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Data cleansing or data cleaning is the process of identifying and correcting (or removing) corrupt, inaccurate, or irrelevant records from a dataset, table, or database. It involves detecting incomplete, incorrect, or inaccurate parts of the data and then replacing, modifying, or deleting the affected data. [ 1 ]
Data sanitization methods are also applied for the cleaning of sensitive data, such as through heuristic-based methods, machine-learning based methods, and k-source anonymity. [ 2 ] This erasure is necessary as an increasing amount of data is moving to online storage, which poses a privacy risk in the situation that the device is resold to ...
Commercial proprietary software: OS X: Yes external [6]? Eraser: Heidi Computers Limited GNU GPL v3: Windows: Yes external [7]? HDDerase: University of California, San Diego: Freeware: OS independent, based on DOS: No internal [8]? hdparm: Mark Lord BSD license: Linux: Yes internal [9] not directly supported without scripting nwipe: Martijn van ...
The process of data exploration may result in additional data cleaning or additional requests for data; thus, the initialization of the iterative phases mentioned in the lead paragraph of this section. [31] Descriptive statistics, such as, the average or median, can be generated to aid in understanding the data.
Such deterioration affects architectural properties such as maintainability and comprehensibility which can lead to a complete re-development of software systems. [9] Code refactoring activities are secured with software intelligence when using tools and techniques providing data about algorithms and sequences of code execution. [10]
Extract, transform, load (ETL) is a three-phase computing process where data is extracted from an input source, transformed (including cleaning), and loaded into an output data container. The data can be collected from one or more sources and it can also be output to one or more destinations.
The cleanroom software engineering process is a software development process intended to produce software with a certifiable level of reliability. The central principles are software development based on formal methods, incremental implementation under statistical quality control, and statistically sound testing.
Data quality assurance is the process of data profiling to discover inconsistencies and other anomalies in the data, as well as performing data cleansing [17] [18] activities (e.g. removing outliers, missing data interpolation) to improve the data quality.