Traditional process models like Petri nets effectively describe the control flow of processes but fail to capture stochastic information such as choice likelihoods. To address this, Stochastic Labeled Petri Nets (SPNs) have recently gained attention, extending Petri nets with transition weights that allow to associate executions with probabilities. The language of an SPN thereby becomes a probability distribution over traces (i.e., sequences of activities). To assess an SPN’s quality, Earth Mover’s Stochastic Conformance (EMSC) emerged as a natural metric that measures the similarity of the SPN’s trace distribution to the observed real-world distribution. In this paper, we propose a locally optimal approach for fine-tuning (or finding) transitions weights to maximize an SPN’s EMSC. Leveraging the relationship between EMSC and the Wasserstein distance, which recently gained attention as a loss function in machine learning, we compute subgradients for EMSC to optimize transition weights via subgradient descent. Besides, we propose a straightforward solution to handle models that allow for infinitely many traces. Our optimization approach is broadly applicable for EMSC—that is, for EMSC using arbitrary trace-to-trace distances—unlike existing works that either to not explicitly consider EMSC or only special variants. We demonstrate the applicability of our approach on several real-life event logs and discovery algorithms, comparing it to state-of-the-art stochastic process discovery methods and a recent full automated simulation approach.

Modern business processes are embedded in a complex environment and, thus, subjected to continuous changes. While current approaches focus on the control flow only, additional perspectives, such as time, are neglected. In this paper, we investigate a more general concept drift detection framework that is based on the Earth Mover's Distance. Our approach is flexible in terms of incorporating additional perspectives thanks to the capability of defining custom feature representations, as well as expressive feature similarity measures. We demonstrate the former by incorporating the time perspective using both a time-binning-based trace descriptor and a suitable similarity measure that considers time and control flow. We evaluate the resulting sliding window detector on different types of control-flow and time drifts, and holistic drifts involving multiple perspectives.

This tutorial aims at providing a unified and comprehensive overview of the state-of-the-art approaches to distance-based multimedia indexing.

The rapid increase in generation and dissemination of online video data has recently raised the demand on efficient and effective query processing techniques in large video databases. In this paper, we first introduce a novel compact video representation model to achieve high effectiveness, and then propose to alleviate computational time complexity of the well-known Earth Mover’s Distance by introducing a filter approximation analyzing earth flows locally and restricting the number of flows globally, ensuring completeness .M oreover, extensive experimental evaluation performed on high dimensional real world datasets points out high efficiency and effectiveness of the proposals, significantly reducing the number of Earth Mover’s Distance computations and outperforming the state of the art by up to two orders of magnitude with respect to selectivity and query processing time.

With the continuous increase of heterogeneous multimedia data, the question of how to access big multimedia data efficiently has become of crucial importance. In order to provide fast access to complex multimedia data, we propose to approximate content-based features of multimedia objects by means of generative models. The proposed gradient-based signatures epitomize a high quality content-based approximation of multimedia objects and facilitate efficient indexing and query processing at large scale.

With the continuous rise of multimedia, the question of how to access large-scale multimedia databases efficiently has become of crucial importance. Given a multimedia database comprising millions of multimedia objects, how to approximate the content-based properties of the corresponding feature representations in order to carry out similarity search efficiently and with high accuracy? In this paper, we propose the concept of gradient-based signatures in order to aggregate content-based features of multimedia objects by means of generative models. We provide theoretical insights into our approach including closed-form expressions for the computation of gradient-based signatures with respect to Gaussian mixture models and additionally investigate different binarization methods for gradient-based signatures in order to query databases comprising millions of multimedia objects with high accuracy in less than one second.

The recent rapid growth of scientific data necessitates efficient similarity search techniques for which convenient object representation models are of vital importance. Feature signatures denoting highly flexible object feature representations have increasingly gained attention for which corresponding efficiency improvement techniques are developed. In this paper, we focus on efficient query processing with the well-known Earth Mover's Distance (EMD) on databases of feature signatures, and propose efficient approximation techniques successfully applicable to high-dimensional feature signatures via dimensionality reduction, guaranteeing both completeness and no false-dismissal within a filter-and-refine architecture. Rigorous experiments on real world data indicate a considerable reduction in the number of EMD computations and high efficiency of the proposed techniques which significantly reduce the query processing time.

The Earth Mover's Distance, proposed in computer vision as a distance-based similarity model reflecting the human perceptual similarity, has been widely utilized in numerous domains for similarity search applicable on both feature histograms and signatures. While efficiency improvement methods towards the Earth Mover's Distance were frequently investigated on feature histograms, not much work is known to study this similarity model on feature signatures denoting object-specific feature representations. Given a very large multimedia database of features signatures, how can k-nearest-neighbor queries be processed efficiently by using the Earth Mover's Distance? In this paper, we propose an efficient filter approximation technique to lower bound the Earth Mover's Distance on feature signatures by restricting the number of earth flows locally. Extensive experiments on real world data indicate the high efficiency of the proposal, attaining order-of-magnitude query processing time cost reduction for high dimensional feature signatures.