0
difference between Data Mining and OLAP
Kobe Considine
Add your answer:
Earn +20 pts
What is the difference between Microsoft SSAS and SPSS?
Microsoft SSAS (SQL Server Analysis Services) and SPSS(Statistical Package for the social sciences) are two different software tools designed for different purposes. Let's discuss the differences between them. Microsoft SSAS is an analytical data engine provided by Microsoft as part of the SQL Server suite. It is used for creating and managing online analytical processing (OLAP). SSAS enables multidimensional and tabular data analysis and provides features for data modelling, data aggregation, and advanced calculations. It is typically used for business intelligence and data warehousing applications, allowing users to analyze large volumes of data and gain insights for decision-making. SPSS is a software package primarily used for statistical analysis and data management in social science research. It provides a comprehensive set of tools and techniques for data exploration, descriptive statistics, hypothesis testing, regression analysis, and more. SPSS offers a user-friendly interface that allows researchers to import data, perform statistical analyses, and generate reports or visualisations of the results. In summary, the main difference between Microsoft SSAS and SPSS is their primary purpose and functionality. SSAS is focused on creating OLAP cubes and data mining models for business intelligence and data warehousing, while SPSS is dedicated to statistical analysis and data management in social science research.
What is the difference between support and confidence data mining?
supoort meand the uninon of probhabilty of A union B and confidance is the probhability of B divide by A
Difference between Online analytical processing and data mining?
online analytical processing uses basic operations such as slice and dice drilldown and roll up on historical data in order to provide multidimensional analysis of data data mining uses knowledge discovery to find out hidden patterns and association constructing analytical models and presenting mining results with visualization tools.
Difference between data mining and database?
Data mining is the process of extracting hidden patterns from data. As more data is gathered, with the amount of data doubling every three years,[1] data mining is becoming an increasingly important tool to transform this data into information. It is commonly used in a wide range of profiling practices, such as marketing, surveillance, fraud detection and scientific discovery. Database is just the system that holds all the data. Or: "A database is a structured collection of records or data that is stored in a computer system." http://en.wikipedia.org/wiki/Database http://en.wikipedia.org/wiki/Data_mining
How do you put data mining in a sentence?
Data mining can uncover interesting patterns. Some cookies will upload solely for the purpose of data mining.
Distinguish between DBMS and OLAP systems?
DBMS (Database Management System) is software that manages and organizes data in a structured way for efficient storage and retrieval. OLAP (Online Analytical Processing) systems are used for complex data analysis and reporting by providing multidimensional views of data for decision-making purposes. DBMS focuses on data storage and retrieval, while OLAP focuses on data analysis and reporting.
Distinguish between Data mining and text mining?
mining the data is called data mining. Mining the text is called text mining
What is the difference between online transaction processing and online analytical processing?
OLTP : customer oriented. OLAP : Market oriented OLTP : ER based application oriented concern OLAP : subject oriented concern. Current data : Historical data used for detailed for decesion making Access patterns are short. : COCancelMPLEX
What company created OLAP?
Nabisco has created the online analytical processing (OLAP) data mart
What are the key relationships between data warehouse and data mining?
Data warehouse is the database on which we apply data mining.
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.
Difference between discrimination and classification in datamining?
There is only a slight difference between discrimination and classification in data mining. Discrimination can be negative and classification is generally just factual.
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 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 difference between Microsoft SSAS and SPSS?
Microsoft SSAS (SQL Server Analysis Services) and SPSS(Statistical Package for the social sciences) are two different software tools designed for different purposes. Let's discuss the differences between them. Microsoft SSAS is an analytical data engine provided by Microsoft as part of the SQL Server suite. It is used for creating and managing online analytical processing (OLAP). SSAS enables multidimensional and tabular data analysis and provides features for data modelling, data aggregation, and advanced calculations. It is typically used for business intelligence and data warehousing applications, allowing users to analyze large volumes of data and gain insights for decision-making. SPSS is a software package primarily used for statistical analysis and data management in social science research. It provides a comprehensive set of tools and techniques for data exploration, descriptive statistics, hypothesis testing, regression analysis, and more. SPSS offers a user-friendly interface that allows researchers to import data, perform statistical analyses, and generate reports or visualisations of the results. In summary, the main difference between Microsoft SSAS and SPSS is their primary purpose and functionality. SSAS is focused on creating OLAP cubes and data mining models for business intelligence and data warehousing, while SPSS is dedicated to statistical analysis and data management in social science research.
What types of business intelligence software are available?
The most common types of business intelligence software are spreadsheets, reporting and querying software, OLAP, digital dashboards, data mining, data warehousing, decision engineering, process mining, business performance management, and local information systems. Each suits specific needs of a business or individual.
What is the difference between support and confidence data mining?
supoort meand the uninon of probhabilty of A union B and confidance is the probhability of B divide by A
