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1. Process Mining Workshops. ICPM 2022 International Workshops, Bozen-Bolzano, Italy, October 23-28, 2022, Revised Selected Papers.

Author

Montali, Marco, Montali, Marco, Senderovich, Arik, and Weidlich, Matthias

Subjects
Business mathematics & systems, Data mining, Health & safety aspects of IT, Information technology: general issues, Machine learning, business process management, conformance checking, data science, deep learning, event data, health informatics, knowledge graphs, machine learning, predictive process monitoring, process analytics, process discovery, process mining, process querying, streaming analytics
Abstract

Summary: This open access book constitutes revised selected papers from the International Workshops held at the 4th International Conference on Process Mining, ICPM 2022, which took place in Bozen-Bolzano, Italy, during October 23-28, 2022. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 42 papers included in this volume were carefully reviewed and selected from 89 submissions. They stem from the following workshops: - 3rd International Workshop on Event Data and Behavioral Analytics (EDBA) - 3rd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) - 3rd International Workshop on Responsible Process Mining (RPM) (previously known as Trust, Privacy and Security Aspects in Process Analytics) - 5th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) - 3rd International Workshop on Streaming Analytics for Process Mining (SA4PM) - 7th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) - 1st International Workshop on Education meets Process Mining (EduPM) - 1st International Workshop on Data Quality and Transformation in Process Mining (DQT-PM)

2. Mining Intelligence and Knowledge Exploration : Third International Conference, MIKE 2015, Hyderabad, India, December 9-11, 2015, Proceedings.

Author

Kathirvalavakumar, T., Prasath, Rajendra, and Vuppala, Anil Kumar

Subjects
Algorithms, Application software, Artificial intelligence, Data mining, Information storage and retrieval, Optical data processing, Artificial Intelligence, Algorithm Analysis and Problem Complexity, Computer Imaging, Vision, Pattern Recognition and Graphics, Data Mining and Knowledge Discovery, Information Storage and Retrieval, Information Systems Applications (incl. Internet)
Abstract

Summary: This book constitutes the refereed proceedings of the Third International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2015, held in Hyderabad, India, in December 2015. The 48 full papers and 8 short papers presented together with 4 doctoral consortium papers were carefully reviewed and selected from 185 submissions. The papers cover a wide range of topics including information retrieval, machine learning, pattern recognition, knowledge discovery, classification, clustering, image processing, network security, speech processing, natural language processing, language, cognition and computation, fuzzy sets, and business intelligence.

Published
2015

3. Modeling Decisions for Artificial Intelligence : 12th International Conference, MDAI 2015, Skövde, Sweden, September 21-23, 2015, Proceedings.

Author

Narukawa, Yasuo and Torra, Vicenc

Subjects
Application software, Artificial intelligence, Data mining, Information storage and retrieval, Numerical analysis, Pattern recognition
Abstract

Summary: This book constitutes the proceedings of the 12th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2015, held in Skövde, Sweden, in September 2015. The 18 revised full papers presented were carefully reviewed and selected from 38 submissions. They discuss theory and tools for modeling decisions, as well as applications that encompass decision making processes and information fusion techniques.

Published
2015

4. Exploring the relationship between the Social Network Profile of S&P1500 Firms and their environmental and financial profiles

Author

Shahgholian, Azar, Theodoulidis, Charalampos, and Papamichail, Konstantinia

Subjects
Social Network of S&P 1500 companies, Data Mining, Text Mining, Financial Profile, Environmental Profile
Abstract

The purpose of this thesis is to enhance our understanding of the relationship between the social network profile of S&P1500 firms and their environmental and financial profiles. The three dimensions of this research are social network profile, financial profile and environmental profile, which are becoming increasingly interlinked. The nature of this research is multidisciplinary and still in its early stages. The existing studies focus mainly on two streams of research, the first of which explores the relationship between firms' environmental and financial profiles, which reveals contradictory results. The second research stream investigates the impact of the social networks between directors on the firms' financial profiles. This thesis is submitted in an alternative format and includes four journal papers, which are interrelated in addressing the purpose of this thesis. First, it is essential to provide effective reviews to create a foundation for developing knowledge in this field of research and to explore the area in which more research is required. Therefore, the first two papers attempt to review systematically the existing research streams, namely: (i) the impact of social network profile on financial profile; and (ii) the relationship between environmental profile and financial profile. Second, the review of the impact of social network profile on financial profile reveals the need to investigate social networks in the organisations from a social network theory perspective. Therefore, the third paper uses quantitative method to provide a concrete definition of social networks and social network centrality metrics in the context of organisations. In addition, a clear process for extracting social networks at both director and board levels from the directors' information repository is defined. Third, through the fourth paper, this thesis explores the impact of the board's roles on environmental governance as an essential component of environmental profile. This paper uses a combination of qualitative and quantitative evaluation. The roles of the board of directors in relation to environmental profile are twofold, namely board monitoring and board resource provision. In this work, the board's social network is examined as a board resource-provision role.

Published
2016

5. Data Analytic Approaches to Predicting Success in Bank Telemarketing

Author

Lentz, Curtis L. and Lentz, Curtis L.

Subjects
Data mining., Telemarketing., Banks and banking., Data Mining, Exploration de données (Informatique), Télémarketing., Banks and banking., Data mining., Telemarketing.
Abstract

This paper uses a large data set from a Portuguese bank to use a data analytic approach to improving the results of telemarketing campaigns. The data was collected from 2008 to 2013, and was obtained from the University of California at Irvine's Machine Learning Repository. It contains 41,118 records and 20 variables, including the target variable of "yes" as a customer's response to the banks offer of a long-term deposit contract. The modeling is performed using IBM's Modeler software. The first section of the paper establishes a foundation for Customer Relationship Management as a powerful and growing method of using data analysis techniques to better understand a company's customers and their relationship with the business. It also outlines the CRISP-DM approach to organizing a data mining project. The data is analyzed in detail, including performing Exploratory Data Analysis (EDA) on all variables to get a detailed view of the data and the distributions of the variables, as well as an understanding of the relationship between the variables and the dependent variable. Further, Principal Component Analysis and K-means clustering is conducted on the data to identify potential correlations in variables and how groups of customers might respond similarly. Logistical regression is used on four different models, the results are described and compared across a variety of criteria. Using the profit criteria, the model with the highest revenue per customer is selected as a model to be used for prediction of how to select the best potential customers for a future campaign. Four other modeling techniques: CART, C5.0, Support Vector Machines, and Neural Networks are tested to see if the predictive results can be improved. Logistic regression is recommended as the best approach due to its higher profit per customer, and its relative ease in interpreting its results over the other modeling techniques. The thesis ends with conclusions, recommendations and suggestions for future research.

Published
2015

6. Computation in Complex Networks.

Author

Pizzuti, Clara, Pizzuti, Clara, and Socievole, Annalisa

Subjects
Technology: general issues, Bayesian networks, Gaussian mixture model, NW network, QoS-based service selection, SciSci, WeChat, active learning, angiogenesis, bridging centrality, cascading failures, chaotic time series, chimera states, city interaction network, cloud computing architecture, community detection, complex network, complex networks, complex system simulation, computational biology, convergence, coupled map lattice, cross-domain recommendation, cross-entropy, data mining, disjoint nodes, disjunct nodes, dissimilarity spaces, entropy, evolution model, genre classification, graph neural network, graph neural networks, graph representation learning, information diffusion, information spreading, inverse preferential attachment, kernel methods, knowledge evolution, language development, language networks, latent sentiment review feature, lemmatization, literary works, machine learning, maximum likelihood, maximum mean discrepancy, membrane algorithm, minimum memory based sign adjustment, modularity, multilayer complex networks, n/a, network embedding, network growth, network properties, network topology, nilpotent matrix, node classification, node similarity, non-linear mapping, null models, online social networks, optimization, overlapping communities, overlapping nodes, preferential attachment, protein contact networks, renormalisation process, self-organizing map network, semantic search framework, sentiment analysis, service-oriented modeling, social media, social networks, socio-ecological system, spreading control, stability, structural balance, stylistic attributes, support vector machines, systems biology, variational autoencoder, variational inference
Abstract

Summary: Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue "Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicine

7. Bioinformatics Applications Based On Machine Learning.

Author

Chamoso, Pablo, Chamoso, Pablo, González-Briones, Alfonso, Mohamad, Mohd, and Rodriguez, Sara

Subjects
Technology: general issues, Affective Computing, Arabidopsis thaliana, Artificial Neural Networks, CTX-M, Decision table, Harmony Search, Machine Learning, PART, Particle Swarm Optimization, Weka, bi-directional LSTM, bioinformatics, biological functions detection, classification, clinical data, clinical implications, cluster, computer vision, convolutional neural network, data mining, deep learning, derivative-free optimization, detection, diabetes, dynamic models, ear detection, epidemiology, evolutionary computation, evolutionary computing, feature selection, forecast, fuel cell, gene clustering, genetic programming, glycolysis, gradient boosting, hybrid systems, hydrogen energy, image recognition, informative genes, intelligent systems, machine learning, metabolism, metagenomics, metrics, mitochondrial protein, parameter estimation, personality assessment, plasmodium falciparum, power management, real-life patients, regression, swarm intelligence, transposable elements, video analysis, yeast
Abstract

Summary: The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems.

8. Essays on residential electricity consumption profiles : weather effects and household behaviour patterns

Author

Kang, Jieyi and Reiner, David

Subjects
333.79, smart metering, data mining, residential electricity, consumption behaviour, weather response
Abstract

The high temporal resolution data created by smart metering, which has now been deployed in many countries, provides an unprecedented opportunity to examine household consumption behaviour in narrow time windows, whereas past studies could only look at monthly or even yearly consumption. However, most studies that have used smart meter data focused either on load management (load forecasting, theft detection, etc.) or linked electricity usage to demographic and/or building characteristics. Few studies have been conducted on the impacts of weather on intraday consumption behaviour. Better appreciation of the influence of weather could improve pricing designs as well as provide better understanding of household behaviour, which could, for example, potentially increase energy efficiency. With knowledge of weather effects on residential consumption, it could also be valuable for utilities to improve grid stability and reduce operation cost. To fill the gap, this dissertation analyses the impact of different weather variables as well as consumption patterns through different tools based on smart metering data. This thesis uses a three article format. Chapter 1 provides a general overview of the literature on smart meters and empirical studies using smart metering data. Chapter 2 presents an econometric analysis of the effect of weather factors in Ireland (such temperature, rainfall and sun duration) at different periods of a day, and contrasts the impacts on consumption for workdays versus weekends versus holidays. Chapter 3 employs machine learning methods - clustering algorithms - to categorise households by their electricity demand response to different weather variables. The results demonstrated that some weather sensitivity patterns are closely associated with household characteristics. In Chapter 4, smart meter data was gathered from a very different location, Chengdu, the capital of Sichuan Province in China, which has more extreme weather and greater variability. Three scenarios are analysed in Chapter 4: (1) weekly consumption profiles in different seasons; (2) festival (major holiday) consumption profiles; and (3) consumption patterns during extreme weather. Finally, the thesis is concluded by Chapter 5, which summarises the main empirical and methodological contributions of the three papers and lays out future work in this area.

Published
2020
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