A novel approach integrating ranking functions discovery, optimization and infernce to improve retrieval performance

The significant roles play by ranking function in the performance and success of Information Retrieval (IR) systems and search engines cannot be underestimated. Diverse ranking functions are available in IR literature. However, empirical studies show that ranking functions do not perform constantly well across different contexts (queries, collections, users). In this study, a novel three-stage integrated ranking framework is proposed for implementing discovering, optimizing and inference rankings used in IR systems. The first phase, discovery process is based on Genetic Programming (GP) approach which smartly combines structural and contents features in the documents while the second phase, optimization process is based on Genetic Algorithm (GA) which combines document retrieval scores of various well-known ranking functions. In the 3rd phase, Fuzzy inference proves as soft search constraints to be applied on documents. We demonstrate how these two features are combined to bring new tasks and processes within the three concept stages of integrated framework for effective IR

Local search: A guide for the information retrieval practitioner

There are a number of combinatorial optimisation problems in information retrieval in which the use of local search methods are worthwhile. The purpose of this paper is to show how local search can be used to solve some well known tasks in information retrieval (IR), how previous research in the field is piecemeal, bereft of a structure and methodologically flawed, and to suggest more rigorous ways of applying local search methods to solve IR problems. We provide a query based taxonomy for analysing the use of local search in IR tasks and an overview of issues such as fitness functions, statistical significance and test collections when conducting experiments on combinatorial optimisation problems. The paper gives a guide on the pitfalls and problems for IR practitioners who wish to use local search to solve their research issues, and gives practical advice on the use of such methods. The query based taxonomy is a novel structure which can be used by the IR practitioner in order to examine the use of local search in IR

Technology in the 21st Century: New Challenges and Opportunities

Although big data, big data analytics (BDA) and business intelligence have attracted growing attention of both academics and practitioners, a lack of clarity persists about how BDA has been applied in business and management domains. In reflecting on Professor Ayre's contributions, we want to extend his ideas on technological change by incorporating the discourses around big data, BDA and business intelligence. With this in mind, we integrate the burgeoning but disjointed streams of research on big data, BDA and business intelligence to develop unified frameworks. Our review takes on both technical and managerial perspectives to explore the complex nature of big data, techniques in big data analytics and utilisation of big data in business and management community. The advanced analytics techniques appear pivotal in bridging big data and business intelligence. The study of advanced analytics techniques and their applications in big data analytics led to identification of promising avenues for future research

On strategies for imbalanced text classification using SVM: A comparative study

Many real-world text classification tasks involve imbalanced training examples. The strategies proposed to address the imbalanced classification (e.g., resampling, instance weighting), however, have not been systematically evaluated in the text domain. In this paper, we conduct a comparative study on the effectiveness of these strategies in the context of imbalanced text classification using Support Vector Machines (SVM) classifier. SVM is the interest in this study for its good classification accuracy reported in many text classification tasks. We propose a taxonomy to organize all proposed strategies following the training and the test phases in text classification tasks. Based on the taxonomy, we survey the methods proposed to address the imbalanced classification. Among them, 10 commonly-used methods were evaluated in our experiments on three benchmark datasets, i.e., Reuters-21578, 20-Newsgroups, and WebKB. Using the area under the Precision–Recall Curve as the performance measure, our experimental results showed that the best decision surface was often learned by the standard SVM, not coupled with any of the proposed strategies. We believe such a negative finding will benefit both researchers and application developers in the area by focusing more on thresholding strategies

A Multidisciplinary Perspective of Big Data in Management Research

In recent years, big data has emerged as one of the prominent buzzwords in business and management. In spite of the mounting body of research on big data across the social science disciplines, scholars have offered little synthesis on the current state of knowledge. To take stock of academic research that contributes to the big data revolution, this paper tracks scholarly work's perspectives on big data in the management domain over the past decade. We identify key themes emerging in management studies and develop an integrated framework to link the multiple streams of research in fields of organisation, operations, marketing, information management and other relevant areas. Our analysis uncovers a growing awareness of big data's business values and managerial changes led by data-driven approach. Stemming from the review is the suggestion for research that both structured and unstructured big data should be harnessed to advance understanding of big data value in informing organisational decisions and enhancing firm competitiveness. To discover the full value, firms need to formulate and implement a data-driven strategy. In light of these, the study identifies and outlines the implications and directions for future research

Uso da web de dados como fonte de informação no processo de inteligência competitiva setorial

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia e Gestão do Conhecimento, Florianópolis, 2014.Aproximadamente oitenta por cento da informação necessária em um processo de Inteligência Competitiva (IC) pode ser obtida de fontes abertas. Porém, a falta de semântica desse tipo de fontes dificulta a dedução dos objetos e de seus relacionamentos. Essas dificuldades restringem a tarefa de recuperação de informação, fazendo da captura de conhecimento uma atividade particularmente difícil. A Web of Data avança nesse sentido ao possibilitar um espaço global de dados com conexões explicitas entre os conjuntos e com mecanismos padrão para acessar e processar os dados. Assim, este trabalho propõe alinhar o processo de IC à esta fonte de dados. Para tanto, é proposto um modelo composto por tarefas estruturadas de identificação, seleção e classificação da informação baseado em setores econômicos, que objetiva facilitar a recuperação e o uso da informação na etapa de coleta do ciclo de IC. Espera-se com isso que organizações possam explorar novas fontes de conhecimento, diminuir os esforços de coleta devido à estruturação da informação, e consequentemente, obter melhor posição estratégica. A verificação do modelo se deu pela sua aplicação no setor de Eletricidade e Gás, pela identificação dos requisitos de IC e pela coleta dos dados pertencentes ao setor escolhido.Abstract : Nearly eighty percent of the information needed for a process of Competitive Intelligence (CI) can be obtained from open sources. However, the lack of semantics of this kind of source complicates the deduction of objects and their relationships. These difficulties restrict the information retrieval task and make knowledge capture a particularly hard activity. The Web of Data moves in this direction, by allowing a global data space with explicit connections between datasets and standard mechanisms to access and process data. So, this paper proposes to align the CI process to this data source. To this end, we propose a model composed for structured identification, selection and classification of information based on economic sectors, which aims to facilitate the retrieval and use of the information in the collection stage of the CI cycle tasks. It is expected that organizations can exploit this new knowledge sources, reduce efforts due to the structuring of information, and hence get better strategic position. The model was validated in the Electricity and Gas sector to identify the requirements of CI and the collection of data belonging to sector