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The document discusses multidimensional databases and data warehousing. It describes multidimensional databases as optimized for data warehousing and online analytical processing to enable interactive analysis of large amounts of data for decision making. It discusses key concepts like data cubes, dimensions, measures, and common data warehouse schemas including star schema, snowflake schema, and fact constellations.
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1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
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- 5. Model for data management where the databases are developed according to user's preferences, in order to be used for specific types of retrievals. Multidimensional database (MDB) is mainly optimized for data warehouse and online analytical processing (OLAP) applications
- 6. Multidimensional data-base technology is a key factor in the interactive analysis of large amounts of data for decision-making purposes MDB mainly useful for sales and marketing applications that involve time series.
- 8. Enables interactive analyses of large amounts of data for decision-making purposes Rapidly process the data in the database so that answers can be generated quickly. Provides “just-in-time” information for effective decision-making in a successful OLAP application View data as multidimensional cubes , which have been particularly well suited for data analyses Enforces simplicity
- 11. Data Cube Model Star Schema Model Snow Flake Schema Model Fact Constellations Schema Model (Global Schema)
- 13. Data is grouped or combined together in multidimensional matrices called Data Cubes. In two Dimension :- row & column or products. In three Dimension :- one regions, products and fiscal quarters.
- 14. data cubes have categories of data called dimensions and measures. measure ◦ represents some fact (or number) such as cost or units of service. dimension ◦ represents descriptive categories of data such as time or location.
- 16. Slicing : Refers to two- dimensional page selected from the cube. Dicing : Dicing provides you the smallest available slice. Define a sub-cube of the original space. Rotation : Rotating changes the dimensional orientation of the report from the cube data.
- 19. It is the simplest form of data warehousing schema. It consists one large central table (fact) containing the bulk of data and a set of smaller dimension tables one for each dimension . Its entity relationship diagram between dimensions and fact table resembles a star where one fact table is connected to multiple dimensions or table.
- 22. It is difficult from a star schema . In this dimensional table are organized into hierarchy by normalization them. The Snow Flake Schema is represented by centralized fact table which are connected to multiple dimensions.
- 25. It is a set of fact tables that shares some dimensional tables. It limits the possible queries for the data warehouse.