Inquiry in Support of the Knowledge Sharing Life Cycle Within a Higher Education Database Practicum: a Case Study at Regis University

The Regis University database practicum provides an opportunity for students to get hands-on experience with Database Administrator (DBA) tasks as they support the virtual lab environment used by students enrolled in database courses at the university. The student DBA team is new each semester and has a short time to become familiar with the environment they are supporting and the tools they will be using. Inefficient communication and organization delay the resolution of production issues. This study analyzed the current knowledge sharing culture, technologies, and processes of the database practicum. The goal of the study was to determine accessibility of core knowledge needed by DBA teams to improve collaboration and reduce time for teams to begin actively supporting the online environment. This paper can be used by any higher education administrator wishing to implement a collaborative information technology

Autonomic Database Management: State of the Art and Future Trends

In recent years, Database Management Systems (DBMS) have increased significantly in size and complexity, increasing the extent to which database administration is a time-consuming and expensive task. Database Administrator (DBA) expenses have become a significant part of the total cost of ownership. This results in the need to develop Autonomous Database Management systems (ADBMS) that would manage themselves without human intervention. Accordingly, this paper evaluates the current state of autonomous database systems and identifies gaps and challenges in the achievement of fully autonomic databases. In addition to highlighting technical challenges and gaps, we identify one human factor, gaining the trust of DBAs, as a major obstacle. Without human acceptance and trust, the goal of achieving fully autonomic databases cannot be realized

Proactive Anomaly Detection in Large-Scale Cloud-Native Databases

This disclosure describes techniques to identify anomalous patterns in customer workloads from database logs and to enable timely, corrective action that ensures uninterrupted operation of the database. Examples of anomalies include sudden increases (bursts) in the number of error messages written to a log file. An adaptive behavior norm is defined for each message type. Time instances or periods when the gap between messages of a given type in the database log deviate from the expected behavior norms are detected. A deviation from the behavior norm is a potential indicator of database problems. An anomaly detection tool outputs a ranked list of log statements exhibiting spikes of activity along with their time intervals that a database administrator (DBA) can examine to take corrective action. By automating anomaly detection, the valuable time of DBAs can be spent acting on issues rather than finding them

Database performance monitoring: use of software tools and traditional practices

A database must perform quickly and accurately to be useful to an organization. To monitor database performance, a database administrator (DBA) analyzes many types of data. As the complexity of database systems increases, so does the amount of required information. Recently available software tools facilitate this information gathering process for the DBA. This study describes a survey of database professionals that used Internet newsgroups and discussion forums to determine DBAs' use of traditional performance management practices, such as system tweaking and benchmarking, and these software tools. Although survey response was small, examination of responses uncovered some common themes. Traditional practices were still used. DBAs overall did not use many tools, for many reasons, but agreed that the generated information would have been useful and necessary. To construct this data, they used scripts or functions built into the database. The study provides a basis for future research into database performance management

CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads

Index tuning, i.e., selecting the indexes appropriate for a workload, is a crucial problem in database system tuning. In this paper, we solve index tuning for large problem instances that are common in practice, e.g., thousands of queries in the workload, thousands of candidate indexes and several hard and soft constraints. Our work is the first to reveal that the index tuning problem has a well structured space of solutions, and this space can be explored efficiently with well known techniques from linear optimization. Experimental results demonstrate that our approach outperforms state-of-the-art commercial and research techniques by a significant margin (up to an order of magnitude).Comment: VLDB201

MEMBANGUN GAME BASED LEARNING DASAR IMPLEMENTASI DATABASE BERBASIS HTML5 PADA MATA KULIAH SISTEM BASIS DATA DENGAN METODE ADDIE

Program studi Sistem Informasi Institut Teknologi Telkom memiliki mata kuliah Sistem Basis Data yang menjadi salah satu sarana untuk menghasilkan Database Administrator (DBA) yang berkompeten sehingga dapat memenuhi kebutuhan pasar dalam bidang DBA. Namun, proses belajar mengajar yang berlangsung saat ini pada mata kuliah Sistem Basis Data dirasa masih kurang baik. Hal ini dapat terlihat dari hasil survei yang telah dilakukan yang menunjukkan bahwa hanya 13% dari 30 mahasiswa yang telah mengikuti mata kuliah Sistem Basis Data dapat memahami keseluruhan materi kuliah Sistem Basis Data.Dalam tugas akhir ini, dilakukan pembangunan Game Based Learning dasar implementasi database pada mata kuliah Sistem Basis Data. Dalam software ini, konten pembelajaran disampaikan dalam bentuk game sehingga dapat meningkatkan ketertarikan dan pemahaman mahasiswa terhadap mata kuliah Sistem Basis Data khususnya untuk materi dasar implementasi database. Dengan game ini, diharapkan dapat membantu proses belajar mengajar materi dasar implementasi database pada mata kuliah Sistem Basis Data khususnya pada 4 materi utama, antara lain Pengenalan DDL (Data Definition Language) dan DML (Data Manipulation Language), Query, PL/SQL (Procedural Language/Structured Query Language), Trigger. Game ini dibangun dengan menggunakan teknologi HTML5 dan model instruksional desain ADDIE.Pada penelitian ini, dilakukan pengujian fungsional dan user acceptance. Dari hasil pengujian fungsional, seluruh fungsi yang ada pada game dapat berjalan dengan baik. Kemudian, dari hasil pengujian user acceptance dihasilkan rata-rata 50% dari 30 mahasiswa setuju bahwa kualitas tampilan, interaksi program, interaksi user, dan penyajian materi pada game baik. Sistem Basis Data, dasar implementasi database , ADDIE, Game Based Learning, HTML5, Database Administrator

Cost of saving natural gas through efficiency programs funded by utility customers: 2012–2017

This study estimates the cost of saving a therm of natural gas from energy efficiency programs funded by utility customers during the period 2012 to 2017. Berkeley Lab researchers compiled and analyzed efficiency program data reported by investor-owned utilities and other program administrators in a dozen states representative of the four U.S. Census regions — Arkansas, California, Connecticut, Iowa, Massachusetts, Michigan, Minnesota, New Jersey, New York, Oklahoma, Rhode Island and Utah. Depending on the year, the dataset accounts for about 50 percent to 70 percent of annual national spending on natural gas efficiency programs. The estimated cost of saving natural gas during the study period is $0.40 per therm. The analysis also includes estimates of the program administrator cost of saved energy for three core sectors for natural gas: commercial and industrial, residential, and low-income households. It aggregates these sectors to provide regional and national values. Our metrics include savings-weighted averages, unweighted medians, and interquartile ranges (25th and 75th percentiles) of the levelized program administrator cost of saving gas, in constant 2017 dollars. In addition, the study analyzes cost trends during the study period, finding that average program costs trended downward. The U.S. Department of Energy’s Building Technologies Office supported this work

Forensic flavour

Databases often receive an uninspired and uninterested response. The curriculum content of a database module generally involves the design of entity-relationship models, SQL programming, application development and advanced database applications such as data warehousing and data mining. These are often taught within the tired and relatively worn case studies of purchase order systems, retail or health care systems. However the current trend for crime scene investigation drama and the frequent stories in the news of personal tragedies involving incorrect data, missing data or data mix-up capture the attention of many. The truth is that crimes require data investigation and expert database witnesses to provide evidence and this requires database knowledge and skill. This project involved the introduction of a ‘forensic flavour’ to the teaching of databases as part of an undergraduate Computing Degree to students. The ‘forensic flavour’ involved introducing investigative and enquiry based learning techniques as well as selecting case studies based around real-life crimes and crime data. The learning objectives remained unchanged for the modules as did the curriculum content. The initial findings are that the students engaged on average 40% better and enjoyed the experience more