Interactive Knowledge Discovery Research Articles

Interactive knowledge discovery and knowledge visualization for decision support in multi-objective optimization

In many practical applications, the end-goal of multi-objective optimization is to select an implementable solution that is close to the Pareto-optimal front while satisfying the decision maker’s preferences. The decision making process is challenging since it involves the manual consideration of all solutions. The field of multi-criteria decision making offers many methods that help the decision maker in this process. However, most methods only focus on analyzing the solutions’ objective values. A more informed decision generally requires the additional knowledge of how different preferences affect the variable values. One difficulty in realizing this is that while the preferences are often expressed in the objective space, the knowledge required to implement a preferred solution exists in the decision space. In this paper, we propose a decision support system that allows interactive knowledge discovery and knowledge visualization to support practitioners by simultaneously considering preferences in the objective space and their impact in the decision space. The knowledge discovery step can use either of two recently proposed data mining techniques for extracting decision rules that conform to given preferences, while the extracted knowledge is visualized via a novel graph-based approach that allows the discovery of important variables, their values and their interactions with other variables. The result is an intuitive and interactive decision support system that aids the entire decision making process — from solution visualization to knowledge visualization. We demonstrate the usefulness of this system on benchmark optimization problems up to 10 objectives and real-world problems with up to six objectives.

Expediting knowledge acquisition by a web framework for Knowledge Graph Exploration and Visualization (KGEV): case studies on COVID-19 and Human Phenotype Ontology

BackgroundKnowledges graphs (KGs) serve as a convenient framework for structuring knowledge. A number of computational methods have been developed to generate KGs from biomedical literature and use them for downstream tasks such as link prediction and question answering. However, there is a lack of computational tools or web frameworks to support the exploration and visualization of the KG themselves, which would facilitate interactive knowledge discovery and formulation of novel biological hypotheses.MethodWe developed a web framework for Knowledge Graph Exploration and Visualization (KGEV), to construct and visualize KGs in five stages: triple extraction, triple filtration, metadata preparation, knowledge integration, and graph database preparation. The application has convenient user interface tools, such as node and edge search and filtering, data source filtering, neighborhood retrieval, and shortest path calculation, that work by querying a backend graph database. Unlike other KGs, our framework allows fast retrieval of relevant texts supporting the relationships in the KG, thus allowing human reviewers to judge the reliability of the knowledge extracted.ResultsWe demonstrated a case study of using the KGEV framework to perform research on COVID-19. The COVID-19 pandemic resulted in an explosion of relevant literature, making it challenging to make full use of the vast and heterogenous sources of information. We generated a COVID-19 KG with heterogenous information, including literature information from the CORD-19 dataset, as well as other existing knowledge from eight data sources. We showed the utility of KGEV in three intuitive case studies to explore and query knowledge on COVID-19. A demo of this web application can be accessed at http://covid19nlp.wglab.org. Finally, we also demonstrated a turn-key adaption of the KGEV framework to study clinical phenotypic presentation of human diseases by Human Phenotype Ontology (HPO), illustrating the versatility of the framework.ConclusionIn an era of literature explosion, the KGEV framework can be applied to many emerging diseases to support structured navigation of the vast amount of newly published biomedical literature and other existing biological knowledge in various databases. It can be also used as a general-purpose tool to explore and query gene-phenotype-disease-drug relationships interactively.

Visualization of WiFi Signals Using Programmable Transfer Functions

In this paper, we show how volume rendering with a Programmable Transfer Function can be used for the effective and comprehensible visualization of WiFi signals. A traditional transfer function uses a low-dimensional lookup table to map the volumetric scalar field to color and opacity. In this paper, we present the concept of a Programmable Transfer Function. We then show how generalizing traditional lookup-based transfer functions to Programmable Transfer Functions enables us to leverage view-dependent and real-time attributes of a volumetric field to depict the data variations of WiFi surfaces with low and high-frequency components. Our Programmable Transfer Functions facilitate interactive knowledge discovery and produce meaningful visualizations.

EmbryoMiner: A new framework for interactive knowledge discovery in large-scale cell tracking data of developing embryos.

State-of-the-art light-sheet and confocal microscopes allow recording of entire embryos in 3D and over time (3D+t) for many hours. Fluorescently labeled structures can be segmented and tracked automatically in these terabyte-scale 3D+t images, resulting in thousands of cell migration trajectories that provide detailed insights to large-scale tissue reorganization at the cellular level. Here we present EmbryoMiner, a new interactive open-source framework suitable for in-depth analyses and comparisons of entire embryos, including an extensive set of trajectory features. Starting at the whole-embryo level, the framework can be used to iteratively focus on a region of interest within the embryo, to investigate and test specific trajectory-based hypotheses and to extract quantitative features from the isolated trajectories. Thus, the new framework provides a valuable new way to quantitatively compare corresponding anatomical regions in different embryos that were manually selected based on biological prior knowledge. As a proof of concept, we analyzed 3D+t light-sheet microscopy images of zebrafish embryos, showcasing potential user applications that can be performed using the new framework.

Interactive knowledge discovery and data mining on genomic expression data with numeric formal concept analysis.

BackgroundGene Expression Data (GED) analysis poses a great challenge to the scientific community that can be framed into the Knowledge Discovery in Databases (KDD) and Data Mining (DM) paradigm. Biclustering has emerged as the machine learning method of choice to solve this task, but its unsupervised nature makes result assessment problematic. This is often addressed by means of Gene Set Enrichment Analysis (GSEA).ResultsWe put forward a framework in which GED analysis is understood as an Exploratory Data Analysis (EDA) process where we provide support for continuous human interaction with data aiming at improving the step of hypothesis abduction and assessment. We focus on the adaptation to human cognition of data interpretation and visualization of the output of EDA.First, we give a proper theoretical background to bi-clustering using Lattice Theory and provide a set of analysis tools revolving around mathcal {K}-Formal Concept Analysis (mathcal {K}-FCA), a lattice-theoretic unsupervised learning technique for real-valued matrices.By using different kinds of cost structures to quantify expression we obtain different sequences of hierarchical bi-clusterings for gene under- and over-expression using thresholds. Consequently, we provide a method with interleaved analysis steps and visualization devices so that the sequences of lattices for a particular experiment summarize the researcher’s vision of the data. This also allows us to define measures of persistence and robustness of biclusters to assess them.Second, the resulting biclusters are used to index external omics databases—for instance, Gene Ontology (GO)—thus offering a new way of accessing publicly available resources. This provides different flavors of gene set enrichment against which to assess the biclusters, by obtaining their p-values according to the terminology of those resources.We illustrate the exploration procedure on a real data example confirming results previously published.ConclusionsThe GED analysis problem gets transformed into the exploration of a sequence of lattices enabling the visualization of the hierarchical structure of the biclusters with a certain degree of granularity. The ability of FCA-based bi-clustering methods to index external databases such as GO allows us to obtain a quality measure of the biclusters, to observe the evolution of a gene throughout the different biclusters it appears in, to look for relevant biclusters—by observing their genes and what their persistence is—to infer, for instance, hypotheses on their function.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1234-z) contains supplementary material, which is available to authorized users.

Interactive knowledge discovery from hidden data through sampling of frequent patterns

In real life, many important datasets are not publicly accessible due to various reasons, including privacy protection and maintenance of business competitiveness. However, Knowledge discovery and pattern mining from these datasets can bring enormous benefit both to the data owner and the external entities. In this paper, we propose a novel solution for this task, which is based on Markov chain Monte Carlo (MCMC) sampling of frequent patterns. Instead of returning all the frequent patterns, the proposed paradigm sends back a small set of randomly selected patterns so that the confidentiality of the dataset can be maintained. Our solution also allows interactive sampling, so that the sampled patterns can fulfill the user's requirement effectively. We show experimental results from several real‐life datasets to validate the capability and usefulness of our solution. In particular, we show examples that by using our proposed solution, an eCommerce marketplace can allow pattern mining on user session data without disclosing the data to the public; such a mining paradigm can help the sellers in the marketplace, which eventually can boost the market's own revenue. © 2016 Wiley Periodicals, Inc. Statistical Analysis and Data Mining: The ASA Data Science Journal, 2016

Dissertation Research Problems in Data Management and Related Areas

Databases and related fields such as Information Retrieval, Data Mining and Knowledge Management offer many topics of interest for dissertation research. Specific areas include, for instance, big data, social networks, Web question answering and interactive knowledge discovery. In this article, we provide a summary and critique of research problems presented in these and related areas at a workshop on dissertation proposals and early doctoral research.

Interactive knowledge discovery with the doctor-in-the-loop: a practical example of cerebral aneurysms research.

Established process models for knowledge discovery find the domain-expert in a customer-like and supervising role. In the field of biomedical research, it is necessary to move the domain-experts into the center of this process with far-reaching consequences for both their research output and the process itself. In this paper, we revise the established process models for knowledge discovery and propose a new process model for domain-expert-driven interactive knowledge discovery. Furthermore, we present a research infrastructure which is adapted to this new process model and demonstrate how the domain-expert can be deeply integrated even into the highly complex data-mining process and data-exploration tasks. We evaluated this approach in the medical domain for the case of cerebral aneurysms research.

Mining Signatures from Event Sequences

This paper proposes a novel secular knowledge representation and learning framework to proposed large- scale secular signature mining of longitudinal heterogeneous occasional data. The framework allows the presentation, extra4ction, and mining of high order latent occasion event structure and relationships between single and many sequences. The prescribed data representation maps the heterogeneous sequences to a image by encoding occasions as a structured spatial-secular shape process. We have suggested clinical assessment for naked interactive knowledge discovery in large electronic health record databases.

Damage detection with streamlined structural health monitoring data.

The huge amounts of sensor data generated by large scale sensor networks in on-line structural health monitoring (SHM) systems often overwhelms the systems’ capacity for data transmission and analysis. This paper presents a new concept for an integrated SHM system in which a streamlined data flow is used as a unifying thread to integrate the individual components of on-line SHM systems. Such an integrated SHM system has a few desirable functionalities including embedded sensor data compression, interactive sensor data retrieval, and structural knowledge discovery, which aim to enhance the reliability, efficiency, and robustness of on-line SHM systems. Adoption of this new concept will enable the design of an on-line SHM system with more uniform data generation and data handling capacity for its subsystems. To examine this concept in the context of vibration-based SHM systems, real sensor data from an on-line SHM system comprising a scaled steel bridge structure and an on-line data acquisition system with remote data access was used in this study. Vibration test results clearly demonstrated the prominent performance characteristics of the proposed integrated SHM system including rapid data access, interactive data retrieval and knowledge discovery of structural conditions on a global level.

Interactive visual knowledge discovery from data-based temporal decision support system

The article aims to present a generic interactive visual analytics solution that provides temporal decision support using knowledge discovery from data modules together with interactive visual representations. It bases its design decisions on classification of visual representation techniques according to the criteria of temporal data type, periodicity, and dimensionality. The design proposal is applied to an existing medical knowledge discovery from data–based decision support system aiming at assisting physicians in the fight against nosocomial infections in the intensive care units. Our solution is fully implemented and evaluated.

Human activity data discovery from triaxial accelerometer sensor: Non-supervised learning sensitivity to feature extraction parametrization

Background: Our methodology describes a human activity recognition framework based on feature extraction and feature selection techniques where a set of time, statistical and frequency domain features taken from 3-dimensional accelerometer sensors are extracted. This framework specifically focuses on activity recognition using on-body accelerometer sensors. We present a novel interactive knowledge discovery tool for accelerometry in human activity recognition and study the sensitivity to the feature extraction parametrization. Results: The implemented framework achieved encouraging results in human activity recognition. We have implemented a new set of features extracted from wearable sensors that are ambitious from a computational point of view and able to ensure high classification results comparable with the state of the art wearable systems (Mannini et al. 2013). A feature selection framework is developed in order to improve the clustering accuracy and reduce computational complexity.1The software OpenSignals (Gomes, Nunes, Sousa, & Gamboa, 2012) was used for signal acquisition and signal processing algorithms were developed in Python Programming Language (Rossum & de Boer, 1991) and Orange Software (Curk et al., 2005).1 Several clustering methods such as K-Means, Affinity Propagation, Mean Shift and Spectral Clustering were applied. The K-means methodology presented promising accuracy results for person-dependent and independent cases, with 99.29% and 88.57%, respectively. Conclusions: The presented study performs two different tests in intra and inter subject context and a set of 180 features is implemented which are easily selected to classify different activities. The implemented algorithm does not stipulate, a priori, any value for time window or its overlap percentage of the signal but performs a search to find the best parameters that define the specific data. A clustering metric based on the construction of the data confusion matrix is also proposed. The main contribution of this work is the design of a novel gesture recognition system based solely on data from a single 3-dimensional accelerometer.

A Web-Based Geovisual Analytical System for Climate Studies

Climate studies involve petabytes of spatiotemporal datasets that are produced and archived at distributed computing resources. Scientists need an intuitive and convenient tool to explore the distributed spatiotemporal data. Geovisual analytical tools have the potential to provide such an intuitive and convenient method for scientists to access climate data, discover the relationships between various climate parameters, and communicate the results across different research communities. However, implementing a geovisual analytical tool for complex climate data in a distributed environment poses several challenges. This paper reports our research and development of a web-based geovisual analytical system to support the analysis of climate data generated by climate model. Using the ModelE developed by the NASA Goddard Institute for Space Studies (GISS) as an example, we demonstrate that the system is able to (1) manage large volume datasets over the Internet; (2) visualize 2D/3D/4D spatiotemporal data; (3) broker various spatiotemporal statistical analyses for climate research; and (4) support interactive data analysis and knowledge discovery. This research also provides an example for managing, disseminating, and analyzing Big Data in the 21st century.

EpiphaNet: An Interactive Tool to Support Biomedical Discoveries

Background. EpiphaNet is an interactive knowledge discovery system which enables researchers to explore visually sets of relations extracted from MEDLINE using a combination of language processing techniques. In this paper, we discuss the theoretical and methodological foundations of the system, and evaluate the utility of the models that underlie it for literature-based discovery. In addition, we present a summary of results drawn from a qualitative analysis of over six hours of interaction with the system by basic medical scientists. The system is able to simulate open and closed discovery, and is shown to generate associations that are both surprising and interesting within the area of expertise of the researchers concerned. EpiphaNet provides an interactive visual representation of associations between concepts, which is derived from distributional statistics drawn from across the spectrum of biomedical citations in MEDLINE. This tool is available online, providing biomedical scientists with the opportunity to identify and explore associations of interest to them.

Development of interactive learning activities in heat transfer

This paper presents ongoing work at Nanyang Technological University (NTU) on the development of electronic resources for thermal-fluid courses, with a focus on the development of interactive learning activities for students to explore and discover heat transfer concepts and mechanisms. Three basic types of Java applets based on the three main types of interactive knowledge discovery activities are presented. Using these three basic applets, different interactive learning activities to assist the students in the learning of heat transfer principles are developed and discussed. The activities can aid the students in the visualisation of certain heat transfer concepts; the user interface is user friendly; and the proposed activities can enable the student to acquire thermal knowledge through interactive exploration.

MedTAKMI-CDI: Interactive knowledge discovery for clinical decision intelligence

This paper describes MedTAKMI-CDI, an online analytical processing system that enables the interactive discovery of knowledge for clinical decision intelligence (CDI). CDI supports decision making by providing in-depth analysis of clinical data from multiple sources. We discuss the fundamental challenges we faced and explain how we met those challenges and developed a prototype experimental CDI system that currently handles clinical information for about 7,000 patients at the National Cancer Center in Japan. We elaborate on a three-layer model (attribute-value pairs, ordered sequences of events, and time-stamped sequences of events) for clinical information, which can represent three different levels of abstraction. This flexibility supports a broad range of analysis, from simple demographic analysis to a mission-critical clinical-path pattern analysis. Rather than a collection of rigid relational schema for clinical information, our relational database system employs a metaschema with patient identifier, time stamp, attribute name, and attribute values. This allows us to modify the representation of clinical information without having to reload the data and rewrite the analytic components. We also describe the analytic functions that are used to understand clinical care practice at the hospital, to obtain an overview of the clinical information, to navigate the clinical information by using the layers of abstraction and the ontologies, and to extract the patterns and rules for clinical paths.

Human-centered concepts for exploration and understanding of Earth observation images

The progress in information retrieval, computer vision, and image analysis makes it possible to establish very complete bases of algorithms and operators. A specialist in remote sensing or image processing now has the tools that allow him, at least in theory, to configure applications solving complex problems of image understanding. However, in reality, earth observation (EO) data analysis is still performed in a very laborious way at the end of repeated cycles of trial and error. To overcome this, we proposed a novel advanced remote sensing information processing system knowledge-driven information mining (KIM). KIM is based on human-centered concepts (HCCs), which implements new features and functions allowing improved feature extraction, search on a semantic level, the availability of collected knowledge, interactive knowledge discovery, and new visual user interfaces. We assess the HCC methodology for solving several difficult tasks in EO image interpretation, using a broad variety of sensor data, from meter-resolution synthetic aperture radar and optical images to hyperspectral data.

A Visualization Model of Interactive Knowledge Discovery Systems and Its Implementations

We briefly introduce an interactive visualization model, RuleViz, for knowledge discovery and data mining, which consists of five components: data preparation and visualization, interactive data reduction, data preprocessing, pattern discovery, and pattern visualization. With this model, the implementation issues are considered and three implementation paradigms, including image-based paradigm, algorithm-embedded paradigm, and interaction-driven paradigm, are discussed. We implement an interactive visualization system, AViz, which discovers 3D numerical association rules from large data sets based on the image-based paradigm. The framework of the AViz system is presented and each component is explored. To discretize numerical attributes, three approaches, including equal-sized, bin-packing-based equal-depth, and interaction-based approaches are proposed, and the algorithm for mining and visualizing numerical association rules is developed. Our experimental result on a census data set is illustrated, which shows that the AViz system is useful and helpful for discovering and visualizing numerical association rules.

Knowledge Discovery Through Self-Organizing Maps: Data Visualization and Query Processing

In data mining, the usefulness of a data pattern depends on the user of the database and does not solely depend on the statistical strength of the pattern. Based on the premise that heuristic search in combinatorial spaces built on computer and human cognitive theories is useful for effective knowledge discovery, this study investigates how the use of self-organizing maps as a tool of data visualization in data mining plays a significant role in human-computer interactive knowledge discovery. This article presents the conceptual foundations of the integration of data visualization and query processing for knowledge discovery, and proposes a set of query functions for the validation of self-organizing maps in data mining.

MINING FOR PROGRAM STRUCTURE

We present a new mining algorithm for discovering program structure built on a warehouse of program-analysis data. We make use of statement definition-use data along with variable typing information to learn about the structure of legacy code. Using concept analysis, we develop an interactive knowledge discovery framework into which different types of analysis data can be integrated. This work is the first in a series of data warehousing experiments that is designed to tap the wealth of information hidden in existing program-analysis data.