MongoDB, taking into account the structure of queries planned for execution to the database. The method is based on set theory. The initial data are the properties of objects, information about which is stored in the database, and the set of queries that are most often executed or whose execution speed should be maximum. In order to determine the need to create embedded documents, our method uses the type of relationship between tables in a relational database. Our studies have shown that this method is in addition to the method of creating collections without embedded documents. In the article, we also describe a methodology for determining in which cases which methods should be used to make working with databases more efficient. It should be noted that this approach can be used for translating data from MySQL to MongoDB and for the consolidation of these databases.

Query Game 2.0: Improvement of a Web-Based Query Game for Cavite State University – Main Campus

Purpose: The study aimed to improve the previous study covering a web-based query game for Cavite State University. The study created a new mechanic and gameplay for the students to learn Structured Query Language (SQL). The enhancements also focused on the interactions of one or more students playing the game. Method: The researchers used iterative development process methodology in the development of the study. The system was assessed and evaluated using different testing methods: unit, integration, and system testing. After passing the tests, 90 students of Information Technology and Computer Science program along with 10 IT experts evaluated the system. Results: The respondents were classified into technical and non-technical respondents and the study garnered an evaluation score of 4.69 and 4.70 respectively. The overall interpretation of the results of the evaluation is Excellent. Conclusion: The study created a system where the user can read and watch lectures and experience the tutorial. Instructors and students may communicate in the system, hence, promoting better relations and healthy competition between students. Recommendations: Based on the conclusions of the study, the system can be used as a supplementary tool in teaching courses with Database Management. To enhance the analysis of query construction of the students, it is recommended to add a module that can analyze the pattern of creating queries and answering questions for every user.

Using Flask for SQLIA Detection and Protection

At present the web applications are used for most of the life activities, these applications are affected by an attack called (Structure Query Language Injection Attack) SQLIA due to the vulnerabilities of the web application. The vulnerabilities of the web application are increased because most of application developers do not care to security in designing.SQL injection is a common attack that infects a web application. The attacker adds (Structured Query Language) SQL code to web page for accessing and changing victim databases.The vital step in securing the database and detecting such an attack in web apps is preparing a tool. Many researchers propose different ways for detection and prevention of such as an attack. In this paper a tool it proposed using a powerful micro-framework web application designer called Flask in Python 3.7 to detect and prevent such attacks. The proposed system is called SQLIAD. SQLIAD analyzed a web application on-line.

Retrieval performance of Google, Yahoo and Bing for navigational queries in the field of “life science and biomedicine”

PurposeThe purpose of this study is to assess the retrieval performance of three search engines, i.e. Google, Yahoo and Bing for navigational queries using two important retrieval measures, i.e. precision and relative recall in the field of life science and biomedicine.Design/methodology/approachTop three search engines namely Google, Yahoo and Bing were selected on the basis of their ranking as per Alexa, an analytical tool that provides ranking of global websites. Furthermore, the scope of study was confined to those search engines having interface in English. Clarivate Analytics' Web of Science was used for the extraction of navigational queries in the field of life science and biomedicine. Navigational queries (classified as one-word, two-word and three-word queries) were extracted from the keywords of the papers representing the top 100 contributing authors in the select field. Keywords were also checked for the duplication. Two important evaluation parameters, i.e. precision and relative recall were used to calculate the performance of search engines on the navigational queries.FindingsThe mean precision for Google scores high (2.30) followed by Yahoo (2.29) and Bing (1.68), while mean relative recall also scores high for Google (0.36) followed by Yahoo (0.33) and Bing (0.31) respectively.Research limitations/implicationsThe study is of great help to the researchers and academia in determining the retrieval efficiency of Google, Yahoo and Bing in terms of navigational query execution in the field of life science and biomedicine. The study can help users to focus on various search processes and the query structuring and its execution across the select search engines for achieving desired result list in a professional search environment. The study can also act as a ready reference source for exploring navigational queries and how these queries can be managed in the context of information retrieval process. It will also help to showcase the retrieval efficiency of various search engines on the basis of subject diversity (life science and biomedicine) highlighting the same in terms of query intention.Originality/valueThough many studies have been conducted highlighting the retrieval efficiency of search engines the current work is the first of its kind to study the retrieval effectiveness of Google, Yahoo and Bing on navigational queries in the field of life science and biomedicine. The study will help in understanding various methods and approaches to be adopted by the users for the navigational query execution across a professional search environment, i.e. “life science and biomedicine”

Deep learning based searching approach for RDF graphs.

The Internet is a remarkably complex technical system. Its rapid growth has also brought technical issues such as problems to information retrieval. Search engines retrieve requested information based on the provided keywords. Consequently, it is difficult to accurately find the required information without understanding the syntax and semantics of the content. Multiple approaches are proposed to resolve this problem by employing the semantic web and linked data techniques. Such approaches serialize the content using the Resource Description Framework (RDF) and execute the queries using SPARQL to resolve the problem. However, an exact match between RDF content and query structure is required. Although, it improves the keyword-based search; however, it does not provide probabilistic reasoning to find the semantic relationship between the queries and their results. From this perspective, in this paper, we propose a deep learning-based approach for searching RDF graphs. The proposed approach treats document requests as a classification problem. First, we preprocess the RDF graphs to convert them into N-Triples format. Second, bag-of-words (BOW) and word2vec feature modeling techniques are combined for a novel deep representation of RDF graphs. The attention mechanism enables the proposed approach to understand the semantic between RDF graphs. Third, we train a convolutional neural network for the accurate retrieval of RDF graphs using the deep representation. We employ 10-fold cross-validation to evaluate the proposed approach. The results show that the proposed approach is accurate and surpasses the state-of-the-art. The average accuracy, precision, recall, and f-measure are up to 97.12%, 98.17%, 95.56%, and 96.85%, respectively.

The effects of database complexity on SQL query formulation

In Structured Query Language (SQL) education, students often execute queries against a simple exercise database. Recently, databases that are more realistic have been utilized to the effect that students find exercises more interesting and useful, as these databases more accurately mimic databases students are likely to encounter in their future work environments. However, using even the most engaging database can be counterproductive to learning, if a student is not able to formulate correct queries due to the complexity of the database schema. Scientific evidence on the effects of database complexity on student’s query formulation is limited, and with queries from 744 students against three databases of varying logical complexity, we set out to study how database complexity affects the success rates in query formulation. The success rates against a simple database were significantly higher than against a semi-complex and a complex database, which indicates that it is easier for students to write SQL queries against simpler databases. This suggests, at least in the scale of our exercise databases, that educators should also consider the negative effects of more realistic databases, even though they have been shown to increase student engagement.

Cassandra-based data repository design for food supply chain traceability

PurposeThe purpose of this paper is to design a supply chain database schema for Cassandra to store real-time data generated by Radio Frequency IDentification technology in a traceability system.Design/methodology/approachThe real-time data generated in such traceability systems are of high frequency and volume, making it difficult to handle by traditional relational database technologies. To overcome this difficulty, a NoSQL database repository based on Casandra is proposed. The efficacy of the proposed schema is compared with two such databases, document-based MongoDB and column family-based Cassandra, which are suitable for storing traceability data.FindingsThe proposed Cassandra-based data repository outperforms the traditional Structured Query Language-based and MongoDB system from the literature in terms of concurrent reading, and works at par with respect to writing and updating of tracing queries.Originality/valueThe proposed schema is able to store the real-time data generated in a supply chain with low latency. To test the performance of the Cassandra-based data repository, a test-bed is designed in the lab and supply chain operations of Indian Public Distribution System are simulated to generate data.

DRT: A Novel Tool for Data Archiving

This article describes a novel data retirement tool (DRT) that removes data without taking the system offline and suffering increased load. Compared to Structured Query Language Delete, the standard data deletion method, DRT is superior in overall deletion time and reduced system load in Oracle.

Linking administrative data sets of inpatient infectious diseases diagnoses in far North Queensland: a cohort profile

PurposeTo design a linked hospital database using administrative and clinical information to describe associations that predict infectious diseases outcomes, including long-term mortality.ParticipantsA retrospective cohort of Townsville Hospital inpatients discharged with...

TransJoin: An Algorithm to Implement Division Operator of Relational Algebra in Structured Query Language

Division operator is one of operators in Relational Algebra which is not implemented directly in SQL (Structured Query Language) standard. Therefore it is not be able to retrieve division query results with an SQL statement in the current relational database management systems (RDBMS). Database programmers have to create a complicated query to perform the task. It is the only relational algebra operators which is not implemented in ANSI SQL standard. This research aimed to study and design an algorithm named TransJoin (transformation and join) to implement it in SQL. TransJoin works to relation P(x, y) divided by Q(y) resulted R(x) through grouping and transforming each y attribute becomes P’(x, y’) and Q’(y’) and each x value is a single tuple with y’ is a composite value of y. Then, result R(x) is resulted of joining P’(x, y’) and Q’(y’) relations by matching y’ attribute. TransJoin was implemented in an open source RDBMS SQLLite. TransJoin tested and delivered valid results by comparing of traditional SQL queries and our proposed SQL queries with various data. The research showed that our algorithm is much more efficient by consuming 0.078 milliseconds compared contrasted the traditional query in 1,974.08 milliseconds for 9,991 tuples.

Query Execution Optimization in Spark SQL

Spark SQL is a big data processing tool for structured data query and analysis. However, due to the execution of Spark SQL, there are multiple times to write intermediate data to the disk, which reduces the execution efficiency of Spark SQL. Targeting on the existing issues, we design and implement an intermediate data cache layer between the underlying file system and the upper Spark core to reduce the cost of random disk I/O. By using the query pre-analysis module, we can dynamically adjust the capacity of cache layer for different queries. And the allocation module can allocate proper memory for each node in cluster. According to the sharing of the intermediate data in the Spark SQL workflow, this paper proposes a cost-based correlation merging algorithm, which can effectively reduce the cost of reading and writing redundant data. This paper develops the SSO (Spark SQL Optimizer) module and integrates it into the original Spark system to achieve the above functions. This paper compares the query performance with the existing Spark SQL by experiment data generated by TPC-H tool. The experimental results show that the SSO module can effectively improve the query efficiency, reduce the disk I/O cost and make full use of the cluster memory resources.

Efficient meta-data structure in top-k queries of combinations and multi-item procurement auctions

Top-k query processing is an important building block for ranked retrieval, with various applications. In this paper, we consider two interesting top-k retrieval problems. In the first problem, we consider the r-combinations of a set S of n real numbers. Precisely, on any input set S, any positive integers k and r, our target is to generate the k best size-r subsets of S efficiently, whose sum of elements is maximized. Our method defines a novel metadata structure G for n and r, even before S and k are known, so that we can later use G to report the top-k r-combinations efficiently when S is available.In the second problem, we consider the top-k procurement decision problem in a multi-item auction, where the input consists of (i) a set of items, where each item is partitioned into equal number of shares, (ii) a set of suppliers, and (iii) for each supplier, her prices of selling different shares of each item; our target is to find k procurements with the least total costs. Our solution extends the novel metadata structure G of the first problem to speed up the reporting steps. For both of the above problems, we further show that the metadata structure can be generated on the fly, thereby saving a considerable amount of storage space.

Random Forest Based Searching Approach for RDF

The blend of digital and physical worlds changed the Internet significantly. Accordingly, trends to collect, access, and deliver information have changed over the Web. Such changes raised the problems of information retrieval. Search engines retrieve requested information based on the provided keywords which is not an efficient way for rich information retrieval. Consequently, the fetching of the required information is difficult without understanding the syntax and semantics of the content. The multiple existing approaches to resolve this problem by exploiting linked data and semantic Web techniques. Such approaches serialize the content leveraging the Resource Description Framework (RDF) and process the queries using SPARQL to resolve the problem. However, an exact match between RDF content and query structure is required. Although it improves the keyword-based search, it does not provide probabilistic reasoning to find the relationship accuracy between the query and results. In this perspective, this paper proposes a machine learning (random forest) based approach to predict the fetching status of RDF by treating RDFs’ requests as a classification problem. First, we preprocess the RDF to convert them into N-Triples format. Then, a feature vector is constructed for each RDF using the preprocessed RDF. After that, a random forest classifier is trained for the prediction of the fetching status of RDFs. The proposed approach is evaluated on an open-source DBpedia dataset. The 10-fold cross-validation results indicate that the performance of the proposed approach is accurate and surpasses the state-of-the-art.

Direct Answers in Google Search Results

This study aims to explore Google's new “featured snippet” (a direct answer that appears in web search results) to better understand the method of extracting and displaying such answers above regular ranked results. The motivation for the study was to investigate form, structure, and relation to query, content, and domain of the direct answer. This study utilized a dataset of 743,798 keywords and the displayed direct answers. It also included web search data for every direct answer, e.g. resulting domain, full URL address and its original ranking, content of the direct answer, and competition (measured as the number of monthly searches for every query). The main finding concerns the form of construction of keywords used in the website content. Keywords should be built in the form of short, two-to-four-word sentences comprising the subject and its attribute. Using relative pronouns, articles, and prepositions, as well as using questions as queries, can help to properly define a query and display the best direct answer. The dataset is relatively small compared to the volume of searches made daily. No other factors were extracted from the URL, and all data concerned the Google search engine only. Implications for webmasters include: keywords used in website content should be close to grammar forms used in queries; use keywords in URLs; write a comprehensive introduction section on the webpage; and use tables and lists HTML markup. The main objective of this study was to examine the structure of the user's queries which caused in appearing of direct answers. It is comprehensive and based on real data used by web search engine users. The data used to support the findings have been deposited in the Zenodo repository (https://doi.org/10.5281/zenodo.3541092).

Research on Data Sharing Architecture for Ecological Monitoring Using Iot Streaming Data

The rapid development of Internet of Things (IoT) technology and the widespread deployment of various sensors around the world have produced a large number of data streams. Thus, current computing systems face the challenge of quickly receiving and managing these large-scale streaming data. This study builds an efficient distributed database based on Greenplum (GP) and focuses on solving the problem of the low efficiency of structured data queries for observed ecological data collected from fragile areas in Northwest China's desert oasis. First, a distributed database is designed and deployed at the physical storage structure level. A database table structure is then established based on the characteristics of the streaming data. On this basis, the data storage strategy is optimized at the data table level. Additionally, the query efficiency of the distributed database is compared with the query efficiency of traditional standalone databases. The results show that the distributed database significantly improves the data query efficiency. The greater the amount of data stored, the better the improvement in efficiency. Finally, based on the optimized distributed database, we develop a data sharing system for streaming data from ecologically fragile areas in the desert oasis in Northwest China, which provides a new approach for the efficient sharing of massive amounts of IoT streaming data for ecological monitoring. Our storage system is still currently working normally, which is highly important to both data managers and users.

Runtime Monitoring for Executable DSLs.

Runtime monitoring is a fundamental technique used throughout the lifecycle of a system for many purposes, such as debugging, testing, or live analytics. While runtime monitoring for general purpose programming languages has seen a great amount of research, developing such complex facilities for any executable Domain Specific Language (DSL) remains a challenging, reoccurring and error prone task. A generic solution must both support a wide range of executable DSLs (xDSLs) and induce as little execution time overhead as possible. Our contribution is a fully generic approach based on a temporal property language with a semantics tailored for runtime verification. Properties can be compiled to efficient runtime monitors that can be attached to any kind of executable discrete event model within an integrated development environment. Efficiency is bolstered using a novel combination of structural model queries and complex event processing. Our evaluation on 3 xDSLs shows that the approach is applicable with an execution time overhead of 121% (on executions shorter than 1s), to 79% (on executions shorter than 20s) making it suitable for model testing and debugging.