WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GPLVM) to determine the latent-space locations of unlabeled signal strength data. We show how GPLVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization.

WiFi-SLAM Using G aussian Process Latent Variable Models

Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated process discovery. An automated process discovery method takes as input an event log, and produces as output a business process model that captures the control-flow relations between tasks that are observed in or implied by the event log. Various automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy, and complexity of the resulting models. However, these methods have been evaluated in an ad-hoc manner, employing different datasets, experimental setups, evaluation measures, and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of closed datasets. This article provides a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark and covering 12 publicly-available real-life event logs, 12 proprietary real-life event logs, and nine quality metrics. The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of some methods and a strong divergence in their performance with respect to the different quality metrics used.

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Automated Discovery of Process Models from Event Logs: Review and Benchmark

This study aims to explore the current status, potential applications, and future directions of blockchain technology in supply chain management. A literature survey, along with an analytical review, of blockchain-based supply chain research was conducted to better understand the trajectory of related research and shed light on the benefits, issues, and challenges in the blockchain-supply-chain paradigm. A selected corpus comprising 106 review articles was analyzed to provide an overview of the use of blockchain and smart contracts in supply chain management. The diverse industrial applications of these technologies in various sectors have increasingly received attention by researchers, engineers, and practitioners. Four major issues: traceability and transparency, stakeholder involvement and collaboration, supply chain integration and digitalization, and common frameworks on blockchain-based platforms, are critical for future orientation. Traditional supply chain activities involve several intermediaries, trust, and performance issues. The potential of blockchain can be leveraged to disrupt supply chain operations for better performance, distributed governance, and process automation. This study contributes to the comprehension of blockchain applications in supply chain management and provides a blueprint for these applications from the perspective of literature analysis. Future efforts regarding technical adoption/diffusion, block-supply chain integration, and their social impacts were highlighted to enrich the research scope.

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When Blockchain Meets Supply Chain: A Systematic Literature Review on Current Development and Potential Applications

Simulation: The Practice of Model Development and Use

Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated process discovery. An automated process discovery method takes as input an event log, and produces as output a business process model that captures the control-flow relations between tasks that are observed in or implied by the event log. Various automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy and complexity of the resulting models. However, these methods have been evaluated in an ad-hoc manner, employing different datasets, experimental setups, evaluation measures and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of closed datasets. This article provides a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark and covering twelve publicly-available real-life event logs, twelve proprietary real-life event logs, and nine quality metrics. The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of some methods and a strong divergence in their performance with respect to the different quality metrics used.

Process discovery is one of the main branches of process mining that allows the user to build a process model representing the process behavior as recorded in the logs. Standard process discovery techniques produce as output a procedural process model (e.g., a Petri net). Recently, several approaches have been developed to derive declarative process models from logs and have been proven to be more suitable to analyze processes working in environments that are less stable and predictable. However, a large part of these techniques are focused on the analysis of the control flow perspective of a business process. Therefore, one of the challenges still open in this field is the development of techniques for the analysis of business processes also from other perspectives, like data, time, and resources. In this paper, we present a full-fledged approach for the discovery of multi-perspective declarative process models from event logs that allows the user to discover declarative models taking into consideration all the information an event log can provide. The approach has been implemented and experimented in real-life case studies.

/pdf/discovery-of-multi-perspective-declarative-process-models-4ts25ip8ed.pdf

Discovery of Multi-perspective Declarative Process Models

Process mining aims at discovering processes by extracting knowledge from event logs. Such knowledge may refer to different business process perspectives. The organisational perspective deals, among other things, with the assignment of human resources to process activities. Information about the resources that are involved in process activities can be mined from event logs in order to discover resource assignment conditions, which is valuable for process analysis and redesign. Prior process mining approaches in this context present one of the following issues: (i) they are limited to discovering a restricted set of resource assignment conditions; (ii) they do not aim at providing efficient solutions; or (iii) the discovered process models are difficult to read due to the number of assignment conditions included.In this paper we address these problems and develop an efficient and effective process mining framework that provides extensive support for the discovery of patterns related to resource assignment. The framework is validated in terms of performance and applicability. A process mining approach for the organisational perspective is proposed.It supports the discovery of resource assignment patterns and how involvement of resources influences the control-flow.The framework consists of an event log pre-processing phase to increase efficiency.A model post-processing phase improves effectiveness by removing redundant rules.

/pdf/a-framework-for-efficiently-mining-the-organisational-fx19ugvd99.pdf

A framework for efficiently mining the organisational perspective of business processes

Flexible business processes can often be modelled more easily using a declarative rather than a procedural modelling approach. Process mining aims at automating the discovery of business process models. Existing declarative process mining approaches either suffer from performance issues with real-life event logs or limit their expressiveness to a specific set of constaint types. Lately, RelationalXES, a relational database architecture for storing event log data, has been introduced. In this paper, we introduce a mining approach that directly works on relational event data by querying the log with conventional SQL. By leveraging database performance technology, the mining procedure is fast without limiting itself to detecting certain control-flow constraints. Queries can be customised and cover process perspectives beyond control flow, e.g., organisational aspects. We evaluated the performance and the capabilities of our approach with regard to several real-life event logs.

/pdf/efficient-and-customisable-declarative-process-mining-with-4q9dj7b3he.pdf

Efficient and Customisable Declarative Process Mining with SQL

Agile processes depend on human resources, decisions and expert knowledge, and they are especially versatile and comprise rather complex scenarios. Declarative, i.e., rule-based, process models are well-suited for modelling these processes. Although there are several mining techniques to discover such declarative process models from event logs, they put less emphasis on the organisational perspective, which specifies how resources are involved in the activities. As a consequence, the resulting models do not specify who should execute which task and with which constraint (like separation of duties) in mind. In this paper, we propose a process mining approach to discover resource-aware, declarative process models. Our specific contribution is the extraction of complex rules for resource assignment that integrate the control-flow and organisational perspectives. Our experiments demonstrate the expressiveness of the mined rules with a reference to the Workflow Resource Patterns and a real-world use case.

Mining the Organisational Perspective in Agile Business Processes

The spectrum of an organization's business processes ranges from routine processes with a well-defined flow to agile processes with a degree of uncertainty. The Process Navigation platform aims at supporting both types of processes as well as combinations of them. It offers execution support for traditional flow-oriented notations like BPMN as they are well-suited for the routine type of processes. Rule-based notations for agile processes like CMMN are on the way of getting established but still have a number of weaknesses. As a consequence, the platform's agile part does not target one single notation but relies on a rule-based cross-perspective and modal intermediate language. CMMN models are then translated to the intermediate language for execution. The contribution of this paper is built up in three parts: first of all, the overall architecture of the execution platform is explained. In a second step, the intermediate language is evaluated on the basis of a comprehensive and acknowledged framework of business process requirements. And finally, the translation of CMMN to the intermediate language is described by means of an example.

/pdf/towards-a-common-platform-for-the-support-of-routine-and-4vsyzashmp.pdf

Stefan Schönig

Papers

Discovery of Multi-perspective Declarative Process Models

A framework for efficiently mining the organisational perspective of business processes

Efficient and Customisable Declarative Process Mining with SQL

Mining the Organisational Perspective in Agile Business Processes

Towards a common platform for the support of routine and agile business processes