Anchor modeling - Agile information modeling in evolving data environments (Rönnbäck and Regardt et al., 2010, pp. 1229--1253) Anchor modeling is suggested in the article as an agile graphical modeling technique that allows for fast adaption through extensions, and yields in older versions to be available as subset of the newest version. Even though this technique originates from data warehouse domain requirements it is to be considered as a “generic modeling approach”. The basic components defined for the anchor schema are: anchors, knots, attributes and ties. In order to preserve information on changes, knots and ties come also in “historized” versions. Furthermore, a specified naming convention and a set of five guidelines, based on a business scenario that deals with stage performance arrangement, are provided. The scenario is extended to handle a schema evolution as a result of new requirements and demonstrates that the proposed model is non-destructive as it results in new additions but existing tables are not altered. The physical implementation of the anchor schema in an anchor database (relational schema in 6NF) can be automated through an on-line open-source tool. Because of the highly normalized form, a layer with views and functions is introduced for easier data retrieval in a time based context. The proposed model is considered from a performance perspective through experiments that compare performance of an anchor database with a less normalized database (3NF). The results of the experiment demonstrated that there are circumstances where anchor databases may result in better performance which is improved by implementation of the following: automatic indexing and partitioning, zero-update strategy and “table eliminat... ... middle of paper ... ...uced by the second generation of data warehouse architecture standard (DW 2.0). According to this standard, a snapshot is taken every time a semantic change appears in the data and this snapshot is delimited by time attributes. At last, “Anchor modeling” is introduced as a graphical modeling technique that incorporates the above mentioned concepts and results in a database design of the 6th Normal Form (6NF). The author provides a general description of the basic constructs of the technique and points out that it is very useful in data warehousing as it can be translated into dimensions and facts by using the Dimension Fact Model (DFM) technique. The article concludes by stating that the focus of their future work will be defining a standardized XML schema to facilitate communication with data warehouses that support temporal data (DW 2.0) Discussion on the article
Smith, W., & Jewett, D. (2009). Tableau software and teradata database the visual approach to the active data warehouse. In Retrieved from http://www.tableausoftware.com/learn/whitepapers
These are covered briefly in appendices in the text. The relational model was first proposed by E.F. Codd in 1970 and the first such systems were developed in 1970s. The relational model is now the dominant model for commercial data processing applications. The relational model can be used in both conceptual and logical database design. The basic structure in the model is a table .Tables consists of rows and columns. Relationships in the relational model are represented implicitly through common attributes between different relations.
As defined by Kroenke Database is an integrated, self-describing collection of related data. Data is stored in a uniform way, typically all in one place- for example, a single physical computer. A database maintains a description of the data it contains and the data has some relationship to other data in the databa...
In 1977, Larry Ellison, Bob Miner, and Ed Oates founded System Development Laboratories. After being inspired by a research paper written in 1970 by an IBM researcher titled “A Relational Model of Data for Large Shared Data Banks” they decided to build a new type of database called a relational database system. The original project on the relational database system was for the government (Central Intelligence Agency) and was dubbed ‘Oracle.’ They thought this would be appropriate because the meaning of Oracle is source of wisdom.
...el that's closely aligned with the software program’s object model. Obviously, an OODBMS may have a physical data model optimized for those types of logical data model it needs.
Web accessibility of a data warehouse is important because many analysis applications are Web-based, because users often access data over the Web (or over an intranet using the same tools) and because data from the Web may feed the DW.
Kasdorf, B. (2014). Welcome to the metadata millenium. Book Business, 17(1), 18-23. Retrieved from http://search.proquest.com/docview/1500945974?accountid=10043
The relational model, as implemented in most RDBMSs, can represent a lot of different models, but has difficulty representing inheritance hierarchies, and complex relationships (many many-to-many's) are costly to process
The Revolution in Database Architecture, by Jim Gray, describes the path that Gray thought that the evolution of the Database Architecture would take after 2004. He considers that databases had been stagnated for several years and that, beginning in 2004, the development of several technologies would pave the way into a revolution in the database world.
The database application design can be improved in a number of ways as described below:
The main components of a data warehouse are the data source, data storage and the end users. The data is extracted from an external source and is passed through an ETL process, which converts the data into an output suitable for analysis. In order for this process to achieve the expected results the data staging process must be done correctly to ensure the needs of the organisation are met. By doing so the chances of a successful system are much higher.
[7] Elmasri & Navathe. Fundamentals of database systems, 4th edition. Addison-Wesley, Redwood City, CA. 2004.
An OLAP application is targeted to deliver most responses to users within about five seconds, with the simplest analyses taking no more than one second and very few taking more than 20 seconds. Impatient users often assume that a process has failed if results are not received with 30 seconds, and they are apt to implement the ‘3 finger salute’ or ‘Alt+Ctrl+Delete’ unless the system warns them that the report will take longer. Even if they have been warned that it will take significantly longer, users are likely to get distracted and lose their chain of thought, so the quality of analysis suffers. This speed is not easy to achieve with large amounts of data, particularly if on-the-fly and ad hoc calculations are required. A wide variety of techniques are used to achieve this goal, including specialized forms of data storage, extensive pre-calculations and specific hardware requirements, but a lot of products are yet fully optimized, so we expect this to be an area of developing technology. In particular, the SAP Business Warehouse is a full pre-calculation approach that fails as the databases simply get too. Likewise, doing everything on-the-fly is much too slow with large databases, even if the most expensive server is used. Slow query response is consistently the most often-cited technical problem with OLAP products.
These basic rules allow user defined as well as built-in data types to define information which allows the schema to be extensible just as XML itself. (N/A, 2001). With these basic structural boundaries in the Schema benefits noted are; (N/A,2001)
A data warehouse comprised of disparate data sources enables the “single version of truth” through shared data repositories and standards and also provides access to the data that will expand frequency and depth of data analysis. Due to these reasons, data warehouse is the foundation for business intelligence.