DOCSLIB.ORG

How to Develop SAS® Data Mart in One Day (With Code Examples)

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
How to Develop SAS® Data Mart in One Day (With Code Examples) NESUG 17 Hands-On Workshops How to Develop SAS® Data Mart In One Day (with code examples) Samuel Berestizhevsky, YieldWise Canada Inc, Canada Tanya Kolosova, YieldWise Canada Inc, Canada ABSTRACT Creating of data marts is usually driven by specific application and business needs. To create useful data mart you need, in first place to answer the question: what are the information needs of the specific business application? An answer to this question is not permanent in time. Learning from the initially required information will inevitably lead to new requirements. In addition, changes in business situation will raise new questions, which will lead to new information needs. It is clear that time of incorporation the new requirements to the data mart is critical and should be counted in days, even hours, rather than weeks or months. How can data mart keep pace with these changes? The answer to this question lays in the data mart development technology [1]. Creating of data mart involves, at least, three following steps: • Design and implementation of data mart model. • Design and implementation of operations on data, like ETL processes, aggregation, integrity and value verification, etc. • Design and implementation of data authorization access. In this paper we describe strategies and techniques intended to reduce data mart development time and simplify modifications. We show you on examples how to implement in SAS three major steps of data mart development in only one day. INTRODUCTION In this paper we describe one of techniques that lets the designer specify data mart visually, in terms of its structure and functionality, and permits the development of mission-critical tasks almost without programming. The main principles of this approach are: • Data mart design is perceived as data and nothing but data. This means that the data mart design is defined in a set of specially structured tables and is stored, updated, and managed in the same way as ordinary data. • Operations on data are defined in terms of what must be done, but not how to do it. These definitions are stored, updated, and managed as ordinary data. • The data mart is managed from a single control point. The table-driven environment is the most fundamental aspect of the described approach. The heart of this environment is the set of specially structured tables forming the data dictionary. The data dictionary contains a variety of information concerning application objects and operations, such as data structures, application activities, and so on. Further in the paper we lead you through examples of data mart development, and show you on how create a data mart, and update and verify data time-saving, flexible and reliable manner. HOW TO IMPLEMENT A DATA MART In the table-driven environment, data mart design involves writing definitions of the data mart objects in the tables of the data dictionary [1]. Description of the data dictionary is out of scope of this paper. However, we want to clarify that we do not mean SAS data dictionary, but specially structured SAS data sets that contain comprehensive information about: • Topology (physical locations) of data mart parts and ways to communicate between those parts • Structure of data mart tables, including detailed information about columns (not only type, label, format, etc. but also information about primary and secondary keys, associated domains, and other constrains) • Relationships between data mart tables in terms of relational data model (foreign keys) Important to mention that this data dictionary can be changed or extended to keep additional information describing your data mart. How to import data from relational data base Let assume, that as a first step in data mart creation we want to create a copy of a relational data base in SAS data sets. 1 NESUG 17 Hands-On Workshops Every relational data base has its data dictionary which contains the most accurate information about data base structure. This information enables us to create exact copy of data base absolutely automatically. Oracle data dictionary The following table shows selected columns from ALL_TAB_COLUMNS Oracle table. OWNER TABLE_NAME COLUMN_NAME DATA_TYPE DATA_LENGTH … … … … … USER CUSTOMER_INFO customer_id VARCHAR2 16 USER CUSTOMER_INFO first_name VARCHAR2 40 USER CUSTOMER_INFO second_name VARCHAR2 40 USER CUSTOMER_INFO … … … USER CUSTOMER_INFO audit_key NUMBER 8 … … … … … As you can see, this table describes a structure of CUSTOMER_INFO table from USER schema. Extracting data from Oracle data dictionary The following SAS macro reads information and creates SAS data set containing image of Oracle data dictionary table: %macro oracle_dd( libname); proc sql ; connect to oracle as mydb (user=USER orapw=PWD path='path'); insert into &libname..source (schema, t_name, c_name, d_type, d_len) select OWNER, TABLE_NAME, COLUMN_NAME, DATA_TYPE, DATA_LENGTH from connection to mydb (select OWNER, TABLE_NAME, COLUMN_NAME, DATA_TYPE,DATA_LENGTH from USER.ALL_TAB_COLUMNS); disconnect from mydb; quit ; %mend; The only information coded in this program is a structure of Oracle data dictionary table (which is quite permanent and does not change even in different Oracle versions) and SOURCE data set which we structure for specific purpose of data import. As a result of this program, the following SAS data set will be created: SOURCE data set SCHEMA T_NAME C_NAME D_TYPE D_LEN DS_NAME VAR_NAME TYPE LEN … … … … … … … … … USER CUSTOMER_INFO customer_id VARCHAR2 16 . USER CUSTOMER_INFO first_name VARCHAR2 40 . USER CUSTOMER_INFO second_nam VARCHAR2 40 . e USER CUSTOMER_INFO … … … . USER CUSTOMER_INFO audit_key NUMBER 8 . … … … … … … … … … Description of data to be loaded Now we know what should be extracted. How we can describe what should be loaded? The following program will fill additional data into SOURCE data set: data &libname..source (drop = t, c) ; set &libname..source; by t_name; retain t, c 0; if first.t_name then c = 0; t + 1; c + 1; ds_name = "&prefixt"||left(t) ; var_name = "&prefixc"||left(c); 2 NESUG 17 Hands-On Workshops if d_type = “DATE” then do ; type = "D" ; len = 8 ; format = "date9." ; end ; if d_type = “NUMBER” then do ; type = "N" ; len = 8 ; format = "&mis" ; end ; if d_type = “VARCHAR2” then do ; type = "C" ; len = data_length ; format = "&mis" ; end ; run ; The rules of translation Oracle data types to corresponding SAS data types are known and easy to define. Conversion of tables’ names to data sets’ names, and fields’ names to variables’ names can be done differently. One of the possibilities is to keep them as is. However, there are several disadvantages of this approach. One of them is that Oracle and SAS naming conventions may differ from version to version, and from platform to platform. Another one is the consideration that your data mart may be fed from different sources, in which case tables with the same name may potentially exist in different data bases. Systematic approach to data sets and variables names solves this problem and creates unique data sets’ and variables’ names. As a result of the previous program, SOURCE data set is populated with data as follows: Populated SOURCE data set SCHEMA T_NAME C_NAME D_TYPE D_LEN DS_NAME VAR_NAME TYPE LEN … … … … … … … … … USER CUSTOMER_INFO customer_id VARCHAR2 16 T23 V1 C 16 USER CUSTOMER_INFO first_name VARCHAR2 40 T23 V2 C 40 USER TRANSACT_INFO second_name VARCHAR2 40 T24 V1 C 40 USER TRANSACT_INFO … … … T24 V2 … … USER TRANSACT_INFO audit_key NUMBER 8 T24 V3 N 8 … … … … … … … … … ETL process Now, our SOURCE data set contains comprehensive information about all Oracle tables from the same schema, and full information about target SAS data sets. However, we probably do not need to extract and load all tables at the same time. ETL processes for each piece of information can be easily and flexibly scheduled. Let consider the following data set. Process data set PROCESS T_NAME DS_NAME LIB_NAME … … … DAILY DAILY_TRANSACTION T315 transact DAILY DAILY_SUBSCRIPTION T403 transact MONTHLY PAYMENT_RECEIPT T315 finance … … … Each process defined in the PROCESS table specifies different portions of data that can be extracted and loaded according with different frequency and at different days. The following program implements required ETL process. Let follow each step of this program, and understand what it does. /* count – is a macro variable containing a number of Oracle tables to be extracted and loaded in the SAS datasets _t - is a series of macro variables containing names of the Oracle tables _d - is a series of macro variables containing names of the SAS datasets _l - is a series of macro variables containing libraries where the SAS datasets should be located */ %_import_ (libname = kernel, process = DAILY) ; … data _null_ ; retain i 1 ; 3 NESUG 17 Hands-On Workshops set &libname..process (where = (process = "&process")) ; call symput("_t" || left(i), trim(left(t_name))) ; call symput("_d" || left(i), trim(left(ds_name))) ; call symput("_l" || left(i), trim(left(lib_name))) ; call symput("count", left(i)) ; i + 1 ; run ; As a result of this data step, macro variables will get the following values: &count = 2 (there are two tables that should be transferred to SAS data sets) &_t1 = DAILY_TRANSACTION &_d1 = T315 &_l1 = daily (the first Oracle table DAILY_TRANSACTION will be imported into daily.T315 SAS data set) &_t2 = DAILY_SUBSCRIPTION &_d2 = T403 &_l2 = daily (the second Oracle table DAILY_SUBSCRIPTION will be imported into daily.T403 SAS data set) /* scount – is a macro variable containing
Load more

Recommended publications
  • SQL Server Column Store Indexes Per-Åke Larson, Cipri Clinciu, Eric N
    SQL Server Column Store Indexes Per-Åke Larson, Cipri Clinciu, Eric N. Hanson, Artem Oks, Susan L. Price, Srikumar Rangarajan, Aleksandras Surna, Qingqing Zhou Microsoft {palarson, ciprianc, ehans, artemoks, susanpr, srikumar, asurna, qizhou}@microsoft.com ABSTRACT SQL Server column store indexes are “pure” column stores, not a The SQL Server 11 release (code named “Denali”) introduces a hybrid, because they store all data for different columns on new data warehouse query acceleration feature based on a new separate pages. This improves I/O scan performance and makes index type called a column store index. The new index type more efficient use of memory. SQL Server is the first major combined with new query operators processing batches of rows database product to support a pure column store index. Others greatly improves data warehouse query performance: in some have claimed that it is impossible to fully incorporate pure column cases by hundreds of times and routinely a tenfold speedup for a store technology into an established database product with a broad broad range of decision support queries. Column store indexes are market. We’re happy to prove them wrong! fully integrated with the rest of the system, including query To improve performance of typical data warehousing queries, all a processing and optimization. This paper gives an overview of the user needs to do is build a column store index on the fact tables in design and implementation of column store indexes including the data warehouse. It may also be beneficial to build column enhancements to query processing and query optimization to take store indexes on extremely large dimension tables (say more than full advantage of the new indexes.
    [Show full text]
  • When Relational-Based Applications Go to Nosql Databases: a Survey
    information Article When Relational-Based Applications Go to NoSQL Databases: A Survey Geomar A. Schreiner 1,* , Denio Duarte 2 and Ronaldo dos Santos Mello 1 1 Departamento de Informática e Estatística, Federal University of Santa Catarina, 88040-900 Florianópolis - SC, Brazil 2 Campus Chapecó, Federal University of Fronteira Sul, 89815-899 Chapecó - SC, Brazil * Correspondence: [email protected] Received: 22 May 2019; Accepted: 12 July 2019; Published: 16 July 2019 Abstract: Several data-centric applications today produce and manipulate a large volume of data, the so-called Big Data. Traditional databases, in particular, relational databases, are not suitable for Big Data management. As a consequence, some approaches that allow the definition and manipulation of large relational data sets stored in NoSQL databases through an SQL interface have been proposed, focusing on scalability and availability. This paper presents a comparative analysis of these approaches based on an architectural classification that organizes them according to their system architectures. Our motivation is that wrapping is a relevant strategy for relational-based applications that intend to move relational data to NoSQL databases (usually maintained in the cloud). We also claim that this research area has some open issues, given that most approaches deal with only a subset of SQL operations or give support to specific target NoSQL databases. Our intention with this survey is, therefore, to contribute to the state-of-art in this research area and also provide a basis for choosing or even designing a relational-to-NoSQL data wrapping solution. Keywords: big data; data interoperability; NoSQL databases; relational-to-NoSQL mapping 1.
    [Show full text]
  • Data Dictionary a Data Dictionary Is a File That Helps to Define The
    Cleveland | v. 216.369.2220 • Columbus | v. 614.291.8456 Data Dictionary A data dictionary is a file that helps to define the organization of a particular database. The data dictionary acts as a description of the data objects or items in a model and is used for the benefit of the programmer or other people who may need to access it. A data dictionary does not contain the actual data from the database; it contains only information for how to describe/manage the data; this is called metadata*. Building a data dictionary provides the ability to know the kind of field, where it is located in a database, what it means, etc. It typically consists of a table with multiple columns that describe relationships as well as labels for data. A data dictionary often contains the following information about fields: • Default values • Constraint information • Definitions (example: functions, sequence, etc.) • The amount of space allocated for the object/field • Auditing information What is the data dictionary used for? 1. It can also be used as a read-only reference in order to obtain information about the database 2. A data dictionary can be of use when developing programs that use a data model 3. The data dictionary acts as a way to describe data in “real-world” terms Why is a data dictionary needed? One of the main reasons a data dictionary is necessary is to provide better accuracy, organization, and reliability in regards to data management and user/administrator understanding and training. Benefits of using a data dictionary: 1.
    [Show full text]
  • USER GUIDE Optum Clinformatics™ Data Mart Database
    USER GUIDE Optum Clinformatics Data Mart Database 1 | P a g e TABLE OF CONTENTS TOPIC PAGE # 1. REQUESTING DATA 3 Eligibility 3 Forms 3 Contact Us 4 2. WHAT YOU WILL NEED 4 SAS Software 4 VPN 5 3. ABSTRACTS, MANUSCRIPTS, THESES, AND DISSERTATIONS 5 Referencing Optum Data 5 Optum Review 5 4. DATA USER SET-UP AND ACCESS INFORMATION 6 Server Log-In After Initial Set-Up 6 Server Access 6 Establishing a Connection to Enterprise Guide 7 Instructions to Add SAS EG to the Cleared Firewall List 8 How to Proceed After Connection 8 5. BEST PRACTICES FOR DATA USE 9 Saving Programs and Back-Up Procedures 9 Recommended Coding Practices 9 6. APPENDIX 11 Version Date: 27-Feb-17 2 | P a g e Optum® ClinformaticsTM Data Mart Database The Optum® ClinformaticsTM Data Mart is an administrative health claims database from a large national insurer made available by the University of Rhode Island College of Pharmacy. The statistically de-identified data includes medical and pharmacy claims, as well as laboratory results, from 2010 through 2013 with over 22 million commercial enrollees. 1. REQUESTING DATA The following is a brief outline of the process for gaining access to the data. Eligibility Must be an employee or student at the University of Rhode Island conducting unfunded or URI internally funded projects. Data will be made available to the following users: 1. Faculty and their research team for projects with IRB approval. 2. Students a. With a thesis/dissertation proposal approved by IRB and the Graduate School (access request form, see link below, must be signed by Major Professor).
    [Show full text]
  • Activant Prophet 21
    Activant Prophet 21 Understanding Prophet 21 Databases This class is designed for… Prophet 21 users that are responsible for report writing System Administrators Operations Managers Helpful to be familiar with SQL Query Analyzer and how to write basic SQL statements Objectives Explain the difference between databases, tables and columns Extract data from different areas of the system Discuss basic SQL statements using Query Analyzer Use Prophet 21 to gather SQL Information Utilize Data Dictionary This course will NOT cover… Basic Prophet 21 functionality Seagate’s Crystal Reports Definitions Columns Contains a single piece of information (fields) Record (rows) One complete set of columns Table A collection of rows View Virtual table created for easier data extraction Database Collection of information organized for easy selection of data SQL Query A graphical user interface used to extract Analyzer data SQL Query Analyzer Accessed through Microsoft SQL Server SQL Server Tools Enterprise Manager Perform administrative functions such as backing up and restoring databases and maintaining SQL Logins Profiler Run traces of activity in your system Basic SQL Commands sp_help sp_help <table_name> select * from <table_name> Select <field_name> from <table_name> Most Common Reporting Areas Address and Contact tables Order Processing Inventory Purchasing Accounts Receivable Accounts Payable Production Orders Address and Contact tables Used in conjunction with other tables These tables hold every address/contact in the
    [Show full text]
  • Uniform Data Access Platform for SQL and Nosql Database Systems
    Information Systems 69 (2017) 93–105 Contents lists available at ScienceDirect Information Systems journal homepage: www.elsevier.com/locate/is Uniform data access platform for SQL and NoSQL database systems ∗ Ágnes Vathy-Fogarassy , Tamás Hugyák University of Pannonia, Department of Computer Science and Systems Technology, P.O.Box 158, Veszprém, H-8201 Hungary a r t i c l e i n f o a b s t r a c t Article history: Integration of data stored in heterogeneous database systems is a very challenging task and it may hide Received 8 August 2016 several difficulties. As NoSQL databases are growing in popularity, integration of different NoSQL systems Revised 1 March 2017 and interoperability of NoSQL systems with SQL databases become an increasingly important issue. In Accepted 18 April 2017 this paper, we propose a novel data integration methodology to query data individually from different Available online 4 May 2017 relational and NoSQL database systems. The suggested solution does not support joins and aggregates Keywords: across data sources; it only collects data from different separated database management systems accord- Uniform data access ing to the filtering options and migrates them. The proposed method is based on a metamodel approach Relational database management systems and it covers the structural, semantic and syntactic heterogeneities of source systems. To introduce the NoSQL database management systems applicability of the proposed methodology, we developed a web-based application, which convincingly MongoDB confirms the usefulness of the novel method. Data integration JSON ©2017 Elsevier Ltd. All rights reserved. 1. Introduction solution to retrieve data from heterogeneous source systems and to deliver them to the user.
    [Show full text]
  • Product Master Data Management Reference Guide
    USAID GLOBAL HEALTH SUPPLY CHAIN PROGRAM Procurement and Supply Management PRODUCT MASTER DATA MANAGEMENT REFERENCE GUIDE Version 1.0 February 2020 The USAID Global Health Supply Chain Program-Procurement and Supply Management (GHSC-PSM) project is funded under USAID Contract No. AID-OAA-I-15-0004. GHSC-PSM connects technical solutions and proven commercial processes to promote efficient and cost-effective health supply chains worldwide. Our goal is to ensure uninterrupted supplies of health commodities to save lives and create a healthier future for all. The project purchases and delivers health commodities, offers comprehensive technical assistance to strengthen national supply chain systems, and provides global supply chain leadership. GHSC-PSM is implemented by Chemonics International, in collaboration with Arbola Inc., Axios International Inc., IDA Foundation, IBM, IntraHealth International, Kuehne + Nagel Inc., McKinsey & Company, Panagora Group, Population Services International, SGS Nederland B.V., and University Research Co., LLC. To learn more, visit ghsupplychain.org DISCLAIMER: The views expressed in this publication do not necessarily reflect the views of the U.S. Agency for International Development or the U.S. government. Contents Acronyms ....................................................................................................................................... 3 Executive Summary ...................................................................................................................... 4 Background
    [Show full text]
  • Master Data Management: Dos & Don’Ts
    Master Data Management: dos & don’ts Keesjan van Unen, Ad de Goeij, Sander Swartjes, and Ard van der Staaij Master Data Management (MDM) is high on the agenda for many organizations. At Board level too, people are now fully aware that master data requires specific attention. This increasing attention is related to the concern for the overall quality of data. By enhancing the quality of master data, disrup- tions to business operations can be limited and therefore efficiency is increased. Also, the availability of reliable management information will increase. However, reaching this goal is not an entirely smooth process. MDM is not rocket science, but it does have specific problems that must be addressed in the right way. This article describes three of these attention areas, including several dos and don’ts. C.J.W.A. van Unen is a senior manager at KPMG Management Consulting, IT Advisory. [email protected] A.S.M de Goeij RE is a manager at KPMG Management Consulting, IT Advisory. [email protected] S. Swartjes is an advisor at KPMG Risk Consulting, IT Advisory. [email protected] A.J. van der Staaij is an advisor at KPMG Risk Consulting, IT Advisory. [email protected] Compact_ 2012 2 International edition 1 Treat master data as an asset Introduction Do not go for gold immediately Every organization has to deal with master data, and seeks Although it may be very tempting, you should not go for ways to manage the quality of this specific group of data in the ‘ideal model’ when (re)designing the MDM organiza- an effective and efficient manner.
    [Show full text]
  • Getting the Data You Can't
    The Marriage of VFE, SQL, Visual Studio John W. Fitzgerald, MD Gregory P. Sanders, MD CHUG 2014 Fall Conference GETTING THE DATA YOU CAN’T GET: WHEN MEL JUST ISN’T ENOUGH The Problem . Built in data symbols are powerful but may not provide the data you need MEDS_AFTER() PROB_LIST_CHANGES() ALL_NEW() ….etc . Mldefs.txt may be available to address your needs but are undocumented, difficult to locate, and may not survive version changes. There are tables with no MEL connections . Complex relationships are generally not available Our Approach . Create SQL Centricity data access statements in Visual Studio, Visual Scripts as an .exe . Store in Centricity Client . Pass arguments to the .exe from VFE . Return results from .exe call . Parse the results as required. Non-trivial Set of Skills Required . VFE . Data Symbols . Centricity table structure and relationships . Model solution in Crystal, Access . SQL statement construction . Visual Studio or equivalent . Lots of debugging THE CLIENT . Folder on each local machine . EXE sits in Client folder for easy access . Code is run locally . Client calls EXE and passes its authentication to it . Client has access to network (ie, SQL server) . EXE must be pushed out to all Clients Dataflow: Call & Query Centricity SQL Centricity calls EXE EXE queries SQL Database EXE Dataflow: Compile & Return Centricity SQL EXE returns data to Centricity SQL returns data to EXE EXE Inside the EXE . Capture Passed Data . Compile SQL Statement . Connect to Database . Organize Queried Data . Pass Back to Centricity Capture Passed Data . Output Data . File name and path designated with “ /o “ primer . Input Data . Data passed to EXE from Centricity .
    [Show full text]
  • MDM Physical Data Dictionary
    IBM InfoSphere Master Data Management InfoSphere Master Data Management Version 11.3 MDM Physical Data Dictionary GI13-2668-01 IBM InfoSphere Master Data Management InfoSphere Master Data Management Version 11.3 MDM Physical Data Dictionary GI13-2668-01 Note Before using this information and the product that it supports, read the information in “Notices and trademarks” on page 195. Edition Notice This edition applies to version 11.3 of IBM InfoSphere Master Data Management and to all subsequent releases and modifications until otherwise indicated in new editions. © Copyright IBM Corporation 1996, 2014. US Government Users Restricted Rights – Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. Contents Chapter 1. Tables...........1 CDEMEMATCHFUNCTIONTP ........46 ACCESSDATEVAL ............1 CDEMEMATCHWORDTP .........47 ADDACTIONTYPE ............2 CDENDREASONTP ...........48 ADDRESS ...............2 CDENTITYLINKSTTP ...........49 ADDRESSGROUP ............5 CDFREQMODETP ............50 AGREEMENTSPECVAL ..........5 CDGENERATIONTP ...........50 BANKACCOUNT ............6 CDHIGHESTEDUTP ...........51 BILLINGSUMMARY............7 CDHOLDINGTP ............52 CAMPAIGN ..............9 CDIDSTATUSTP.............53 CAMPAIGNASSOCIAT ..........10 CDIDTP ...............53 CATEGORY ..............11 CDINACTREASONTP...........54 CATEGORYNLS .............12 CDINCOMESRCTP............55 CATEQUIV ..............13 CDINDUSTRYTP ............56 CATHIERARCHY ............14 CDLANGTP ..............56 CATHIERARCHYNLS ...........15 CDLINKREASONTP
    [Show full text]
  • The Database/Data Dictionary Interface
    PANEL : THE DATABASE/DATA DICTIONARY INTERFACE Larry KERSCHBERG (Chairperson)-Un iversity of South Carolina, Paul FEHDER-IBM Santa Teresa Lab, Georqe GAGNAC-Cincom Systems , Inc., John MXIIRE and llichael JAKES-Software A.G. of North America, Inc., Shamkant NAVATHE-University of Florida, Edgar SIBLEY-Alpha Omega Group, Inc. The purpose of this panel is to review the state- . Privacy, integrity, and security of-the-art in Data Dictionary systems and to issues. explore future capabilities and architectures for such systems. In particular, the interface . How can the dictionary bottleneck between Data Dictionarv svstems and Database be ameliorated? Flanaaement systems wili be discussed in relation to the following issues: 2. The Role of Data Dictionaries in Information Resources Management 1. Architecture of Data Dictionary Systems As data dictionaries are endowed with Current thinking regarding data dictionaries increased intelligence and enhanced capa- indicates that the dictionary is to be a bilities, they will play a key role in repository of information about an enter- information resource management. We wish prise. Thus, this repository, or knowledge to explore several aspects of this role, base, not only contains information about namely: the "semantics" of data stored in databases, but also knows about enterprise concepts A. Tools for enterprise administration. such as organizational structure and dyna- mics, forms, memos and other documents B. Application development tools. together with their organizational flows, and the description of information resources C. Managing computer hardware and software such as hardware and software systems. resources. In this framework we consider the following D. Communication mechanisms among data issues: dictionaries. A.
    [Show full text]
  • A Dictionary of DBMS Terms
    A Dictionary of DBMS Terms Access Plan Access plans are generated by the optimization component to implement queries submitted by users. ACID Properties ACID properties are transaction properties supported by DBMSs. ACID is an acronym for atomic, consistent, isolated, and durable. Address A location in memory where data are stored and can be retrieved. Aggregation Aggregation is the process of compiling information on an object, thereby abstracting a higher-level object. Aggregate Function A function that produces a single result based on the contents of an entire set of table rows. Alias Alias refers to the process of renaming a record. It is alternative name used for an attribute. 700 A Dictionary of DBMS Terms Anomaly The inconsistency that may result when a user attempts to update a table that contains redundant data. ANSI American National Standards Institute, one of the groups responsible for SQL standards. Application Program Interface (API) A set of functions in a particular programming language is used by a client that interfaces to a software system. ARIES ARIES is a recovery algorithm used by the recovery manager which is invoked after a crash. Armstrong’s Axioms Set of inference rules based on set of axioms that permit the algebraic mani- pulation of dependencies. Armstrong’s axioms enable the discovery of minimal cover of a set of functional dependencies. Associative Entity Type A weak entity type that depends on two or more entity types for its primary key. Attribute The differing data items within a relation. An attribute is a named column of a relation. Authorization The operation that verifies the permissions and access rights granted to a user.
    [Show full text]