Rumble: Data Independence for Large Messy Data Sets
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QUERYING JSON and XML Performance Evaluation of Querying Tools for Offline-Enabled Web Applications
QUERYING JSON AND XML Performance evaluation of querying tools for offline-enabled web applications Master Degree Project in Informatics One year Level 30 ECTS Spring term 2012 Adrian Hellström Supervisor: Henrik Gustavsson Examiner: Birgitta Lindström Querying JSON and XML Submitted by Adrian Hellström to the University of Skövde as a final year project towards the degree of M.Sc. in the School of Humanities and Informatics. The project has been supervised by Henrik Gustavsson. 2012-06-03 I hereby certify that all material in this final year project which is not my own work has been identified and that no work is included for which a degree has already been conferred on me. Signature: ___________________________________________ Abstract This article explores the viability of third-party JSON tools as an alternative to XML when an application requires querying and filtering of data, as well as how the application deviates between browsers. We examine and describe the querying alternatives as well as the technologies we worked with and used in the application. The application is built using HTML 5 features such as local storage and canvas, and is benchmarked in Internet Explorer, Chrome and Firefox. The application built is an animated infographical display that uses querying functions in JSON and XML to filter values from a dataset and then display them through the HTML5 canvas technology. The results were in favor of JSON and suggested that using third-party tools did not impact performance compared to native XML functions. In addition, the usage of JSON enabled easier development and cross-browser compatibility. Further research is proposed to examine document-based data filtering as well as investigating why performance deviated between toolsets. -
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. -
A Platform-Independent Framework for Managing Geo-Referenced JSON Data Sets
electronics Article J-CO: A Platform-Independent Framework for Managing Geo-Referenced JSON Data Sets Giuseppe Psaila * and Paolo Fosci Department of Management, Information and Production Engineering, University of Bergamo, 24044 Dalmine (BG), Italy; [email protected] * Correspondence: [email protected]; Tel.: +39-035-205-2355 Abstract: Internet technology and mobile technology have enabled producing and diffusing massive data sets concerning almost every aspect of day-by-day life. Remarkable examples are social media and apps for volunteered information production, as well as Open Data portals on which public administrations publish authoritative and (often) geo-referenced data sets. In this context, JSON has become the most popular standard for representing and exchanging possibly geo-referenced data sets over the Internet.Analysts, wishing to manage, integrate and cross-analyze such data sets, need a framework that allows them to access possibly remote storage systems for JSON data sets, to retrieve and query data sets by means of a unique query language (independent of the specific storage technology), by exploiting possibly-remote computational resources (such as cloud servers), comfortably working on their PC in their office, more or less unaware of real location of resources. In this paper, we present the current state of the J-CO Framework, a platform-independent and analyst-oriented software framework to manipulate and cross-analyze possibly geo-tagged JSON data sets. The paper presents the general approach behind the Framework, by illustrating J-CO the query language by means of a simple, yet non-trivial, example of geographical cross-analysis. The paper also presents the novel features introduced by the re-engineered version of the execution Citation: Psaila, G.; Fosci, P. -
Applying the ETL Process to Blockchain Data. Prospect and Findings
information Article Applying the ETL Process to Blockchain Data. Prospect and Findings Roberta Galici 1, Laura Ordile 1, Michele Marchesi 1 , Andrea Pinna 2,* and Roberto Tonelli 1 1 Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, Italy; [email protected] (R.G.); [email protected] (L.O.); [email protected] (M.M.); [email protected] (R.T.) 2 Department of Electrical and Electronic Engineering (DIEE), University of Cagliari, Piazza D’Armi, 09100 Cagliari, Italy * Correspondence: [email protected] Received: 7 March 2020; Accepted: 7 April 2020; Published: 10 April 2020 Abstract: We present a novel strategy, based on the Extract, Transform and Load (ETL) process, to collect data from a blockchain, elaborate and make it available for further analysis. The study aims to satisfy the need for increasingly efficient data extraction strategies and effective representation methods for blockchain data. For this reason, we conceived a system to make scalable the process of blockchain data extraction and clustering, and to provide a SQL database which preserves the distinction between transaction and addresses. The proposed system satisfies the need to cluster addresses in entities, and the need to store the extracted data in a conventional database, making possible the data analysis by querying the database. In general, ETL processes allow the automation of the operation of data selection, data collection and data conditioning from a data warehouse, and produce output data in the best format for subsequent processing or for business. We focus on the Bitcoin blockchain transactions, which we organized in a relational database to distinguish between the input section and the output section of each transaction. -
XML Prague 2012
XML Prague 2012 Conference Proceedings University of Economics, Prague Prague, Czech Republic February 10–12, 2012 XML Prague 2012 – Conference Proceedings Copyright © 2012 Jiří Kosek ISBN 978-80-260-1572-7 Table of Contents General Information ..................................................................................................... vii Sponsors ........................................................................................................................... ix Preface .............................................................................................................................. xi The eX Markup Language? – Eric van der Vlist ........................................................... 1 XML and HTML Cross-Pollination: A Bridge Too Far? – Norman Walsh and Robin Berjon .................................................................................... 11 XML5's Story – Anne van Kesteren ................................................................................ 23 XProc: Beyond application/xml – Vojtěch Toman ........................................................ 27 The Anatomy of an Open Source XProc/XSLT implementation of NVDL – George Bina ...................................................................................................................... 49 JSONiq – Jonathan Robie, Matthias Brantner, Daniela Florescu, Ghislain Fourny, and Till Westmann .................................................................................................................. 63 Corona: Managing and -
Download the Result from the Cluster
UC Riverside UC Riverside Electronic Theses and Dissertations Title Interval Joins for Big Data Permalink https://escholarship.org/uc/item/7xb001cz Author Carman, Jr., Eldon Preston Publication Date 2020 License https://creativecommons.org/licenses/by/4.0/ 4.0 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA RIVERSIDE Interval Joins for Big Data A Dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science by Eldon Preston Carman, Jr. September 2020 Dissertation Committee: Dr. Vassilis J. Tsotras, Chairperson Dr. Michael J. Carey Dr. Ahmed Eldawy Dr. Vagelis Hristidis Dr. Eamonn Keogh Copyright by Eldon Preston Carman, Jr. 2020 The Dissertation of Eldon Preston Carman, Jr. is approved: Committee Chairperson University of California, Riverside Acknowledgments I am grateful to my advisor, Professor Vassilis Tsotras, without whose support, I would not have been able to complete this journey. Thank you for your continued encour- agement and guidance through this learning experience. I especially would like to thank my dissertation committee members, Dr. Ahmed Eldawy, Dr. Vagelis Hristidis, Dr. Eamonn Keogh, and Dr. Michael Carey, for reviewing my dissertation. I also like to thank Dr. Rajiv Gupta for his support in the beginning of this dissertation. I would like to thank everyone in AsterixDB’s team for their support. Especially Professor Michael Carey for his guidance in understanding the AsterixDB stack and managing the research in an active software product. Thank you to my lab partners, Ildar Absalyamov and Steven Jacobs, for all the lively discussions on our regular drives out to Irvine. -
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. -
Recent Trends in JSON Filters Atul Jain*, Dr
International Journal of Scientific Research in Computer Science, Engineering and Information Technology ISSN : 2456-3307 (www.ijsrcseit.com) https://doi.org/10.32628/CSEIT217116 Recent Trends in JSON Filters Atul Jain*, Dr. ShashiKant Gupta CSA Department, ITM University, Gwalior, Madhya Pradesh, India ABSTRACT Article Info JavaScript Object Notation is a text-based data exchange format for structuring Volume 7, Issue 1 data between a server and web application on the client-side. It is basically a Page Number: 87-93 data format, so it is not limited to Ajax-style web applications and can be used Publication Issue : with API’s to exchange or store information. However, the whole data never to January-February-2021 be used by the system or application, It needs some extract of a piece of requirement that may vary person to person and with the changing of time. The searching and filtration from the JSON string are very typical so most of the studies give only basics operation to query the data from the JSON object. The aim of this paper to find out all the methods with different technology to search and filter with JSON data. It explains the extensive results of previous research on the JSONiq Flwor expression and compares it with the json-query module of npm to extract information from JSON. This research has the intention of achieving the data from JSON with some advanced operators with the help of a prototype in json-query package of NodeJS. Thus, the data can be filtered out more efficiently and accurately Article History without the need for any other programming language dependency. -
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. -
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. -
Chapter 9 – Designing the Database
Systems Analysis and Design in a Changing World, seventh edition 9-1 Chapter 9 – Designing the Database Table of Contents Chapter Overview Learning Objectives Notes on Opening Case and EOC Cases Instructor's Notes (for each section) ◦ Key Terms ◦ Lecture notes ◦ Quick quizzes Classroom Activities Troubleshooting Tips Discussion Questions Chapter Overview Database management systems provide designers, programmers, and end users with sophisticated capabilities to store, retrieve, and manage data. Sharing and managing the vast amounts of data needed by a modern organization would not be possible without a database management system. In Chapter 4, students learned to construct conceptual data models and to develop entity-relationship diagrams (ERDs) for traditional analysis and domain model class diagrams for object-oriented (OO) analysis. To implement an information system, developers must transform a conceptual data model into a more detailed database model and implement that model in a database management system. In the first sections of this chapter students learn about relational database management systems, and how to convert a data model into a relational database schema. The database sections conclude with a discussion of database architectural issues such as single server databases versus distributed databases which are deployed across multiple servers and multiple sites. Many system interfaces are electronic transmissions or paper outputs to external agents. Therefore, system developers need to design and implement integrity controls and security controls to protect the system and its data. This chapter discusses techniques to provide the integrity controls to reduce errors, fraud, and misuse of system components. The last section of the chapter discusses security controls and explains the basic concepts of data protection, digital certificates, and secure transactions.