Ontology of core data mining entities

In this article, we present OntoDM-core, an ontology of core data mining entities. OntoDM-core defines themost essential datamining entities in a three-layered ontological structure comprising of a specification, an implementation and an application layer. It provides a representational framework for the description of mining structured data, and in addition provides taxonomies of datasets, data mining tasks, generalizations, data mining algorithms and constraints, based on the type of data. OntoDM-core is designed to support a wide range of applications/use cases, such as semantic annotation of data mining algorithms, datasets and results; annotation of QSAR studies in the context of drug discovery investigations; and disambiguation of terms in text mining. The ontology has been thoroughly assessed following the practices in ontology engineering, is fully interoperable with many domain resources and is easy to extend

Data mining workflow templates for intelligent discovery assistance in RapidMiner

Knowledge Discovery in Databases (KDD) has evolved during the last years and reached a mature stage offering plenty of operators to solve complex tasks. User support for building workflows, in contrast, has not increased proportionally. The large number of operators available in current KDD systems make it difficult for users to successfully analyze data. Moreover, workflows easily contain a large number of operators and parts of the workflows are applied several times, thus it is hard for users to build them manually. In addition, workflows are not checked for correctness before execution. Hence, it frequently happens that the execution of the workflow stops with an error after several hours runtime. In this paper we address these issues by introducing a knowledge-based representation of KDD workflows as a basis for cooperative-interactive planning. Moreover, we discuss workflow templates that can mix executable operators and tasks to be refined later into sub-workflows. This new representation helps users to structure and handle workflows, as it constrains the number of operators that need to be considered. We show that workflows can be grouped in templates enabling re-use and simplifying KDD worflow construction in RapidMiner

Metalearning

This open access book as one of the fastest-growing areas of research in machine learning, metalearning studies principled methods to obtain efficient models and solutions by adapting machine learning and data mining processes. This adaptation usually exploits information from past experience on other tasks and the adaptive processes can involve machine learning approaches. As a related area to metalearning and a hot topic currently, automated machine learning (AutoML) is concerned with automating the machine learning processes. Metalearning and AutoML can help AI learn to control the application of different learning methods and acquire new solutions faster without unnecessary interventions from the user. This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and AutoML, covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. It can thus help developers to develop systems that can improve themselves through experience. This book is a substantial update of the first edition published in 2009. It includes 18 chapters, more than twice as much as the previous version. This enabled the authors to cover the most relevant topics in more depth and incorporate the overview of recent research in the respective area. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining, data science and artificial intelligence. ; Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence

Metalearning

This open access book as one of the fastest-growing areas of research in machine learning, metalearning studies principled methods to obtain efficient models and solutions by adapting machine learning and data mining processes. This adaptation usually exploits information from past experience on other tasks and the adaptive processes can involve machine learning approaches. As a related area to metalearning and a hot topic currently, automated machine learning (AutoML) is concerned with automating the machine learning processes. Metalearning and AutoML can help AI learn to control the application of different learning methods and acquire new solutions faster without unnecessary interventions from the user. This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and AutoML, covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. It can thus help developers to develop systems that can improve themselves through experience. This book is a substantial update of the first edition published in 2009. It includes 18 chapters, more than twice as much as the previous version. This enabled the authors to cover the most relevant topics in more depth and incorporate the overview of recent research in the respective area. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining, data science and artificial intelligence. ; Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence

NEW ARTIFACTS FOR THE KNOWLEDGE DISCOVERY VIA DATA ANALYTICS (KDDA) PROCESS

Recently, the interest in the business application of analytics and data science has increased significantly. The popularity of data analytics and data science comes from the clear articulation of business problem solving as an end goal. To address limitations in existing literature, this dissertation provides four novel design artifacts for Knowledge Discovery via Data Analytics (KDDA). The first artifact is a Snail Shell KDDA process model that extends existing knowledge discovery process models, but addresses many existing limitations. At the top level, the KDDA Process model highlights the iterative nature of KDDA projects and adds two new phases, namely Problem Formulation and Maintenance. At the second level, generic tasks of the KDDA process model are presented in a comparative manner, highlighting the differences between the new KDDA process model and the traditional knowledge discovery process models. Two case studies are used to demonstrate how to use KDDA process model to guide real world KDDA projects. The second artifact, a methodology for theory building based on quantitative data is a novel application of KDDA process model. The methodology is evaluated using a theory building case from the public health domain. It is not only an instantiation of the Snail Shell KDDA process model, but also makes theoretical contributions to theory building. It demonstrates how analytical techniques can be used as quantitative gauges to assess important construct relationships during the formative phase of theory building. The third artifact is a data mining ontology, the DM3 ontology, to bridge the semantic gap between business users and KDDA expert and facilitate analytical model maintenance and reuse. The DM3 ontology is evaluated using both criteria-based approach and task-based approach. The fourth artifact is a decision support framework for MCDA software selection. The framework enables users choose relevant MCDA software based on a specific decision making situation (DMS). A DMS modeling framework is developed to structure the DMS based on the decision problem and the users\u27 decision preferences and. The framework is implemented into a decision support system and evaluated using application examples from the real-estate domain

Distributed Load Testing by Modeling and Simulating User Behavior

Modern human-machine systems such as microservices rely upon agile engineering practices which require changes to be tested and released more frequently than classically engineered systems. A critical step in the testing of such systems is the generation of realistic workloads or load testing. Generated workload emulates the expected behaviors of users and machines within a system under test in order to find potentially unknown failure states. Typical testing tools rely on static testing artifacts to generate realistic workload conditions. Such artifacts can be cumbersome and costly to maintain; however, even model-based alternatives can prevent adaptation to changes in a system or its usage. Lack of adaptation can prevent the integration of load testing into system quality assurance, leading to an incomplete evaluation of system quality. The goal of this research is to improve the state of software engineering by addressing open challenges in load testing of human-machine systems with a novel process that a) models and classifies user behavior from streaming and aggregated log data, b) adapts to changes in system and user behavior, and c) generates distributed workload by realistically simulating user behavior. This research contributes a Learning, Online, Distributed Engine for Simulation and Testing based on the Operational Norms of Entities within a system (LODESTONE): a novel process to distributed load testing by modeling and simulating user behavior. We specify LODESTONE within the context of a human-machine system to illustrate distributed adaptation and execution in load testing processes. LODESTONE uses log data to generate and update user behavior models, cluster them into similar behavior profiles, and instantiate distributed workload on software systems. We analyze user behavioral data having differing characteristics to replicate human-machine interactions in a modern microservice environment. We discuss tools, algorithms, software design, and implementation in two different computational environments: client-server and cloud-based microservices. We illustrate the advantages of LODESTONE through a qualitative comparison of key feature parameters and experimentation based on shared data and models. LODESTONE continuously adapts to changes in the system to be tested which allows for the integration of load testing into the quality assurance process for cloud-based microservices

Generation and Applications of Knowledge Graphs in Systems and Networks Biology

The acceleration in the generation of data in the biomedical domain has necessitated the use of computational approaches to assist in its interpretation. However, these approaches rely on the availability of high quality, structured, formalized biomedical knowledge. This thesis has the two goals to improve methods for curation and semantic data integration to generate high granularity biological knowledge graphs and to develop novel methods for using prior biological knowledge to propose new biological hypotheses. The first two publications describe an ecosystem for handling biological knowledge graphs encoded in the Biological Expression Language throughout the stages of curation, visualization, and analysis. Further, the second two publications describe the reproducible acquisition and integration of high-granularity knowledge with low contextual specificity from structured biological data sources on a massive scale and support the semi-automated curation of new content at high speed and precision. After building the ecosystem and acquiring content, the last three publications in this thesis demonstrate three different applications of biological knowledge graphs in modeling and simulation. The first demonstrates the use of agent-based modeling for simulation of neurodegenerative disease biomarker trajectories using biological knowledge graphs as priors. The second applies network representation learning to prioritize nodes in biological knowledge graphs based on corresponding experimental measurements to identify novel targets. Finally, the third uses biological knowledge graphs and develops algorithmics to deconvolute the mechanism of action of drugs, that could also serve to identify drug repositioning candidates. Ultimately, the this thesis lays the groundwork for production-level applications of drug repositioning algorithms and other knowledge-driven approaches to analyzing biomedical experiments

Exploiting semantic web knowledge graphs in data mining

Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in that field are knowledge intensive and can often benefit from using additional knowledge from various sources. Therefore, many approaches have been proposed in this area that combine Semantic Web data with the data mining and knowledge discovery process. Semantic Web knowledge graphs are a backbone of many information systems that require access to structured knowledge. Such knowledge graphs contain factual knowledge about real word entities and the relations between them, which can be utilized in various natural language processing, information retrieval, and any data mining applications. Following the principles of the Semantic Web, Semantic Web knowledge graphs are publicly available as Linked Open Data. Linked Open Data is an open, interlinked collection of datasets in machine-interpretable form, covering most of the real world domains. In this thesis, we investigate the hypothesis if Semantic Web knowledge graphs can be exploited as background knowledge in different steps of the knowledge discovery process, and different data mining tasks. More precisely, we aim to show that Semantic Web knowledge graphs can be utilized for generating valuable data mining features that can be used in various data mining tasks. Identifying, collecting and integrating useful background knowledge for a given data mining application can be a tedious and time consuming task. Furthermore, most data mining tools require features in propositional form, i.e., binary, nominal or numerical features associated with an instance, while Linked Open Data sources are usually graphs by nature. Therefore, in Part I, we evaluate unsupervised feature generation strategies from types and relations in knowledge graphs, which are used in different data mining tasks, i.e., classification, regression, and outlier detection. As the number of generated features grows rapidly with the number of instances in the dataset, we provide a strategy for feature selection in hierarchical feature space, in order to select only the most informative and most representative features for a given dataset. Furthermore, we provide an end-to-end tool for mining the Web of Linked Data, which provides functionalities for each step of the knowledge discovery process, i.e., linking local data to a Semantic Web knowledge graph, integrating features from multiple knowledge graphs, feature generation and selection, and building machine learning models. However, we show that such feature generation strategies often lead to high dimensional feature vectors even after dimensionality reduction, and also, the reusability of such feature vectors across different datasets is limited. In Part II, we propose an approach that circumvents the shortcomings introduced with the approaches in Part I. More precisely, we develop an approach that is able to embed complete Semantic Web knowledge graphs in a low dimensional feature space, where each entity and relation in the knowledge graph is represented as a numerical vector. Projecting such latent representations of entities into a lower dimensional feature space shows that semantically similar entities appear closer to each other. We use several Semantic Web knowledge graphs to show that such latent representation of entities have high relevance for different data mining tasks. Furthermore, we show that such features can be easily reused for different datasets and different tasks. In Part III, we describe a list of applications that exploit Semantic Web knowledge graphs, besides the standard data mining tasks, like classification and regression. We show that the approaches developed in Part I and Part II can be used in applications in various domains. More precisely, we show that Semantic Web graphs can be exploited for analyzing statistics, building recommender systems, entity and document modeling, and taxonomy induction. %In Part III, we focus on semantic annotations in HTML pages, which are another realization of the Semantic Web vision. Semantic annotations are integrated into the code of HTML pages using markup languages, like Microformats, RDFa, and Microdata. While such data covers various domains and topics, and can be useful for developing various data mining applications, additional steps of cleaning and integrating the data need to be performed. In this thesis, we describe a set of approaches for processing long literals and images extracted from semantic annotations in HTML pages. We showcase the approaches in the e-commerce domain. Such approaches contribute in building and consuming Semantic Web knowledge graphs