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Handling errors in this manner is considered bad practice [1] and an anti-pattern in computer programming. In languages with exception handling support, this practice is called exception swallowing. Errors and exceptions have several purposes:
To aid in ensuring exception safety, C++ standard library developers have devised a set of exception safety levels, contractual guarantees of the behavior of a data structure's operations with regards to exceptions. Library implementers and clients can use these guarantees when reasoning about exception handling correctness. The exception ...
The implementation of exception handling in programming languages typically involves a fair amount of support from both a code generator and the runtime system accompanying a compiler. (It was the addition of exception handling to C++ that ended the useful lifetime of the original C++ compiler, Cfront. [18]) Two schemes are most common.
Data cleansing may also involve harmonization (or normalization) of data, which is the process of bringing together data of "varying file formats, naming conventions, and columns", [2] and transforming it into one cohesive data set; a simple example is the expansion of abbreviations ("st, rd, etc." to "street, road, etcetera").
VBA can, however, control one application from another using OLE Automation. For example, VBA can automatically create a Microsoft Word report from Microsoft Excel data that Excel collects automatically from polled sensors. VBA can use, but not create, ActiveX/COM DLLs, and later versions add support for class modules.
This page was last edited on 23 April 2004, at 12:33 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may ...
The output of a cryptographic hash function, also known as a message digest, can provide strong assurances about data integrity, whether changes of the data are accidental (e.g., due to transmission errors) or maliciously introduced. Any modification to the data will likely be detected through a mismatching hash value.
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]