Applying the ETL Process to Blockchain Data. Prospect and Findings
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Module 8 Wiki Guide
Best Practices for Biomedical Research Data Management Harvard Medical School, The Francis A. Countway Library of Medicine Module 8 Wiki Guide Learning Objectives and Outcomes: 1. Emphasize characteristics of long-term data curation and preservation that build on and extend active data management ● It is the purview of permanent archiving and preservation to take over stewardship and ensure that the data do not become technologically obsolete and no longer permanently accessible. ○ The selection of a repository to ensure that certain technical processes are performed routinely and reliably to maintain data integrity ○ Determining the costs and steps necessary to address preservation issues such as technological obsolescence inhibiting data access ○ Consistent, citable access to data and associated contextual records ○ Ensuring that protected data stays protected through repository-governed access control ● Data Management ○ Refers to the handling, manipulation, and retention of data generated within the context of the scientific process ○ Use of this term has become more common as funding agencies require researchers to develop and implement structured plans as part of grant-funded project activities ● Digital Stewardship ○ Contributions to the longevity and usefulness of digital content by its caretakers that may occur within, but often outside of, a formal digital preservation program ○ Encompasses all activities related to the care and management of digital objects over time, and addresses all phases of the digital object lifecycle 2. Distinguish between preservation and curation ● Digital Curation ○ The combination of data curation and digital preservation ○ There tends to be a relatively strong orientation toward authenticity, trustworthiness, and long-term preservation ○ Maintaining and adding value to a trusted body of digital information for future and current use. -
Database Systems Administrator FLSA: Exempt
CLOVIS UNIFIED SCHOOL DISTRICT POSITION DESCRIPTION Position: Database Systems Administrator FLSA: Exempt Department/Site: Technology Services Salary Grade: 127 Reports to/Evaluated by: Chief Technology Officer Salary Schedule: Classified Management SUMMARY Gathers requirements and provisions servers to meet the district’s need for database storage. Installs and configures SQL Server according to the specifications of outside software vendors and/or the development group. Designs and executes a security scheme to assure safety of confidential data. Implements and manages a backup and disaster recovery plan. Monitors the health and performance of database servers and database applications. Troubleshoots database application performance issues. Automates monitoring and maintenance tasks. Maintains service pack deployment and upgrades servers in consultation with the developer group and outside vendors. Deploys and schedules SQL Server Agent tasks. Implements and manages replication topologies. Designs and deploys datamarts under the supervision of the developer group. Deploys and manages cubes for SSAS implementations. DISTINGUISHING CAREER FEATURES The Database Systems Administrator is a senior level analyst position requiring specialized education and training in the field of study typically resulting in a degree. Advancement to this position would require competency in the design and administration of relational databases designated for business, enterprise, and student information. Advancement from this position is limited to supervisory management positions. ESSENTIAL DUTIES AND RESPONSIBILITIES Implements, maintains and administers all district supported versions of Microsoft SQL as well as other DBMS as needed. Responsible for database capacity planning and involvement in server capacity planning. Responsible for verification of backups and restoration of production and test databases. Designs, implements and maintains a disaster recovery plan for critical data resources. -
Undergraduate Biocuration: Developing Tomorrow’S Researchers While Mining Today’S Data
The Journal of Undergraduate Neuroscience Education (JUNE), Fall 2015, 14(1):A56-A65 ARTICLE Undergraduate Biocuration: Developing Tomorrow’s Researchers While Mining Today’s Data Cassie S. Mitchell, Ashlyn Cates, Renaid B. Kim, & Sabrina K. Hollinger Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, GA 30332. Biocuration is a time-intensive process that involves managerial positions. We detail the undergraduate extraction, transcription, and organization of biological or application and training processes and give detailed job clinical data from disjointed data sets into a user-friendly descriptions for each position on the assembly line. We database. Curated data is subsequently used primarily for present case studies of neuropathology curation performed text mining or informatics analysis (bioinformatics, entirely by undergraduates, namely the construction of neuroinformatics, health informatics, etc.) and secondarily experimental databases of Amyotrophic Lateral Sclerosis as a researcher resource. Biocuration is traditionally (ALS) transgenic mouse models and clinical data from ALS considered a Ph.D. level task, but a massive shortage of patient records. Our results reveal undergraduate curators to consolidate the ever-mounting biomedical “big biocuration is scalable for a group of 8-50+ with relatively data” opens the possibility of utilizing biocuration as a minimal required resources. Moreover, with average means to mine today’s data while teaching students skill accuracy rates greater than 98.8%, undergraduate sets they can utilize in any career. By developing a biocurators are equivalently accurate to their professional biocuration assembly line of simplified and counterparts. Initial training to be completely proficient at compartmentalized tasks, we have enabled biocuration to the entry-level takes about five weeks with a minimal be effectively performed by a hierarchy of undergraduate student time commitment of four hours/week. -
The Relational Data Model and Relational Database Constraints
chapter 33 The Relational Data Model and Relational Database Constraints his chapter opens Part 2 of the book, which covers Trelational databases. The relational data model was first introduced by Ted Codd of IBM Research in 1970 in a classic paper (Codd 1970), and it attracted immediate attention due to its simplicity and mathematical foundation. The model uses the concept of a mathematical relation—which looks somewhat like a table of values—as its basic building block, and has its theoretical basis in set theory and first-order predicate logic. In this chapter we discuss the basic characteristics of the model and its constraints. The first commercial implementations of the relational model became available in the early 1980s, such as the SQL/DS system on the MVS operating system by IBM and the Oracle DBMS. Since then, the model has been implemented in a large num- ber of commercial systems. Current popular relational DBMSs (RDBMSs) include DB2 and Informix Dynamic Server (from IBM), Oracle and Rdb (from Oracle), Sybase DBMS (from Sybase) and SQLServer and Access (from Microsoft). In addi- tion, several open source systems, such as MySQL and PostgreSQL, are available. Because of the importance of the relational model, all of Part 2 is devoted to this model and some of the languages associated with it. In Chapters 4 and 5, we describe the SQL query language, which is the standard for commercial relational DBMSs. Chapter 6 covers the operations of the relational algebra and introduces the relational calculus—these are two formal languages associated with the relational model. -
A Brief Introduction to the Data Curation Profiles
Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success A Brief Introduction to the Data Curation Profiles Jake Carlson Data Services Specialist Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success Setting the stage Since 2004: Purdue Interdisciplinary Research Initiative revealed many data needs on campus What faculty said… • Not sure how or whether to share data • Lack of time to organize data sets • Need help describing data for discovery • Want to find new ways to manage data • Need help archiving data sets/collections Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success “Investigating Data Curation Profiles across Research Domains” This lead to a research award from IMLS • Goals of the project: – A better understanding of the practices, attitudes and needs of researchers in managing and sharing their data. – Identify possible roles for librarians and the skill sets they will need to facilitate data sharing and curation. – Develop “data curation profiles” – a tool for librarians and others to gather information on researcher needs for their data and to inform curation services. – Purdue: Brandt—PI, Carlson—Proj Mgr, Witt—coPI; GSLIS UIUC: Palmer—co-PI, Cragin—Proj Mgr Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success Data Curation Profiles Methodology • Initial interview— based on “Conducting a Data Interview” poster, then pre-profile based on model of data in a few published papers • Interviews were transcribed, reviewed • Coding, wanted more detail -> 2nd Interview • Developed and revised a template for Profile “In our experience, one of the most effective tactics for eliciting datasets for the collection is a simple librarian- researcher interview. -
Data Definition Language
1 Structured Query Language SQL, or Structured Query Language is the most popular declarative language used to work with Relational Databases. Originally developed at IBM, it has been subsequently standard- ized by various standards bodies (ANSI, ISO), and extended by various corporations adding their own features (T-SQL, PL/SQL, etc.). There are two primary parts to SQL: The DDL and DML (& DCL). 2 DDL - Data Definition Language DDL is a standard subset of SQL that is used to define tables (database structure), and other metadata related things. The few basic commands include: CREATE DATABASE, CREATE TABLE, DROP TABLE, and ALTER TABLE. There are many other statements, but those are the ones most commonly used. 2.1 CREATE DATABASE Many database servers allow for the presence of many databases1. In order to create a database, a relatively standard command ‘CREATE DATABASE’ is used. The general format of the command is: CREATE DATABASE <database-name> ; The name can be pretty much anything; usually it shouldn’t have spaces (or those spaces have to be properly escaped). Some databases allow hyphens, and/or underscores in the name. The name is usually limited in size (some databases limit the name to 8 characters, others to 32—in other words, it depends on what database you use). 2.2 DROP DATABASE Just like there is a ‘create database’ there is also a ‘drop database’, which simply removes the database. Note that it doesn’t ask you for confirmation, and once you remove a database, it is gone forever2. DROP DATABASE <database-name> ; 2.3 CREATE TABLE Probably the most common DDL statement is ‘CREATE TABLE’. -
SQL Vs Nosql: a Performance Comparison
SQL vs NoSQL: A Performance Comparison Ruihan Wang Zongyan Yang University of Rochester University of Rochester [email protected] [email protected] Abstract 2. ACID Properties and CAP Theorem We always hear some statements like ‘SQL is outdated’, 2.1. ACID Properties ‘This is the world of NoSQL’, ‘SQL is still used a lot by We need to refer the ACID properties[12]: most of companies.’ Which one is accurate? Has NoSQL completely replace SQL? Or is NoSQL just a hype? SQL Atomicity (Structured Query Language) is a standard query language A transaction is an atomic unit of processing; it should for relational database management system. The most popu- either be performed in its entirety or not performed at lar types of RDBMS(Relational Database Management Sys- all. tems) like Oracle, MySQL, SQL Server, uses SQL as their Consistency preservation standard database query language.[3] NoSQL means Not A transaction should be consistency preserving, meaning Only SQL, which is a collection of non-relational data stor- that if it is completely executed from beginning to end age systems. The important character of NoSQL is that it re- without interference from other transactions, it should laxes one or more of the ACID properties for a better perfor- take the database from one consistent state to another. mance in desired fields. Some of the NOSQL databases most Isolation companies using are Cassandra, CouchDB, Hadoop Hbase, A transaction should appear as though it is being exe- MongoDB. In this paper, we’ll outline the general differences cuted in iso- lation from other transactions, even though between the SQL and NoSQL, discuss if Relational Database many transactions are execut- ing concurrently. -
The Temporal Query Language Tquel
The Temporal Query Language TQuel RICHARD SNODGRASS University of North Carolina Recently, attention has been focused on temporal datubases, representing an enterprise over time. We have developed a new language, TQuel, to query a temporal database. TQuel was designed to be a minimal extension, both syntactically and semantically, of Quel, the query language in the Ingres relational database management system. This paper discusses the language informally, then provides a tuple relational calculus semantics for the TQuel statements that differ from their Quel counterparts, including the modification statements. The three additional temporal constructs defined in TQuel are shown to be direct semantic analogues of Quel’s where clause and target list. We also discuss reducibility of the semantics to Quel’s semantics when applied to a static database. TQuel is compared with ten other query languages supporting time. Categories and Subject Descriptors: H.2.1 [Database Management]: Logical Design-data models; H.2.3 [Database Management]: Languages-query lunguages; H.2.7 [Database Management]: Database Administration-logging and recovery General Terms: Languages, Theory Additional Key Words and Phrases: Historical database, Quel, relational calculus, relational model, rollback database, temporal database, tuple calculus 1. INTRODUCTION Most conventional databases represent the state of an enterprise at a single moment of time. Although the contents of the database continue to change as new information is added, these changes are viewed as modifications to the state, with the old, out-of-date data being deleted from the database. The current contents of the database may be viewed as a snapshot of the enterprise. Recently, attention has been focused on temporal dutubases (Z’DBs), repre- senting the progression of states of an enterprise over an interval of time. -
Choosing a Data Model and Query Language for Provenance
Choosing a Data Model and Query Language for Provenance The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Holland, David A., Uri Braun, Diana Maclean, Kiran-Kumar Muniswamy-Reddy, and Margo I. Seltzer. 2008. Choosing a data model and query language for provenance. In Provenance and Annotation of Data and Processes: Proceedings of the 2nd International Provenance and Annotation Workshop (IPAW '08), June 17-18, 2008, Salt Lake City, UT, ed. Juliana Freire, David Koop, and Luc Moreau. Berlin: Springer. Special Issue. Lecture Notes in Computer Science 5272. Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:8747959 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#OAP Choosing a Data Model and Query Language for Provenance David A. Holland, Uri Braun, Diana Maclean, Kiran-Kumar Muniswamy-Reddy, Margo I. Seltzer Harvard University, Cambridge, Massachusetts [email protected] Abstract. The ancestry relationships found in provenance form a di- rected graph. Many provenance queries require traversal of this graph. The data and query models for provenance should directly and naturally address this graph-centric nature of provenance. To that end, we set out the requirements for a provenance data and query model and discuss why the common solutions (relational, XML, RDF) fall short. A semistruc- tured data model is more suited for handling provenance. -
Reactive Relational Database Connectivity
R2DBC - Reactive Relational Database Connectivity Ben Hale<[email protected]>, Mark Paluch <[email protected]>, Greg Turnquist <[email protected]>, Jay Bryant <[email protected]> Version 0.8.0.RC1, 2019-09-26 © 2017-2019 The original authors. Copies of this document may be made for your own use and for distribution to others, provided that you do not charge any fee for such copies and further provided that each copy contains this Copyright Notice, whether distributed in print or electronically. 1 Preface License Specification: R2DBC - Reactive Relational Database Connectivity Version: 0.8.0.RC1 Status: Draft Specification Lead: Pivotal Software, Inc. Release: 2019-09-26 Copyright 2017-2019 the original author or authors. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at https://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. Foreword R2DBC brings a reactive programming API to relational data stores. The Introduction contains more details about its origins and explains its goals. This document describes the first and initial generation of R2DBC. Organization of this document This document is organized into the following parts: • Introduction • Goals • Compliance • Overview • Connections • Transactions 2 • Statements • Batches • Results • Column and Row Metadata • Exceptions • Data Types • Extensions 3 Chapter 1. -
Database Design & SQL Programming
FAQ’s Q: What is SQL? A: Structured Query Language (SQL) is a specialized language for updating, Objectives: deleting, and requesting information held in a relational database management OAK RIDGE HIGH SCHOOL Students will: system (RDBMS). learn about database management Q: What type of credit do I receive from taking this course? systems, both in terms of use and Database Design implementation and design A: This course has been approved for UC & understand the stages of database elective “g” credit. This course also articulates with Folsom Lake College design and implementation CISP: 351 Introduction to Relational SQL Programming understand basic database Database Design and SQL. See reverse for more information. programming theory collaborate with team members on Q: Am I required to take the Oracle a year long project to create a Certification exam? database and mobile app around a A: No. If you decide to take the exam, business idea of their choice you will want to take a few practice exams develop a professional relationship on your own beforehand. There are several preparation books for Exam 1Z0- Oracle Online Curriculum with an Intel mentor through the 047. PC Pals program Q: Where do I take the exam and is Oracle Certification demonstrate the ability to enter there a fee? Earn College Credit and advance within professional A: Exams are administered at a testing organizations by creating a center in the Sacramento area. Students receive a 25% discount voucher off the Software Development Skills resume and learning interviewing published price. skills Intel Mentors have an opportunity to earn Oracle Database Design & Android App Development SQL Certified Expert Certification SQL Programming Resume Building & Interviewing Skills Few subjects will open as many doors for Prerequisites: students in the twenty-first century as computer science (CS) and engineering. -
Examples of Relational Database Model
Examples Of Relational Database Model Discerning Ajai never dally so hardheadedly or die-cast any macaronics warily. Flutiest Noble necessitating: he syllabicating his magnificoes inexplicably and deprecatorily. Taliped Ram zeroes, his decerebration revving strangle verbally. Several products exist to support such databases. Simply click the image would make changes online. Note that a constraint implies an index on the same label and property. So why should we use a database? Gain new system that data typing sql programs operate, documents can write business logic in relational database examples of organization might like i have all elements which is. In sql and domains like spreadsheet, difference and gathering data? DRDA enables network connected relational databases to cooperate to fulfill SQL requests. So what is our use case? And because these databases are expensive, you tend to put everything in there. Individual tables to? It will take us a couple of weeks going over these skills and concepts. When there is a one to one relationship, one of two actions typically occur. In database examples of relational model was one. Graph models and graph queries are two sides of pain same coin. Some research data stores replicate a given blob across multiple server nodes, which enables fast parallel reads. Why relational structure of relational schema? Each model database model? But what if item prices were negotiated on each order or there were special promotions? Other data on database relational databases went from. The domain is better user not relational model support multiple bills in other. There is called relation which columns? In english as a model developed procedures for example, music collection of models are generic visual basic skill every tuple, it natural join.