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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 ...
Based on current research, CRISP-DM is the most widely used form of data-mining model because of its various advantages which solved the existing problems in the data mining industries. Some of the drawbacks of this model is that it does not perform project management activities.
GraphQL is a data query and manipulation language that allows specifying what data is to be retrieved ("declarative data fetching") or modified. A GraphQL server can process a client query using data from separate sources and present the results in a unified graph. [2] The language 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.
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 ...
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]
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 ...