Collaborative tagging supported knowledge discovery

Web 2.0 has brought about a new sort of user centred services which rely a great deal on flexible organizational capabilities designed for user-supplied organization. Collaborative tagging is especially interesting in this context and this article explores what this kind of organization in connection with some Web 2.0 principles means for knowledge discovery in various ways. To fully explore this, the article defines collaborative tagging and gives an overview of collaborative tagging in general, of services using it and of tags themselves. It concludes with mechanisms this kind of approach to knowledge organization provides for knowledge discovery

Grid Service Discovery in the Financial Markets Sector

Investment Banking requires a diverse system set in supporting a range of markets from bonds to trading options on weather. The challenge to this community is the ability to adapt to new business requirements in an effective manner, utilizing their network of capabilities in a flexible, dynamic way. A semantic approach to discovery can be used in a pragmatic, practical manner. The use of richer explicit knowledge, that is system readable, provides the basis for discovering capabilities on this exemplar Business Grid—“the grid of services”. This design research project focuses on the utilization of disparate knowledge during discovery

KNODWAT: A scientific framework application for testing knowledge discovery methods for the biomedical domain

BACKGROUND: Professionals in the biomedical domain are confronted with an increasing mass of data. Developing methods to assist professional end users in the field of Knowledge Discovery to identify, extract, visualize and understand useful information from these huge amounts of data is a huge challenge. However, there are so many diverse methods and methodologies available, that for biomedical researchers who are inexperienced in the use of even relatively popular knowledge discovery methods, it can be very difficult to select the most appropriate method for their particular research problem. RESULTS: A web application, called KNODWAT (KNOwledge Discovery With Advanced Techniques) has been developed, using Java on Spring framework 3.1. and following a user-centered approach. The software runs on Java 1.6 and above and requires a web server such as Apache Tomcat and a database server such as the MySQL Server. For frontend functionality and styling, Twitter Bootstrap was used as well as jQuery for interactive user interface operations. CONCLUSIONS: The framework presented is user-centric, highly extensible and flexible. Since it enables methods for testing using existing data to assess suitability and performance, it is especially suitable for inexperienced biomedical researchers, new to the field of knowledge discovery and data mining. For testing purposes two algorithms, CART and C4.5 were implemented using the WEKA data mining framework

Clinically driven semi-supervised class discovery in gene expression data

Abstract Motivation: Unsupervised class discovery in gene expression data relies on the statistical signals in the data to exclusively drive the results. It is often the case, however, that one is interested in constraining the search space to respect certain biological prior knowledge while still allowing a flexible search within these boundaries. Results: We develop an approach to semi-supervised class discovery. One component of our approach uses clinical sample information to constrain the search space and guide the class discovery process to yield biologically relevant partitions. A second component consists of using known biological annotation of genes to drive the search, seeking partitions that manifest strong differential expression in specific sets of genes. We develop efficient algorithmics for these tasks, implementing both approaches and combinations thereof. We show that our method is robust enough to detect known clinical parameters in accordance with expected clinical values. We also use our method to elucidate cardiovascular disease (CVD) putative risk factors. Availability: MonoClaD (Monotone Class Discovery). See http://bioinfo.cs.technion.ac.il/people/zohar/MonoClad/ Supplementary information: Supplementary data is available at http://bioinfo.cs.technion.ac.il/people/zohar/MonoClad/software.html Contact: [email protected]

Towards Role Based Hypothesis Evaluation for Health Data Mining

Data mining researchers have long been concerned with the application of tools to facilitate and improve data analysis on large, complex data sets. The current challenge is to make data mining and knowledge discovery systems applicable to a wider range of domains, among them health. Early work was performed over transactional, retail based data sets, but the attraction of finding previously unknown knowledge from the ever increasing amounts of data collected from the health domain is an emerging area of interest and specialisation. The problem is finding a solution that is suitably flexible to allow for generalised application whilst being specific enough to provide functionality that caters for the nuances of each role within the domain. The need for a more granular approach to problem solving in other areas of information technology has resulted in the use of role based solutions. This paper discusses the progress to date in developing a role oriented solution to the problem of providing for the diverse requirements of health domain data miners and defining the foundation for determining what constitutes an interesting discovery in an area as complex as health

A Network-Graph Based IT Artifact Aiding the Theory Building Process

To support theory building, we introduce a network-graph based IT artifact to provide high recall during exploratory searches and high precision using knowledge gained through the literature discovery process. The use of network graphs, where all data is represented as a node, relationship, or property of either, offers a flexible and tailorable methodology able to accommodate the highly iterative process of theory building. This IT artifact was developed to enable aggregation and normalization of data from varied sources and formats to support the acquisition and assessment of literature needed throughout this process. Our goal in presenting this IT artifact is to promote an accessible and pragmatic approach addressing the varied challenges of Information Systems researchers during the information seeking process

Visualization-driven Structural and Statistical Analysis of Turbulent Flows

Knowledge extraction from data volumes of ever increasing size requires ever more flexible tools to facilitate interactive query. In- teractivity enables real-time hypothesis testing and scientific discovery, but can generally not be achieved without some level of data reduction. The approach described in this paper combines multi-resolution access, region-of-interest extraction, and structure identification in order to pro- vide interactive spatial and statistical analysis of a terascale data volume. Unique aspects of our approach include the incorporation of both local and global statistics of the flow structures, and iterative refinement fa- cilities, which combine geometry, topology, and statistics to allow the user to effectively tailor the analysis and visualization to the science. Working together, these facilities allow a user to focus the spatial scale and domain of the analysis and perform an appropriately tailored mul- tivariate visualization of the corresponding data. All of these ideas and algorithms are instantiated in a deployed visualization and analysis tool called VAPOR, which is in routine use by scientists internationally. In data from a 10243 simulation of a forced turbulent flow, VAPOR allowed us to perform a visual data exploration of the flow properties at interac- tive speeds, leading to the discovery of novel scientific properties of the flow, in the form of two distinct vortical structure populations. These structures would have been very difficult (if not impossible) to find with statistical overviews or other existing visualization-driven analysis ap- proaches. This kind of intelligent, focused analysis/refinement approach will become even more important as computational science moves to- wards petascale applications

A Network-Graph Based IT Artifact Aiding the Theory Building Process

Proceedings of the 55th Hawaii International Conference on System Sciences | 2022The article of record at published may be found at https://hdl.handle.net/10125/80136To support theory building, we introduce a network-graph based IT artifact to provide high recall during exploratory searches and high precision using knowledge gained through the literature discovery process. The use of network graphs, where all data is represented as a node, relationship, or property of either, offers a flexible and tailorable methodology able to accommodate the highly iterative process of theory building. This IT artifact was developed to enable aggregation and normalization of data from varied sources and formats to support the acquisition and assessment of literature needed throughout this process. Our goal in presenting this IT artifact is to promote an accessible and pragmatic approach addressing the varied challenges of Information Systems researchers during the information seeking process