- Scheduling and executing the batch jobs.

- Configuring, starting and stopping the real-time services

- Adapters configuration and managing them.

- Repository usage, Job Server configuration.

- Access Server configuration.

- Batch job publishing.

- Real-time services publishing through web services.

•  NO_CACHE – It is used for not caching values.


•  PRE_LOAD_CACHE – Result column preloads and compares the column into the memory, prior to executing the lookup.


•  PRE_LOAD_CACHE is used when the table can exactly fit in the memory space.


•  DEMAND_LOAD_CACHE – Result column loads and compares the column into the memory when a function performs the execution.


•  DEMAND_LOAD_CACHE is suitable while looking up the highly repetitive values with small subset of data.

Cascade:

- Cascade process involves taking values from various other prompts.

- The result is a single report.

- The result is used when a criteria is to be implemented.

Drill Through:

- Drill Through process is implemented when navigation from summary to detailed information.

- Drill Through has a parent and a child report.

- Data of another report can be seen based on the current details of data.

- Agglomerative Hierarchical clustering method allows the clusters to be read from bottom to top and it follows this approach so that the program always reads from the sub-component first then moves to the parent. Whereas, divisive uses top-bottom approach in which the parent is visited first then the child. 

- Agglomerative hierarchical method consists of objects in which each object creates its own clusters and these clusters are grouped together to create a large cluster. It defines a process of merging that carries on till all the single clusters are merged together into a complete big cluster that will consists of all the objects of child clusters. Whereas, in divisive the parent cluster is divided into smaller cluster and it keeps on dividing till each cluster has a single object to represent.

There are many algorithms that can be used to analyze the database to check the maintenance of all the data sets that are already present. The different types of cluster models include as follows:

- Connectivity models: these are the models that connect one cluster to another cluster. This includes the example of hierarchical clustering that is based on the distance connectivity of one model to another model. 

- Centroid models: these are the models that are used to find the clusters using the single mean vector. It includes the example of k-means algorithm.

- Distribution models: it includes the specification of the models that are statistically distributed for example multivariate normal distribution model.

- Density models: deals with the clusters that are densely connected with one another in the regions having the data space. 

- Group models: specifies the model that doesn’t provide the refined model for the output and just gives the grouping information

Cluster analysis is used to define the object without giving the class label. It analyzes all the data that is present in the data warehouse and compare the cluster with the cluster that is already running. It performs the task of assigning some set of objects into the groups are also known as clusters. It is used to perform the data mining job using the technique like statistical data analysis. It includes all the information and knowledge around many fields like machine learning, pattern recognition, image analysis and bio-informatics. Cluster analysis performs the iterative process of knowledge discovery and includes trials and failures. It is used with the pre-processing and other parameters as a result to achieve the properties that are desired to be used.

At the time of updating the data warehouse, a lookup table is used. When placed on the fact table or warehouse based upon the primary key of the target, the update is takes place only by allowing new records or updated records depending upon the condition of lookup.

The materialized views are aggregate tables. It contains summarized data. For example, to generate sales reports on weekly or monthly or yearly basis instead of daily basis of an application, the date values are aggregated into week values, week values are aggregated into month values and month values into year values. To perform this process aggregate function is used.

The primary keys of entity tables are the foreign keys of dimension tables.

The Primary keys of fact dimensional table are the foreign keys of fact tablels.