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Anomaly detection is crucial in the petroleum industry for monitoring critical machinery. [20] Martí et al. used a novel segmentation algorithm to analyze sensor data for real-time anomaly detection. [20] This approach helps promptly identify and address any irregularities in sensor readings, ensuring the reliability and safety of petroleum ...
An insertion anomaly. Until the new faculty member, Dr. Newsome, is assigned to teach at least one course, their details cannot be recorded. An update anomaly. Employee 519 is shown as having different addresses on different records. A deletion anomaly. All information about Dr. Giddens is lost if they temporarily cease to be assigned to any ...
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").
In anomaly detection, the local outlier factor (LOF) is an algorithm proposed by Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng and Jörg Sander in 2000 for finding anomalous data points by measuring the local deviation of a given data point with respect to its neighbours.
The modified Thompson Tau test is a method used to determine if an outlier exists in a data set. [23] The strength of this method lies in the fact that it takes into account a data set's standard deviation, average and provides a statistically determined rejection zone; thus providing an objective method to determine if a data point is an outlier.
In order not to fill the data store with useless information, there is a policy to remove tombstones completely. For this, the system checks the age of the tombstone and removes it after a prescribed time has elapsed. In Apache Cassandra, this elapsed time is set with the GCGraceSeconds parameter [1] and the process is named Compaction. [2]
Denormalization is a strategy used on a previously-normalized database to increase performance. In computing, denormalization is the process of trying to improve the read performance of a database, at the expense of losing some write performance, by adding redundant copies of data or by grouping data.
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