23-bit Metaknowledge Template Towards Big Data Knowledge Discovery and Management

The global influence of Big Data is not only growing but seemingly endless. The trend is leaning towards knowledge that is attained easily and quickly from massive pools of Big Data. Today we are living in the technological world that Dr. Usama Fayyad and his distinguished research fellows discussed in the introductory explanations of Knowledge Discovery in Databases (KDD) predicted nearly two decades ago. Indeed, they were precise in their outlook on Big Data analytics. In fact, the continued improvement of the interoperability of machine learning, statistics, database building and querying fused to create this increasingly popular science- Data Mining and Knowledge Discovery. The next generation computational theories are geared towards helping to extract insightful knowledge from even larger volumes of data at higher rates of speed. As the trend increases in popularity, the need for a highly adaptive solution for knowledge discovery will be necessary. In this research paper, we are introducing the investigation and development of 23 bit-questions for a Metaknowledge template for Big Data Processing and clustering purposes. This research aims to demonstrate the construction of this methodology and proves the validity and the beneficial utilization that brings Knowledge Discovery from Big Data.Comment: IEEE Data Science and Advanced Analytics (DSAA'2014

Novel Metaknowledge-based Processing Technique for Multimedia Big Data clustering challenges

Past research has challenged us with the task of showing relational patterns between text-based data and then clustering for predictive analysis using Golay Code technique. We focus on a novel approach to extract metaknowledge in multimedia datasets. Our collaboration has been an on-going task of studying the relational patterns between datapoints based on metafeatures extracted from metaknowledge in multimedia datasets. Those selected are significant to suit the mining technique we applied, Golay Code algorithm. In this research paper we summarize findings in optimization of metaknowledge representation for 23-bit representation of structured and unstructured multimedia data in order toComment: IEEE Multimedia Big Data (BigMM 2015

FSL-BM: Fuzzy Supervised Learning with Binary Meta-Feature for Classification

This paper introduces a novel real-time Fuzzy Supervised Learning with Binary Meta-Feature (FSL-BM) for big data classification task. The study of real-time algorithms addresses several major concerns, which are namely: accuracy, memory consumption, and ability to stretch assumptions and time complexity. Attaining a fast computational model providing fuzzy logic and supervised learning is one of the main challenges in the machine learning. In this research paper, we present FSL-BM algorithm as an efficient solution of supervised learning with fuzzy logic processing using binary meta-feature representation using Hamming Distance and Hash function to relax assumptions. While many studies focused on reducing time complexity and increasing accuracy during the last decade, the novel contribution of this proposed solution comes through integration of Hamming Distance, Hash function, binary meta-features, binary classification to provide real time supervised method. Hash Tables (HT) component gives a fast access to existing indices; and therefore, the generation of new indices in a constant time complexity, which supersedes existing fuzzy supervised algorithms with better or comparable results. To summarize, the main contribution of this technique for real-time Fuzzy Supervised Learning is to represent hypothesis through binary input as meta-feature space and creating the Fuzzy Supervised Hash table to train and validate model.Comment: FICC201

A Prototype Method and Tool to Facilitate Knowledge Sharing in the New Product Development Process

New Product Development (NPD) plays a critical role in the success of manufacturing firms. Activities in the product development process are dependent on the exchange of knowledge among NPD project team members. Increasingly, many organisations consider effective knowledge sharing to be a source of competitive advantage. However, the sharing of knowledge is often inhibited in various ways. This doctoral research presents an exploratory case study conducted at a multinational physical goods manufacturer. This investigation uncovered three, empirically derived and theoretically informed, barriers to knowledge sharing. They have been articulated as the lack of an explicit definition of information about the knowledge used and generated in the product development process, and the absence of mechanisms to make this information accessible in a multilingual environment and to disseminate it to NPD project team members. Collectively, these barriers inhibit a shared understanding of product development process knowledge. Existing knowledge management methodologies have focused on the capture of knowledge, rather than providing information about the knowledge and have not explicitly addressed issues regarding knowledge sharing in a multilingual environment. This thesis reports a prototype method and tool to facilitate knowledge sharing that addresses all three knowledge sharing barriers. Initially the research set out to identify and classify new product development process knowledge and then sought to determine what information about specific knowledge items is required by project teams. Based on the exploratory case findings, an ontology has been developed that formally defines information about this knowledge and allows it to be captured in a knowledge acquisition tool, thereby creating a knowledge base. A mechanism is provided to permit language labels to be attached to concepts and relations in the ontology, making it accessible to speakers of different languages. A dissemination tool allows the ontology and knowledge base to be viewed via a Web browser client. Essentially, the ontology and mechanisms facilitate a knowledge sharing capability. Some initial validation was conducted to better understand implementation issues and future deployment of the prototype method and tool in practice

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

The Impact of Social media on Innovation in Small and Medium-Sized Businesses

This research explores the impact of social media on innovation in small and medium-sized businesses. Research during the recent years suggest that information systems in general and social media platforms in particular play a significant role in empowering open innovation networks, which involve a diverse set of partners, and have been known a key driver for the sustainable development of new products and services in organizations. Social media platforms present an opportunity for firms to create online communities where users engage in collaborative practices to create value by submitting product reviews, providing feedback, generating ideas, suggesting new solutions to the problems, and identifying new sources of innovation. There is a growing body of literature suggesting SMEs can reap significant benefits if they use social media to collaborate with their external partners, suppliers, customers, and other stakeholders, and to engage in open innovation activities with them, perhaps because they lack sufficient resources such as time, budget, and expertise, to innovate on their own. These benefits can be co-creation of new solutions, increased efficiency saving and economies of scale, improved metadata (knowledge of who knows what and who knows whom), and enhanced individual and organizational learning. However, previous studies have rarely examined the complexity of actual implementation of open innovation in the context of SMEs. Particularly, there have been few empirical studies to examine how social media can be integrated into the innovation process of SMEs. To examine the entire process of social media-enabled innovation in SMEs, this research has set out to address a main research question by exploring two sub-research questions as follow: How do social media-based interactions influence the innovation practices of small and medium-sized businesses? I. How does social media influence information sharing between small and medium-sized businesses and their external stakeholders? II. How is information from social media used internally by small and medium-sized businesses to support their innovation practices? The research focuses on two qualitative case studies of UK-based SMEs active in the education resources development, and legal aid services sectors. Netnography and semi-structured interviews were selected as the main methods for developing the case studies. In each case study, netnographic data was collected from the company’s social media interactions with external stakeholders. This was followed by semi-structured interviews with the key informants from each organization. The case studies were guided by the grounded theory principles, which also informed the assessment and analysis of the collected data to develop a new theoretical model that conceptualizes the social media-enabled innovation in the context of case studies. Hence, the newly-developed model has emerged from the empirical data and has been verified against the identified concepts from the literature review. The new model includes four main stages which are: Branding and socialization, information sharing, information use, and maturity. Each stage consists of two key components contributing to the fulfilment of the objectives set out for that stage. The research also identified two contextual factors that are likely to impact the successful adoption of the model in organizations. These two factors are: community culture and company size. This research is among the few empirical studies which have attempted to examine the end-to-end process of social media-enabled innovation in the context of SMEs and the methodological approach is novel in research into education resources development and legal aid services sectors