Nonparametric statisticsNonparametric statistics is the type of statistics that is not restricted by assumptions concerning the nature of the population from which a sample is drawn. This is opposed to parametric statistics, for which a problem is restricted a priori by assumptions concerning the specific distribution of the population (such as the normal distribution) and parameters (such the mean or variance).
Data qualityData quality refers to the state of qualitative or quantitative pieces of information. There are many definitions of data quality, but data is generally considered high quality if it is "fit for [its] intended uses in operations, decision making and planning". Moreover, data is deemed of high quality if it correctly represents the real-world construct to which it refers. Furthermore, apart from these definitions, as the number of data sources increases, the question of internal data consistency becomes significant, regardless of fitness for use for any particular external purpose.
Data lineageData lineage includes the data origin, what happens to it, and where it moves over time. Data lineage provides visibility and simplifies tracing errors back to the root cause in a data analytics process. It also enables replaying specific portions or inputs of the data flow for step-wise debugging or regenerating lost output. Database systems use such information, called data provenance, to address similar validation and debugging challenges.
Completeness (statistics)In statistics, completeness is a property of a statistic in relation to a parameterised model for a set of observed data. A complete statistic T is one for which any proposed distribution on the domain of T is predicted by one or more prior distributions on the model parameter space. In other words, the model space is 'rich enough' that every possible distribution of T can be explained by some prior distribution on the model parameter space. In contrast, a sufficient statistic T is one for which any two prior distributions will yield different distributions on T.
Query by ExampleQuery by Example (QBE) is a database query language for relational databases. It was devised by Moshé M. Zloof at IBM Research during the mid-1970s, in parallel to the development of SQL. It is the first graphical query language, using visual tables where the user would enter commands, example elements and conditions. Many graphical front-ends for databases use the ideas from QBE today. Originally limited only for the purpose of retrieving data, QBE was later extended to allow other operations, such as inserts, deletes and updates, as well as creation of temporary tables.
