Category: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

A systematic review on detection and adaptation of concept drift in streaming data using machine learning techniques

nShruti Arora, nRinkle Rani, nNitin Saxenan Edit "A systematic review on detection and adaptation of concept drift in streaming data using machine learning techniques"
A systematic review on detection and adaptation of concept drift in streaming data using machine learning techniques

Detecting concept drift: A visual guide.


Abstract

Last decade demonstrate the massive growth in organizational data which keeps on increasing multi-fold as millions of records get updated every second. Handling such vast and continuous data is challenging which further opens up many research areas. The continuously flowing data from various sources and in real-time is termed as streaming data. While deriving valuable statistics from data streams, the variation that occurs in data distribution is called concept drift. These drifts play a significant role in a variety of disciplines, including data mining, machine learning, ubiquitous knowledge discovery, quantitative decision theory, and so forth. As a result, a substantial amount of research is carried out for studying methodologies and approaches for dealing with drifts. However, the available material is scattered and lacks guidelines for selecting an effective technique for a particular application. The primary novel objective of this survey is to present an understanding of concept drift challenges and allied studies. Further, it assists researchers from diverse domains to accommodate detection and adaptation algorithms for concept drifts in their applications. Overall, this study aims to contribute to deeper insights into the classification of various types of drifts and methods for detection and adaptation along with their key features and limitations. Furthermore, this study also highlights performance metrics used to evaluate the concept drift detection methods for streaming data. This paper presents the future research scope by highlighting gaps in the existing literature for the development of techniques to handle concept drifts.

This article is categorized under: Algorithmic Development > Ensemble Methods Application Areas > Data Mining Software Tools Fundamental Concepts of Data and Knowledge > Big Data Mining

Causality and causal inference for engineers: Beyond correlation, regression, prediction and artificial intelligence

nM. Z. Nasern Edit "Causality and causal inference for engineers: Beyond correlation, regression, prediction and artificial intelligence"
Causality and causal inference for engineers: Beyond correlation, regression, prediction and artificial intelligence

This primer presents fundamental principles behind causal discovery, causal inference, and counterfactuals from an engineering perspective and contrasts that to those pertaining to correlation, regression, and AI.


Abstract

In order to engineer new materials, structures, systems, and processes that address persistent challenges, engineers seek to tie causes to effects and understand the effects of causes. Such a pursuit requires a causal investigation to uncover the underlying structure of the data generating process (DGP) governing phenomena. A causal approach derives causal models that engineers can adopt to infer the effects of interventions (and explore possible counterfactuals). Yet, and for the most part, we continue to design experiments in the hope of empirically observing engineered intervention(s). Such experiments are idealized, complex, and costly and hence are narrow in scope. On the contrary, a causal investigation will allow us to peek into the how and why of a DGP and provide us with the essential means to articulate a causal model that accurately describes the phenomenon on hand and better predicts the outcome of possible interventions. Adopting a causal approach in engineering is perhaps more warranted than ever—especially with the rise of big data and the adoption of artificial intelligence (AI); wherein AI models are naivety presumed to describe causal ties. To bridge such knowledge gap, this primer presents fundamental principles behind causal discovery, causal inference, and counterfactuals from an engineering perspective and contrasts that to those pertaining to correlation, regression, and AI.

This article is categorized under: Application Areas > Industry Specific Applications Algorithmic Development > Causality Discovery Application Areas > Science and Technology Technologies > Machine Learning

Knowledge graph‐driven data processing for business intelligence

nLipika Deyn Edit "Knowledge graph‐driven data processing for business intelligence"
Knowledge graph-driven data processing for business intelligence

Occupational safety ontology built from news articles published by OSHA helps in getting a comprehensive view of incidents, causes, regions and so on.


Abstract

With proliferation of Big Data, organizational decision making has also become more complex. Business Intelligence (BI) is no longer restricted to querying about marketing and sales data only. It is more about linking data from disparate applications and also churning through large volumes of unstructured data like emails, call logs, social media, News, and so on in an attempt to derive insights that can also provide actionable intelligence and better inputs for future strategy making. Semantic technologies like knowledge graphs have proved to be useful tools that help in linking disparate data sources intelligently and also enable reasoning through complex networks that are created as a result of this linking. Over the last decade the process of creation, storage, and maintenance of knowledge graphs have sufficiently matured, and they are now making inroads into business decision making also. Very recently, these graphs are also seen as a potential way to reduce hallucinations of large language models, by including these during pre-training as well as generation of output. There are a number of challenges also. These include building and maintaining the graphs, reasoning with missing links, and so on. While these remain as open research problems, we present in this article a survey of how knowledge graphs are currently used for deriving business intelligence with use-cases from various domains.

This article is categorized under: Algorithmic Development > Text Mining Application Areas > Business and Industry

A survey of episode mining

nOualid Ouarem, nFarid Nouioua, nPhilippe Fournier‐Vigern Edit "A survey of episode mining"
A survey of episode mining

A search space of frequent episodes (sequences of events that appear frequently in a sequence of events).


Abstract

Episode mining is a research area in data mining, where the aim is to discover interesting episodes, that is, subsequences of events, in an event sequence. The most popular episode-mining task is frequent episode mining (FEM), which consists of identifying episodes that appear frequently in an event sequence, but this task has also been extended in various ways. It was shown that episode mining can reveal insightful patterns for numerous applications such as web stream analysis, network fault management, and cybersecurity, and that episodes can be useful for prediction. Episode mining is an active research area, and there have been numerous advances in the field over the last 25 years. However, due to the rapid evolution of the pattern mining field, there is no prior study that summarizes and gives a detailed overview of this field. The contribution of this article is to fill this gap by presenting an up-to-date survey that provides an introduction to episode mining and an overview of recent developments and research opportunities. This advanced review first gives an introduction to the field of episode mining and the first algorithms. Then, the main concepts used in these algorithms are explained. After that, several recent studies are reviewed that have addressed some limitations of these algorithms and proposed novel solutions to overcome them. Finally, the paper lists some possible extensions of the existing frameworks to mine more meaningful patterns and presents some possible orientations for future work that may contribute to the evolution of the episode mining field.

This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Algorithmic Development > Association Rules Technologies > Association Rules

Multispectral data mining: A focus on remote sensing satellite images

nSin Liang Lim, nJaya Sreevalsan‐Nair, nB. S. Daya Sagarn Edit "Multispectral data mining: A focus on remote sensing satellite images"
Multispectral data mining: A focus on remote sensing satellite images

(Left) The multispectral image of Kerala, India, that is captured by Sentinel-2, shows the aftermath of its devastating floods in August 2018. Data mining of such an image would lead to knowledge discovery, which is critical for the estimation of damages, risk assessment, and so on. The data mining in this scenario involves processes, such as segmentation and change detection from multi-source and/or time-series images. These processes lead to the knowledge of flood extent. (Right) Our article reviews all such processes in the context of the entire data science workflow for multispectral images from satellite sensors. (Image generated by authors. (Left) Satellite images and data story courtesy: https://earthobservatory.nasa.gov/images/92669/before-and-after-the-kerala-floods)


Abstract

This article gives a brief overview of various aspects of data mining of multispectral image data. We focus on specifically the remote sensing satellite images acquired using multispectral imaging (MSI), given the technology used across multiple knowledge domains, such as chemistry, medical imaging, remote sensing, and so on with a sufficient amount of variation. In this article, the different data mining processes are reviewed along with state-of-the-art methods and applications. To study data mining, it is important to know how the data are acquired and preprocessed. Hence, those topics are briefly covered in the article. The article concludes with applications demonstrating the knowledge discovery from data mining, modern challenges, and promising future directions for MSI data mining research.

This article is categorized under: Application Areas > Science and Technology Fundamental Concepts of Data and Knowledge > Knowledge Representation Fundamental Concepts of Data and Knowledge > Big Data Mining

Deepfake detection using deep learning methods: A systematic and comprehensive review

nArash Heidari, nNima Jafari Navimipour, nHasan Dag, nMehmet Unaln Edit "Deepfake detection using deep learning methods: A systematic and comprehensive review"
Deepfake detection using deep learning methods: A systematic and comprehensive review

The suggested DL-deepfake detection taxonomy separated four distinct method.


Abstract

Deep Learning (DL) has been effectively utilized in various complicated challenges in healthcare, industry, and academia for various purposes, including thyroid diagnosis, lung nodule recognition, computer vision, large data analytics, and human-level control. Nevertheless, developments in digital technology have been used to produce software that poses a threat to democracy, national security, and confidentiality. Deepfake is one of those DL-powered apps that has lately surfaced. So, deepfake systems can create fake images primarily by replacement of scenes or images, movies, and sounds that humans cannot tell apart from real ones. Various technologies have brought the capacity to change a synthetic speech, image, or video to our fingers. Furthermore, video and image frauds are now so convincing that it is hard to distinguish between false and authentic content with the naked eye. It might result in various issues and ranging from deceiving public opinion to using doctored evidence in a court. For such considerations, it is critical to have technologies that can assist us in discerning reality. This study gives a complete assessment of the literature on deepfake detection strategies using DL-based algorithms. We categorize deepfake detection methods in this work based on their applications, which include video detection, image detection, audio detection, and hybrid multimedia detection. The objective of this paper is to give the reader a better knowledge of (1) how deepfakes are generated and identified, (2) the latest developments and breakthroughs in this realm, (3) weaknesses of existing security methods, and (4) areas requiring more investigation and consideration. The results suggest that the Conventional Neural Networks (CNN) methodology is the most often employed DL method in publications. According to research, the majority of the articles are on the subject of video deepfake detection. The majority of the articles focused on enhancing only one parameter, with the accuracy parameter receiving the most attention.

This article is categorized under: Technologies > Machine Learning Algorithmic Development > Multimedia Application Areas > Science and Technology

The state‐of‐art review of ultra‐precision machining using text mining: Identification of main themes and recommendations for the future direction

nWai Sze YIP, nHengzhou Edward Yan, nBaolong Zhang, nSuet Ton Edit "The state‐of‐art review of ultra‐precision machining using text mining: Identification of main themes and recommendations for the future direction"
The state-of-art review of ultra-precision machining using text mining: Identification of main themes and recommendations for the future direction

The graphical abstract of the study including research procedures of the text mining approach (bottom left) and the text mining/thematic network of ultra-precision machining (bottom right).


Abstract

Ultra-precision machining (UPM), one of the most advanced machining techniques that can produce exact components, significantly impacts the technological community. The significance of UPM attracts the attention of academic and industrial partners. As a result of the rapid development of UPM caused by technological advancement, it is necessary to revisit the current stages and evolution of UPM to sustain and advance this technology. The state of the art in UPM is first investigated systematically in this study by identifying the current four major UPM themes. The UPM thematic network is then built, along with a structural analysis of the network, to determine the interactions between each theme and the primary roles of theme members responsible for the interactions. Furthermore, the “bridge” role is assigned to the specific UPM theme content. On the other hand, Sentiment analysis is conducted to determine how the academic community at UPM feels about the themes for UPM research to focus on those themes with a need for more confidence. Considering the above findings, the future perspective of UPM and suggestions for its advancement are discussed and provided. This study provides a comprehensive understanding and the current state-of-the-art review of UPM technology by a text mining technique to critically analyze its research content, as well as suggestions to enhance UPM development by focusing on its current challenges, thereby assisting academia and institutions in leveraging this technology to benefit society.

This article is categorized under: Algorithmic Development > Text Mining Application Areas > Science and Technology Application Areas > Industry Specific Applications

Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022

nCheng Zhang, nNilam Nur Amir Sjarif, nRoslina Ibrahimn Edit "Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022"
Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022

A review of recent research on the application of deep learning models to price forecast of financial time series, with information on model architectures, applications, advantages and disadvantages, and directions for future research.


Abstract

Accurately predicting the prices of financial time series is essential and challenging for the financial sector. Owing to recent advancements in deep learning techniques, deep learning models are gradually replacing traditional statistical and machine learning models as the first choice for price forecasting tasks. This shift in model selection has led to a notable rise in research related to applying deep learning models to price forecasting, resulting in a rapid accumulation of new knowledge. Therefore, we conducted a literature review of relevant studies over the past 3 years with a view to aiding researchers and practitioners in the field. This review delves deeply into deep learning-based forecasting models, presenting information on model architectures, practical applications, and their respective advantages and disadvantages. In particular, detailed information is provided on advanced models for price forecasting, such as Transformers, generative adversarial networks (GANs), graph neural networks (GNNs), and deep quantum neural networks (DQNNs). The present contribution also includes potential directions for future research, such as examining the effectiveness of deep learning models with complex structures for price forecasting, extending from point prediction to interval prediction using deep learning models, scrutinizing the reliability and validity of decomposition ensembles, and exploring the influence of data volume on model performance.

This article is categorized under: Technologies > Prediction Technologies > Artificial Intelligence

Pre‐trained language models: What do they know?

nNuno Guimarães, nRicardo Campos, nAlípio Jorgen Edit "Pre‐trained language models: What do they know?"
Pre-trained language models: What do they know?

Diagram of pretrained language models common sense capabilities and possible domains of application.


Abstract

Large language models (LLMs) have substantially pushed artificial intelligence (AI) research and applications in the last few years. They are currently able to achieve high effectiveness in different natural language processing (NLP) tasks, such as machine translation, named entity recognition, text classification, question answering, or text summarization. Recently, significant attention has been drawn to OpenAI's GPT models' capabilities and extremely accessible interface. LLMs are nowadays routinely used and studied for downstream tasks and specific applications with great success, pushing forward the state of the art in almost all of them. However, they also exhibit impressive inference capabilities when used off the shelf without further training. In this paper, we aim to study the behavior of pre-trained language models (PLMs) in some inference tasks they were not initially trained for. Therefore, we focus our attention on very recent research works related to the inference capabilities of PLMs in some selected tasks such as factual probing and common-sense reasoning. We highlight relevant achievements made by these models, as well as some of their current limitations that open opportunities for further research.

This article is categorized under: Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining Technologies > Artificial Intelligence

A review on client selection models in federated learning

nMonalisa Panigrahi, nSourabh Bharti, nArun Sharman Edit "A review on client selection models in federated learning"
A review on client selection models in federated learning

Basic federated learning architecture.


Abstract

Federated learning (FL) is a decentralized machine learning (ML) technique that enables multiple clients to collaboratively train a common ML model without them having to share their raw data with each other. A typical FL process involves (1) FL client(s) selection, (2) global model distribution, (3) local training, and (4) aggregation. As such FL clients are heterogeneous edge devices (i.e., mobile phones) that differ in terms of computational resources, training data quality, and distribution. Therefore, FL client(s) selection has a significant influence on the execution of the remaining steps of an FL process. There have been a variety of FL client(s) selection models proposed in the literature, however, their critical review and/or comparative analysis is much less discussed. This paper brings the scattered FL client(s) selection models onto a single platform by first categorizing them into five categories, followed by providing a detailed analysis of the benefits/shortcomings and the applicability of these models for different FL scenarios. Such understanding can help researchers in academia and industry to develop improved FL client(s) selection models to address the requirement challenges and shortcomings of the current models. Finally, future research directions in the area of FL client(s) selection are also discussed.

This article is categorized under: Technologies > Machine Learning Technologies > Artificial Intelligence