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Process mining, especially process discovery, has been utilized to extract process models from event logs. One challenge faced by process discovery is to identify concurrency effectively. State-of-the-art approaches employ activity orders in traces to undertake process discovery and they require stringent completeness notions of event logs. Thus, they may fail to extract appropriate processes when...
Nowadays, mining approximate frequent itemsets from noisy data has attracted much attention in real applications. However, there is not widely accepted algorithm at present to solve the problem under noisy databases, which dues to two key issues. Firstly, the anti-monotonicity property does not hold which is used to prune candidate itemsets efficiently. And secondly, the computation of support counting...
This paper presents a chance constrained approach to extracting linear models from reference data to be used in subsequence identification or pattern matching. Due to the ordered nature of time series data, the extracted models are sequential, with feasible domains separated by transition points. In a sequence of models, a transition point is defined as the point where one model is invalid and the...
According to the situation that many workflow instances may deviate from the predefined model, this paper proposed a new process mining approach based on analyzing the workflow log to realize the workflow process reconstruction. First, build the Markov transition matrix based on the workflow log, then design a multi-step process mining algorithm to mine the structural relationships between the activities,...
This paper presents a new RANSAC based method for extracting planes from 3D range data. The generic RANSAC Plane Extranction (PE) method may over-extract a plane. It may fail in the case of a multi-step scene where the RANSAC process results in multiple inlier patches that form a slant plane straddling the steps. The CC-RANSAC algorithm overcomes the latter limitation if the inlier patches are separate...
Process mining has been widely applied in lots of fields, which can discover workflow models from event logs, helps to design or re-design process models, and brings convenience to workflow management system. The main function of process mining algorithms is to provide us with valuable objective information hidden in event logs, which plays a crucial important role on the implementation of new operation...
The Petaflow project aims to contribute to the use of high performance computational resources to the benefit of society. To this goal the emergence of adequate information and communication technologies with respect to high performance computing-networking-visualisation and their mutual awareness is required. The developed technology and algorithms will be applied to a real peta-scale data intensive...
Bayesian linear classifier is the basic scheme to solve model classification basing on statistics. Face with the classification of three different nectar plant, the near infrared spectrum data was acquired. The character of the near infrared spectrums is known as litter sample and higher dimension. In this paper, the method has developed to acquire the feature wavelength based on genetic algorithm...
Existing sequence mining algorithms mostly focus on mining for subsequences. However, a large class of applications, such as biological DNA and protein motif mining, require efficient mining of ??approximate?? patterns that are contiguous. The few existing algorithms that can be applied to find such contiguous approximate pattern mining have drawbacks like poor scalability, lack of guarantees in finding...
Data Stream mining (DSM) is claimed to be the successor of traditional data mining where it is capable of mining continuous incoming data streams in real-time with an acceptable performance. Nowadays many computer applications evolved to online and on-demand basis, fresh data are feeding in at high speeds. Not only a decision response needs to be made rapidly, the trained decision tree models would...
In this paper, we present a novel approach to clustering multivariate time series. In contrast to previous approaches, we base our cluster notion on the interactions between the univariate time series within a data object. Our objective is to assign objects with a similar intrinsic interaction pattern to a common cluster. To formalize this idea, we define a cluster by a set of mathematical models...
In this paper, a simple, robust algorithm is proposed for detection crest lines on triangular meshes with noise. We update principal curvature when computing it on noisy models, and use two thresholds to remove unessential ridge and ravine points to guarantee that ridge and ravine points are perceptually salient points. Experimental results demonstrate our algorithm is effective.
An anisotropic smoothing algorithm for removing noise of triangular mesh models is proposed. First, the desirable normals of the triangles are approximated using our desirable normal computing schemes, and then each vertex is repositioned according to desirable normals of adjacent triangular faces. The desirable normals estimated by a weighted sum of normals at neighboring faces. If the face differs...
In sequential decision making under uncertainty, as in many other modeling endeavors, researchers observe a dynamical system and collect data measuring its behavior over time. These data are often used to build models that explain relationships between the measured variables, and are eventually used for planning and control purposes. However, these measurements cannot always be exact, systems can...
This article presents an instrumental variable method dedicated to non-linear Hammerstein systems operating in closed loop. The linear process is a Box-Jenkins model and the non-linear part is a sum of known basis functions. The performance of the proposed algorithm is illustrated by a numerical example.
The subject area of this paper is discrete-time linear time-invariant systems composed of subsystems whose state updating is asynchronous due to the clock signal arriving with delays. This leads to the synchronization error identification problem which is to find from a trajectory of the system the order in which the subsystems' states are updated. A solution to this problem based on a direct search...
Coupling between two general interconnect structures, each viewed as the aggressor and victim sub-systems, respectively, are modeled with a 4-port network. A new method is introduced to estimate the coupling effects on the victim subsystem with known activities in the aggressor sub-system. It is verified with an idealized example, and is illustrated with a real-world PCB design scenario for noise...
Local perturbations around contours strongly disturb the final result of computer vision tasks. It is common to introduce a priori information in the estimation process. Improvement can be achieved via a deformable model such as the snake model. In recent works, the deformable contour is modeled by means of B-spline snakes which allows local control, concise representation, and the use of fewer parameters...
In recent years, several devices allow to directly measure real vector fields, leading to a better understanding of fundamental phenomena such as fluid simulation or brainwater movement. This turns vector field visualization and analysis important tools for many applications in engineering and in medicine. However, real data is generally corrupted by noise, puzzling the understanding provided by those...
A novel simulator is presented for interferometric synthetic aperture radar (InSAR), which contains three simulation levels: the raw signal level (RSL), the SLC image level (SIL) and the interferometric phase level (IPL). In this simulator, a new model of complex backscattering coefficients for the extended scene with low complexity is presented and validated. Furthermore the implementation details...
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