Enhancing student learning in database courses with large data sets

To enhance and grow the database certificate program and enhance database courses offered by the Computer Science/Information Technology department (CSIT) at the Community College of Baltimore County (CCBC), we are in the process of developing the EDNA Project. The EDNA Project- Extraction of Diverse Datasets and Analysis will be an educational database and project set encompassing several large real-world datasets that will be accessible to students in the database courses. This project was created out of the need for more elaborate examples and hands-on experience with database concepts. This repository will help serve to teach database concepts, data analytics, and lead to topics related to big data to students in these courses and certificates programs. In addition, this paper describes collaboration with the University of Baltimore and the beginnings of Project RED. This paper discusses our current work in developing this project.

An important issue to extend database management systems functionality is to allow the expression of imprecise queries to make these systems able to satisfy user needs more closely. In the last few years, some authors have dealt with the problem of relaxing the relational model in order to admit some imprecision. Two significant data mining problems have been addressed recently, namely, mining fuzzy association. rules, and set-oriented mining for association rules in relational database (SETM algorithms). At present, the problem of how to express the mining fuzzy association rules algorithm as SQL queries has not been proposed. In this paper, we attempt to develop a fuzzy version of SETM algorithms that can be expressed as SQL queries in relational databases and discuss optimization of the algorithm.

Motion capture data have been widely used in applications ranging from video games and animations to simulations and virtual environments. Moreover, all data-driven approaches for analysis and synthesis of motions are depending on motion capture data. Although multiple large motion capture data sets are freely available for research, there is no system which can provide a centralized access to all of them in an organized manner. In this paper we show that using a relational database management system (RDBMS) to store data does not only provide such a centralized access to the data, but also allows to include other sensor modalities (e.g. accelerometer data) and various semantic annotations. We present two applications for our system: A motion capture player where motions sequences can be retrieved from large datasets using SQL queries and the automatic construction of statistical models which can further be used for complex motion analysis and motions synthesis tasks.KeywordsMotion CaptureZero Moment PointMotion Capture DataRelational Database Management SystemSynchronization FrameThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Performance tuning is the one area where the Oracle DBA will probably spend the most of his or her time, whether the DBA is working with a development or a production database. Performance tuning could be proactive as well as reactive. If you’re a DBA helping developers to tune their SQL, you can improve performance by suggesting more efficient queries or table- and index-organization schemes. If you’re a production DBA, you’ll be dealing with user perceptions of a “slow” database, batch jobs taking longer and longer to complete, and so on.KeywordsQuery ProcessingExecution PlanQuery OptimizationTrace FilePerformance TuningThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The main problem has been managing databases because they are continuously expanding quickly and getting more complicated in terms of volume, variety, and velocity. Currently, Relational Database Management Systems (RDMS), also known as SQL, or traditional search engines are primarily used to manage and utilize data collection. Due to their highly semantic properties and applications, relational databases have emerged as powerful and superior during the previous two decades. The handling of a high volume of data and the variability in data type and structure has become a laborious task since Big Data entered the IT industry. Relational databases are inadequate to handle such big data due to their rigid data limitations, structure, relations, and other factors. As a result, data aggregation is rendered impossible. NoSQL databases offer a practical and understandable foundation for combining massive amounts of data, structures, and interactions. Data modeling and migration are now needed in order to define the issue. There isn't yet a tool for switching from relational to no-SQL databases. For this migration, relational (SQL) database queries must be converted to NoSQL database queries. Since relational databases (RDBMS) have found it difficult to keep up with modernization, NoSQL has emerged as the most practical database. This essay aims to highlight and assess the applications of NoSQL, the notion of NoSQL data modeling, and the NoSQL database migration procedure. NoSQL, NoSQL Roadmap, Data Modeling, ETL Approach for Data Migration, Data Loading, and Data Extraction.

The translation of the one-to-one relationship from the Entity/Relationship model to the relational model while preserving one-to-one correspondence between the members of the participating entity sets is a topic that has been neglected in introductory database textbooks. In this paper, we provide a simple method, intended for students taking a first course in relational database design, that accomplishes this without introducing nulls or violating the relational model.

This paper describes the introduction of a feedback-revision-resubmission cycle for homework assignments in two introductory courses. The goal was to increase student engagement with homework assignments as an important tool for concept practice and mastery. A review of the effects in both an object-oriented programming course and a relational databases course shows positive outcomes as reflected in both course evaluations and a decision tree analysis of student performance on assignments.

SQL queries are usually tested for correctness by executing them on one or more datasets, to see if they give the desired results on each dataset. Erroneous queries are often the result of small changes, or mutations, of the correct query. Earlier work on the XData system showed how to generate datasets that kill all mutations in a class of mutations that included join type and comparison operation mutations. However, the system could not handle a number of commonly used SQL features.In this paper we extend the XData data generation techniques to handle features such as null values, string constraints, aggregation with constraints on aggregation results, and a class of subqueries, amongst others. We present a study of the effectiveness of our data generation approach for correcting student SQL assignments that were part of a database course. The datasets generated by XData outperform publicly available datasets, as well as manual grading done earlier by teaching assistants.

On the selection of secondary indices in relational databases

A data lake refers to an extremely large data resource or repository. Data lakes store large amounts of data and use advanced analytics to pair data from multiple sources with different types of structured, semi-structured, and unstructured information. NoSQL databases such as Mongodb, Redis, Neo4j, and Cassandra are nontabular and they store data differently rather than use relational tables. NoSQL databases come in many forms, mostly documents, key values, wide columns, and graphs based on their data model. NoSQL gives less complicated scalability and higher overall performance as compared with traditional relational databases. NoSQL databases can store different types of data, but they cannot fully support Automation, Consistency, Isolation, and endurance (ACID) features, i.e., trigger functions in multi-transaction management, because a NoSQL database uses a non-relational database system. Thus, an interpreter is necessary for SQL-to-NoSQL queries. We used ANTLR (ANother Tool for Language Recognition), which has five main stages: The input SQL query, the tokenizer, the parser, the parser tree, and lastly the generation of the query results in NoSQL. The tool gave users to write a flexible multi-pass language parser that is expected to solve problems in querying complex ACID functions and other problems in complex queries in NoSQL databases. In the measurement, analysis, and evaluation of the translation results through the comparison of each NoSQL criterion against a Relational Database Management System (MySQL), the scores obtained were as follows. The performance criterion achieved the highest score (98.40%) by the MongoDB database, followed by scalability (97.40%) and accuracy (97.00%). The criterion with the lowest score was complexity (91.65%).

A survey of business schools accredited by the Assembly of American Collegiate Schools of Business has provided data on the hardware and software used in introductory database courses. Survey results show that the size computer used most often in the course is the microcomputer, followed in popularity by upper-end minicomputers. Relational database management software is used in preference over hierarchical and network DBMS.

The Structured Query Language (SQL) is the most widely used declarative language for accessing relational databases, and an essential topic in introductory database courses in higher learning institutions. Despite the intuitiveness of SQL, formulating and comprehending written queries can be confusing, especially for undergraduate students. One major reason for this is that the simple syntax of SQL is often misleading and hard to comprehend. A number of tools have been developed to aid the comprehension of queries and to improve the mental models of students concerning the underlying logic of SQL. Some of these tools employed visualisation and animation in their approach to aid the comprehension of SQL. This paper presents an interactive comprehension aid based on visualisation, specifically designed to support the SQL SELECT statement, an area identified in the literature as problematic for students. The visualisation tool uses visual specifications depicting SQL operations to build queries. This is expected to reduce the cognitive load of a student who is learning SQL. We have shown with an online survey that adopting visual specifications in teaching systems assist students in attaining a richer learning experience in introductory database courses.

The author conducts research on data management in the Graha Kasih Nursing Home in Kubu Raya District, which is to find problems with data processing that still uses paper so that it is susceptible to errors in the data recording process and the risk of data loss is very large, making it difficult to access data. Therefore the author designed a Relational Database for elderly data at the Graha Kasih Nursing Home of Kubu Raya District to overcome the problem. The form of research used in this study uses a case study with R & D (Research and Development) method which is a descriptive method which is the initial method of R & D method to collect data about existing conditions. While the data collection techniques used are interviews, observation and documentation studies. The method of analysis and design used in this research is the Database Method of Life Cycle (DBLC), with the focus of the discussion including Conceptual Database Design, Logical Database design, and Physical Database Design. Flowchat is used to describe existing running systems, while the proposed system modeling and design uses UML (Unified Modeling Language). The design of this Relational Database is made with Visual Basic .NET programming language as its design tool and MYSQL as its database. The designed database resulted in an application that could help the Graha Kasih Nursing Home of Kubu Raya District to facilitate the management of occupants 'data, contribution data, data on residents' transfers, contributor data, donation data, and employee data, as well as minimizing the risk of data damage and data loss. , and facilitate the process of accessing data easily and quickly.

Exponential growth in scientific research data demands novel measures for managing the extremely large datasets. In particular, due to advancements in high-resolution microscopy, the nanoscopy scientific research community is producing datasets up to the range of multiple TeraBytes (TB). Systematically acquired datasets of biological specimens are composed of multiple high-resolution images, in the range of 150-200 TB. The management of these extremely large datasets requires an optimized Generic Client Service (GCS) API with an integration into a data repository system. The novel API proposed in this paper provides an abstract interface that connects various disparate systems. The API is optimized to provide an efficient and automated ingest, download of the data and management of its metadata. The ingest and download processes are based on well-defined workflows stated in this paper. The base metadata model for comprehensive description of the datasets is also stated in the paper. The API is seamlessly integrated with a digital data repository system, namely KIT Data Manager to make it adaptable for a wide range of communities. Finally, a simple and easy to use command line tool is realized based on GCS API to manage large datasets of nanoscopy research community.

There are few sources from which to obtain clinical and genetic data for use in research in Saudi Arabia. Numerous obstacles led to the difficulty of integrating these data from silos and scattered sources to provide standardized access to large data sets for patients with common health conditions. To this end, we sought to contribute to this area and offer a practical and easy-to-implement solution. In this paper, we aim to design and implement a “not only SQL” (NoSQL) based integration framework to generate an Integrated Data Repository of Genetic Disorders Data (GENE2D) to integrate data from various genetic clinics and research centers in Saudi Arabia and provide an easy-to-use query interface for researchers to conduct their studies on large datasets. The major components involved in the GENE2D architecture consists of the data sources, the integrated data repository (IDR) as a central database, and the application interface. The IDR uses a NoSQL document store via MongoDB (an open source document-oriented database program) as a backend database. The application interface called Query Builder provides multiple services for data retrieval from the database using a custom query to answer simple or complex research questions. The GENE2D system demonstrates its potential to help grow and develop a national genetic disorders database in Saudi Arabia.