The Experts below are selected from a list of 404985 Experts worldwide ranked by ideXlab platform

Carlos Agon - One of the best experts on this subject based on the ideXlab platform.

  • Time-series Data Mining
    ACM Computing Surveys, 2012
    Co-Authors: Philippe Esling, Carlos Agon
    Abstract:

    In almost every scientific field, measurements are performed over time. These observations lead to a collection of organized Data called time series. The purpose of time-series Data Mining is to try to extract all meaningful knowledge from the shape of Data. Even if humans have a natural capacity to perform these tasks, it remains a complex problem for computers. In this article we intend to provide a survey of the techniques applied for time-series Data Mining. The first part is devoted to an overview of the tasks that have captured most of the interest of researchers. Considering that in most cases, time-series task relies on the same components for implementation, we divide the literature depending on these common aspects, namely representation techniques, distance measures, and indexing methods. The study of the relevant literature has been categorized for each individual aspects. Four types of robustness could then be formalized and any kind of distance could then be classified. Finally, the study submits various research trends and avenues that can be explored in the near future.We hope that this article can provide a broad and deep understanding of the time-series Data Mining research field.

Philippe Esling - One of the best experts on this subject based on the ideXlab platform.

  • Time-series Data Mining
    ACM Computing Surveys, 2012
    Co-Authors: Philippe Esling, Carlos Agon
    Abstract:

    In almost every scientific field, measurements are performed over time. These observations lead to a collection of organized Data called time series. The purpose of time-series Data Mining is to try to extract all meaningful knowledge from the shape of Data. Even if humans have a natural capacity to perform these tasks, it remains a complex problem for computers. In this article we intend to provide a survey of the techniques applied for time-series Data Mining. The first part is devoted to an overview of the tasks that have captured most of the interest of researchers. Considering that in most cases, time-series task relies on the same components for implementation, we divide the literature depending on these common aspects, namely representation techniques, distance measures, and indexing methods. The study of the relevant literature has been categorized for each individual aspects. Four types of robustness could then be formalized and any kind of distance could then be classified. Finally, the study submits various research trends and avenues that can be explored in the near future.We hope that this article can provide a broad and deep understanding of the time-series Data Mining research field.

Melanie Hilario - One of the best experts on this subject based on the ideXlab platform.

  • The Data Mining OPtimization Ontology
    Journal of Web Semantics, 2015
    Co-Authors: C. Maria Keet, Agnieszka Ławrynowicz, Alexandros Kalousis, Raul Palma, Phong Nguyen, Claudia Amato, Robert Stevens, Melanie Hilario
    Abstract:

    Abstract The Data Mining OPtimization Ontology (DMOP) has been developed to support informed decision-making at various choice points of the Data Mining process. The ontology can be used by Data miners and deployed in ontology-driven information systems. The primary purpose for which DMOP has been developed is the automation of algorithm and model selection through semantic meta-Mining that makes use of an ontology-based meta-analysis of complete Data Mining processes in view of extracting patterns associated with Mining performance. To this end, DMOP contains detailed descriptions of Data Mining tasks (e.g., learning, feature selection), Data, algorithms, hypotheses such as mined models or patterns, and workflows. A development methodology was used for DMOP, including items such as competency questions and foundational ontology reuse. Several non-trivial modeling problems were encountered and due to the complexity of the Data Mining details, the ontology requires the use of the OWL 2 DL profile. DMOP was successfully evaluated for semantic meta-Mining and used in constructing the Intelligent Discovery Assistant, deployed at the popular Data Mining environment RapidMiner.

Ali Bou Nassif - One of the best experts on this subject based on the ideXlab platform.

  • Data Mining techniques in social media
    Neurocomputing, 2016
    Co-Authors: Mohammadnoor Injadat, Fadi Salo, Ali Bou Nassif
    Abstract:

    Today, the use of social networks is growing ceaselessly and rapidly. More alarming is the fact that these networks have become a substantial pool for unstructured Data that belong to a host of domains, including business, governments and health. The increasing reliance on social networks calls for Data Mining techniques that is likely to facilitate reforming the unstructured Data and place them within a systematic pattern. The goal of the present survey is to analyze the Data Mining techniques that were utilized by social media networks between 2003 and 2015. Espousing criterion-based research strategies, 66 articles were identified to constitute the source of the present paper. After a careful review of these articles, we found that 19 Data Mining techniques have been used with social media Data to address 9 different research objectives in 6 different industrial and services domains. However, the Data Mining applications in the social media are still raw and require more effort by academia and industry to adequately perform the job. We suggest that more research be conducted by both the academia and the industry since the studies done so far are not sufficiently exhaustive of Data Mining techniques.

Wee Keong Ng - One of the best experts on this subject based on the ideXlab platform.

  • research issues in web Data Mining
    Data Warehousing and Knowledge Discovery, 1999
    Co-Authors: Sanjay Kumar Madria, Sourav S Bhowmick, Wee Keong Ng
    Abstract:

    In this paper, we discuss Mining with respect to web Data referred here as web Data Mining. In particular, our focus is on web Data Mining research in context of our web warehousing project called WHOWEDA (Warehouse of Web Data). We have categorized web Data Mining into threes areas; web content Mining, web structure Mining and web usage Mining. We have highlighted and discussed various research issues involved in each of these web Data Mining category. We believe that web Data Mining will be the topic of exploratory research in near future.