User interfaces for structural engineering relational data base

Chapter 2 - Querying

Abstract: Structured Query Language (SQL) is a important query language for storing, manipulating and retrieving the data from database. So every individual needs the skill to access the data and analyze it. Since SQL is used while designing most of the softwares in the industry, students should have in-depth knowledge of firing the queries on the database. So the mobile app for the SQL is developed. This app contains SQL material, practice part to run the query, test, question bank and videos related to SQL topics. In this study, step by step process is given to access each part of this mobile app. The research question is “Whether an android mobile app for the course SQL is useful and helps students to understand this language by going through the notes & PPTs and practicing the SQL query through the practice section given in app?” Also the feedback is given to know the perception of students about this app. Keywords: SQL, Android Mobile Application, Quiz, Likert’s scale, Feedback

Web based environments will typically need to maintain many documents (e.g. simulation model information and data results from multiple sites). Ideally, this information would be held in a widely utilized and highly viewable format. The eXtensible Markup Language (XML) is a new standard that meets this criteria for World Wide Web based applications. It is sophisticated enough so that complex real world structures and relationships may be captured. Large collections of XML documents can be stored for efficient retrieval using database management systems (DBMSs). Current database management is represented by the object oriented (OO) and object-relational (OR) database systems. Each type has their own query languages, Object Query Language (OQL, v.2) and Structured Query Language (SQL), respectively. These database query languages provide a high-level way to ask for information from a database. The article looks at storing and retrieving XML documents. It surveys work being done on query languages and tools for XML. It then discusses a simple graphical XML query tool for front end use in the Java Simulation (JSIM) Web based simulation environment.

In this paper we introduce an integration system that consists of two subsystems (tools): integration sub-system (tool) and query (sub-system) tool. The integration tool has been built for integrating data from different data stores (databases) that were created with different database engines. The query sub-system (tool) has been built to help a user to query in a structured natural language or structured query language. The integration system has been built based on the web services technology to be adaptable, reusable, maintainable, and distributed. The integration subsystem collects data from heterogeneous data sources, unifies them based on ontology and stores the unified data in a data warehousing, which its schema is generated automatically by the tool. The integration tool is a database engine independent, domain independent and based on ontology scheme. The query tool has been built to accept the requests from a user and manipulate data in the data warehouse and return the results to the user. The query tool generates queries automatically based on the user requirements and data warehouse schema. The user can write his query as structured natural language or structured query language. The system has been implemented and tested.

Transact-SQL (T-SQL) is the dialect of the Structured Query Language (SQL) adopted by Microsoft’s SQL Server database engine. T-SQL has been around since the early days of the Sybase code in the early 1990s. It has evolved throughout the years and today is a robust programming language based on the ANSI SQL: 2008 standard. In this chapter, you will learn how to access data using T-SQL statements as well as how to use many of the programming capabilities within the database engine, including stored procedures and functions.

Multidimensional data analysis or On‐line analytical processing (OLAP) offers a single subject‐oriented source for analyzing summary data based on various dimensions. We demonstrate that the OLAP approach gives a promising starting point for advanced analysis and comparison among summary data in informetrics applications. At the moment there is no single precise, commonly accepted logical/conceptual model for multidimensional analysis. This is because the requirements of applications vary considerably. We develop a conceptual/logical multidimensional model for supporting the complex and unpredictable needs of informetrics. Summary data are considered with respect of some dimensions. By changing dimensions the user may construct other views on the same summary data. We develop a multidimensional query language whose basic idea is to support the definition of views in a way, which is natural and intuitive for lay users in the informetrics area. We show that this view‐oriented query language has a great expressive power and its degree of declarativity is greater than in contemporary operation‐oriented or SQL (Structured Query Language)‐like OLAP query languages.

The SQL (structured query language) Access Group, an open industry consortium that includes many major SQL database vendors and a number of database tool developers, is developing a common embedded SQL language interface for client-server interoperability in OSI (open systems interconnection) environments. This specification covers most of the features in the current ANSI/ISO SQL standard, includes some new features from the proposed SQL2 standard, and maintains upward compatibility with X/Open applications. As part of this development process, SQL Access has seen the need for enhancements to SQL for remote access and has contributed to the ANSI and ISO standardization efforts. X/Open is a member of the SQL Access Group and enjoys an excellent working relationship in extending the X-Open specification to include SQL Access Group requirements. >

Automated extraction of structured data from Web sources often leads to large heterogeneous knowledge bases (KB), with data and schema items numbering in the hundreds of thousands or millions. Formulating information needs with conventional structured query languages is difficult due to the sheer size of schema information available to the user. We address this challenge by proposing a new query language that blends keyword search with structured query processing over large information graphs with rich semantics. Our formalism for structured queries based on keywords combines the flexibility of keyword search with the expressiveness of structures queries.

In the present scenario, every user is not familiar with the use of Structured Query Language (SQL). So, user is not able to understand or write complex queries in SQL. An enhanced application with intelligent interface is needed to improve communication between the naive user and the databases application. The database is an efficient structure for handling data. To understand the structure of the database, a user has to learn SQL. The non-expert users who are not familiar with the use of SQL, need a system by which the user can interact with the database in their natural language. The system must have the ability to understand the natural language and interact with the database accordingly. In this research, an improved system with three-tier architecture is developed. Pattern matching and semantic matching techniques are used to develop the system which transforms natural language into SQL queries. The SQL query is generated with the production rules and the predefined data dictionary. The predefined data dictionary contains semantics for attributes and relation among attributes. The input given by the user is transformed by the system into SQL query by passing through steps like tokenization, escape word removal, classification of elements and query formation. Finally, the output is in the form of a SQL query. The results are compared with the existing system. The results given by the proposed system are better than the existing system. The proposed system has better recall value, accuracy, and precision in comparison to existing systems.

Copyright © 2019 by author(s) and Scientific Research Publishing Inc. The performance and reliability of converting natural language into structured query language can be problematic in handling nuances that are prevalent in natural language. Relational databases are not designed to understand language nuance, therefore the question why we must handle nuance has to be asked. This paper is looking at an alternative solution for the conversion of a Natural Language Query into a Structured Query Language (SQL) capable
\nof being used to search a relational database. The process uses the natural language concept, Part of Speech to identify words that can be used to identify database tables and table columns. The use of Open NLP based grammar files, as well as additional configuration files, assist in the translation from natural language to query language. Having identified which tables and which columns contain the pertinent data the next step is to create the SQL statement.

Structured Query Language (SQL) is commonly used in Relational Database Management Systems (RDBMS) and is currently one of the most popular data definition and manipulation languages. Its core functionality is implemented, with only some minor variations, throughout all RDBMS products. It is an effective tool in the process of managing and querying data in relational databases. This paper describes a method to effectively automate the conversion of a data query from a Natural Language Query (NLQ) to Structured Query Language (SQL) with Online Analytical Processing (OLAP) cube data warehouse objects. To obtain or manipulate the data from relational databases, the user must be familiar with SQL and must also write an appropriate and valid SQL statement. However, users who are not familiar with SQL are unable to obtain relevant data through relational databases. To address this, we propose a Natural Language Processing (NLP) model to convert an NLQ into an SQL query. This allows novice users to obtain the required data without having to know any complicated SQL details. The model is also capable of handling complex queries using the OLAP cube technique, which allows data to be pre-calculated and stored in a multi-dimensional and ready-to-use format. A multi-dimensional cube (hypercube) is used to connect with the NLP interface, thereby eliminating long-running data queries and enabling self-service business intelligence. The study demonstrated how the use of hypercube technology helps to increase the system response speed and the ability to process very complex query sentences. The system achieved impressive performance in terms of NLP and the accuracy of generating different query sentences. Using OLAP hypercube technology, the study achieved distinguished results compared to previous studies in terms of the speed of the response of the model to NLQ analysis, the generation of complex SQL statements, and the dynamic display of the results. As a plan for future work, it is recommended to use infinite-dimension (n-D) cubes instead of 4-D cubes to enable ingesting as much data as possible in a single object and to facilitate the execution of query statements that may be too complex in query interfaces running in a data warehouse. The study demonstrated how the use of hypercube technology helps to increase system response speed and process very complex query sentences.

One of the most important considerations when selecting a database is how relational (SQL) and non-relational (NoSQL) data structures will interact. While all options are viable, consumers should take certain distinctions into account before choosing. Since SQL databases are vertically scalable, you can typically scale server components like CPU, RAM, or SSD. NoSQL databases, on the other hand, support horizontal scaling. As a result, you can increase the capacity of your NoSQL database by fragmenting (data partitioning), or by adding extra servers. Why, then, is it still challenging to choose the instance that is most appropriate for a given application and requires the least amount of runtime? Because data that will be conveyed via the internet uses a cloud in computer networks as a metaphor. To determine which model to utilize, it is required to conduct a comparison study of SQL-oriented database engines. SQL has a form created for another side of non-productive data and is offered in the form of ordered data, but NoSQL databases are horizontally expandable. Workload management solutions are therefore also in charge of automating organizational procedures, i.e., they carry out activities without requiring manual employee attendance. For businesses trying to implement continuous delivery methods and enhance the effectiveness of customer service delivery, they are unavoidable.

Multiple criteria decision making usually means selection of the best objects or their parameters which best meet conditions or criteria. Human decision making often involves also uncertain and vague criteria. The terms as a short distance, a high building or long time are commonly used in human speech. Nevertheless, it is not necessary to describe them exactly. In case of decision making based on lot of data and multiple criteria, data are usually stored in databases and a query language is used for handling and querying them. In this case, the criteria should be defined in a formal computer language. The standard for querying data in relational databases is the Structured Query Language (SQL). All objects and phenomena, and everything people do, is experience in space and time. Space and time, therefore, should be used as a framework for querying and reasoning about information stored in database systems. Databases, in which spatial and temporal data types are included and functions for their handling are supported, are called the spatio-temporal databases. To spatio-temporal query a database, an extension of standard SQL to support spatial and temporal data is needed. Uncertain spatio-temporal queries are not yet standardized and they are a current topic of research. For uncertain queries creation and expression of uncertain decision criteria, development of new methods and techniques is required. One of the most widely used approach to model, analyse and process uncertain and vague data is fuzzy set theory. Therefore, in this paper, we propose a new way of application fuzzy set theory to spatio-temporal querying. For the case study realisation, the open source PostgreSQL database system extended by the PostGIS we have used. The implementation of the proposed principles of fuzzy set theory to spatio-temporal querying databases can bring an opportunity for the efficient decision making based on multiple uncertain criteria.

A key function of a software system is its ability to facilitate the manipulation of data, which is often implemented using a flavour of the Structured Query Language (SQL). To develop the data operations of software (i.e, creating, retrieving, updating, and deleting data), developers are required to excel in writing and combining both SQL and application code. The problem is that writing SQL code in itself is already challenging (e.g., SQL anti-patterns are commonplace) and combining SQL with application code (i.e., for SQL development tasks) is even more demanding. Meanwhile, we have little empirical understanding regarding the characteristics of SQL development tasks. Do SQL development tasks typically need more code changes? Do they typically have a longer time-to-completion? Answers to such questions would prepare the community for the potential challenges associated with such tasks. Our results obtained from 20 Apache projects reveal that SQL development tasks have a significantly longer time-to-completion than SQL-unrelated tasks and require significantly more code changes. Through our qualitative analyses, we observe that SQL development tasks require more spread out changes, effort in reviews and documentation. Our results also corroborate previous research highlighting the prevalence of SQL anti-patterns. The software engineering community should make provision for the peculiarities of SQL coding, in the delivery of safe and secure interactive software.

The DBMS applications are becoming increasingly popular in the scientific community to support the interactive exploration of huge data. A result of heavy usage has also lead to a lot of tavles generated in the warehouse. This has tremendously increased the need for recommendation system & various tools for the user as user is not capable to explore such huge data by using Structured Query language. This motivates to turn towards the idea of query recommendation system which will assist the technical & non-technical users to generate top-N query recommendations as per their needs. In this survey paper, the focus is given on opportunities that exist in domain of query recommendation, brief description of recommendation outline and techniques. Along with this, the terminology to identify the similarity and comparison of fragment based approach & matrix factorization approach is also explored. The Database Management Systems are used to organize, maintain and retrieve the huge amount of data. To acquire the essential data the user need to interact with the database by taking the support of declarative Structured Query Language (SQL). Most of times, it is observed that user faces the difficulties in interacting with the database by using SQL query language as the database have hundreds of schema's and thousands of attributes. This problem has been solved with the support of Database Management Applications such as SAP, Easy Query, and Microsoft Access. These applications involve the user interaction and manual editing of the SQL query components but when the user is unacquainted about the underlying database structure then it becomes difficult for the user to formulate the query. To formulate the query, the research work has been done (1) and that results into the query assistant tool which helps the user to either formulate the query or to recommend the relevant query items based on formulated query. This work is the reflection of the web recommendation techniques those are used in gaining the on-line marketing intelligence. The web recommendation techniques and algorithms have their strong relation with the Information Retrieval Science (IR). In IR, the recommendation techniques and algorithms are used to retrieve the most relevant top K documents as per the formulated query. Most of times the Business Verticals have their own off-line database and the user will be interested in exploration of the database and faces the difficulties. Therefore, it may also possible to extend the idea specified by the authors (1) to provide the Query Recommendation Assistant Tool that enable the user to formulate and refine the SQL query. This take off to cataloged view on how to make the imperative use of IR and the web recommendation techniques to generate and recommend the top K relevant query objects by identifying the similarities among the SQL query fragments. This becomes the novel approach that handles crucial responsibility in proposing the assistant tool to retrieve data over the database. The rest of the paper is organized as follows: section-2 describes the related work and the section-3 concentrates on recommendation outline. The section-4 describes the recommendation techniques and similarity metrics those have been used in finding the similarity among the query fragments. The section-5 focuses on terminology used to calculate weights of items required for finding similarity. The section-6 states the comparison between the fragment based approach and matrix factorization approach.