a dimension of data definition
For example,“The data in the database at a particular moment in time is called a database state. In a given database state. . . .Every time we insert or delete a record, or change the value of a data item, we change one state of the database into another state [8].”Finally, we note two implications of our model. Overview. Every report in Google Analytics is made up of dimensions and metrics. Dimensions describe characteristics of your users, their sessions and actions. The dimension City describes a characteristic of sessions and indicates the city, for example, Paris or New York, from which each session originated. Each dimension and metric has a scope: users, sessions, or actions. In most cases, it only makes sense to combine dimensions and metrics that share the same scope. For example, Sessions is a session-based metric so it can only be used with session-level dimensions like Source or City. A dimension is a structure that categorizes facts and measures in order to enable users to answer business questions. Commonly used dimensions are people, products, place and time. In a data warehouse, dimensions provide structured labeling information to otherwise unordered numeric measures. The dimension is a data set composed of individual, non-overlapping data elements. The primary functions of dimensions are threefold: to provide filtering, grouping and labelling. These functions are often described as slice and dice. Slicing refers to filtering data. Metrics are quantitative measurements. The metric Sessions is the total number of sessions. The metric Pages/Session is the average number of pages viewed per session. The tables in most Google Analytics reports organize dimension values into rows, and metrics into columns. For example, this table shows one dimension (City) and two metrics (Sessions and Pages/Session). Thus,our analysis concentrates on the internal view, and isoriented towards system design and data production.This has two important implications. First, since theinternal view is use-independent, it supports a set of def-initions of dimensions of data quality that are compa-rable across applications. What may be con-sidered good data in one case (for a specific applicationor user) may not be sufficient in another case. Forexample, analysis of the financial position of a firm mayrequire data in units of thousands of dollars, whereasauditing requires precision to the cent. A conformed dimension is a set of data attributes that have been physically referenced in multiple database tables using the same key value to refer to the same structure, attributes, domain values, definitions and concepts. A conformed dimension cuts across many facts. A real-world system is said to be properlyrepresented if: (1) there exists an exhaustive mapping,Rep: RW L →IS L, and (2) no two states in RW L aremapped into the same state in IS L (the inverse mappingis a function).Our analysis of data deficiencies is based on deviationsfrom the conditions of Definition 3. Representation deficienciesare defined in terms of the difference between the viewof the real-world system as inferred from the informa-tion system and the view that is obtained by directlyobserving the real-world system. From various types ofrepresentation deficiencies, we derive a set of data qual-ity dimensions.
It is a data warehouse, dimensions provide structured labeling information to otherwise unordered numeric measures.