Knowledge Discovery Applications Research Articles

Application of Knowledge Discovery in Databases (KDD) to environmental, economic, and social indicators used in BIM workflow to support sustainable design

Life Cycle Sustainability Assessment (LCSA) can help to predict the impact of products and services, such as buildings, during their entire life cycle. However, it is an extensive data method. The Building Information Modelling (BIM) method can contribute towards reducing the effort involved and simplifying the data collection relating to the building elements. To this end, databases adjusted to the BIM workflow are needed to systematise and harmonise the structure of the environmental, economic and social data of said elements. This paper provides a solution to this problem by presenting an innovative Triple Bottom Line (TBL) database with environmental, economic, and social indicators of building elements to support the triple assessment adapted to the BIM workflow. An analysis employing Knowledge Discovery in Databases (KDD) was performed for the first time on this type of database to better understand the correlations between the dimensions. The key contributions include correlation detection, 83 % of which were direct, which showed that, overall, the environmental (CO2 emissions), economic (cost), and social (labour) dimensions experience similar growth trends. Strong correlations between economic and social variables were found in 68 % of the cases, followed by those of the economic and environmental (32 %), and social and environmental (18 %) variables. Findings from the correlation analysis between the three dimensions reveal their influence on the type of building system, element and material. Four scenarios were thereby identified in accordance with these correlations, to aid in sustainable decision-making. Various growth trends were detected, which can facilitate the implementation of the LCSA.

Dynamic knowledge graph applications for augmented built environments through “The World Avatar”

The proliferation of digital building models in recent years has led to a corresponding rise in specialised, non-interoperable models. These models impede sustainable developments by forming data silos that hinder cross-application data exchange and knowledge discovery processes. Although Semantic Web solutions hold promise in addressing these silos, current approaches primarily focus on developing novel ontologies, yielding similar outcomes. But it is unclear how these methodologies could support broader knowledge discovery processes and application requirements. This paper addresses these research challenges by introducing a dynamic knowledge graph as implemented within The World Avatar for interoperable building models. We demonstrate its value through two distinct applications in urban energy management and laboratory automation. The dynamic knowledge graph revolves around a comprehensive structured knowledge model constructed from ontologies and agents. Ontologies semantically annotate data and represent domain knowledge and their relationships with standardised definitions. When augmented with an agent architecture, the resulting knowledge model can align stakeholder perspectives and accommodate the dynamic and scalable nature of urban data. Moreover, the dynamic knowledge graph fosters innovative human-machine interactions through visualisation interfaces to augment knowledge discovery processes in the built environment for greater efficiencies and innovation. As the knowledge model expands, users gain access to a broader spectrum of private and public data sources and technologies, while reducing integration barriers. This is especially pertinent for smaller and less influential entities like municipal and local governments with limited resources, who can realise substantial benefits at reduced costs.

Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review.

The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large-scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields-Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence-aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy-are discussed.

APPLICATIONS OF KNOWLEDGE DISCOVERY AND DATABASE (KDD) IN CLINICAL DECISION SUPPORT SYSTEM

Applications of Knowledge Discovery and Database (KDD) in Clinical Decision Support Systems) is a research work that aims to develop a medical system that has the ability to detect and suggest cure for an ailment with minimal effort. Healthcare system has been a major source of worry across the globe in recent time due to emergence of different types of diseases and epidemics. The number of health care personnel in various hospitals falls short of the number required most especially in terms of specialists. The medical system in Nigeria today suffers from lack of fast, accurate, reliable and intelligent software solutions that can help healthcare practitioners make decisions that would solve urgent, and in some cases, complex medical problems in real-time. Also, the cost of processing and analyzing large volumes of data in a medical environment is high most especially in terms of time consumption. So in this research design and implement clinical decision support system using knowledge discovery in database (KDD) was the major focus. The design required a computerized database for storing medical records as well as software for improved intelligence-based medical procedures that were going to be simpler to use, versatile, adaptable, savvy, and effortless at combining and evaluating medical data, allowing medical practitioners at all levels to make realistic, intelligent, and real-time decisions on critical health issues. The system was designed using the Object-Oriented Design and Analysis Methodology (OOADM).

Potential Target Discovery and Drug Repurposing for Coronaviruses: Study Involving a Knowledge Graph-Based Approach.

The global pandemics of severe acute respiratory syndrome, Middle East respiratory syndrome, and COVID-19 have caused unprecedented crises for public health. Coronaviruses are constantly evolving, and it is unknown which new coronavirus will emerge and when the next coronavirus will sweep across the world. Knowledge graphs are expected to help discover the pathogenicity and transmission mechanism of viruses. The aim of this study was to discover potential targets and candidate drugs to repurpose for coronaviruses through a knowledge graph-based approach. We propose a computational and evidence-based knowledge discovery approach to identify potential targets and candidate drugs for coronaviruses from biomedical literature and well-known knowledge bases. To organize the semantic triples extracted automatically from biomedical literature, a semantic conversion model was designed. The literature knowledge was associated and integrated with existing drug and gene knowledge through semantic mapping, and the coronavirus knowledge graph (CovKG) was constructed. We adopted both the knowledge graph embedding model and the semantic reasoning mechanism to discover unrecorded mechanisms of drug action as well as potential targets and drug candidates. Furthermore, we have provided evidence-based support with a scoring and backtracking mechanism. The constructed CovKG contains 17,369,620 triples, of which 641,195 were extracted from biomedical literature, covering 13,065 concept unique identifiers, 209 semantic types, and 97 semantic relations of the Unified Medical Language System. Through multi-source knowledge integration, 475 drugs and 262 targets were mapped to existing knowledge, and 41 new drug mechanisms of action were found by semantic reasoning, which were not recorded in the existing knowledge base. Among the knowledge graph embedding models, TransR outperformed others (mean reciprocal rank=0.2510, Hits@10=0.3505). A total of 33 potential targets and 18 drug candidates were identified for coronaviruses. Among them, 7 novel drugs (ie, quinine, nelfinavir, ivermectin, asunaprevir, tylophorine, Artemisia annua extract, and resveratrol) and 3 highly ranked targets (ie, angiotensin converting enzyme 2, transmembrane serine protease 2, and M protein) were further discussed. We showed the effectiveness of a knowledge graph-based approach in potential target discovery and drug repurposing for coronaviruses. Our approach can be extended to other viruses or diseases for biomedical knowledge discovery and relevant applications.

Information Lattice Learning

We propose Information Lattice Learning (ILL) as a general framework to learn rules of a signal (e.g., an image or a probability distribution). In our definition, a rule is a coarsened signal used to help us gain one interpretable insight about the original signal. To make full sense of what might govern the signal’s intrinsic structure, we seek multiple disentangled rules arranged in a hierarchy, called a lattice. Compared to representation/rule-learning models optimized for a specific task (e.g., classification), ILL focuses on explainability: it is designed to mimic human experiential learning and discover rules akin to those humans can distill and comprehend. This paper details the math and algorithms of ILL, and illustrates how it addresses the fundamental question “what makes X an X” by creating rule-based explanations designed to help humans understand. Our focus is on explaining X rather than (re)generating it. We present applications in knowledge discovery, using ILL to distill music theory from scores and chemical laws from molecules and further revealing connections between them. We show ILL’s efficacy and interpretability on benchmarks and assessments, as well as a demonstration of ILL-enhanced classifiers achieving human-level digit recognition using only one or a few MNIST training examples (1–10 per class).

The construction of a TCM knowledge graph and application of potential knowledge discovery in diabetic kidney disease by integrating diagnosis and treatment guidelines and real-world clinical data.

Background: The complexity and rapid progression of lesions in diabetic kidney disease pose significant challenges for clinical diagnosis and treatment. The advantages of Traditional Chinese Medicine (TCM) in diagnosing and treating this condition have gradually become evident. However, due to the disease's complexity and the individualized approach to diagnosis and treatment in Traditional Chinese Medicine, Traditional Chinese Medicine guidelines have limitations in guiding the treatment of diabetic kidney disease. Most medical knowledge is currently stored in the process of recording medical records, which hinders the understanding of diseases and the acquisition of diagnostic and treatment knowledge among young doctors. Consequently, there is a lack of sufficient clinical knowledge to support the diagnosis and treatment of diabetic kidney disease in Traditional Chinese Medicine. Objective: To build a comprehensive knowledge graph for the diagnosis and treatment of diabetic kidney disease in Traditional Chinese Medicine, utilizing clinical guidelines, consensus, and real-world clinical data. On this basis, the knowledge of Traditional Chinese Medicine diagnosis and treatment of diabetic kidney disease was systematically combed and mined. Methods: Normative guideline data and actual medical records were used to construct a knowledge graph of Traditional Chinese Medicine diagnosis and treatment for diabetic kidney disease and the results obtained by data mining techniques enrich the relational attributes. Neo4j graph database was used for knowledge storage, visual knowledge display, and semantic query. Utilizing multi-dimensional relations with hierarchical weights as the core, a reverse retrieval verification process is conducted to address the critical problems of diagnosis and treatment put forward by experts. Results: 903 nodes and 1670 relationships were constructed under nine concepts and 20 relationships. Preliminarily a knowledge graph for Traditional Chinese Medicine diagnosis and treatment of diabetic kidney disease was constructed. Based on the multi-dimensional relationships, the diagnosis and treatment questions proposed by experts were validated through multi-hop queries of the graphs. The results were confirmed by experts and showed good outcomes. Conclusion: This study systematically combed the Traditional Chinese Medicine diagnosis and treatment knowledge of diabetic kidney disease by constructing the knowledge graph. Furthermore, it effectively solved the problem of "knowledge island". Through visual display and semantic retrieval, the discovery and sharing of diagnosis and treatment knowledge of diabetic kidney disease were realized.

Prototype Theory Meets Word Embedding: A Novel Approach for Text Categorization via Granular Computing

The problem of the information representation and interpretation coming from senses by the brain has plagued scientists for decades. The same problems, from a different perspective, hold in automated Pattern Recognition systems. Specifically, in solving various NLP tasks, an ever better and richer semantic representation of text as a set of features is needed and a plethora of text embedding techniques in algebraic spaces are continuously provided by researchers. These spaces are well suited to be conceived as conceptual spaces in light of the Gärdenfors’s Conceptual Space theory, which, within the Cognitive Science paradigm, seeks a geometrization of thought that bridges the gap between an associative lower level and a symbolic higher level in which information is organized and processed and where inductive reasoning is appropriate. Granular Computing can offer the toolbox for granulating text that can be represented by more abstract entities than words, offering a good hierarchical representation of the text embedded in an algebraic space driving Machine Learning applications, specifically, in text mining tasks. In this paper, the Conceptual Space Theory, the Granular Computing approach and Machine Learning are bound in a novel common framework for solving some text categorization tasks with both standard classifiers suited for working with mathbb {R}^n vectors and a Recurrent Neural Network (RNN) — an LSTM — able to deal with sequences. Instead of working with word vectors, the algorithms process more abstract entities (concepts), where patterns, in a first approach, are obtained through the construction of a symbolic histogram starting from a suitable set of information granules, representing a document as a distribution of concepts. For the RNN case, as a further novelty, a text is represented as a random walk over prototypes within the conceptual space synthesized over a suitable text embedding procedure. A comparison of the performance and a critical discussion are offered for both a neural embedding technique and the well-known LSA, showing how the conceptual level leads also to Knowledge Discovery applications.

A gradient boosting machine-based framework for electricity energy knowledge discovery

Knowledge discovery in databases (KDD) has an important effect on various fields with the development of information science. Electricity energy forecasting (EEF), a primary application of KDD, aims to explore the inner potential rule of electrical data for the purpose to serve electricity-related organizations or groups. Meanwhile, the advent of the information society attracts more and more scholars to pay attention to EEF. The existing methods for EEF focus on using high-techs to improve the experimental results but fail to construct an applicable electricity energy KDD framework. To complement the research gap, our study aims to propose a gradient boosting machine-based KDD framework for electricity energy prediction and enrich knowledge discovery applications. To be specific, we draw on the traditional knowledge discovery process and techniques to make the framework reliable and extensible. Additionally, we leverage Gradient Boosting Machine (GBM) to improve the efficiency and accuracy of our approach. We also devise three metrics for the evaluation of the proposed framework including R-square (R2), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Besides, we collect the electricity energy consumption (EEC) as well as meteorological data from 2013 to 2016 in New York state and take the EEC prediction of New York State as an example. Finally, we conduct extensive experiments to verify the superior performance of our framework and the results show that our model achieves outstanding results for the three metrics (around 0.87 for R2, 60.15 for MAE, and 4.79 for MAPE). Compared with real value and the official prediction model, our approach also has a remarkable prediction ability. Therefore, we find that the proposed framework is feasible and reliable for EEF and could provide practical references for other types of energy KDD.

Possibility of Applying Data Mining in Health Insurance

In this paper, a great range of possible applications of data mining, data warehousing and knowledge discovery is described. With the increase of costs and complexity of healthcare systems, application of the following techniques and algorithms can help decision makers in health insurance organisations to increase productivity, cut costs and lower the complexity of their daily processes. The application of data mining is described by using CRISP-DM methodology, while example of possible application is given for each type of data mining approach.

Unsupervised Learning Using Expectation Propagation Inference of Inverted Beta-Liouville Mixture Models for Pattern Recognition Applications

Learning statistical models successfully is both an essential and a challenging task for various pattern recognition and knowledge discovery applications. In particular, generative models such as finite and infinite mixture models have demonstrated to be efficient in terms of overall performance. In this paper, a robust framework based on an expectation propagation (EP) inference is developed to learn inverted Beta-Liouville (IBL) mixture models which is proper choice for positive data classification. Within the proposed EP learning method, the full posterior distribution is estimated accurately, the model complexity and all related parameters are evaluated simultaneously in a single optimization scheme. Extensive experiments using challenging real-world applications including recognition of facial expression, automatic human action categorization, and hand gesture recognition show the merit of our approach in terms of achieving better results than comparable techniques.

Application of Data Mining and Knowledge Discovery in Medical Databases

While technical improvements in the form of computer-based healthcare information applications as well as hardware are enabling collecting of and access to healthcare data wieldier. In this context, there are tools to analyse and examine this medical data once it has been acquired and saved. Analysis of documented medical data records may help in the identification of hidden features and patterns that could significantly increase our understanding of disease onset and treatment therapies. Significantly, the progress in information and communications technologies (ICT) has outpaced our capacity to assess summarise, and extract insight from the data. Today, database management system has equipped us with the fundamental tools for the effective storage as well as lookup of massive data sets, but the topic of how to allow human beings to interpret and analyse huge data remains a challenging and unsolved challenge. So, sophisticated methods for automated data mining and knowledge discovery are required to deal with large data. In this study, an effort was made employing machine learning approach to acquire knowledge that will aid various personnel in taking decisions that will guarantee that the sustainability objectives on Health is achieved. Finally, the present data mining methodologies with data mining methods and also its deployment tools that are more helpful for healthcare services are addressed in depth.

TOWARDS THE AUTOMATIC ONTOLOGY GENERATION AND ALIGNMENT OF BIM AND GIS DATA FORMATS

Abstract. Establishing semantic interoperability between BIM and GIS is vital for geospatial information exchange. Semantic web have a natural ability to provide seamless semantic representation and integration among the heterogeneous domains like BIM and GIS through employing ontology. Ontology models can be defined (or generated) using domain-data representations and further aligned across other ontologies by the semantic similarity of their entities - introducing cross-domain ontologies to achieve interoperability of heterogeneous information. However, due to extensive semantic features and complex alignment (mapping) relations between BIM and GIS data formats, many approaches are far from generating semantically-rich ontologies and perform effective alignment to address geospatial interoperability. This study highlights the fundamental perspectives to be addressed for BIM and GIS interoperability and proposes a comprehensive conceptual framework for automatic ontology generation followed by ontology alignment of open-standards for BIM and GIS data formats. It presents an approach based on transformation patterns to automatically generate ontology models, and semantic-based and structure-based alignment techniques to form cross-domain ontology. Proposed two-phase framework provides ontology model generation for input XML schemas (i.e. of IFC and CityGML formats), and illustrates alignment technique to potentially develop a cross-domain ontology. The study concludes anticipated results of cross-domain ontology can provides future perspectives in knowledge-discovery applications and seamless information exchange for BIM and GIS.

MACHINE LEARNING FOR DETECTING EPISTASIS INTERACTIONS AND ITS RELEVANCE TO PERSONALIZED MEDICINE IN ALZHEIMER’S DISEASE: SYSTEMATIC REVIEW

Alzheimer’s disease (AD) is a progressive disease that attacks the brain’s neurons and causes problems in memory, thinking, and reasoning skills. Personalized Medicine (PM) needs a better and more accurate understanding of the relationship between human genetic data and complex diseases like AD. The goal of PM is to tailor the treatment of a case person to his individual properties. PM requires the prediction of a person’s disease from genetic data, and its success depends on the accurate detection of genetic biomarkers. Single Nucleotide polymorphisms (SNPs) are considered the most prevalent type of variation in the human genome. Epistasis has a biological relevance to complex diseases and has an important impact on PM. Detection of the most significant epistasis interactions associated with complex diseases is a big challenge. This paper reviews several machine learning techniques and algorithms to detect the most significant epistasis interactions in Alzheimer’s disease. We discuss many machine learning techniques that can be used for detecting SNPs’ combinations like Random Forests, Support Vector Machines, Multifactor Dimensionality Reduction, Neural Network, and Deep Learning. This review paper highlights the pros and cons of these techniques and explains how they can be applied in an efficient framework to apply knowledge discovery and data mining in AD disease.

AgroSupportAnalytics: A Cloud-based Complaints Management and Decision Support System for Sustainable Farming in Egypt

Sustainable Farming requires up-to-date advice on crop diseases, patterns, and adequate prevention actions to face developing circumstances. Currently, in developing countries like Egypt, farmers’ access to such information is extremely limited due to the agriculture support being either not available, inconsistent, or unreliable. The presented Cloud-based Complaints Management and Decision Support System for Sustainable Farming in Egypt, named as AgroSupportAnalytics, aims to resolve the problem of both the lack of support and advice for farmers, and the inconsistencies in doing so by current manual approach provided by agricultural experts. Key contribution is the development of an automated complaint management and decision support strategy, on the basis of extensive research on requirement analysis tailored for Egypt. The solution is grounded on the application of knowledge discovery and analysis on agricultural data and farmers’ complaints, deployed on a Cloud platform, to provide farming stakeholders in Egypt with timely and suitable support. This paper presents the overall system architectural framework along with the information and storage services, which have been based on the requirements specifications phases of the project along with the historical data sets of past 10 year of farmers complaints and enquiries in Egypt.

Mining Implicit Equations From Data Using Gene Expression Programming

Symbolic regression is an active research topic that has various applications in data mining and knowledge discovery. Existing methods for symbolic regression mainly focus on mining explicit equations. In contrast, implicit equations are more flexible and powerful than explicit equations with regard to describing the relationships between variables in the given dataset. However, evaluating the quality of an implicit equation is more difficult than evaluating an explicit equation. The traditional method for implicit equation evaluation is based on derivative calculation, which requires much time consumption and dense training data. To address the above issues, this article proposes a new evolutionary framework to efficiently mine implicit equations from data. In the proposed framework, a new mechanism (named CL-FEM) is proposed to evaluate implicit equations, and it can efficiently evaluate the accuracy and validity of implicit equations. In addition, a multiple sub-chromosome encoding method based on a least-squares estimator is proposed in the framework to further improve its search efficiency. Based on the proposed evolutionary framework, an efficient algorithm named gene expression programming with a least-squares estimator (LSE-GEP) is developed to mine implicit equations from data. Experimental results demonstrate that the proposed LSE-GEP method performs much better than the recently published methods, in terms of its success rate, accuracy and readability.

Top-down spatially-explicit probabilistic estimation of building energy performance at a scale

Achieving the energy-related and environmental targets for nations and municipalities is largely dependent on the existing built stock. It plays a pivotal role in the accomplishment of these targets through the implementation of energy efficiency and flexibility programs, involving the deployment of distributed energy resource management technologies, refurbishment of building envelopes and upgrading of indoor environmental control equipment. Spatial awareness about urban energy use enables to prioritise the areas where these solutions will be most effective and balanced with the plans for new constructions. Large-scale building energy mapping, however, must cope with heterogeneity of buildings within the built stock, absence of detailed information and multiple sources of uncertainty that stem from the complex and dynamic properties of the phenomenon at a building level. One of the key challenges in the discipline is to account for these uncertainties while maintaining the rational model complexities and data needs. This study, therefore, suggests a parsimonious top-down probabilistic modelling recipe to enable geospatial energy mapping and analysis. Under such modelling principles, an inverse propagation of uncertainties is carried out from the status quo of the built stock. The proposed framework is based on probabilistic sampling with prior parametric univariate density estimation and statistical hypothesis testing. Consolidation with the exogenous influencing factors is facilitated through the measure of statistically significant difference. This approach is exemplified with the data from two sources: the cadastral system and the energy performance certificates registry. A case study developed for Trondheim (Norway) quantified the central tendency and dispersion in the distributions of the simulated bulk total annual energy use by buildings per 1×1km grid cell over the urban territory. The results suggest that best estimates of these values vary between 11MWh·y-1 and 141GWh·y-1 depending on the grid cell. A measure of dispersion in the simulated results is highly correlated with these estimates. Robust handling of uncertainties and the possibility to accommodate a variety of modelling objectives make this approach practical for energy mapping with a flexible spatial resolution that may facilitate numerous applications in energy planning. A collection of methods for univariate density estimation discussed in this study together with the empirical data are accessible through Built Stock Explorer:https://builtstockexplorer.indecol.ntnu.no. This open web application for knowledge discovery in building energy data enables to reproduce some of the results presented in the article.

Knowledge discovery in Chinese herbal medicine: a machine learning perspective

Traditional Chinese Medicine (TCM) has attracted more and more attention due to its remarkable effects on treating diseases, and Chinese herbal medicine (CHM) is an important partition of TCM, rich in natural active ingredients. Researchers are trying multiple analytical methods to dig out more valuable information about CHM and reveal the principle of TCM. Machine learning is playing an important role in the studies. Knowledge discovery of CHM using machine learning mainly includes quality control of CHM, network pharmacology in CHM, and medical prescriptions composed by CHM, aiming to understand TCM better, provide more efficiency methods in the production of CHM and find novel treatment of disease not curable nowadays. In this paper, we summarized the basic idea of frequently used classification and clustering machine learning algorithms, introduced pre-processing algorithms commonly used to simplify and accelerate machine learning procedure, presented current status of machine learning algorithms’ applications in knowledge discovery of CHM, discussed challenges and future trends of machine learning’s application in CHM. It is believed that the paper provides a valuable insight for the starters trying to apply machine learning in the study of CHM and catch up the recent status of related researches.

General method for automated feature extraction and selection and its application for gender classification and biomechanical knowledge discovery of sex differences in spinal posture during stance and gait

Modern technologies enable to capture multiple biomechanical parameters often resulting in relational data. The current work proposes a generally applicable method comprising automated feature extraction, ensemble feature selection and classification to best capture the potentials of the data also for generating new biomechanical knowledge. Its benefits are demonstrated in the concrete biomechanically and medically relevant use case of gender classification based on spinal data for stance and gait. Very good results for accuracy were obtained using gait data. Dynamic movements of the lumbar spine in sagittal and frontal plane and of the pelvis in frontal plane best map gender differences.

Using Data Sciences in Digital Marketing: Framework, methods, and performance metrics

In the last decade, the use of Data Sciences, which facilitate decision-making and extraction of actionable insights and knowledge from large datasets in the digital marketing environment, has remarkably increased. However, despite these advances, relevant evidence on the measures to improve the management of Data Sciences in digital marketing remains scarce. To bridge this gap in the literature, the present study aims to review (i) methods of analysis, (ii) uses, and (iii) performance metrics based on Data Sciences as used in digital marketing techniques and strategies. To this end, a comprehensive literature review of major scientific contributions made so far in this research area is undertaken. The results present a holistic overview of the main applications of Data Sciences to digital marketing and generate insights related to the creation of innovative Data Mining and knowledge discovery techniques. Important theoretical implications are discussed, and a list of topics is offered for further research in this field. The review concludes with formulating recommendations on the development of digital marketing strategies for businesses, marketers, and non-technical researchers and with an outline of directions of further research on innovative Data Mining and knowledge discovery applications.