Nonprocedural Communication between Users and Application Software

This report is a survey of nonprocedural communication between users and application software in interactive data-processing systems. It includes a description of the main features of interactive systems, a classification of the potential users of application software, and a definition of the nonprocedural interface. Nonprocedural languages are classified into a number of broad groups and illustrated with examples. Finally, future trends in user-computer interfaces and possible developments in manager-oriented languages are discussed

Impact of Query Specification Mode and Problem Complexity on Query Specification Productivity of Novice Users of Database Systems

With the increased demand for the utilization of computerized information systems by business users, the need for investigating the impact of various user interfaces has been well recognized. It is usually assumed that providing the user with assistance in the usage o-f a system would significantly increase the user's productivity. There is, however, a dearth of systematic inquiry into this commonly held notion to verify its validity in a scientific fashion. The purpose of this study is to investigate the impact of system-provided user assistance and complexity level of the problem on novice users' productivity in specifying database queries. The study is theoretical in the sense that it presents an approach adopted from research in deductive database systems to attack problems concerning user interface design. It is empirical in that it conducts an experiment in a controlled laboratory setting to collect primary data for the testing of a series of hypotheses. The two independent variables are system-provided user assistance and problem complexity, while the dependent variable is the user's query specification productivity. Three measures are used as separate indicators of query specification productivity: number of syntactic errors, number of semantic errors, and time required for completing a query task. Due to the lack of a well-defined metric for user assistance, the study first presents a generic classification scheme for relational query specification. Based on this classification scheme, two quantitative metrics for measuring the amount of user assistance in terms of prompts and defaults were developed. The user assistance is operationally defined with these two metrics. Four findings emerge as significant results of the study. First, user assistance has a significant main effect on all of the three dependent measures at the 1 percent significance level. Second, problem complexity also has a significant impact on the three productivity measures at the 1 percent significance level. Third, the interaction effect of user assistance and problem complexity on the number of semantic errors and the amount of time for completion is significant at the 1 percent level. Fourth, Although this interaction effect on the number of syntactic errors is not significant at the 5 percent level, it is at the 10 percent level. More research is needed to permit a thorough understanding of the issue of user interface design. A list of topics is suggested for future research to confirm or to modify the findings of this study

Students' syntactic mistakes in writing seven different types of SQL queries and its application to predicting students' success

© 2016 ACM. The computing education community has studied extensively the errors of novice programmers. In contrast, little attention has been given to student's mistake in writing SQL statements. This paper represents the first large scale quantitative analysis of the student's syntactic mistakes in writing different types of SQL queries. Over 160 thousand snapshots of SQL queries were collected from over 2000 students across eight years. We describe the most common types of syntactic errors that students make. We also describe our development of an automatic classifier with an overall accuracy of 0.78 for predicting student performance in writing SQL queries

The Query Cube: A Framework for Assessing User Productivity with Database Information Retrieval

Three key factors that affect user productivity on database information retrieval are representation realism, expressive ease, and task complexity. Representation realism is the level of abstraction used in formulating queries. Expressive ease is the syntactic flexibility of a query language. Task complexity is the level of difficulty of queries. These factors formed a three dimensional query cube. A laboratory experiment was conducted to evaluate user productivity on database information retrieval corresponding to different vertices of the query cube. The results show that the query cube is a viable framework for assessing user productivity, both on effectiveness and efficiency perspective

Programming Course Sequence and Prior Knowledgeof Programming Languages:Do They Affect Students\u27 Grades?

Research in the field of education has shown that learning a new skill or subject is enhanced when prior learning on a similar topic has already taken place. Conversely, articles in the popular press have reported that object-oriented programming languages are more difficult to learn if the programmer already knows a non object-oriented language. This study will survey 400 students in Cobol, C++, and Visual Basic to determine if prior programming courses affect students\u27 grades and if so, if there is an optimal sequence to learning the languages. Results can be used by IS educators to plan programming course sequence, by practitioners to design better training programs, and by researchers to further examine cognitive issues when learning programming language

A Tutorial on Visual Representations of Relational Queries

Query formulation is increasingly performed by systems that need to guess a user's intent (e.g. via spoken word interfaces). But how can a user know that the computational agent is returning answers to the "right" query? More generally, given that relational queries can become pretty complicated, how can we help users understand existing relational queries, whether human-generated or automatically generated? Now seems the right moment to revisit a topic that predates the birth of the relational model: developing visual metaphors that help users understand relational queries. This lecture-style tutorial surveys the key visual metaphors developed for visual representations of relational expressions. We will survey the history and state-of-the art of relationally-complete diagrammatic representations of relational queries, discuss the key visual metaphors developed in over a century of investigating diagrammatic languages, and organize the landscape by mapping their used visual alphabets to the syntax and semantics of Relational Algebra (RA) and Relational Calculus (RC).Comment: 4 page tutorial paper at VLDB 2023, tutorial web page with slides to be posted in time: https://northeastern-datalab.github.io/visual-query-representation-tutorial/. arXiv admin note: text overlap with arXiv:2208.0161

Principles of Query Visualization

Query Visualization (QV) is the problem of transforming a given query into a graphical representation that helps humans understand its meaning. This task is notably different from designing a Visual Query Language (VQL) that helps a user compose a query. This article discusses the principles of relational query visualization and its potential for simplifying user interactions with relational data.Comment: 20 pages, 12 figures, preprint for IEEE Data Engineering Bulleti

The effects of data models and conceptual models of the structured query language on the task of query writing by end users

This research is an empirical investigation of human factors on the use of database systems. The problem motivating the study is the difficulty encountered by end-users in retrieving data from a database

The Effects of Conceptual and Logical Interfaces On Visual Query Performance of End Users

To the end users, the interface is the system. A better interface not only facilitates end user interaction with the database, it also enables them to formulate queries more efficiently and effectively. Two of the most important user-database interfaces are the conceptual and logical interfaces. With the conceptual interface, the user communicates with the database system in terms of entities, objects and relationships. On the other hand, the current user-database interaction is mainly based on the logical interface where the user expresses the queries in terms of relations and join operations. Because the concepts at the logical interface are abstract and convoluted to ordinary users, many researchers argue that end users will be better off with the conceptual interface. This research will test this claim by comparing the effects of coneeptual and logical interfaces on the visual query performance of end users. The experimental study involves three tests: an initial test, a retention test and a relearning test. This allows us to assess the learning effect over time. The results show that users of the conceptual interface achieve higher accuracy, are more confident in their answers, and spend less time on the queries than users of the logical interface in all three tests