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Relational OLAP (ROLAP) serversThese are the intermediate servers that stand in among a relational back-end server and client front-end tools. They facilitate a relational or extended-relational DBMS to save and manage warehouse data, and OLAP middleware to provide the missing item.ROLAP servers involve optimization for each DBMS back end, implementation of aggregation navigation logic, and more tools and services. ROLAP technology tends to have higher scalability than MOLAP technology. The DSS server of Micro strategy, for instance, adopts the ROLAP techniques.ROLAP systems work generally from the data that resides in a relational database, where the base data and dimension tables ... Read More
In R, the divisibility of a number by a certain number can be found by using the modulus operator %%. If we want to check the divisibility of a set of numbers by a certain number then for loop will be used.Check out the below given examples to understand how it can be done.Example 1To check which numbers between 1 and 100 are divisible by 10, use the following code −for(x in 1:100){ + if(x%%10==0){ + print(x) + } + }If you execute the above given code, it generates the following output −[1] 10 ... Read More
OLAM stands for Online analytical mining. It is also known as OLAP Mining. It integrates online analytical processing with data mining and mining knowledge in multi-dimensional databases. There are several paradigms and structures of data mining systems.Various data mining tools must work on integrated, consistent, and cleaned data. This requires costly pre-processing for data cleaning, data transformation, and data integration. Thus, a data warehouse constructed by such pre-processing is a valuable source of high-quality information for both OLAP and data mining. Data mining can serve as a valuable tool for data cleaning and data integration.OLAM is particularly important for the ... Read More
Data warehouses and data marts are used in a broad area of applications. Business executives use the data in data warehouses and data marts to implement data analysis and create strategic decisions. In some firms, data warehouses are used as an integral element of a plan-execute-assess “closed-loop” feedback system for enterprise administration.Data warehouses are used widely in banking and financial services, consumer goods and retail distribution sectors, and controlled manufacturing, including demand-based production. Generally, the longer a data warehouse has been in use, the more it will have developed. This evolution takes place throughout various phases.Initially, the data warehouse is ... Read More
Data warehouses contain huge volumes of data. OLAP servers demand that decision support queries be acknowledged in the order of seconds. Thus, it is essential for data warehouse systems to provide highly effective cube computation techniques, access techniques, and query processing techniques.Efficient Computation of Data CubesAt the core of multidimensional data analysis is the efficient computation of aggregations across many sets of dimensions. In SQL terms, these aggregations are referred to as group-by’s. Each group-by can be represented by a cuboid, where the set of group-by’s forms a lattice of cuboids defining a data cube.There are three choices for data ... Read More
OLAP stands for On-Line Analytical Processing. OLAP is a categorization of software technology that authorizes analysts, managers, and executives to profit insight into information through quick, consistent, interactive access in a broad variety of possible views of information that has been transformed from raw data to reflect the real dimensionality of the enterprise as learned by the users.OLAP servers present business users with multidimensional data from data warehouses or data marts, without concerns regarding how or where the data are stored. The physical architecture and implementation of OLAP servers must consider data storage issues.There are three main types of OLAP ... Read More
Data Warehousing is a technique that is mainly used to collect and manage data from various sources to give the business a meaningful business insight. A data warehouse is specifically designed to support management decisions.In simple terms, a data warehouse refers to a database that is maintained separately from an organization’s operational databases. Data warehouse systems enables the integration of multiple application systems. They provide data processing by supporting a solid platform of consolidated, historical information for analysis.Data warehouses generalize and consolidate information in the multidimensional area. The construction of data warehouses includes data cleaning, data integration, and data transformation ... Read More
Practically, the scatterplots are well visualized on white background just like on white paper. If we want to create a scatterplot with white background and without gridlines using ggplot2 then we can apply classic theme to the plot.Check out the below given example to understand how it can be done.ExampleFollowing snippet creates a sample data frame −x
Data Warehouses usually have a three-level (tier) architecture that involves −The bottom tier is a warehouse database server that is relatively always a relational database system. Back-end tools and utilities are used to feed records into the bottom tier from operational databases or other external sources (including user profile data supported by external consultants).These tools and utilities implement data extraction, cleaning, and transformation (e.g., to merge the same data from multiple sources into a unified format), and load and refresh functions to update the data warehouse. The data are extracted using application program interfaces referred to as gateways.A gateway is ... Read More
If we have comma separated values that contains duplicate and unique values then we might want to find the number of unique values within each comma separated value. To find the unique values in comma separated strings stored in an R data frame column, we can use stri_extract_all_regex function of stringi package along with sapply function.Check out the below examples to understand how it can be done.Example 1Following snippet creates a sample data frame −x