Data Cleaning: Check Null Rule

Data Cleaning involves the use of simple domain knowledge, such as spell-check, to detect errors and make corrections.

Data Integration involves combining data from a single source into a coherent store.

ETL tools allow users to specify transformations through a command-line interface.

The Entity identification problem in Data Integration involves identifying aliens from multiple data sources.

Data cleaning involves adding noise to the data.

Data Reduction is one of the major tasks in Data Preprocessing.

Data integration combines data from various sources into a coherent data store like a data warehouse.

Check null rule specifies the use of numbers or mathematical formulas to indicate the null condition.

Data reduction can expand the size of the data by duplicating features.

Data transformation involves scaling data within a smaller range like $0.0$ to $1.0$.

Believability is a measure of data quality related to how trustable the data are correct.

Accuracy in data quality refers to the timeliness of the data update.

Data cleaning involves routines that work to fill in missing values, smooth noisy data, identify or remove outliers, and resolve inconsistencies.

Data preprocessing involves major tasks such as data cleaning, data manipulation, and data visualization.

One possible reason for faulty data is when users accidentally submit incorrect data values for mandatory fields.

Errors in data transmission can lead to faulty data.

Limited buffer size for coordinating synchronized data transfer is an example of a technology limitation that may lead to faulty data.

Data preprocessing only includes tasks like data cleaning and data integration.

Discretization involves mapping the entire set of values of a given attribute to a new set of replacement values.

Simple random sampling always performs better than stratified sampling in the presence of skewed data.

Normalization ensures that data is scaled to fall within a larger, specified range.

Data compression aims to obtain an expanded representation of the original data.

Stratified sampling involves drawing samples from each partition of the data set proportionally.

Attribute construction is a method in data transformation that involves adding noise to the data.

Data discretization can only be performed once on a given attribute.

Concept hierarchies in data warehouses facilitate drilling and rolling to view data in a single granularity.

Concept hierarchy generation for nominal data always requires explicit specification of a total ordering of attributes.

Data preprocessing includes tasks like data cleaning, data integration, and data reduction, but does not involve data transformation.

Data quality aspects include accuracy, consistency, and timeliness, but not interpretability.

Automatic generation of hierarchies for a set of attributes is done solely by analyzing the number of distinct values for each attribute.