Perform Knowledge Discovery Research Articles

Survey on A Smart Medical System on Drug Analysis using ML Algorithms

Online health communities continue to offer huge variety of medical information useful for medical practitioners, system administrators and patients alike. In this system we collect real time health posts from reputed websites, where patients express their views, including their experiences and side-effects on drugs used by them. We propose to perform Summarization of user posts per drug, and come out with useful conclusions for medical fraternity as well as patient community at a glance. Further, we propose to classify the users based on their ‘emotional state of mind’. Also, we shall perform knowledge discovery from user posts, whereby useful ‘patterns’ about the triad ‘drugs-symptoms-medicine’ is done by Association Rule Mining. Association rule mining is a popular and widely-known machine learning task. It is used to find out interesting relations between variables in large database. Rules generated by association have two disjoint set of items having form LHS (Left Hand Side) => RHS (Right Hand Side). The rule says that RHS is likely to occur whenever the LHS set occurs.

Non-Axiomatic Logic Modeling of English Texts for Knowledge Discovery and Commonsense Reasoning

Non-axiomatic logic (NAL) is a term-based, non-monotonic, multi-valued logic with evidence-based formal semantics. All those characteristics position NAL as an excellent candidate for modeling natural language expressions and supporting artificial agents while performing knowledge discovery and commonsense reasoning tasks. In this article, we propose a set of rules for the automatic translation of natural language (NL) text into the formal language of non-axiomatic logic (NAL). Several free available tools are used to support a previous linguistic analysis, and a common sense ontology is used to populate a background knowledge base that helps to delimit the scope and the semantics of logical formulas translated. Experimentation shows our set to be the most comprehensive NL-to-NAL translation rule set known so far. Furthermore, we included an extensive set of examples to show how our proposed set of rules can be used for translating a wide range of English statements with varying grammatical structures.

A Two-Phase Dynamic Throughput Optimization Model for Big Data Transfers

The amount of data transferred over dedicated and non-dedicated network links has been increasing much faster than the increase in the network capacity. On the other hand, the current data transfer solutions fail to guarantee even the promised achievable transfer throughput. In this article, we propose a novel two-phase dynamic throughput optimization model based on mathematical modeling with offline knowledge discovery/analysis and adaptive online decision making. In the offline analysis, we mine historical transfer logs to perform knowledge discovery about the transfer characteristics. The online phase uses the discovered knowledge from the offline analysis along with the real-time investigation of the network condition to optimize the protocol parameters. As the real-time investigation is expensive and provides partial knowledge about the current network status, our model uses historical knowledge about the network and data characteristics to reduce the real-time investigation overhead while ensuring near-optimal throughput for each transfer. Our novel approach is tested over different networks with different datasets, and it has outperformed its closest competitor by 1.7x and the default case by 5x. It also achieved up to 93 percent accuracy compared to the optimal achievable throughput possible on those networks.

Recommender-as-a-Service with Chatbot Guided Domain-science Knowledge Discovery in a Science Gateway.

Scientists in disciplines such as neuroscience and bioinformatics are increasingly relying on science gateways for experimentation on voluminous data, as well as analysis and visualization in multiple perspectives. Though current science gateways provide easy access to computing resources, datasets and tools specific to the disciplines, scientists often use slow and tedious manual efforts to perform knowledge discovery to accomplish their research/education tasks. Recommender systems can provide expert guidance and can help them to navigate and discover relevant publications, tools, data sets, or even automate cloud resource configurations suitable for a given scientific task. To realize the potential of integration of recommenders in science gateways in order to spur research productivity, we present a novel "OnTimeRecommend" recommender system. The OnTimeRecommend comprises of several integrated recommender modules implemented as microservices that can be augmented to a science gateway in the form of a recommender-as-a-service. The guidance for use of the recommender modules in a science gateway is aided by a chatbot plug-in viz., Vidura Advisor. To validate our OnTimeRecommend, we integrate and show benefits for both novice and expert users in domain-specific knowledge discovery within two exemplar science gateways, one in neuroscience (CyNeuro) and the other in bioinformatics (KBCommons).

A Comprehensive Survey on Cloud Data Mining (CDM) Frameworks and Algorithms

Data mining is used for finding meaningful information out of a vast expanse of data. With the advent of Big Data concept, data mining has come to much more prominence. Discovering knowledge out of a gigantic volume of data efficiently is a major concern as the resources are limited. Cloud computing plays a major role in such a situation. Cloud data mining fuses the applicability of classical data mining with the promises of cloud computing. This allows it to perform knowledge discovery out of huge volumes of data with efficiency. This article presents the existing frameworks, services, platforms, and algorithms for cloud data mining. The frameworks and platforms are compared among each other based on similarity, data mining task support, parallelism, distribution, streaming data processing support, fault tolerance, security, memory types, storage systems, and others. Similarly, the algorithms are grouped on the basis of parallelism type, scalability, streaming data mining support, and types of data managed. We have also provided taxonomies on the basis of data mining techniques such as clustering, classification, and association rule mining. We also have attempted to discuss and identify the major applications of cloud data mining. The various taxonomies for cloud data mining frameworks, platforms, and algorithms have been identified. This article aims at gaining better insight into the present research realm and directing the future research toward efficient cloud data mining in future cloud systems.

YouTube as a Source of Information in Understanding Autonomous Vehicle Consumers: Natural Language Processing Study

The automotive industry is currently experiencing a revolution with the advent and deployment of autonomous vehicles. Several countries are conducting large-scale testing of autonomous vehicles on private and even public roads. It is important to examine the attitudes and potential concerns of end users towards autonomous cars before mass deployment. To facilitate the transition to autonomous vehicles, the automotive industry produces many videos on its products and technologies. The largest video sharing website, YouTube.com, hosts many videos on autonomous vehicle technology. Content analysis and text mining of the comments related to the videos with large numbers of views can provide insight about potential end-user feedback. This study examines two questions: first, how do people view autonomous vehicles? Second, what polarities exist regarding (a) content and (b) automation level? The researchers found 107 videos on YouTube using a related keyword search and examined comments on the 15 most-viewed videos, which had a total of 60.9 million views and around 25,000 comments. The videos were manually clustered based on their content and automation level. This study used two natural language processing (NLP) tools to perform knowledge discovery from a bag of approximately seven million words. The key issues in the comment threads were mostly associated with efficiency, performance, trust, comfort, and safety. The perception of safety and risk increased in the textual contents when videos presented full automation level. Sentiment analysis shows mixed sentiments towards autonomous vehicle technologies, however, the positive sentiments were higher than the negative.

Vehicle Consumer Complaint Reports Involving Severe Incidents: Mining Large Contingency Tables

According to 2010–2014 Fatality Analysis Reporting System (FARS) data, nearly 6.35% of fatal crashes happened as a result of vehicles’ pre-existing manufacturing defects. The National Highway Traffic Safety Administration’s (NHTSA) vehicle complaint database incorporates more than 1.37 million complaint reports (as of June 1, 2017). These reports contain extended information on vehicle-related disruptions. Around 5% of these reports involve some level of injury or fatalities. This study had two principal objectives, namely (1) perform knowledge discovery to understand the latent trends in consumer complaints, and (2) identify clusters with high relative reporting ratios from a large contingency table of vehicle models and associated complaints. To accomplish these objectives, 67,201 detailed reports associated with injury or fatalities from the NHTSA vehicle complaint database were examined. Exploratory text mining and empirical Bayes (EB) data mining were performed. Additionally, this study analyzed five years (2010–2014) of FARS data to examine the research findings. Results show that major vehicular defects are associated with air bags, brake systems, seat belts, and speed controls. The EB metrics identified several key ‘ vehicle model with major defect’ groups that require more attention. This study demonstrates the applicability of consumer complaints in identifying major vehicular defects as well as key groups of ‘ vehicle model with major defect.’ The findings of this study will provide a significant contribution to the reduction of crashes from vehicle-related disruptions. The research presented in this paper is crucial given the ongoing advancement of connected and automated vehicle technologies.

Modelling of e-Governance Framework for Mining Knowledge from Massive Grievance Redressal Data

With the massive proliferation of online applications for the citizens with abundant resources, there is a tremendous hike in usage of e-governance platforms. Right from entrepreneur, players, politicians, students, or anyone who are highly depending on web-based grievance redressal networking sites, which generates loads of massive grievance data that are not only challenging but also highly impossible to understand. The prime reason behind this is grievance data is massive in size and they are highly unstructured. Because of this fact, the proposed system attempts to understand the possibility of performing knowledge discovery process from grievance Data using conventional data mining algorithms. Designed in Java considering massive number of online e-governance framework from civilian’s grievance discussion forums, the proposed system evaluates the effectiveness of performing datamining for Big data.

Knowledge Discovery from Dynamic Data on a Nonlinear System

A method is presented for performing knowledge discovery on the dynamic data of a nonlinear system. In the proposed approach, a synchronized phasor measurement technique is used to acquire the dynamic data of the nonlinear system and a hyper-rectangular type neural network (HRTNN) is then applied to extract crisp and fuzzy rules with which to estimate the system stability. The effectiveness of the proposed methodology is verified using the dynamic data of a typical real-world nonlinear system, namely an AEP-14 bus, and the extracted rules are relating to the knowledge discovery of the stability levels for the nonlinear system. The discovered relationships among the dynamic data (i.e., the operating state), the extracted rules, and the system stability are confirmed by means of a two-stage confirmatory factor analysis.

Collaborative management of a repository of KDD processes

Knowledge Discovery in Databases (KDD) is a complex and computationally intensive process that requires a repeated interaction between tools and users, often in a distributed environment. Given the complexity of the process, both naive and expert users need some support to effectively perform knowledge discovery. In this paper, we present a user-and knowledge-centric approach to support the design of KDD projects. Semantic technologies are exploited to support sharing and (re)use of KDD computational resources and processes in a distributed collaborative environment. In particular, functionalities for tool publishing, service and process discovery, and versioning of processes greatly enhance process management, and provide a learn-by-example and trial-and-test environment for collaborative KDD design. The systematic use of semantic information, a loosely coupled and layered service-oriented architecture and a cooperative and flexible approach results in a platform natively conceived for an open, distributed and collaborative environment.

Abstract 2379: A Comprehensive Imaging Informatics System with Decision-Support Tools for the Interdisciplinary Comprehensive Arm Rehabilitation Trial

BACKGROUND: Stroke is one of the major causes of death and disability in America. The Interdisciplinary Comprehensive Arm Rehabilitation Evaluation (ICARE) project aims to compare a theoetically-defensible, evidence-based arm therapy, Accelerated Skill Acquisition Program, with traditional therapies. Imaging of the brain provides evidence of the location and severity of the stroke lesion. Researchers can analyze selected images acquired prior to enrollment and perform knowledge discovery of correlation between the size and location of the lesion and specific rehabilitation outcomes. However, large-scale trials such as ICARE encounter challenges for integrating imaging data from multiple healthcare institutions across the country. The ICARE project has 7 clinical sites distributed across the country, with more than 160 patients randomized thus far, tens of gigabytes of imaging data are involved. Therefore, we propose to develop a web-based system with tools that can support imaging and informatics related data within such a randomized controlled trial and allow for tele-consultation and collaboration. EVALUATION: The system includes a database and file storage system, a Health Insurance Portability and Accountability Act (HIPAA)-compliant anonymizer, a web-based Graphical User Interface (GUI) and decision-support tools such as lesion quantifying tools and image analysis. The GUI allows users to upload, search and review the images, patient information and annotations. The HIPAA-compliant anonymizer will anonymize any private information according to HIPAA. The lesion quantifying tool will help clinicians to measure, quantify, and characterize the size of lesion and evaluate the rehabilitation progress. The system will be used within 7 clinical sites and evaluated by researchers in the ICARE trial. DISCUSSION: The system allows for tele-consultation and is facilitated through the development of a thin-client image viewing application. Clinicians and researchers across the country can manage the imaging data, make and store annotations, measure the lesion size, and collaborate to identify the imaging biomarkers that would ultimately enhance the existing ICARE clinical trial database. Moreover, the anonymizer tool can be used not only for imaging data in this rehabilitation trial, but also for other data from multi-site clinical trials since most studies are required to be anonymized. CONCLUSION: A comprehensive imaging informatics system for a large-scale controlled stroke rehabilitation trial is developed. Physicians and clinical researchers are able to collect, organize and analyze stroke cases efficiently and effectively across multiple sites.

An automated reasoning framework for translational research

In this paper we propose a novel approach to the design and implementation of knowledge-based decision support systems for translational research, specifically tailored to the analysis and interpretation of data from high-throughput experiments. Our approach is based on a general epistemological model of the scientific discovery process that provides a well-founded framework for integrating experimental data with preexisting knowledge and with automated inference tools.In order to demonstrate the usefulness and power of the proposed framework, we present its application to Genome-Wide Association Studies, and we use it to reproduce a portion of the initial analysis performed on the well-known WTCCC dataset. Finally, we describe a computational system we are developing, aimed at assisting translational research. The system, based on the proposed model, will be able to automatically plan and perform knowledge discovery steps, to keep track of the inferences performed, and to explain the obtained results.

Easier surveillance of climate-related health vulnerabilities through a Web-based spatial OLAP application

BackgroundClimate change has a significant impact on population health. Population vulnerabilities depend on several determinants of different types, including biological, psychological, environmental, social and economic ones. Surveillance of climate-related health vulnerabilities must take into account these different factors, their interdependence, as well as their inherent spatial and temporal aspects on several scales, for informed analyses. Currently used technology includes commercial off-the-shelf Geographic Information Systems (GIS) and Database Management Systems with spatial extensions. It has been widely recognized that such OLTP (On-Line Transaction Processing) systems were not designed to support complex, multi-temporal and multi-scale analysis as required above. On-Line Analytical Processing (OLAP) is central to the field known as BI (Business Intelligence), a key field for such decision-support systems. In the last few years, we have seen a few projects that combine OLAP and GIS to improve spatio-temporal analysis and geographic knowledge discovery. This has given rise to SOLAP (Spatial OLAP) and a new research area. This paper presents how SOLAP and climate-related health vulnerability data were investigated and combined to facilitate surveillance.ResultsBased on recent spatial decision-support technologies, this paper presents a spatio-temporal web-based application that goes beyond GIS applications with regard to speed, ease of use, and interactive analysis capabilities. It supports the multi-scale exploration and analysis of integrated socio-economic, health and environmental geospatial data over several periods. This project was meant to validate the potential of recent technologies to contribute to a better understanding of the interactions between public health and climate change, and to facilitate future decision-making by public health agencies and municipalities in Canada and elsewhere. The project also aimed at integrating an initial collection of geo-referenced multi-scale indicators that were identified by Canadian specialists and end-users as relevant for the surveillance of the public health impacts of climate change. This system was developed in a multidisciplinary context involving researchers, policy makers and practitioners, using BI and web-mapping concepts (more particularly SOLAP technologies), while exploring new solutions for frequent automatic updating of data and for providing contextual warnings for users (to minimize the risk of data misinterpretation). According to the project participants, the final system succeeds in facilitating surveillance activities in a way not achievable with today's GIS. Regarding the experiments on frequent automatic updating and contextual user warnings, the results obtained indicate that these are meaningful and achievable goals but they still require research and development for their successful implementation in the context of surveillance and multiple organizations.ConclusionSurveillance of climate-related health vulnerabilities may be more efficiently supported using a combination of BI and GIS concepts, and more specifically, SOLAP technologies (in that it facilitates and accelerates multi-scale spatial and temporal analysis to a point where a user can maintain an uninterrupted train of thought by focussing on "what" she/he wants (not on "how" to get it) and always obtain instant answers, including to the most complex queries that take minutes or hours with OLTP systems (e.g., aggregated, temporal, comparative)). The developed system respects Newell's cognitive band of 10 seconds when performing knowledge discovery (exploring data, looking for hypotheses, validating models). The developed system provides new operators for easily and rapidly exploring multidimensional data at different levels of granularity, for different regions and epochs, and for visualizing the results in synchronized maps, tables and charts. It is naturally adapted to deal with multiscale indicators such as those used in the surveillance community, as confirmed by this project's end-users.

Investigating the use of a multi-agent system for knowledge discovery in databases

This paper presents an agent-based system which is capable of performing knowledge discovery from database, including image databases. The proposed system is composed of several knowledge discovery (KD) agents which are responsible for data preparation and data mining. In the system, each agent has its own data mining method and they can have different answers to the same input pattern. In this sense, a negotiation method was proposed. As a result of this method, a commonly agreed output for an input pattern is provided by the system.

Case-Based Reasoning for Knowledge Discovery

The Georgia Tech Research Institute's goal in the Novel Intelligence in Massive Data (NIMD) Program, is to investigate certain aspects of an intelligence analyst's preferences and analytic strategies used in the process of discovering new knowledge. We are analyzing search strategies used by analysts in an attempt to understand their current task model. Based on this understanding, we will design and prototype a software tool that applies case-based reasoning in combination with other advanced reasoning techniques to help analysts perform knowledge discovery. The main objective of our work is the development, validation and incremental improvement of a set of knowledge discovery automation aids that significantly reduces the manual searches done by intelligence analysts and increases the quality and quantity of derived intelligence. We believe that these technical improvements will depend on our explicit understanding of the cognitive issues implied by the use of advanced reasoning techniques, integrated into a next generation NIMD prototype.

Knowledge discovery in textual documentation: qualitative and quantitative analyses

This paper presents an approach for performing knowledge discovery in texts through qualitative and quantitative analyses of high‐level textual characteristics. Instead of applying mining techniques on attribute values, terms or keywords extracted from texts, the discovery process works over conceptss identified in texts. Concepts represent real world events and objects, and they help the user to understand ideas, trends, thoughts, opinions and intentions present in texts. The approach combines a quasi‐automatic categorisation task (for qualitative analysis) with a mining process (for quantitative analysis). The goal is to find new and useful knowledge inside a textual collection through the use of mining techniques applied over concepts (representing text content). In this paper, an application of the approach to medical records of a psychiatric hospital is presented. The approach helps physicians to extract knowledge about patients and diseases. This knowledge may be used for epidemiological studies, for training professionals and it may be also used to support physicians to diagnose and evaluate diseases.

Perspective of webometrics

Since the mid-1990s has emerged a new research field, webometrics, investigating the nature and properties of the Web drawing on modern informetric methodologies. The article attempts to point to selected areas of webometric research that demonstrate interesting progress and space for development as well as to some currently less promising areas. Recent investigations of search engine coverage and performance are reviewed as a frame for selected quality and content analyses. Problems with measuring Web Impact Factors (Web-IF) are discussed. Concluding the article, new directions of webometrics are outlined for performing knowledge discovery and issue tracking on the Web, partly based on bibliometric methodologies used in bibliographic and citation databases. In this framework graph theoretic approaches, including path analysis, transversal links, “weak ties” and “small-world” phenomena are integrated.

Some privacy issues in knowledge discovery: the OECD personal privacy guidelines

Several countries have generated principles to protect individuals from the potential invasion of privacy that data collection and retrieval poses. The Organization for Economic Cooperation and Development (OECD) has provided probably the best known set of guidelines. A number of countries have adopted these guidelines as statutory law, in whole or in part. The OECD has specific guidelines pertaining to data privacy that directly affect those performing knowledge discovery generally, and those who use so called "personal data" in particular. The article addresses such questions as: What are the implications of the existing privacy guidelines, especially those of the OECD, for knowledge discovery? What are the limitations of these guidelines? How do the restrictions on knowledge discovery about individuals affect knowledge discovery on groups? How do legal systems influence knowledge discovery?.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>