A Covering Classification Rule Induction Approach for Big Datasets

Kolias, Vasilis; Anagnostopoulos, Ioannis; Kayafas, E.

doi:10.1109/bdc.2014.17

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…Since our experimental results are quite encouraging, a next step could be the evaluation of the proposed framework for the prediction of stock exchange index movement and for forecasting the value of stock price indices and prices. Furthermore, another interesting aspect for future research could be the utilization of rule induction and discovery methods or even the use of synthetic data for further accuracy improvement based on the insights received in their training/testing periods (see [30,31] and the references therein). Finally, we intend to conduct extensive empirical experiments by applying the proposed algorithm in specific scientific fields and to evaluate its performance on large real-world datasets, such as educational, healthcare, etc.…”

Section: Discussionmentioning

confidence: 99%

Forecasting Economy-Related Data Utilizing Weight-Constrained Recurrent Neural Networks

Livieris

2019

Algorithms

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During the last few decades, machine learning has constituted a significant tool in extracting useful knowledge from economic data for assisting decision-making. In this work, we evaluate the performance of weight-constrained recurrent neural networks in forecasting economic classification problems. These networks are efficiently trained with a recently-proposed training algorithm, which has two major advantages. Firstly, it exploits the numerical efficiency and very low memory requirements of the limited memory BFGS matrices; secondly, it utilizes a gradient-projection strategy for handling the bounds on the weights. The reported numerical experiments present the classification accuracy of the proposed model, providing empirical evidence that the application of the bounds on the weights of the recurrent neural network provides more stable and reliable learning.

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Section: Discussionmentioning

confidence: 99%

Forecasting Economy-Related Data Utilizing Weight-Constrained Recurrent Neural Networks

Livieris

2019

Algorithms

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“…In the context of a Smart City, a vast number of diverse devices with various sensing capacities provide raw data. These devices are often dispersed around the city, allowing for real-time monitoring and the creation of a comprehensive picture of the city's current situation [32][33]. According to the proposed solution, to monitor and analyze the data, there are a few steps involved as shown in Figure 2.…”

Section: Basic Idea Of Processing Unified Data In Iot Systemmentioning

confidence: 99%

Optimal method to monitor network for IoT devices based on anomaly detection

ALİ,

CALİS

2024

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Many challenges have been identified to monitor, manage, process, and store the big data that accumulates from different sources in the IoT concept. The focus of this paper is very significant and limited to solving the problem of monitoring classified big data. Detection of anomalies in a grouping of classified data made it easy to monitor and help to make decisions for action to operate. There is no need to store, process, or manage the redundant data further that is already within the range of the group. So, the main concern is abnormal values in the groups that need to be processed further and require focus. The method proposed in this paper serves as an optimal solution designed to address the visualization challenges associated with dense and high-volume datasets. Our approach involves a strategic process of categorizing data into groups and pinpointing anomalies within these groups. This systematic classification not only enhances data organization but also plays a pivotal role in simplifying the visualization of intricate data patterns. Additionally, this method brings about significant cost efficiencies by strategically optimizing the expenses incurred in processing operations and the allocation of storage space for the equipment.

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“…Data are being generated from multiple scientific sources, including Smart Cities, the IoT, scientific modeling, or different big data simulations [1][2][3]; but also from users' social, professional or everyday activities. These daily fresh data are accumulated over other historical repositories, setting up the vast and complex universe of digital data.…”

Section: Introductionmentioning

confidence: 99%

Towards a comprehensive data lifecycle model for big data environments

Sinaeepourfard

García

Masip-Bruin

et al. 2016

Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies

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A huge amount of data is constantly being produced in the world. Data coming from the IoT, from scientific simulations, or from any other field of the eScience, are accumulated over historical data sets and set up the seed for future Big Data processing, with the final goal to generate added value and discover knowledge. In such computing processes, data are the main resource; however, organizing and managing data during their entire life cycle becomes a complex research topic. As part of this, Data LifeCycle (DLC) models have been proposed to efficiently organize large and complex data sets, from creation to consumption, in any field, and any scale, for an effective data usage and big data exploitation.Several DLC frameworks can be found in the literature, each one defined for specific environments and scenarios. However, we realized that there is no global and comprehensive DLC model to be easily adapted to different scientific areas. For this reason, in this paper we describe the Comprehensive Scenario Agnostic Data LifeCycle (COSA-DLC) model, a DLC model which: i) is proved to be comprehensive as it addresses the 6Vs challenges (namely Value, Volume, Variety, Velocity, Variability and Veracity; and ii), it can be easily adapted to any particular scenario and, therefore, fit the requirements of a specific scientific field. In this paper we also include two use cases to illustrate the ease of the adaptation in different scenarios. We conclude that the comprehensive scenario agnostic DLC model provides several advantages, such as facilitating global data management, organization and integration, easing the adaptation to any kind of scenario, guaranteeing good data quality levels and, therefore, saving design time and efforts for the scientific and industrial communities. CCS Concepts• Information systems➝Data management systems • Computer systems organization➝Architectures➝Other architectures➝ Data flow architectures • Software and its engineering➝ Software system structures➝ Data flow architectures

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A Covering Classification Rule Induction Approach for Big Datasets

Cited by 3 publications

References 15 publications

Forecasting Economy-Related Data Utilizing Weight-Constrained Recurrent Neural Networks

Forecasting Economy-Related Data Utilizing Weight-Constrained Recurrent Neural Networks

Optimal method to monitor network for IoT devices based on anomaly detection

Towards a comprehensive data lifecycle model for big data environments

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