Attribute generalization depends on the following rule: If there is a huge collection of distinct values for an attribute in the original working relation, and there exists a group of generalization operators on the attribute, thus a generalization operator should be choose and utilized to the attribute.This rule depends on the following reasoning. The use of a generalization services to generalize an attribute value inside a tuple, or rule, in the working relation will create the rule cover more of the initial data tuples, therefore generalizing the concept it defines. This corresponds to the generalization rule defined as climbing generalization ... Read More

AOI stands for Attribute-Oriented Induction. The attribute-oriented induction approach to concept description was first proposed in 1989, a few years before the introduction of the data cube approach. The data cube approach is essentially based on materialized views of the data, which typically have been pre-computed in a data warehouse.In general, it implements off-line aggregation earlier an OLAP or data mining query is submitted for processing. In other words, the attribute-oriented induction approach is generally a query-oriented, generalization-based, on-line data analysis methods.The general idea of attribute-oriented induction is to first collect the task-relevant data using a database query and then ... Read More

Data generalization summarizes data by replacing relatively low-level values (such as numeric values for an attribute age) with higher-level concepts (such as young, middleaged, and senior). Given the high amount of data saved in databases, it is beneficial to be able to define concepts in concise and succinct terms at generalized (rather than low) methods of abstraction.It is allowing data sets to be generalized at multiple levels of abstraction facilitates users in examining the general behavior of the data. Given the AllElectronics database, for instance, rather than examining single customer transactions, sales managers can prefer to view the data generalized ... Read More

The curse of dimensionality and the need for understandable results pose serious challenges for finding an efficient and scalable solution to the cubegrade problem. It can be confined but interesting version of the cubegrade problem, called constrained multidimensional gradient analysis. It can reduces the search space and derives interesting results.There are the following types of constraints which are as follows −Significance constraint − This provide that it can test only the cells that have specific “statistical significance” in the data, including containing at least a defined number of base cells or at least a specific total sales. In the data ... Read More

There are three measures are used as exception indicators to support recognize data anomalies. These measures denotes the degree of surprise that the quantity in a cell influence, concerning its expected value.The measures are computed and associated with every cell, for all levels of aggregation. They are as follows including the SelfExp, InExp, and PathExp measures are based on a numerical approaches for table analysis.A cell value is treated an exception depends on how much it differs from its expected value, where its expected value is decided with a statistical model. The difference among a given cell value and its ... Read More

Discovery-driven exploration is such a cube exploration approach. In discovery-driven exploration, precomputed measures indicating data exceptions are used to guide the user in the data analysis process, at all levels of aggregation. It refer to these measures as exception indicators.Intuitively, an exception is a data cube cell value that is significantly different from the value anticipated, based on a statistical model. The model treated variations and patterns in the measure value across all of the dimensions to which a cell apply.For instance, if the analysis of item-sales data acknowledge an increase in sales in December in comparison to several months, ... Read More

Data Warehousing is an approach that can collect and manage information from multiple sources to support the business a significant business insight. A data warehouse is specifically created for the goals of support management decisions.A data warehouse defines a database that is maintained separately from a company operational databases. Data warehouse systems enable the integration of several application systems. They support data processing by supporting a solid platform of consolidated, historical records for analysis.A data warehouse can be considered as a group of materialized views defined over remote base areas. When a query is formal, it is computed locally, using ... Read More

Data Warehousing is an approach that can collect and handle data from multiple sources to provide the business a significant business insight. A data warehouse is specifically created for the goals of support management decisions.In simple terms, a data warehouse defines a database that is maintained independently from an organization’s operational databases. Data warehouse systems enable the integration of several application systems. They provide data processing by supporting a solid platform of consolidated, historical data for analysis.A data warehouse supports an OLTP system by providing a place for the OLTP database to offload records as it accumulates, and by providing ... Read More

Measures can be organized into three elements including distributive, algebraic, and holistic. It depends on the type of aggregate functions used.Distributive − An aggregate function is distributive if it can be calculated in a delivered manner as follows. Consider the data are independent into n sets. It can use the service to each partition, resulting in n aggregate values.If the result changed by using the function to the n aggregate values is the same as that derived by using the function to the whole data set (without partitioning), the function can be evaluated in a distributed way.For instance, count() can ... Read More

Entropy-based discretization is a supervised, top-down splitting approach. It explores class distribution data in its computation and preservation of split-points (data values for separation an attribute range). It can discretize a statistical attribute, A, the method choose the value of A that has the minimum entropy as a split-point, and recursively divisions the resulting intervals to appear at a hierarchical discretization.Specific discretization forms a concept hierarchy for A. Let D includes data tuples described by a group of attributes and a class-label attribute. The class-label attribute supports the class data per tuple. The basic approach for the entropy-based discretization of ... Read More