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Olap reports use multidimensional data stored in data warehouses, allowing for complex queries and analysis across various dimensions. They enable users to perform operations like slicing, dicing, and drilling down into data to uncover insights. The data is typically aggregated and pre-calculated, facilitating fast query performance and enabling users to view data from multiple perspectives. Additionally, OLAP reports are often designed to support decision-making processes by presenting historical and predictive analytics.
What else can I help you with?
Media reports can be evaluated for them and effectiveness is this true?
true
What are examples of true unclassified data?
True unclassified data includes publicly available information that is not subject to any restrictions or classification, such as government reports, academic research, and statistical data released by organizations like the U.S. Census Bureau. Other examples include news articles, social media posts, and general knowledge found in encyclopedias. This type of data can be freely accessed and shared without concerns about confidentiality or security.
What is multidimensional Data?
Multidimensional data refers to data that can be represented in multiple dimensions, allowing for complex analysis and insights. This type of data is often structured in a way that enables the exploration of relationships across various attributes or dimensions, such as time, geography, and product categories. It is commonly used in data warehousing and analytics, particularly in applications like OLAP (Online Analytical Processing), where users can navigate through the data in a more interactive and comprehensive manner. Multidimensional data structures, such as cubes, facilitate efficient querying and reporting.
What is data cube?
A data cube is a multi-dimensional array of values used to represent data in a way that enables efficient querying and analysis. It organizes data into dimensions (such as time, geography, and product) and measures (quantitative data like sales or profit), allowing users to perform operations like slicing, dicing, and aggregating information. This structure facilitates complex analytical queries and helps in visualizing data trends and patterns effectively. Data cubes are commonly used in online analytical processing (OLAP) applications.
Once you have collected your data the data needs to be analyzed?
true
What is the difference between OLAP and data mining?
difference between Data Mining and OLAP
What company created OLAP?
Nabisco has created the online analytical processing (OLAP) data mart
What is olap?
OLAP stands for Online Analytical Processing. An OLAP system can be considered as a category of applications and technologies that are used for collecting, managing, processing and presenting multi-dimensional data for analysis and management purposes.
What is the disadvantage of olap?
OLAP, and its reliance on the data warehousing environment, are two of the most significant new technology areas. Moreover, the use of relational design and relational database technology are not feasible implementations to support OLAP design because of the complexity of the queries. The business problem is that OLAP queries are not real-time queries because of the refresh cycle of data into the OLAP data repository. Conventional designs call for integration of data into an operational data store where it can be cleansed, transformed, extracted, & then loaded into the OLAP data repository. This is accomplished through the use of (ETL) tools. The ETL process is generally complicated because data must be integrated and transformed for loading into the nonnormalized relational schema usually associated with OLAP environments. As such, the process can be complicated and time consuming, and with large amounts of data may only occur at monthly or quarterly time intervals. This creates the problem of not having real-time data in the OLAP repository. Real-time data exists in the OLTP environment where the time horizon of data within the OLTP environment is much shorter because performance decreases can occur with growing amounts of data. This is opposite of the nature and goals of the OLAP environment where data is aggregated and the time horizon of data grows to some large amount as determined by the information life cycle policy of the organization. The main problems you have to face using OLAP as a source is that OLAP engines, in general, are designed to return small result sets from highly aggregated data, whereas data mining, in general, is designed to perform operations on large sets of raw (or preprocessed) data. The implementation of OLAP in Analysis Services, requires that all of the result set be materialized in memory before returning to the client. This generally isn't a big deal for typical OLAP queries, but if you are, for instance, trying to mine all of your transaction data for the past 10 years, you will run into difficulties, in short the data gathered may not be (relatively) recent enough to qualify as real-time data for business intelligence purposes.
Distinguish between DBMS and OLAP systems?
The issue is, what distinguishes relational database systems and multidimensional data base systems. It is certainly possible to have an OLAP DMBS, and indeed a number of them have been on the market in the past. The defining difference is how the data is stored. An OLAP system has specialized data structures for optimizing performance with multidimensional data. A relational system uses data tables and SQL to store data. An native OLAP system (a.k.a MOLAP) does not store the data in relational tables. ...At least not directly. For example Oracle embeds their MOLAP system into relational tables. That can make it confusion, but for simplicities sake, just consider, a conventional, relational DBMS stores data in tables and uses SQL, an OLAP system uses something else and a different language, depending on the vendor. Examples are store data in variables, use Oracle OLAP DML, store data in Microsoft Analysis Services, use MDX, Store data in Essbase, use MDX, etc. For detailed information on using a native OLAP system see "The Multidimensional Data Modeling Tool Kit" on Amazon.
Is it true or false that ICMP reports on the success or faliure of data delivery?
true
Why need of separate data warehouse?
3 Main reasons: 1.OLTP systems require high concurrency, reliability, locking which provide good performance for short and simple OLTP queries. An OLAP query is very complex and does not require these properties. Use of OLAP query on OLTP system degrades its performance. 2.An OLAP query reads HUGE amount of data and generates the required result. The query is very complex too. Thus special primitiveshave to provided to support this kind of data access. 3.OLAP systems access historical data and not current volatile data while OLTP systems access current up-to-date data and do not need historical data.
Explain the OLAP and OLTP system with example?
OLTP vs. OLAPWe can divide IT systems into transactional (OLTP) and analytical (OLAP). In general we can assume that OLTP systems provide source data to data warehouses, whereas OLAP systems help to analyze it.- OLTP (On-line Transaction Processing) is characterized by a large number of short on-line transactions (INSERT, UPDATE, DELETE). The main emphasis for OLTP systems is put on very fast query processing, maintaining data integrity in multi-access environments and an effectiveness measured by number of transactions per second. In OLTP database there is detailed and current data, and schema used to store transactional databases is the entity model (usually 3NF).- OLAP (On-line Analytical Processing) is characterized by relatively low volume of transactions. Queries are often very complex and involve aggregations. For OLAP systems a response time is an effectiveness measure. OLAP applications are widely used by Data Mining techniques. In OLAP database there is aggregated, historical data, stored in multi-dimensional schemas (usually star schema).
What does OLAP allow?
OLAP allows Nabisco to accurately track sales and consumer preferences, with the largest data mart holding sales, price discount, spoilage, transportation, and promotional information for two years
What is oltp and olap?
OLTP (Online Transaction Processing) refers to systems that manage transactional data in real-time, allowing for fast query processing and maintaining data integrity in multi-access environments. It is commonly used in applications like banking and retail for day-to-day operations. OLAP (Online Analytical Processing), on the other hand, is designed for complex queries and data analysis, enabling users to perform multidimensional analysis of business data. OLAP systems support decision-making processes by providing insights through data aggregation and historical analysis.
What is the Advantages of OLAP?
The primary advantage of OLAP data storage is better performance for accessing multidimensional data. OLAP systems are also accompanied by calculation engines and data manipulation languages. So a second advantage is that it gives analytical capabilities that are not in SQL or are more difficult to obtain. Finally, if you know how to use it, it is easier to work with multidimensional data in a multidimensional system. There are no table joins, storage is set up to include aggregates along with leaf level data, data is articulated in terms of functional categories (rather than rows and columns, or integer indexes), and so on. This is discussed in, The Multidimensional Data Modeling Toolkit, if you want more information.
What does the OLAP do?
The OLAP allows Nabisco to accurately track sales and consumer preferences
