The World As Database: on the Relation of Software Development, Query Methods, and Interpretative Independence
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Evolution of Database Technology
Introduction and Database Technology By EM Bakker September 11, 2012 Databases and Data Mining 1 DBDM Introduction Databases and Data Mining Projects at LIACS Biological and Medical Databases and Data Mining CMSB (Phenotype Genotype), DIAL CGH DB Cyttron: Visualization of the Cell GRID Computing VLe: Virtual Lab e-Science environments DAS3/DAS4 super computer Research on Fundamentals of Databases and Data Mining Database integration Data Mining algorithms Content Based Retrieval September 11, 2012 Databases and Data Mining 2 DBDM Databases (Chapters 1-7): The Evolution of Database Technology Data Preprocessing Data Warehouse (OLAP) & Data Cubes Data Cubes Computation Grand Challenges and State of the Art September 11, 2012 Databases and Data Mining 3 DBDM Data Mining (Chapters 8-11): Introduction and Overview of Data Mining Data Mining Basic Algorithms Mining data streams Mining Sequence Patterns Graph Mining September 11, 2012 Databases and Data Mining 4 DBDM Further Topics Mining object, spatial, multimedia, text and Web data Mining complex data objects Spatial and spatiotemporal data mining Multimedia data mining Text mining Web mining Applications and trends of data mining Mining business & biological data Visual data mining Data mining and society: Privacy-preserving data mining September 11, 2012 Databases and Data Mining 5 [R] Evolution of Database Technology September 11, 2012 Databases and Data Mining 6 Evolution of Database Technology 1960s: (Electronic) Data collection, database creation, IMS -
Oracle Vs. Nosql Vs. Newsql Comparing Database Technology
Oracle vs. NoSQL vs. NewSQL Comparing Database Technology John Ryan Senior Solution Architect, Snowflake Computing Table of Contents The World has Changed . 1 What’s Changed? . 2 What’s the Problem? . .. 3 Performance vs. Availability and Durability . 3 Consistecy vs. Availability . 4 Flexibility vs . Scalability . 5 ACID vs. Eventual Consistency . 6 The OLTP Database Reimagined . 7 Achieving the Impossible! . .. 8 NewSQL Database Technology . 9 VoltDB . 10 MemSQL . 11 Which Applications Need NewSQL Technology? . 12 Conclusion . 13 About the Author . 13 ii The World has Changed The world has changed massively in the past 20 years. Back in the year 2000, a few million users connected to the web using a 56k modem attached to a PC, and Amazon only sold books. Now billions of people are using to their smartphone or tablet 24x7 to buy just about everything, and they’re interacting with Facebook, Twitter and Instagram. The pace has been unstoppable . Expectations have also changed. If a web page doesn’t refresh within seconds we’re quickly frustrated, and go elsewhere. If a web site is down, we fear it’s the end of civilisation as we know it. If a major site is down, it makes global headlines. Instant gratification takes too long! — Ladawn Clare-Panton Aside: If you’re not a seasoned Database Architect, you may want to start with my previous articles on Scalability and Database Architecture. Oracle vs. NoSQL vs. NewSQL eBook 1 What’s Changed? The above leads to a few observations: • Scalability — With potentially explosive traffic growth, IT systems need to quickly grow to meet exponential numbers of transactions • High Availability — IT systems must run 24x7, and be resilient to failure. -
Support Aggregate Analytic Window Function Over Large Data by Spilling
Support Aggregate Analytic Window Function over Large Data by Spilling Xing Shi and Chao Wang Guangdong University of Technology, Guangzhou, Guangdong 510006, China North China University of Technology, Beijing 100144, China Abstract. Analytic function, also called window function, is to query the aggregation of data over a sliding window. For example, a simple query over the online stock platform is to return the average price of a stock of the last three days. These functions are commonly used features in SQL databases. They are supported in most of the commercial databases. With the increasing usage of cloud data infra and machine learning technology, the frequency of queries with analytic window functions rises. Some analytic functions only require const space in memory to store the state, such as SUM, AVG, while others require linear space, such as MIN, MAX. When the window is extremely large, the memory space to store the state may be too large. In this case, we need to spill the state to disk, which is a heavy operation. In this paper, we proposed an algorithm to manipulate the state data in the disk to reduce the disk I/O to make spill available and efficiency. We analyze the complexity of the algorithm with different data distribution. 1. Introducion In this paper, we develop novel spill techniques for analytic window function in SQL databases. And discuss different various types of aggregate queries, e.g., COUNT, AVG, SUM, MAX, MIN, etc., over a relational table. Aggregate analytic function, also called aggregate window function, is to query the aggregation of data over a sliding window. -
The Declarative Imperative Experiences and Conjectures in Distributed Logic
The Declarative Imperative Experiences and Conjectures in Distributed Logic Joseph M. Hellerstein University of California, Berkeley [email protected] ABSTRACT The juxtaposition of these trends presents stark alternatives. The rise of multicore processors and cloud computing is putting Will the forecasts of doom and gloom materialize in a storm enormous pressure on the software community to find solu- that drowns out progress in computing? Or is this the long- tions to the difficulty of parallel and distributed programming. delayed catharsis that will wash away today’s thicket of im- At the same time, there is more—and more varied—interest in perative languages, preparing the ground for a more fertile data-centric programming languages than at any time in com- declarative future? And what role might the database com- puting history, in part because these languages parallelize nat- munity play in shaping this future, having sowed the seeds of urally. This juxtaposition raises the possibility that the theory Datalog over the last quarter century? of declarative database query languages can provide a foun- Before addressing these issues directly, a few more words dation for the next generation of parallel and distributed pro- about both crisis and opportunity are in order. gramming languages. 1.1 Urgency: Parallelism In this paper I reflect on my group’s experience over seven years using Datalog extensions to build networking protocols I would be panicked if I were in industry. and distributed systems. Based on that experience, I present — John Hennessy, President, Stanford University [35] a number of theoretical conjectures that may both interest the database community, and clarify important practical issues in The need for parallelism is visible at micro and macro scales. -
CMU 15-445/645 Database Systems (Fall 2018 :: Query Optimization
Query Optimization Lecture #13 Database Systems Andy Pavlo 15-445/15-645 Computer Science Fall 2018 AP Carnegie Mellon Univ. 2 ADMINISTRIVIA Mid-term Exam is on Wednesday October 17th → See mid-term exam guide for more info. Project #2 – Checkpoint #2 is due Friday October 19th @ 11:59pm. CMU 15-445/645 (Fall 2018) 4 QUERY OPTIMIZATION Remember that SQL is declarative. → User tells the DBMS what answer they want, not how to get the answer. There can be a big difference in performance based on plan is used: → See last week: 1.3 hours vs. 0.45 seconds CMU 15-445/645 (Fall 2018) 5 IBM SYSTEM R First implementation of a query optimizer. People argued that the DBMS could never choose a query plan better than what a human could write. A lot of the concepts from System R’s optimizer are still used today. CMU 15-445/645 (Fall 2018) 6 QUERY OPTIMIZATION Heuristics / Rules → Rewrite the query to remove stupid / inefficient things. → Does not require a cost model. Cost-based Search → Use a cost model to evaluate multiple equivalent plans and pick the one with the lowest cost. CMU 15-445/645 (Fall 2018) 7 QUERY PLANNING OVERVIEW System Catalog Cost SQL Query Model Abstract Syntax Annotated Annotated Tree AST AST Parser Binder Rewriter Optimizer (Optional) Name→Internal ID Query Plan CMU 15-445/645 (Fall 2018) 8 TODAY'S AGENDA Relational Algebra Equivalences Plan Cost Estimation Plan Enumeration Nested Sub-queries Mid-Term Review CMU 15-445/645 (Fall 2018) 9 RELATIONAL ALGEBRA EQUIVALENCES Two relational algebra expressions are equivalent if they generate the same set of tuples. -
Multiple Condition Where Clause Sql
Multiple Condition Where Clause Sql Superlunar or departed, Fazeel never trichinised any interferon! Vegetative and Czechoslovak Hendrick instructs tearfully and bellyings his tupelo dispensatorily and unrecognizably. Diachronic Gaston tote her endgame so vaporously that Benny rejuvenize very dauntingly. Codeigniter provide set class function for each mysql function like where clause, join etc. The condition into some tests to write multiple conditions that clause to combine two conditions how would you occasionally, you separate privacy: as many times. Sometimes, you may not remember exactly the data that you want to search. OR conditions allow you to test multiple conditions. All conditions where condition is considered a row. Issue date vary each bottle of drawing numbers. How sql multiple conditions in clause condition for column for your needs work now query you take on clauses within a static list. The challenge join combination for joining the tables is found herself trying all possibilities. TOP function, if that gives you no idea. New replies are writing longer allowed. Thank you for your feedback! Then try the examples in your own database! Here, we have to provide filters or conditions. The conditions like clause to make more content is used then will identify problems, model and arrangement of operators. Thanks for your help. Thanks for war help. Multiple conditions on the friendly column up the discount clause. This sql where clause, you have to define multiple values you, we have to basic syntax. But your suggestion is more readable and straight each way out implement. Use parenthesis to set of explicit groups of contents open source code. -
Evolution of Database Technology 1960S: (Electronic) Data Collection, Database Creation, IMS (Hierarchical Database System by IBM) and Network DBMS
Database Technology Erwin M. Bakker & Stefan Manegold https://homepages.cwi.nl/~manegold/DBDM/ http://liacs.leidenuniv.nl/~bakkerem2/dbdm/ [email protected] [email protected] 9/21/18 Databases and Data Mining 1 Evolution of Database Technology 1960s: (Electronic) Data collection, database creation, IMS (hierarchical database system by IBM) and network DBMS 1970s: Relational data model, relational DBMS implementation 1980s: RDBMS, advanced data models (extended-relational, OO, deductive, etc.) Application-oriented DBMS (spatial, scientific, engineering, etc.) 9/21/18 Databases and Data Mining 2 Evolution of Database Technology 1990s: Data mining, data warehousing, multimedia databases, and Web databases 2000 - Stream data management and mining Data mining and its applications Web technology Data integration, XML Social Networks (Facebook, etc.) Cloud Computing global information systems Emerging in-house solutions In Memory Databases Big Data 9/21/18 Databases and Data Mining 3 1960’s Companies began automating their back-office bookkeeping in the 1960s COBOL and its record-oriented file model were the work-horses of this effort Typical work-cycle: 1. a batch of transactions was applied to the old-tape-master 2. a new-tape-master produced 3. printout for the next business day. COmmon Business-Oriented Language (COBOL 2002 standard) 9/21/18 Databases and Data Mining 4 COBOL A quote by Prof. dr. E.W. Dijkstra (Turing Award 1972) 18 June 1975: “The use of COBOL cripples the mind; its teaching should, therefore, be regarded as a criminal offence.” September 2015: 9/21/18 Databases and Data Mining 5 COBOL Code (just an example!) 01 LOAN-WORK-AREA. -
SQL DELETE Table in SQL, DELETE Statement Is Used to Delete Rows from a Table
SQL is a standard language for storing, manipulating and retrieving data in databases. What is SQL? SQL stands for Structured Query Language SQL lets you access and manipulate databases SQL became a standard of the American National Standards Institute (ANSI) in 1986, and of the International Organization for Standardization (ISO) in 1987 What Can SQL do? SQL can execute queries against a database SQL can retrieve data from a database SQL can insert records in a database SQL can update records in a database SQL can delete records from a database SQL can create new databases SQL can create new tables in a database SQL can create stored procedures in a database SQL can create views in a database SQL can set permissions on tables, procedures, and views Using SQL in Your Web Site To build a web site that shows data from a database, you will need: An RDBMS database program (i.e. MS Access, SQL Server, MySQL) To use a server-side scripting language, like PHP or ASP To use SQL to get the data you want To use HTML / CSS to style the page RDBMS RDBMS stands for Relational Database Management System. RDBMS is the basis for SQL, and for all modern database systems such as MS SQL Server, IBM DB2, Oracle, MySQL, and Microsoft Access. The data in RDBMS is stored in database objects called tables. A table is a collection of related data entries and it consists of columns and rows. SQL Table SQL Table is a collection of data which is organized in terms of rows and columns. -
IBM Cloudant: Database As a Service Fundamentals
Front cover IBM Cloudant: Database as a Service Fundamentals Understand the basics of NoSQL document data stores Access and work with the Cloudant API Work programmatically with Cloudant data Christopher Bienko Marina Greenstein Stephen E Holt Richard T Phillips ibm.com/redbooks Redpaper Contents Notices . 5 Trademarks . 6 Preface . 7 Authors. 7 Now you can become a published author, too! . 8 Comments welcome. 8 Stay connected to IBM Redbooks . 9 Chapter 1. Survey of the database landscape . 1 1.1 The fundamentals and evolution of relational databases . 2 1.1.1 The relational model . 2 1.1.2 The CAP Theorem . 4 1.2 The NoSQL paradigm . 5 1.2.1 ACID versus BASE systems . 7 1.2.2 What is NoSQL? . 8 1.2.3 NoSQL database landscape . 9 1.2.4 JSON and schema-less model . 11 Chapter 2. Build more, grow more, and sleep more with IBM Cloudant . 13 2.1 Business value . 15 2.2 Solution overview . 16 2.3 Solution architecture . 18 2.4 Usage scenarios . 20 2.5 Intuitively interact with data using Cloudant Dashboard . 21 2.5.1 Editing JSON documents using Cloudant Dashboard . 22 2.5.2 Configuring access permissions and replication jobs within Cloudant Dashboard. 24 2.6 A continuum of services working together on cloud . 25 2.6.1 Provisioning an analytics warehouse with IBM dashDB . 26 2.6.2 Data refinement services on-premises. 30 2.6.3 Data refinement services on the cloud. 30 2.6.4 Hybrid clouds: Data refinement across on/off–premises. 31 Chapter 3. -
SQL Stored Procedures
Agenda Key:31MA Session Number:409094 DB2 for IBM i: SQL Stored Procedures Tom McKinley ([email protected]) DB2 for IBM i consultant IBM Lab Services 8 Copyright IBM Corporation, 2009. All Rights Reserved. This publication may refer to products that are not currently available in your country. IBM makes no commitment to make available any products referred to herein. What is a Stored Procedure? • Just a called program – Called from SQL-based interfaces via SQL CALL statement • Supports input and output parameters – Result sets on some interfaces • Follows security model of iSeries – Enables you to secure your data – iSeries adopted authority model can be leveraged • Useful for moving host-centric applications to distributed applications 2 © 2009 IBM Corporation What is a Stored Procedure? • Performance savings in distributed computing environments by dramatically reducing the number of flows (requests) to the database engine – One request initiates multiple transactions and processes R R e e q q u u DB2 for i5/OS DB2DB2 for for i5/OS e e AS/400 s s t t SP o o r r • Performance improvements further enhanced by the option of providing result sets back to ODBC, JDBC, .NET & CLI clients 3 © 2009 IBM Corporation Recipe for a Stored Procedure... 1 Create it CREATE PROCEDURE total_val (IN Member# CHAR(6), OUT total DECIMAL(12,2)) LANGUAGE SQL BEGIN SELECT SUM(curr_balance) INTO total FROM accounts WHERE account_owner=Member# AND account_type IN ('C','S','M') END 2 Call it (from an SQL interface) over and over CALL total_val(‘123456’, :balance) 4 © 2009 IBM Corporation Stored Procedures • DB2 for i5/OS supports two types of stored procedures 1. -
The Best Nurturers in Computer Science Research
The Best Nurturers in Computer Science Research Bharath Kumar M. Y. N. Srikant IISc-CSA-TR-2004-10 http://archive.csa.iisc.ernet.in/TR/2004/10/ Computer Science and Automation Indian Institute of Science, India October 2004 The Best Nurturers in Computer Science Research Bharath Kumar M.∗ Y. N. Srikant† Abstract The paper presents a heuristic for mining nurturers in temporally organized collaboration networks: people who facilitate the growth and success of the young ones. Specifically, this heuristic is applied to the computer science bibliographic data to find the best nurturers in computer science research. The measure of success is parameterized, and the paper demonstrates experiments and results with publication count and citations as success metrics. Rather than just the nurturer’s success, the heuristic captures the influence he has had in the indepen- dent success of the relatively young in the network. These results can hence be a useful resource to graduate students and post-doctoral can- didates. The heuristic is extended to accurately yield ranked nurturers inside a particular time period. Interestingly, there is a recognizable deviation between the rankings of the most successful researchers and the best nurturers, which although is obvious from a social perspective has not been statistically demonstrated. Keywords: Social Network Analysis, Bibliometrics, Temporal Data Mining. 1 Introduction Consider a student Arjun, who has finished his under-graduate degree in Computer Science, and is seeking a PhD degree followed by a successful career in Computer Science research. How does he choose his research advisor? He has the following options with him: 1. Look up the rankings of various universities [1], and apply to any “rea- sonably good” professor in any of the top universities. -
15-445/645 Database Systems (Fall 2020) Carnegie Mellon University Prof
Lecture #02: Intermediate SQL 15-445/645 Database Systems (Fall 2020) https://15445.courses.cs.cmu.edu/fall2020/ Carnegie Mellon University Prof. Andy Pavlo 1 Relational Languages Edgar Codd published a major paper on relational models in the early 1970s. Originally, he only defined the mathematical notation for how a DBMS could execute queries on a relational model DBMS. The user only needs to specify the result that they want using a declarative language (i.e., SQL). The DBMS is responsible for determining the most efficient plan to produce that answer. Relational algebra is based on sets (unordered, no duplicates). SQL is based on bags (unordered, allows duplicates). 2 SQL History Declarative query lanaguage for relational databases. It was originally developed in the 1970s as part of the IBM System R project. IBM originally called it “SEQUEL” (Structured English Query Language). The name changed in the 1980s to just “SQL” (Structured Query Language). The language is comprised of different classes of commands: 1. Data Manipulation Language (DML): SELECT, INSERT, UPDATE, and DELETE statements. 2. Data Definition Language (DDL): Schema definitions for tables, indexes, views, and other objects. 3. Data Control Language (DCL): Security, access controls. SQL is not a dead language. It is being updated with new features every couple of years. SQL-92 is the minimum that a DBMS has to support to claim they support SQL. Each vendor follows the standard to a certain degree but there are many proprietary extensions. 3 Aggregates An aggregation function takes in a bag of tuples as its input and then produces a single scalar value as its output.