OLAP

Bitmaps are very useful in start schema to join large databases to small databases. Answer queries and bit arrays are used to perform logical operations on the databases. Bit map indexes are very efficient in handling Gender differentiation; also repetitive tasks are performed with much larger efficiency.

Bitmaps commonly use one bitmap for every single distinct value. Number of bitmaps used can be reduced by opting for a different type of encoding. Space can be optimized but when a query is generated bitmaps have to be accessed.

Often calculating all the data is not possible by aggregations for this reason some of the complex data problems are solved. In order to determine which data should be solved and calculated, developers use View selection application. This solution is often used to reduce calculation problem.

OLAP can process complex queries and give the output in less than 0.1 seconds, for it to achieve such a performance OLAP uses aggregations. Aggregations are built by aggregating and changing the data along the dimensions. Possible combination of aggregations can be determined by the combination possibilities of dimension granularities.

Functioning of ROLAP occurs simultaneously with relational databases. Data and tables are stored as relational tables. To hold new information or data new tables are created. Functioning of ROLAP depends upon specialized schema design.

Classic form of OLAP is known as MOLAP and it is often called as OLAP. Simple database structures such as time period, product, location, etc are used. Functioning of each and every dimension or data structure is defined by one or more hierarchies.

Hyper cube or multidimensional cube forms the core of OLAP system. This consists of measures which are arranged according to dimensions. Hyper cube Meta data is created by star or snow flake schema of tables in RDBMS. Dimensions are extracted from dimension table and measures from the fact table.

OLAP is known as online analytical processing which provides answers to queries which are multi dimensional in nature. It composes relational reporting and data mining for providing solutions to business intelligence. This term OLAP is created from the term OLTP.

a) A condition when each cell of the cube is filled with data and that leads to more processing time.

d) Both 1 and 2

OLAP is an acronym for Online Analytical Processing. OLAP performs multidimensional analysis of business data and provides the capability for complex calculations, trend analysis, and sophisticated data modeling.

A data cube stores data in a summarized version which helps in a faster analysis of data. The data is stored in such a way that it allows reporting easily. E.g. using a data cube a user may want to analyze weekly, monthly performance of an employee. Here, month and week could be considered as the dimensions of the cube.

Database level roles are used to manage the security of the database. The role can be either fixed or flexible. Fixed roles are predefined while flexible roles can be created. Examples of some fixed database level roles are db_owner, db_securityadmin, db_datawriter etc.

b) 4

Explanation:

{ (item name, color, clothes size), (item name, color), (item name), () }.

Data source is where the data comes from in data warehousing. The data collected from various sources and is cleaned. The data source can be internal or external. Efficient Analysis and cleansing of source data is the key success to data warehousing.

Data in a warehouse comes from the transactions. Fact table in a data warehouse consists of facts and/or measures. The nature of data in a fact table is usually numerical. e.g. If I want to know the number of resources used for a task, my fact table will store the actual measure (of resources) while my Dimension table will store the task and resource details.

d) DECODE (expression, search, result [, search, result]… [, default])

d) All of the above

a) Multidimensional

Explanation:

Given a relation used for data analysis, we can identify some of its attributes as measure attributes, since they measure some value, and can be aggregated upon.Dimension attribute define the dimensions on which measure attributes, and summaries of measure attributes, are viewed.

Database marketing tool or application helps a user or marketing professional in determining the right tool or strategy for his valuable add campaign. This tool collects data from all sources and gives relevant information the specialist with their add campaign. It gives a complete picture to the developer.