# Anomaly Monitoring of Process Based on Recurrent Timeliness Rules (AMP-RTR)
## 2 Related Work  
There are many well-known and important contributions to data timeliness assessment and they are also used in many real-world scenarios. First, we discuss the existing contributions from the perspective of timeliness of the data quality dimension. Then, we summarize the application scenarios of the existing timeliness evaluation methods. Finally, we determine the research direction according to the actual needs of the manufacturing industry. There are many different terms in the field of representing data timeliness: currency, timeliness, staleness, up-to-date, freshness, temporal validity. In the area of data timeliness, [PERSON] et al. [7] was one of the earliest and most famous contributions. They define a measure as a function that depends on the age of the attribute value at the time the currency is evaluated, the (fixed and given) shelf life of the attribute value, and the sensitivity parameters that make the measure suitable for the application environment. The value of this indicator depends on the average frequency with which attribute values (approximately)  
Figure 1: The AMP-RTR research flow diagram. are updated in practice and the time elapsed from the moment the currency is evaluated to the time the attribute values are obtained. [PERSON] and [PERSON] [8] propose another indicator in terms of functions that depend on the length of time between the moment the currency is evaluated and the moment the attribute value is obtained (\(t_{1}-t_{0}\)) or updated or confirmed (\(t_{1}-t_{0}\)). The main idea of their approach is to map measures across \([0-1]\), where a value of 1 represents a perfectly good currency and a value of 0 represent a totally bad currency. [PERSON] et al. [9] used volatility as an index of an exponential function that multiplies as a proportion factor of money, and volatility is the probability that data will be updated over time. [PERSON] and [PERSON] [10] proposed a measure based on conditional expectation and additional metadata, which produces interpretable and interval scaled measures. The classification methods used include support vector machines [11; 12; 13] and logistic regression [14] to deep neural networks [15]. All the above-mentioned authors have proposed a metric for currency in a general environment, and there are no specific scenarios. Of course, a lot of attempts and contributions have been made by predecessors in the field of data timeliness application. [PERSON], [PERSON] and [PERSON] [16] applied the timeliness index to a German financial service provider (FSP) to support the valuation of customers. [PERSON] [17] describes how to evaluate the data timeliness described in the literature in the research information system (RIS). [PERSON] and [PERSON] [18], by quantifying the timeliness of the data quality dimension, implemented efficient Customer Relationship Management (CRM) for the activities of a large mobile service provider. [PERSON] and [PERSON] [19] apply probability-based data currency assessment methods and event-driven mechanisms to reflect the likelihood that Wikipedia articles are still up-to-date (that is, no currency-related events change the state of the corresponding entity), so they can distinguish between current and past articles very well. [PERSON] et al. [20] explained the time impact through seasonal autoregressive comprehensive moving average (SARIMA) based on the time series model, and proposed a scheme to predict the popularity and evolution of video content. [PERSON] [21] proposed an exception detection framework MuSADET for timing exceptions found in real-time embedded system event tracking. MuSADET compares the frequency domain characteristics of the unknown trajectory with the normal model trained from the good execution of the system. This method uses static event generator, signal processing and distance measurement to classify the time series between arrivals as normal/abnormal, which can better detect the system exceptions. The \(AARD\) (Average Absolute Relative Deviations) method proposed by [PERSON] et al.