Process mining: A recent framework for extracting a model from event logs

Business Process Management (BPM) is a well-known discipline, with roots in previous theories related with optimizing management and improving businesses results. One can trace BPM back to the beginning of this century, although it was in more recent years when it gained a special focus of attention. Usually, traditional BPM approaches start from top and analyse the organization according some known rules from its structure or from the type of business. Process Mining (PM) is a completely different approach, since it aims to extract knowledge from event logs, which are widely present in many of today’s organizations. PM uses specialized data-mining algorithms, trying to uncover patterns and trends in these logs, and it is an alternative approach where formal process specification is not easily obtainable or is not cost-effective. This paper makes a literature review of major works issued about this theme.(undefined

AVERAGE MONTHLY RAINFALL FORECAST IN ROMANIA BY USING k-NEAREST NEIGHBORS REGRESSION

The discovery of the best strategies for achieving future values forecast of a time series represents a permanent concern in time series analysis, highly motivated from a theoretical point of view, but especially from a practical point of view.In the context of the explosive growth of machine learning techniques, their usein time series forecast is a natural step to find modern alternatives to overcome existing limitations of traditional techniques. Although it is a relatively a simple method of learning, (k-nearest neighbor) regression seems to be a good competitor to traditional methods.The purpose of this paper is to describe how to use this method for forecasting time series and for achieving Monthly Average Rainfall (AMR) forecast in Romania

Design of an Online Optimisation Tool for Smart Home Heating Control

The performance of model predictive smart home heating control (SHHC) heavily depends on the accuracy of the initial setup for individual building characteristics. Since owners or renters of residential buildings are predominantly not experts, users’ acceptance of SHHC requires ease of use in the setup and minimal user intervention (e.g. only declaration of preferences), but at the same time high reliability of the initial parameter settings and flexibility to handle different preferences. In contrast, the training time of self-learning SHHC (e.g. based on artificial neural networks) to reach a reliable control status could conflict with the users’ request for comfortable heating from the very beginning. Dealing with this trade-off, this paper follows the tradition of design science research and presents a prototype of an online optimisation tool (OOT) for SHHC. The OOT is multi objective (e.g. minimising lifecycle energy (cost) or carbon emissions) under constraints such as thermal comfort. While the OOT is based on a discrete dynamic model, its self-adaptation is accelerated by a database of physically simulated characteristic buildings, which allows parameter setting at the beginning by a similarity measurement. The OOT artefact provides a base for empirically testing advantages of different SHHC design alternatives

Predicting lorawan behavior. How machine learning can help

Large scale deployments of Internet of Things (IoT) networks are becoming reality. From a technology perspective, a lot of information related to device parameters, channel states, network and application data are stored in databases and can be used for an extensive analysis to improve the functionality of IoT systems in terms of network performance and user services. LoRaWAN (Long Range Wide Area Network) is one of the emerging IoT technologies, with a simple protocol based on LoRa modulation. In this work, we discuss how machine learning approaches can be used to improve network performance (and if and how they can help). To this aim, we describe a methodology to process LoRaWAN packets and apply a machine learning pipeline to: (i) perform device profiling, and (ii) predict the inter-arrival of IoT packets. This latter analysis is very related to the channel and network usage and can be leveraged in the future for system performance enhancements. Our analysis mainly focuses on the use of k-means, Long Short-Term Memory Neural Networks and Decision Trees. We test these approaches on a real large-scale LoRaWAN network where the overall captured traffic is stored in a proprietary database. Our study shows how profiling techniques enable a machine learning prediction algorithm even when training is not possible because of high error rates perceived by some devices. In this challenging case, the prediction of the inter-arrival time of packets has an error of about 3.5% for 77% of real sequence cases

Predicting lorawan behavior. How machine learning can help

Large scale deployments of Internet of Things (IoT) networks are becoming reality. From a technology perspective, a lot of information related to device parameters, channel states, network and application data are stored in databases and can be used for an extensive analysis to improve the functionality of IoT systems in terms of network performance and user services. LoRaWAN (Long Range Wide Area Network) is one of the emerging IoT technologies, with a simple protocol based on LoRa modulation. In this work, we discuss how machine learning approaches can be used to improve network performance (and if and how they can help). To this aim, we describe a methodology to process LoRaWAN packets and apply a machine learning pipeline to: (i) perform device profiling, and (ii) predict the inter-arrival of IoT packets. This latter analysis is very related to the channel and network usage and can be leveraged in the future for system performance enhancements. Our analysis mainly focuses on the use of k-means, Long Short-Term Memory Neural Networks and Decision Trees. We test these approaches on a real large-scale LoRaWAN network where the overall captured traffic is stored in a proprietary database. Our study shows how profiling techniques enable a machine learning prediction algorithm even when training is not possible because of high error rates perceived by some devices. In this challenging case, the prediction of the inter-arrival time of packets has an error of about 3.5% for 77% of real sequence cases

Forecasting South Africa’s inflation rate using deep neural networks

Mini Dissertation (MSc eScience)--University of Pretoria, 2022.Inflation forecasting is crucial for efficient monetary policy and decision-making in an economy. This paper examines the feasibility of including deep neural networks in the macroeconomic forecasting toolbox for the South African economy. This study focuses on South Africa’s annual headline inflation rate and applies two different deep neural network architectures for forecasting. The deep neural network’s performance is compared to the autoregressive integrated moving average (ARIMA) benchmark, where root mean squared error (RMSE) is used as a performance measure. The results show that the multiple layer perceptron (MLP) outperformed the benchmark and its peer, the convolutional recurrent neural network model. Admittedly, the convolutional long-short term memory network (CNN-LSTM) is sensitive to architectural design, especially when the amount of training data is in short supply. In conclusion, the study finds that the ARIMA model predicts inflation inconsistently in the presence of endogenous and exogenous structural breaks in the time series and consequently gives non-unique forecasts. The MLP becomes a viable addition to the macroeconomic forecasting toolbox in such a case.DSI-NICIS National e-Science Postgraduate Teaching and Training Platform (NEPTTP)EconomicsMSc eScienceUnrestricte

A Risk Analysis and Data Driven Approach to Combating Sex Trafficking

Sex trafficking is a heinous criminal act that compels victims in the United States and worldwide to perform commercial sex acts through force, fraud, coercion, or age (TVPA, 2000). This dissertation takes a risk-analysis and data-driven approach to attain a better understanding of the problem, with the goal of showing that such an approach can help comprehend misallocation of resources, reform policy, reinforce social services, or support populations vulnerable to sex trafficking. Sex trafficking is a complex problem and must be studied both qualitatively and quantitatively in order to provide those in a position of influence with an improved basis for decision-making. In Chapter 2 of this dissertation, I outline the risks associated with sex trafficking and suggest that risk analysis tools can be useful for antitrafficking efforts, as they can provide context-sensitive, empirical knowledge as well as a way to communicate neutrally about a charged topic. Building on the understanding of this complex crime, in Chapter 3 I analyze online commercial sex work advertisements to draw conclusions about the COVID-19 pandemic’s impact on sex trafficking, showing a measurable impact of the pandemic-related stay-at-home orders on advertising, and likely on the vulnerability of at-risk populations to trafficking. Finally, in Chapter 4 I use data collected by myself and a collaborator on sex work advertisements as a basis to explore three quantitative methods for detecting anomalies in time-series data. Based on the results of this sex trafficking case study, I evaluate the benefits and drawbacks of each method for risk-based decision-makers and discuss how these methods can be integrated into a broader risk framework. This dissertation contributes to the field of sex trafficking research by offering improved methods for detecting anomalous behaviors in the system and advancing the application of these techniques for the risk analysis community. Although they are specifically designed for sex trafficking, analysts can apply these methods to many of the risk-related challenges of our future.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/170041/1/juliaoh_1.pd

A Comprehensive Techno-Economic Framework for Shale Gas Exploitation and Distribution in the United States

Over the past years, shale gas has turned into one of the most significant sources of energy in the United States. Technological advancements have provided the energy industry with the necessary tools to allow the economic exploitation of an enormous volume of natural gas trapped in shale formations. This has boosted the domestic gas production and generated a boom in other sectors of the economy in the country. However, major challenges are involved in the development of shale gas resources. A drastic decline of wells’ productivity, the costs involved in the gas production and distribution facets, and the volatile behavior of the energy market represent some of the complexities faced by a gas operator. In this context, the utilization of a comprehensive frameworks to analyze and develop long-term strategies can represent a meaningful supporting tool for shale gas operators. The main objective of this research work is the development and implementation a novel techno-economic framework for the optimal exploitation and delivery of shale gas in the United States. The proposed framework is based on an interdisciplinary approach that combines data driven techniques, petroleum engineering practices, reservoir simulations and mathematical programming methods. Data analysis algorithms are implemented to guide the decision-making processes involved in the unconventional reservoir and define the predominant trends of certain exogenous parameters of the system. Petroleum engineering practices and reservoir simulation models are required for a realistic description of the formations and the proper definition of strategies to extract the gas from the shale rock. Finally, the mathematical programming is required for describing the surface facilities design and operations to ensure the allocation of the shale gas in the different commercialization points. The output of this framework will provide the optimal operations and infrastructure by maximizing the net present value (NPV). To demonstrate the efficacy of the proposed decision-making structure, a case study based on the liquid-rich region of the Marcellus play is considered in this work. The application of the proposed framework depicts the influence of reservoir complexities and external factors in establishing optimal strategic decisions for the exploitation, processing and allocation of shale gas. The coordination of the different facets including the drilling and completion activities and the design and operation of the surface facilities has a key role in maintaining the economy of a shale gas venture above its economic threshold

Sustainability Reporting Process Model using Business Intelligence

Sustainability including the reporting requirements is one of the most relevant topics for companies. In recent years, many software providers have launched new software tools targeting companies committed to implementing sustainability reporting. But it’s not only companies willing to use their Business Intelligence (BI) solution, there are also basic principles such as the single source of truth and tendencies to combine sustainability reporting with the financial reporting (Integrated Reporting) The IT integration of sustainability reporting has received limited attention by scientific research and can be facilitated using BI systems. This has to be done both to anticipate the economic demand for integrated reporting from an IT perspective as well as for ensuring the reporting of revisable data. Through the adaption of BI systems, necessary environmental and social changes can be addressed rather than merely displaying sustainability data from additional, detached systems or generic spreadsheet applications. This thesis presents research in the two domains sustainability reporting and Business Intelligence and provides a method to support companies willing to implement sustainability reporting with BI. SureBI presented within this thesis is developed to address experts from both sustainability and BI. At first BI is researched from a IT and project perspective and a novel BI reporting process is developed. Then, sustainability reporting is researched focusing on the reporting content and a sustainability reporting process is derived. Based on these two reporting processes SureBI is developed, a step-by-step process method, aiming to guide companies through the process of implementing sustainability reporting using their BI environment. Concluding, an evaluation and implementation assesses the suitability and correctness of the process model and exemplarily implements crucial IT tasks of the process. The novel combination of these two topics indicates challenges from both fields. In case of BI, users face problems regarding historically grown systems and lacking implementation strategies. In case of sustainability, the mostly voluntary manner of this reporting leads to an uncertainty as to which indicators have to be reported. The resulting SureBI addresses and highlights these challenges and provides methods for the addressing and prioritization of new stakeholders, the prioritization of the reporting content and describes possibilities to integrate the high amount of estimation figures using BI. Results prove that sustainability reporting could and should be implemented using existing BI solutions