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"Using graph as a fundamental representation for data modeling is an emerging approach in data management. In this approach, the data set is modeled as a graph, representing each data entity as a vertex (also called a node) of the graph and each relationship between two entities as an edge between corresponding vertices. The graph data model ...
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.
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.
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 ...
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 ...
The growth of the use of semi-structured data has created new opportunities for data mining, which has traditionally been concerned with tabular data sets, reflecting the strong association between data mining and relational databases. Much of the world's interesting and mineable data does not easily fold into relational databases, though a ...
Semantic data mining is a subset of data mining that specifically seeks to incorporate domain knowledge, such as formal semantics, into the data mining process.Domain knowledge is the knowledge of the environment the data was processed in. Domain knowledge can have a positive influence on many aspects of data mining, such as filtering out redundant or inconsistent data during the preprocessing ...
Examples of what businesses use data mining for is to include performing market analysis to identify new product bundles, finding the root cause of manufacturing problems, to prevent customer attrition and acquire new customers, cross-selling to existing customers, and profiling customers with more accuracy. [1]