Single Dimensional Generalized Kalman Filter

Khan, Zainullah; Bugti, Hairbyar; Bugti, Ali Sheraz

doi:10.1109/icecube.2018.8610960

Cited by 1 publication

(3 citation statements)

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“…measuring error) with a set of equations and consecutive data inputs (measurements). Kalman Filter eliminates these errors or variations from the data measurements and projects the measurements onto the estimate [6], [10]. A model of the procedure of a Kalman Filter is depicted in Fig.…”

Section: B One-dimensional Kalman Filtermentioning

confidence: 99%

“…One of its most famous applications is the moon landing [13]. Even today, Kalman Filter is used extensively in state estimation of linear and non-linear systems such as in the inertial navigation system of aeroplanes [13], in the tracking method of autonomous vehicles [13], in early failure prediction [22], and many more applications [10], [13].…”

Section: Related Workmentioning

confidence: 99%

“…This approach uses graph theory to determine all possible paths from the current state to an end state of a business process. With these possible paths, the estimation of a one-dimensional Kalman Filter [6], [7], [10], [13], [22] is used to predict the remaining path of a business process without utilising any log data. With this estimation, in turn, the current progress of a lifecycle can be determined.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A One-Dimensional Kalman Filter for Real-Time Progress Prediction in Object Lifecycle Processes

Arnold¹,

Breitmayer²,

Reichert³

2021

2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW)

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Real-time monitoring of business processes offers promising perspectives to discover problems and optimisation potentials. Early detection is a key part in this endeavour. One crucial aspect of real-time monitoring is to determine the current progress of a running business process. This is particularly challenging for business processes that consist of a multitude of loosely coupled, smaller processes that interact with each other, like object lifecycle processes in data-centric approaches to business process management. In this paper, an approach to predict the remaining portion of the process path to be still executed in relation to the overall process is proposed. This prediction is based on a one-dimensional Kalman Filter. As a major benefit of this approach, real-time progress determination can start directly with the first run of the process, i.e., without need for comprehensive event log data. This becomes possible due to the procedure applied by the Kalman Filter, which requires no log data. A quantitative study with 250 progress estimations for large object lifecycle processes results in a deviation of the average estimated progress from the real progress, calculated after the completion of the process, of about 5%. This emphasises that reasonable progress predictions are possible even in the absence of an event log, as it is the case when deploying new or changed processes to the run-time system.

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Section: B One-dimensional Kalman Filtermentioning

confidence: 99%