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With the spreading prevalence of Big Data, many advances have recently been made in this field. Frameworks such as Apache Hadoop and Apache Spark have gained a lot of traction over the past decades and have become massively popular, especially in industries. It is becoming increasingly evident that effective big data analysis is key to solving artificial intelligence problems. Thus, a multi-algorithm...
This paper presents an implementation of the conformal voting scheme using a reconfigurable hardware approach in the frame of conformal geometric algebra $\mathcal {G}_{3,1}$. This algorithm is able to extract geometric entities, such as circles and lines from edged images. The conformal voting scheme is divided into two main stages: a local stage computed using neighborhoods in the image, and a...
To facilitate system architecture development using DoDAF, we propose an architecture development methodology based on DoDAF 2.0 in this paper. First, based on experiences in practice, gaps between Operational Viewpoint (OV), Capability Viewpoint (CV) and System/Service Viewpoint (SV/SvcV) are analyzed. Second, to bridge the gaps, two architecture viewpoints, i.e. Information activity Viewpoint (IaV)...
Re-engineering is essential for maintaining the competitiveness of enterprises. Enterprise re-engineering involves addressing (emergent) changes, re-organizing, outsourcing, realigning, etc. In this paper, we investigate how to re-engineer enterprises by coupling high-level requirements and the data warehouse, leading to a process re-design that makes the business layer of an enterprise more effective...
In this paper we present a reference architecture for ETL stages of EDM and LA that works with different data formats and different extraction sites, ensuring privacy and making easier for new participants to enter into the process without demanding more computing power. Considering scenarios with a multitude of virtual environments hosting educational activities, accessible through a common infrastructure,...
Due to data intensive and sophisticated tasks in scientific experiments, workflows have been widely used to enable repetitive task automation and data reproducibility. This yields to the need for effective and efficient search mechanisms for scientific workflows discovery as workflow retrieval systems require a model which fulfills several requirements: unification, accuracy, and rich representations...
Research data repositories are necessary infrastructures that ensure the data generated for research are accessible, stable, reliable, and reusable. Based on years of accumulated data work experience, the Computer Network Information Center of the Chinese Academy of Sciences has built a multi-disciplinary data repository ScienceDB for research users and teams using its big data storage, analysis and...
The paper proposes a methodology for the development of a marketing decision support system using Big Data technology and data mining techniques. The approach was inspired by the CRISP-DM methodology, which is not oriented towards Big Data projects. Therefore, we have modified this methodology with respect to the purpose and technological requirements of the project. The proposed methodology was tested...
Data with high volume, velocity, variety and veracity brings the new experience curve of analytics. Big data in higher education comes from different sources that include blogs, social networks, student information systems, learning management systems, research, and other machine-generated data. Once the data is analysed it promises better student placement processes; more accurate enrolment forecasts,...
Prediction markets have substantially grown during the last years. In particular, sports forecasting is an important field of application. In this paper we present a new forecasting system oriented to sports. This proposal is a multi-agent system conformed by several intelligent and independent agents. The agents perform different complementary tasks. This heterogeneous approach provides a powerful...
Extreme Learning Machine (ELM) is a neural network architecture with Single Layer Feed-forward Neural Network (SLFN). For meaningful results, the structure of ELM has to be optimized through the inclusion of regularization and the ℓ2 — norm based regularization is mostly used. ℓ2-norm based regularization achieves better performance than the traditional ELM. The estimate of the regularization parameter...
In recent years, the increasing number of cyberattacks has gained the development of innovative tools to quickly detect new threats. A recent approach to this problem is to analyze the content of Social Networks to discover the rising of new malicious software. Twitter is a popular social network which allows millions of users to share their opinions on what happens all over the world. The subscribers...
Software refactoring aims at optimizing software modularization by improving internal software structure without altering its external behavior. There exists various approaches for suggesting refactoring opportunities, based on different sources of information, e.g., structural, semantic, and historical. In this paper, we propose a data fusion model to combine different sources of information in order...
The exponential growth of complex, heterogeneous, dynamic, and unbounded data, generated by a variety of fields including health, genomics, physics, climatology, and social networks pose significant challenges in data processing and desired speed-performance. Existing processor-based software-only algorithms are incapable of analyzing and processing this enormous amount of data, efficiently and effectively...
Intrusion detection systems (IDS) support the recognition of attacks, based on the analysis of either network traffic data (Network-based IDS) or application/system logs stored in a host (Host-based IDS). Exploiting heterogeneous data coming from both kinds of sources could be useful to detect coordinated attacks and to reduce the number of false alarms, but poses challenges in terms of both information...
Different information visualization techniques can be found in the literature due to the quantity and variety of data stored in computational systems. In this context, the classification of chart images becomes important because it allows various types of graphs to be detected automatically in different contexts, allowing a more specific processing for each type of visualization, for example, data...
In the context of Internet of Things (IoT), the amount of data collected from physical objects is ever increasing. However, in the whole infrastructure there are several bottlenecks related especially to hardware limitations (low end processors on devices and communication means), but also time, therefore analyzing this data requires knowledge about the available computational resources. In this article...
Image categorization is the process of categorizing the images into its respective class or bins. It is still challenging problem in computer vision key area. The existing methodologies for image categorization like semantic modelling approaches, neural network approaches does not provides an accurate solution. This is due to inefficient feature extraction and their processing. Deep Learning is a...
Personalization in the field of Technology Enhanced Learning (TEL) is a topic that received a lot of concern by researchers. At the same time, there is a growing amount of Open Educational Resources (OER) indexed according to the W3C standards. Relevant OERs can usefully complement the contents delivered to a learner during an online course. Computing the best OERs to offer to the learner at each...
The need of smart information retrieval systems is in contrast with the difficulties to deal with huge amount of data. In this paper we present a Big Data Analytics architecture used to implement a semantic similarity search tool for natural language texts in biomedical domain. The implemented methodology is based on Word Embeddings (WEs) models obtained using the word2vec algorithm. The system has...
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