Leveraging the metadata model, enterprise users can then apply elementary data analysis techniques to gather business knowledge. For example, ad hoc queries can be run against the data warehouse ...

Some popular data modeling techniques include Hierarchical, Relational, Network, Entity-relationship, and Object-oriented. Hierarchical Technique.

Data Vault modeling: addressing business needs. Data Vault is a data modeling approach that is detail-oriented – keeping track of data and its history. It gives organizations more agility and ...

Time series-based data mining techniques help businesses to mine data to analyze periodic trends. This practice is also helpful in analyzing random events which occur outside the normal series of ...

For example, the Knowledge Discovery Databases model has nine steps, the CRISP-DM model has six steps, and the SEMMA process model has five steps. Applications of Data Mining ...

Data warehouses must support data extraction from multiple databases to keep up with the trend. For example, three heterogeneous data mining programs are needed to model the behavior of telecom ...

U1: (Volume) Data Warehousing – how to ask analytical queries over huge datasets that are generated via integration from traditional databases as sources. U2: (Velocity) Streaming Data – what are the ...

Logistic Model Trees. Logistic model trees combine logistic regression and decision-trees. In that respect they are optimizing the advantages of both modeling techniques, both with their accuracy and ...

Some of this added ambiguity arises from the data warehousing community. ... At other times, a business wants to hire a data architect who is very expert at data modeling techniques. Other occasions ...

Data modeling is the ... This database structure is optimized for online queries and data warehousing tools. Entity-rich model: ... bullet points and a host of other methods to deliver ...