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Firstly, the graph model can be a natural fit for data sets that have hierarchical, complex, or even arbitrary structures. Such structures can be easily encoded into the graph model as edges. This can be more convenient than the relational model, which requires the normalization of the data set into a set of tables with fixed row types.
GraphQL is a data query and manipulation language for APIs that allows a client to specify what data it needs ("declarative data fetching"). A GraphQL server can fetch data from separate sources for a single client query and present the results in a unified graph. [2] It is not tied to any specific database or storage engine.
Data Mining Extensions (DMX) is a query language for data mining models supported by Microsoft's SQL Server Analysis Services product. [1] Like SQL, it supports a data definition language (DDL), data manipulation language (DML) and a data query language (DQL), all three with SQL-like syntax. Whereas SQL statements operate on relational tables ...
Since its launch a few years ago, GraphQL platform Hasura has made it easier for developers to use GraphQL by simplifying the process of connecting their data sources to their applications and ...
Gremlin is an Apache2-licensed graph traversal language that can be used by graph system vendors. There are typically two types of graph system vendors: OLTP graph databases and OLAP graph processors.
Some depend on a relational engine and "store" the graph data in a table (although a table is a logical element, therefore this approach imposes another level of abstraction between the graph database, the graph database management system and the physical devices where the data is actually stored). Others use a key–value store or document ...
For exchanging the extracted models—in particular for use in predictive analytics—the key standard is the Predictive Model Markup Language (PMML), which is an XML-based language developed by the Data Mining Group (DMG) and supported as exchange format by many data mining applications. As the name suggests, it only covers prediction models ...
Spatial data mining is the application of data mining methods to spatial data. The end objective of spatial data mining is to find patterns in data with respect to geography. So far, data mining and Geographic Information Systems (GIS) have existed as two separate technologies, each with its own methods, traditions, and approaches to ...