3,883 research outputs found
Semi-automatic assessment of basic SQL statements
Learning and assessing the Structured Query Language (SQL) is an important step in developing students' database skills. However, due to the increasing numbers of students learning SQL, assessing and providing detailed feedback to students' work can be time consuming and prone to errors. The main purpose of this research is to reduce or remove as many of the repetitive tasks in any phase of the assessment process of SQL statements as possible to achieve the consistency of marking and feedback on SQL answers.This research examines existing SQL assessment tools and their limitations by testing them on SQL questions, where the results reveal that students must attaint essential skills to be able to formulate basic SQL queries. This is because formulating SQL statements requires practice and effort by students. In addition, the standard steps adopted in many SQL assessment tools were found to be insufficient in successfully assessing our sample of exam scripts. The analysis of the outcomes identified several ways of solving the same query and the categories of errors based on the common student mistakes in SQL statements. Based on this, this research proposes a semi-automated assessment approach as a solution to improve students’ SQL formulation process, ensure the consistency of SQL grading and the feedback generated during the marking process. The semi-automatic marking method utilities both the Case-Based Reasoning (CBR) system and Rule-Based Reasoning (RBR) system methodologies. The approach aims to reduce the workload of marking tasks by reducing or removing as many of the repetitive tasks in any phase of the marking process of SQL statements as possible. It also targets the improvement of feedback dimensions that can be given to students.In addition, the research implemented a prototype of the SQL assessment framework which supports the process of the semi-automated assessment approach. The prototype aims to enhance the SQL formulation process for students and minimise the required human effort for assessing and evaluating SQL statements. Furthermore, it aims to provide timely, individual and detailed feedback to the students. The new prototype tool allows students to formulate SQL statements using the point-and-click approach by using the SQL Formulation Editor (SQL-FE). It also aims to minimise the required human effort for assessing and evaluating SQL statements through the use of the SQL Marking Editor (SQL-ME). To ensure the effectiveness of the SQL-FE tool, the research conducted two studies which compared the newly implemented tool with the paper-based manual method in the first study (pilot study), and with the SQL Management Studio tool in the second study (full experiment). The results provided reasonable evidence that using SQL-FE can have a beneficial effect on formulating SQL statements and improve students’ SQL learning. The results also showed that students were able to solve and formulate the SQL query on time and their performance showed significant improvement. The research also carried out an experiment to examine the viability of the SQL Marking Editor by testing the SQL partial marking, grouping of identical SQL statements, and the resulting marking process after applying the generic marking rules. The experimental results presented demonstrated that the newly implemented editor was able to provide consistent marking and individual feedback for all SQL parts. This means that the main aim of this research has been fulfilled, since the workload of the lecturers has been reduced, and students’ performance in formulating SQL statements has been improved.</div
TLAD 2010 Proceedings:8th international workshop on teaching, learning and assesment of databases (TLAD)
This is the eighth in the series of highly successful international workshops on the Teaching, Learning and Assessment of Databases (TLAD 2010), which once again is held as a workshop of BNCOD 2010 - the 27th International Information Systems Conference. TLAD 2010 is held on the 28th June at the beautiful Dudhope Castle at the Abertay University, just before BNCOD, and hopes to be just as successful as its predecessors.The teaching of databases is central to all Computing Science, Software Engineering, Information Systems and Information Technology courses, and this year, the workshop aims to continue the tradition of bringing together both database teachers and researchers, in order to share good learning, teaching and assessment practice and experience, and further the growing community amongst database academics. As well as attracting academics from the UK community, the workshop has also been successful in attracting academics from the wider international community, through serving on the programme committee, and attending and presenting papers.This year, the workshop includes an invited talk given by Richard Cooper (of the University of Glasgow) who will present a discussion and some results from the Database Disciplinary Commons which was held in the UK over the academic year. Due to the healthy number of high quality submissions this year, the workshop will also present seven peer reviewed papers, and six refereed poster papers. Of the seven presented papers, three will be presented as full papers and four as short papers. These papers and posters cover a number of themes, including: approaches to teaching databases, e.g. group centered and problem based learning; use of novel case studies, e.g. forensics and XML data; techniques and approaches for improving teaching and student learning processes; assessment techniques, e.g. peer review; methods for improving students abilities to develop database queries and develop E-R diagrams; and e-learning platforms for supporting teaching and learning
TLAD 2010 Proceedings:8th international workshop on teaching, learning and assesment of databases (TLAD)
This is the eighth in the series of highly successful international workshops on the Teaching, Learning and Assessment of Databases (TLAD 2010), which once again is held as a workshop of BNCOD 2010 - the 27th International Information Systems Conference. TLAD 2010 is held on the 28th June at the beautiful Dudhope Castle at the Abertay University, just before BNCOD, and hopes to be just as successful as its predecessors.The teaching of databases is central to all Computing Science, Software Engineering, Information Systems and Information Technology courses, and this year, the workshop aims to continue the tradition of bringing together both database teachers and researchers, in order to share good learning, teaching and assessment practice and experience, and further the growing community amongst database academics. As well as attracting academics from the UK community, the workshop has also been successful in attracting academics from the wider international community, through serving on the programme committee, and attending and presenting papers.This year, the workshop includes an invited talk given by Richard Cooper (of the University of Glasgow) who will present a discussion and some results from the Database Disciplinary Commons which was held in the UK over the academic year. Due to the healthy number of high quality submissions this year, the workshop will also present seven peer reviewed papers, and six refereed poster papers. Of the seven presented papers, three will be presented as full papers and four as short papers. These papers and posters cover a number of themes, including: approaches to teaching databases, e.g. group centered and problem based learning; use of novel case studies, e.g. forensics and XML data; techniques and approaches for improving teaching and student learning processes; assessment techniques, e.g. peer review; methods for improving students abilities to develop database queries and develop E-R diagrams; and e-learning platforms for supporting teaching and learning
An empirical study of the effects of data model and query language on novice user query performance
Master'sMASTER OF SCIENC
SQL query log analysis for identifying user interests and query recommendations
In the sciences and elsewhere, the use of relational databases has become ubiquitous.
To get maximum profit from a database, one should have in-depth knowledge in both
SQL and a domain (data structure and meaning that a database contains). To assist
inexperienced users in formulating their needs, SQL query recommendation system
(SQL QRS) has been proposed. It utilizes the experience of previous users captured by
SQL query log as well as the user query history to suggest. When constructing such
a system, one should solve related problems: (1) clean the query log and (2) define
appropriate query similarity functions. These two tasks are not only necessary for
building SQL QRS, but they apply to other problems. In what follows, we describe
three scenarios of SQL query log analysis: (1) cleaning an SQL query log, (2) SQL
query log clustering when testing SQL query similarity functions and (3) recommending
SQL queries. We also explain how these three branches are related to each other.
Scenario 1. Cleaning SQL query log as a general pre-processing step
The raw query log is often not suitable for query log analysis tasks such as clustering,
giving recommendations. That is because it contains antipatterns and robotic data
downloads, also known as Sliding Window Search (SWS). An antipattern in software
engineering is a special case of a pattern. While a pattern is a standard solution, an
antipattern is a pattern with a negative effect.
When it comes to SQL query recommendation, leaving such artifacts in the log during
analysis results in a wrong suggestion. Firstly, the behaviour of "mortal" users who
need a recommendation is different from robots, which perform SWS. Secondly, one
does not want to recommend antipatterns, so they need to be excluded from the query
pool. Thirdly, the bigger a log is, the slower a recommendation engine operates. Thus,
excluding SWS and antipatterns from the input data makes the recommendation
better and faster.
The effect of SWS and antipatterns on query log clustering depends on the chosen
similarity function. The result can either (1) do not change or (2) add clusters which
cover a big part of data. In any case, having antipatterns and SWS in an input log
increases only the time one need to cluster and do not increase the quality of results.
Scenario 2. Identifying User Interests via Clustering
To identify the hot spots of user interests, one clusters SQL queries. In a scientific
domain, it exposes research trends. In business, it points to popular data slices which
one might want to refactor for better accessibility. A good clustering result must be
precise (match ground truth) and interpretable.
Query similarity relies on SQL query representation. There are three strategies to
represent an SQL query. FB (feature-based) query representation sees a query as
structure, not considering the data, a query accesses. WB (witness-based) approach
treat a query as a set of tuples in the result set. AAB (access area-based) representation
considers a query as an expression in relational algebra. While WB and FB query
similarity functions are straightforward (Jaccard or cosine similarities), AAB query
similarity requires additional definition. We proposed two variants of AAB similarity
measure – overlap (AABovl) and closeness (AABcl). In AABovl, the similarity of two
queries is the overlap of their access areas. AABcl relies on the distance between two
access areas in the data space – two queries may be similar even if their access areas
do not overlap.
The extensive experiments consist of two parts. The first one is clustering a rather
small dataset with ground truth. This experiment serves to study the precision of
various similarity functions by comparing clustering results to supervised insights. The
second experiment aims to investigate on the interpretability of clustering results with
different similarity functions. It clusters a big real-world query log. The domain expert
then evaluates the results. Both experiments show that AAB similarity functions
produce better results in both precision and interpretability.
Scenario 3. SQL Query Recommendation
A sound SQL query recommendation system (1) provides a query which can be run
directly, (2) supports comparison operators and various logical operators, (3) is scalable
and has low response times, (4) provides recommendations of high quality. The existing
approaches fail to fulfill all the requirements. We proposed DASQR, scalable and
data-aware query recommendation to meet all four needs. In a nutshell, DASQR is
a hybrid (collaborative filtering + content-based) approach. Its variations utilize all
similarity functions, which we define or find in the related work.
Measuring the quality of SQL query recommendation system (QRS) is particularly
challenging since there is no standard way approaching it. Previous studies have
evaluated the results using quality metrics which only rely on the query representations
used in these studies. It is somewhat subjective since a similarity function and a
quality metric are dependent. We propose AAB quality metrics and then evaluate
each approach based on all the metrics.
The experiments test DASQR approaches and competitors. Both performance and
runtime experiments indicate that DASQR approaches outperform the existing ones
Evaluation in natural language processing
quot; European Summer School on Language Logic and Information(ESSLLI 2007)(Trinity College Dublin Ireland 6-17 August 2007
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