Processing Fuzzy Relational Queries Using Fuzzy Views Emmanuel Doumard, Olivier Pivert, Grégory Smits, Virginie Thion

Total Page:16

File Type:pdf, Size:1020Kb

Processing Fuzzy Relational Queries Using Fuzzy Views Emmanuel Doumard, Olivier Pivert, Grégory Smits, Virginie Thion Processing Fuzzy Relational Queries Using Fuzzy Views Emmanuel Doumard, Olivier Pivert, Grégory Smits, Virginie Thion To cite this version: Emmanuel Doumard, Olivier Pivert, Grégory Smits, Virginie Thion. Processing Fuzzy Relational Queries Using Fuzzy Views. IEEE International Conference on Fuzzy Systems, Jun 2019, New-Orleans, United States. hal-02116133 HAL Id: hal-02116133 https://hal.inria.fr/hal-02116133 Submitted on 30 Apr 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Processing Fuzzy Relational Queries Using Fuzzy Views Emmanuel Doumard, Olivier Pivert, Gregory´ Smits, and Virginie Thion Univ Rennes, IRISA - UMR 6074, F-22305 Lannion, France Email: folivier.pivert,gregory.smits,[email protected] Abstract—This paper proposes two original approaches to the experimentation aimed to compare the performances of the processing of fuzzy queries in a relational database context. The two solutions described before. The experimental results are general idea is to use views, either materialized or not. In the first discussed and the pros and cons of each method are pointed case, materialized views are used to store the satisfaction degrees related to user-defined fuzzy predicates, instead of calculating out. Finally, Section VI recalls the main contributions of the them at runtime by means of user functions embedded in the paper and outlines perspectives for future research. query (which induces an important overhead). In the second case, abstract views are used to efficiently access the tuples that belong II. CONTEXT AND RELATED WORK to the α-cut of the query result, by means of a derived Boolean In this section, we recall some useful notions concerning selection condition. fuzzy queries in a relational database context, with a par- ticular focus on the SQLf language [5] which constitutes I. INTRODUCTION the framework considered in this work. We first deal with The idea of making database management systems more syntactic aspects, then we briefly discuss query-processing- flexible by switching from Boolean logic to fuzzy logic for related issues. interpreting queries is already quite old, as the first work on A. Refresher about Fuzzy Relational Queries this topic dates back to the mid 70’s. Indeed, the first paper considering fuzzy relational queries was authored by V. Tahani Dealing with fuzzy queries in a relational database context — then a Ph.D. student supervised by L.A. Zadeh — in 1977 first implies to extend the classical notion of a relation into [16]. Since that time, a great amount of work has of course that of a fuzzy relation. A fuzzy relation is associated with a been done, both for defining highly expressive fuzzy query fuzzy concept and is simply a relation where every tuple is languages (see for instance [12], [5], [1], [14]) and for devising assigned a membership degree in the unit interval, that reflects efficient processing techniques suitable for such queries (see the extent to which the tuple satisfies . The operations from e.g. [4], [6], [17], [15]). As a matter of fact, it is of prime the relational algebra can be extended to fuzzy relations along importance to have available efficient evaluation algorithms so two lines: first, by considering fuzzy relations as fuzzy sets; that the gain in terms of expressiveness be not counterbalanced second by introducing gradual predicates in the appropriate by a severe performance degradation. So far, fuzzy query operations (selections and joins especially). The definitions processing relies on a technique first introduced by Bosc and of the extended relational operators can be found in [1]. As Pivert in [2], called derivation, whose basic principle is to an illustration, we give the definition of the fuzzy selection compute a Boolean query from the fuzzy one to be evaluated. operator hereafter: The derived Boolean query, which corresponds to the desired µ (t) = >(µ (t); µ (t)) α-cut of the fuzzy query to be evaluated (or a superset thereof), select(r; ') r ' can then be efficiently processed by a regular DBMS, and the where r denote a fuzzy relation, ' is a fuzzy predicate and > satisfaction degree of each answer can be computed either by is a triangular norm (most usually, min is used). means of an external program or by a call to user functions Relational algebra is obviously too abstruse for non-experts in the derived query (which proves rather costful). In the to use, this is why no commercial DBMS offers it as a query present paper, we introduce an alternative processing strategy, language. More user-oriented languages (based on relational based on the use of fuzzy views. Two variants are studied: one algebra) had to be defined, and the most famous one is of where the views are materialized, the other where they are course SQL, in which queries are expressed by means of kept abstract. “blocks” involving clauses such as select, from, where, etc. The The remainder of the paper is organized as follows. In language called SQLf, whose initial version was presented in Section II, we recall some useful notions about fuzzy rela- [5], extends SQL so as to support fuzzy queries. The general tional queries and their evaluation. We also provide a brief principle consists in introducing gradual predicates wherever survey of related work. Sections III and IV are devoted to it makes sense. The three clauses select, from and where of the the description of the two approaches involving materialized base block of SQL are kept in SQLf and the from clause re- and abstract views respectively. In Section V, we present an mains unchanged (except when fuzzy joins are used, in which case the join condition involves a fuzzy comparison operator). RDBMS, which entails better performances. Above all, The principal differences concern mainly two points: fuzzy queries are directly submitted to the DBMS. • the filtering of the result, which can be achieved through • tight coupling: the new features are incorporated into the a number of desired answers (k), a minimal level of RDBMS inner core. This solution, which is of course satisfaction (α), or both, and the most efficient in terms of query evaluation, implies • the nature of the authorized conditions as mentioned to entirely rewrite the query engine (including the parser previously. and query optimizer), which is a quite heavy task. Therefore, the SQLf base block is expressed as: In [15], we presented an implementation at the junction select [distinct][k j α j k, α] attributes between mild coupling and tight coupling where i) the mem- from relations where fuzzy-cond bership functions corresponding to the user-specified fuzzy predicates are defined as stored procedures and ii) the gradual where fuzzy-cond may involve both Boolean and fuzzy pred- connectives (fuzzy conjunction, disjunction, and quantifiers) icates. From a conceptual point of view, this expression is are implemented in C and integrated in the query processing interpreted as: engine of the RDBMS PostgreSQL. • the fuzzy selection (by fuzzy-cond) of the Cartesian Since commercial RDBMSs are not able to natively interpret product of the relations appearing in the from clause, fuzzy queries, some previous works (see [2], [6]) suggested • a projection over the attributes of the select clause (du- to perform a derivation step in order to generate a regular plicates are kept by default, and if distinct is specified Boolean query used to prefilter the relation (or Cartesian the maximal degree among the duplicates is retained), product of relations) concerned. The idea is to restrict the • the filtering of the result (top k elements and/or those degree computation phase only to those tuples that belong whose score is over the threshold α). to the α-cut of the result of the fuzzy query (assuming that Besides, SQLf also preserves (and extends) the constructs α is a qualitative threshold specified by the user; 0+ is used specific to SQL, e.g. nesting operators, relation partitioning, by default). With this type of evaluation strategy, fuzzy query etc., see [14] for more detail. In the following, we only processing involves three steps: consider single-block Selection-Projection-Join SQLf queries. 1) derivation of a Boolean query from the fuzzy one, using Any fuzzy querying system must provide users with a the threshold α and the membership functions of the convenient way to define the fuzzy terms that they wish to fuzzy predicates involved in the where clause; include in their queries. In practice, the membership function 2) processing of the derived query, which produces a clas- F associated with a fuzzy set is often chosen to be of a sical relation; F trapezoidal shape. Then, may be expressed by a quadruplet 3) computation of the satisfaction degree attached to each (a; b; c; d) core(F ) = [b; c] support(F ) = (a; d) where and . tuple (followed by a tuple filtering step if necessary1), In a previous work [15], we described a graphical interface which yields the fuzzy relation that constitutes the final aimed at helping the user express his/her fuzzy queries. result. B. About Fuzzy Query Processing: Related Work In terms of performances, the interest of using a mild coupling Fuzzy query processing [8], [12], [9], [3] raises several architecture lies in the fact that the resulting fuzzy relation is issues, among which the main two ones are listed hereafter: directly computed during the tuple selection phase (no external program needs to be called to perform step 3).
Recommended publications
  • Schema in Database Sql Server
    Schema In Database Sql Server Normie waff her Creon stringendo, she ratten it compunctiously. If Afric or rostrate Jerrie usually files his terrenes shrives wordily or supernaturalized plenarily and quiet, how undistinguished is Sheffy? Warring and Mahdi Morry always roquet impenetrably and barbarizes his boskage. Schema compare tables just how the sys is a table continues to the most out longer function because of the connector will often want to. Roles namely actors in designer slow and target multiple teams together, so forth from sql management. You in sql server, should give you can learn, and execute this is a location of users: a database projects, or more than in. Your sql is that the view to view of my data sources with the correct. Dive into the host, which objects such a set of lock a server database schema in sql server instance of tables under the need? While viewing data in sql server database to use of microseconds past midnight. Is sql server is sql schema database server in normal circumstances but it to use. You effectively structure of the sql database objects have used to it allows our policy via js. Represents table schema in comparing new database. Dml statement as schema in database sql server functions, and so here! More in sql server books online schema of the database operator with sql server connector are not a new york, with that object you will need. This in schemas and history topic names are used to assist reporting from. Sql schema table as views should clarify log reading from synonyms in advance so that is to add this game reports are.
    [Show full text]
  • Sql Create Table Variable from Select
    Sql Create Table Variable From Select Do-nothing Dory resurrect, his incurvature distasting crows satanically. Sacrilegious and bushwhacking Jamey homologising, but Harcourt first-hand coiffures her muntjac. Intertarsal and crawlier Towney fanes tenfold and euhemerizing his assistance briskly and terrifyingly. How to clean starting value inside of data from select statements and where to use matlab compiler to store sql, and then a regular join You may not supported for that you are either hive temporary variable table. Before we examine the specific methods let's create an obscure procedure. INSERT INTO EXEC sql server exec into table. Now you can show insert update delete and invent all operations with building such as in pay following a write i like the Declare TempTable. When done use t or t or when to compact a table variable t. Procedure should create the temporary tables instead has regular tables. Lesson 4 Creating Tables SQLCourse. EXISTS tmp GO round TABLE tmp id int NULL SELECT empire FROM. SQL Server How small Create a Temp Table with Dynamic. When done look sir the Execution Plan save the SELECT Statement SQL Server is. Proc sql create whole health will select weight married from myliboutdata ORDER to weight ASC. How to add static value while INSERT INTO with cinnamon in a. Ssrs invalid object name temp table. Introduction to Table Variable Deferred Compilation SQL. How many pass the bash array in 'right IN' clause will select query. Creating a pope from public Query Vertica. Thus attitude is no performance cost for packaging a SELECT statement into an inline.
    [Show full text]
  • (DDL) Reference Manual
    Data Definition Language (DDL) Reference Manual Abstract This publication describes the DDL language syntax and the DDL dictionary database. The audience includes application programmers and database administrators. Product Version DDL D40 DDL H01 Supported Release Version Updates (RVUs) This publication supports J06.03 and all subsequent J-series RVUs, H06.03 and all subsequent H-series RVUs, and G06.26 and all subsequent G-series RVUs, until otherwise indicated by its replacement publications. Part Number Published 529431-003 May 2010 Document History Part Number Product Version Published 529431-002 DDL D40, DDL H01 July 2005 529431-003 DDL D40, DDL H01 May 2010 Legal Notices Copyright 2010 Hewlett-Packard Development Company L.P. Confidential computer software. Valid license from HP required for possession, use or copying. Consistent with FAR 12.211 and 12.212, Commercial Computer Software, Computer Software Documentation, and Technical Data for Commercial Items are licensed to the U.S. Government under vendor's standard commercial license. The information contained herein is subject to change without notice. The only warranties for HP products and services are set forth in the express warranty statements accompanying such products and services. Nothing herein should be construed as constituting an additional warranty. HP shall not be liable for technical or editorial errors or omissions contained herein. Export of the information contained in this publication may require authorization from the U.S. Department of Commerce. Microsoft, Windows, and Windows NT are U.S. registered trademarks of Microsoft Corporation. Intel, Itanium, Pentium, and Celeron are trademarks or registered trademarks of Intel Corporation or its subsidiaries in the United States and other countries.
    [Show full text]
  • Exploiting Fuzzy-SQL in Case-Based Reasoning
    Exploiting Fuzzy-SQL in Case-Based Reasoning Luigi Portinale and Andrea Verrua Dipartimentodi Scienze e Tecnoiogie Avanzate(DISTA) Universita’ del PiemonteOrientale "AmedeoAvogadro" C.so Borsalino 54 - 15100Alessandria (ITALY) e-mail: portinal @mfn.unipmn.it Abstract similarity-basedretrieval is the fundamentalstep that allows one to start with a set of relevant cases (e.g. the mostrele- The use of database technologies for implementingCBR techniquesis attractinga lot of attentionfor severalreasons. vant products in e-commerce),in order to apply any needed First, the possibility of usingstandard DBMS for storing and revision and/or refinement. representingcases significantly reduces the effort neededto Case retrieval algorithms usually focus on implement- developa CBRsystem; in fact, data of interest are usually ing Nearest-Neighbor(NN) techniques, where local simi- alreadystored into relational databasesand used for differ- larity metrics relative to single features are combinedin a ent purposesas well. Finally, the use of standardquery lan- weightedway to get a global similarity betweena retrieved guages,like SQL,may facilitate the introductionof a case- and a target case. In (Burkhard1998), it is arguedthat the basedsystem into the real-world,by puttingretrieval on the notion of acceptancemay represent the needs of a flexible sameground of normaldatabase queries. Unfortunately,SQL case retrieval methodologybetter than distance (or similar- is not able to deal with queries like those neededin a CBR ity). Asfor distance, local acceptancefunctions can be com- system,so different approacheshave been tried, in orderto buildretrieval engines able to exploit,at thelower level, stan- bined into global acceptancefunctions to determinewhether dard SQL.In this paper, wepresent a proposalwhere case a target case is acceptable(i.e.
    [Show full text]
  • Learning About Women and Urban Services N Latin America and the Caribbean
    P w- N :jk)- (,. -2 LEARNING ABOUT WOMEN AND URBAN SERVICES N LATIN AMERICA AND THE CARIBBEAN A Report on the Women, Low-h;come Households and Urban Services Project of The Population Council Wikh Selected Contributions from The International Center for Research on Women The Equity Policy Center The Development Planning Unit of University College LEARNING ABOUT WOMEN AND URBAN SERVICES IN LATIN AMERICA AND THE CARIBBEAN A Report on the Wbmen, Low-income Householdsand UrbanServices Project of The Population Council With Selected Contributions from The InternationalCenter for Research on Women The Equity Policy Cenier The Development Planning Unit of University College Marianne Schmink Judith Bruce and Marilyn Kohn Editors © 1986 The Population Council, Inc. Selections of this volume may be reproduced for teaching purposes in naga7ines and newspapers with acknowledgment to this report, to the authors, and the sponsoring institutions. The majority of the articles in this volume are an outgrowth of working group deliberations, the research and related activities of the Women, Low-Income Households, and Urban Services Project of the Population Council. The first phase of this project was supported by the United States Agency for International Development under Cooperative Agreement No. AID/OTR-0007-A-00-1154-00. The views expressed by the authors are their own. CONTENTS LEARNING ABOUT WOMEN AND URBAN SERVICES IN LATIN AMERICA AND THE CARIBBEAN A Report on the Women, Low-Income Households and Urban Services Project of tho Population Council With Selected Contributions from: The International Center for Research on Women The Equity Policy Center The Development Planning Unit of University College Preface Judith Bruce Part I.
    [Show full text]
  • Look out the Window Functions and Free Your SQL
    Concepts Syntax Other Look Out The Window Functions and free your SQL Gianni Ciolli 2ndQuadrant Italia PostgreSQL Conference Europe 2011 October 18-21, Amsterdam Look Out The Window Functions Gianni Ciolli Concepts Syntax Other Outline 1 Concepts Aggregates Different aggregations Partitions Window frames 2 Syntax Frames from 9.0 Frames in 8.4 3 Other A larger example Question time Look Out The Window Functions Gianni Ciolli Concepts Syntax Other Aggregates Aggregates 1 Example of an aggregate Problem 1 How many rows there are in table a? Solution SELECT count(*) FROM a; • Here count is an aggregate function (SQL keyword AGGREGATE). Look Out The Window Functions Gianni Ciolli Concepts Syntax Other Aggregates Aggregates 2 Functions and Aggregates • FUNCTIONs: • input: one row • output: either one row or a set of rows: • AGGREGATEs: • input: a set of rows • output: one row Look Out The Window Functions Gianni Ciolli Concepts Syntax Other Different aggregations Different aggregations 1 Without window functions, and with them GROUP BY col1, . , coln window functions any supported only PostgreSQL PostgreSQL version version 8.4+ compute aggregates compute aggregates via by creating groups partitions and window frames output is one row output is one row for each group for each input row Look Out The Window Functions Gianni Ciolli Concepts Syntax Other Different aggregations Different aggregations 2 Without window functions, and with them GROUP BY col1, . , coln window functions only one way of aggregating different rows in the same for each group
    [Show full text]
  • Firebird 3 Windowing Functions
    Firebird 3 Windowing Functions Firebird 3 Windowing Functions Author: Philippe Makowski IBPhoenix Email: pmakowski@ibphoenix Licence: Public Documentation License Date: 2011-11-22 Philippe Makowski - IBPhoenix - 2011-11-22 Firebird 3 Windowing Functions What are Windowing Functions? • Similar to classical aggregates but does more! • Provides access to set of rows from the current row • Introduced SQL:2003 and more detail in SQL:2008 • Supported by PostgreSQL, Oracle, SQL Server, Sybase and DB2 • Used in OLAP mainly but also useful in OLTP • Analysis and reporting by rankings, cumulative aggregates Philippe Makowski - IBPhoenix - 2011-11-22 Firebird 3 Windowing Functions Windowed Table Functions • Windowed table function • operates on a window of a table • returns a value for every row in that window • the value is calculated by taking into consideration values from the set of rows in that window • 8 new windowed table functions • In addition, old aggregate functions can also be used as windowed table functions • Allows calculation of moving and cumulative aggregate values. Philippe Makowski - IBPhoenix - 2011-11-22 Firebird 3 Windowing Functions A Window • Represents set of rows that is used to compute additionnal attributes • Based on three main concepts • partition • specified by PARTITION BY clause in OVER() • Allows to subdivide the table, much like GROUP BY clause • Without a PARTITION BY clause, the whole table is in a single partition • order • defines an order with a partition • may contain multiple order items • Each item includes
    [Show full text]
  • Using the Set Operators Questions
    UUSSIINNGG TTHHEE SSEETT OOPPEERRAATTOORRSS QQUUEESSTTIIOONNSS http://www.tutorialspoint.com/sql_certificate/using_the_set_operators_questions.htm Copyright © tutorialspoint.com 1.Which SET operator does the following figure indicate? A. UNION B. UNION ALL C. INTERSECT D. MINUS Answer: A. Set operators are used to combine the results of two ormore SELECT statements.Valid set operators in Oracle 11g are UNION, UNION ALL, INTERSECT, and MINUS. When used with two SELECT statements, the UNION set operator returns the results of both queries.However,if there are any duplicates, they are removed, and the duplicated record is listed only once.To include duplicates in the results,use the UNION ALL set operator.INTERSECT lists only records that are returned by both queries; the MINUS set operator removes the second query's results from the output if they are also found in the first query's results. INTERSECT and MINUS set operations produce unduplicated results. 2.Which SET operator does the following figure indicate? A. UNION B. UNION ALL C. INTERSECT D. MINUS Answer: B. UNION ALL Returns the combined rows from two queries without sorting or removing duplicates. sql_certificate 3.Which SET operator does the following figure indicate? A. UNION B. UNION ALL C. INTERSECT D. MINUS Answer: C. INTERSECT Returns only the rows that occur in both queries' result sets, sorting them and removing duplicates. 4.Which SET operator does the following figure indicate? A. UNION B. UNION ALL C. INTERSECT D. MINUS Answer: D. MINUS Returns only the rows in the first result set that do not appear in the second result set, sorting them and removing duplicates.
    [Show full text]
  • Exploring the Visualization of Schemas for Aggregate-Oriented Nosql Databases?
    Exploring the Visualization of Schemas for Aggregate-Oriented NoSQL Databases? Alberto Hernández Chillón, Severino Feliciano Morales, Diego Sevilla Ruiz, and Jesús García Molina Faculty of Computer Science, University of Murcia Campus Espinardo, Murcia, Spain {alberto.hernandez1,severino.feliciano,dsevilla,jmolina}@um.es Abstract. The lack of an explicit data schema (schemaless) is one of the most attractive NoSQL database features for developers. Being schema- less, these databases provide a greater flexibility, as data with different structure can be stored for the same entity type, which in turn eases data evolution. This flexibility, however, should not be obtained at the expense of losing the benefits provided by having schemas: When writ- ing code that deals with NoSQL databases, developers need to keep in mind at any moment some kind of schema. Also, database tools usu- ally require the knowledge of a schema to implement their functionality. Several approaches to infer an implicit schema from NoSQL data have been proposed recently, and some utilities that take advantage of inferred schemas are emerging. In this article we focus on the requisites for the vi- sualization of schemas for aggregate-oriented NoSQL Databases. Schema diagrams have proven useful in designing and understanding databases. Plenty of tools are available to visualize relational schemas, but the vi- sualization of NoSQL schemas (and the variation that they allow) is still in an immature state, and a great R&D effort is required to achieve tools with the desired capabilities. Here, we study the main challenges to be addressed, and propose some visual representations. Moreover, we outline the desired features to be supported by visualization tools.
    [Show full text]
  • SQL: Triggers, Views, Indexes Introduction to Databases Compsci 316 Fall 2014 2 Announcements (Tue., Sep
    SQL: Triggers, Views, Indexes Introduction to Databases CompSci 316 Fall 2014 2 Announcements (Tue., Sep. 23) • Homework #1 sample solution posted on Sakai • Homework #2 due next Thursday • Midterm on the following Thursday • Project mixer this Thursday • See my email about format • Email me your “elevator pitch” by Wednesday midnight • Project Milestone #1 due Thursday, Oct. 16 • See project description on what to accomplish by then 3 Announcements (Tue., Sep. 30) • Homework #2 due date extended to Oct. 7 • Midterm in class next Thursday (Oct. 9) • Open-book, open-notes • Same format as sample midterm (from last year) • Already posted on Sakai • Solution to be posted later this week 4 “Active” data • Constraint enforcement: When an operation violates a constraint, abort the operation or try to “fix” data • Example: enforcing referential integrity constraints • Generalize to arbitrary constraints? • Data monitoring: When something happens to the data, automatically execute some action • Example: When price rises above $20 per share, sell • Example: When enrollment is at the limit and more students try to register, email the instructor 5 Triggers • A trigger is an event-condition-action (ECA ) rule • When event occurs, test condition ; if condition is satisfied, execute action • Example: • Event : some user’s popularity is updated • Condition : the user is a member of “Jessica’s Circle,” and pop drops below 0.5 • Action : kick that user out of Jessica’s Circle http://pt.simpsons.wikia.com/wiki/Arquivo:Jessica_lovejoy.jpg 6 Trigger example
    [Show full text]
  • Oracle Nosql Database EE Data Sheet
    Oracle NoSQL Database 21.1 Enterprise Edition (EE) Oracle NoSQL Database is a multi-model, multi-region, multi-cloud, active-active KEY BUSINESS BENEFITS database, designed to provide a highly-available, scalable, performant, flexible, High throughput and reliable data management solution to meet today’s most demanding Bounded latency workloads. It can be deployed in on-premise data centers and cloud. It is well- Linear scalability suited for high volume and velocity workloads, like Internet of Things, 360- High availability degree customer view, online contextual advertising, fraud detection, mobile Fast and easy deployment application, user personalization, and online gaming. Developers can use a single Smart topology management application interface to quickly build applications that run in on-premise and Online elastic configuration cloud environments. Multi-region data replication Enterprise grade software Applications send network requests against an Oracle NoSQL data store to and support perform database operations. With multi-region tables, data can be globally distributed and automatically replicated in real-time across different regions. Data can be modeled as fixed-schema tables, documents, key-value pairs, and large objects. Different data models interoperate with each other through a single programming interface. Oracle NoSQL Database is a sharded, shared-nothing system which distributes data uniformly across multiple shards in a NoSQL database cluster, based on the hashed value of the primary keys. An Oracle NoSQL Database data store is a collection of storage nodes, each of which hosts one or more replication nodes. Data is automatically populated across these replication nodes by internal replication mechanisms to ensure high availability and rapid failover in the event of a storage node failure.
    [Show full text]
  • “A Relational Model of Data for Large Shared Data Banks”
    “A RELATIONAL MODEL OF DATA FOR LARGE SHARED DATA BANKS” Through the internet, I find more information about Edgar F. Codd. He is a mathematician and computer scientist who laid the theoretical foundation for relational databases--the standard method by which information is organized in and retrieved from computers. In 1981, he received the A. M. Turing Award, the highest honor in the computer science field for his fundamental and continuing contributions to the theory and practice of database management systems. This paper is concerned with the application of elementary relation theory to systems which provide shared access to large banks of formatted data. It is divided into two sections. In section 1, a relational model of data is proposed as a basis for protecting users of formatted data systems from the potentially disruptive changes in data representation caused by growth in the data bank and changes in traffic. A normal form for the time-varying collection of relationships is introduced. In Section 2, certain operations on relations are discussed and applied to the problems of redundancy and consistency in the user's model. Relational model provides a means of describing data with its natural structure only--that is, without superimposing any additional structure for machine representation purposes. Accordingly, it provides a basis for a high level data language which will yield maximal independence between programs on the one hand and machine representation and organization of data on the other. A further advantage of the relational view is that it forms a sound basis for treating derivability, redundancy, and consistency of relations.
    [Show full text]