Olap data mining warehousing data marts essay

Oracle OLAP provides native multidimensional storage and speed-of-thought response times when analyzing data across multiple dimensions. The database provides rich support for analytics such as time series calculations, forecasting, advanced aggregation with additive and non additive operators, and allocation operators.

Olap data mining warehousing data marts essay

Multi-dimensional business tasks ODS is abbreviated as Operational Data Store and it is a repository of real time operational data rather than long term trend data. What is the difference between View and Materialized View?

A view is nothing but a virtual table which takes the output of the query and it can be used in place of tables.

Olap data mining warehousing data marts essay

A materialized view is nothing but an indirect access to the table data by storing the results of a query in a separate schema. ETL is a software which is used to reads the data from the specified data source and extracts a desired subset of data.

Next, it transform the data using rules and lookup tables and convert it to a desired state. Then, load function is used to load the resulting data to the target database. These are decision support systems which is used to server large number of users.

What is real-time datawarehousing? Real-time datawarehousing captures the business data whenever it occurs. When there is business activity gets completed, that data will be available in the flow and become available for use instantly.

What are Aggregate tables? Aggregate tables are the tables which contain the existing warehouse data which has been grouped to certain level of dimensions.

It is easy to retrieve data from the aggregated tables than the original table which has more number of records. This table reduces the load in the database server and increases the performance of the query. What is factless fact tables?

How can we load the time dimension? Time dimensions are usually loaded through all possible dates in a year and it can be done through a program. Here, years can be represented with one row per day.

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What are Non-additive facts? Non-Addictive facts are said to be facts that cannot be summed up for any of the dimensions present in the fact table.

If there are changes in the dimensions, same facts can be useful. What is conformed fact? A Datamart is a specialized version of Datawarehousing and it contains a snapshot of operational data that helps the business people to decide with the analysis of past trends and experiences.

A data mart helps to emphasizes on easy access to relevant information. What is Active Datawarehousing? An active datawarehouse is a datawarehouse that enables decision makers within a company or organization to manage customer relationships effectively and efficiently.

Datawarehouse is a place where the whole data is stored for analyzing, but OLAP is used for analyzing the data, managing aggregations, information partitioning into minor level information. What is ER Diagram?

ER diagram is abbreviated as Entity-Relationship diagram which illustrates the interrelationships between the entities in the database. This diagram shows the structure of each tables and the links between the tables.

What are the key columns in Fact and dimension tables? Foreign keys of dimension tables are primary keys of entity tables. Foreign keys of fact tables are the primary keys of the dimension tables.

SCD is defined as slowly changing dimensions, and it applies to the cases where record changes over time. What are the types of SCD? There are three types of SCD and they are as follows: What is BUS Schema?

BUS schema consists of suite of confirmed dimension and standardized definition if there is a fact tables. What is Star Schema?

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Star schema is nothing but a type of organizing the tables in such a way that result can be retrieved from the database quickly in the data warehouse environment. What is Snowflake Schema? Snowflake schema which has primary dimension table to which one or more dimensions can be joined.Data-warehousing and data-mining techniques provide this capability.

A data warehouse is a modern reporting environment that provides users direct access to their data. A Data warehousing is the sum of all its Data Marts. In large data warehouse environments, many different types of analysis can occur.

You can enrich your data warehouse with advance analytics using OLAP (On-Line Analytic Processing) and data mining. Rather than having a separate OLAP or data mining engine, Oracle has integrated OLAP and data mining.

Data warehousing and data marts. 11/28/; 12 minutes to read An orchestration process populates the data marts from data maintained in an operational data store. A data warehouse can help consolidate data from different software.

Data mining tools can help you find hidden patterns using automatic methodologies against data stored in. Data warehouse concepts. Please provide a detailed expkanation of the following concepts o An explanation of data warehouse concepts o The benefits of data mining.

Olap data mining warehousing data marts essay

Frank Dehne, Todd Eavis, and Andrew Rau-Chaplin, Coarse grained parallel on-line analytical processing (OLAP) for data mining, ICCS, Soroush Momen-Pour and Alan Wagner, Parallel partitioned-cube algorithm., PDPTA, apply OLAP and data mining techniques to provide more analytical reports and provide answers of miscellaneous questions to decision makers [10].

OLAP is a way to look at these preaggregated query results- in real time.

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