An Autonomic Approach to Real-Time Predictive Analytics Using Open Data and Internet of Things

Derguech, Wassim; Bruke, Eanna; Curry, Edward

doi:10.1109/uic-atc-scalcom.2014.137

Cited by 28 publications

(13 citation statements)

References 13 publications

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“…The objective of the models applied in these algorithms is to process and train data of high velocity. For example [48,46] applied linear regression algorithm for real-time prediction. Another fast training algorithm is the classification and regression tree described in 5.3.1, applied in classifying Smart Citizen behaviors [48,47].…”

Section: Discussion On Taxonomy Of Machine Learning Algorithmsmentioning

confidence: 99%

Machine learning for internet of things data analysis: a survey

Mahdavinejad

Rezvan

Barekatain

et al. 2018

Digital Communications and Networks

862

378

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Rapid developments in hardware, software, and communication technologies have allowed the emergence of Internet-connected sensory devices that provide observation and data measurement from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25-50 billion. As the numbers grow and technologies become more mature, the volume of data published will increase. Internet-connected devices technology, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interaction between the physical and cyber worlds. In addition to increased volume, the IoT generates Big Data characterized by velocity in terms of time and location dependency, with a

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Section: Discussion On Taxonomy Of Machine Learning Algorithmsmentioning

confidence: 99%

Machine learning for internet of things data analysis: a survey

Mahdavinejad

Rezvan

Barekatain

et al. 2018

Digital Communications and Networks

862

378

View full text Add to dashboard Cite

show abstract

“…However, the use of fixed basis functions can lead to significant shortcomings (e.g., an upsurge in input space dimensionality leads to a precipitous increase in the cardinality of the fundamental functions) [39]. Linear regression algorithms have a high execution rate [40]. For example, this algorithm is adept for analyzing and predicting buildings energy usage.…”

Section: Linear Regressionmentioning

confidence: 99%

Machine Learning Algorithms for Smart Data Analysis in Internet of Things Environment: Taxonomies and Research Trends

et al. 2020

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Machine learning techniques will contribution towards making Internet of Things (IoT) symmetric applications among the most significant sources of new data in the future. In this context, network systems are endowed with the capacity to access varieties of experimental symmetric data across a plethora of network devices, study the data information, obtain knowledge, and make informed decisions based on the dataset at its disposal. This study is limited to supervised and unsupervised machine learning (ML) techniques, regarded as the bedrock of the IoT smart data analysis. This study includes reviews and discussions of substantial issues related to supervised and unsupervised machine learning techniques, highlighting the advantages and limitations of each algorithm, and discusses the research trends and recommendations for further study.

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“…According to [20], the Internet of Things (IoT) and Open Data are particularly promising in real time predictive data analytics for effective decision support, and the dynamic selection of Open Data and IoT sources for that purpose is the main challenge. Data quality is tackled in [28], [29] and [30], where data quality problems in Semantic Web data are identified by means of data validation rules.…”

Section: Semantic Web Technologies For Kddmentioning

confidence: 99%

“…Smart Home Weather 20 is an OWL ontology that covers both the weather data and the concepts required to perform weather-related tasks within smart homes [70]. Apart from concepts such as weather phenomena and states that can be used to model external climatic condition, this ontology covers near future weather forecasting, making it suitable to use in a smart home scenario.…”

Section: Smarthomeweather Ontologymentioning

confidence: 99%

Semantic prediction assistant approach applied to energy efficiency in Tertiary buildings

Esnaola-Gonzalez

Bermúdez

Fernández

et al. 2018

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Fulfilling occupants' comfort whilst reducing energy consumption is still an unsolved problem in most of tertiary buildings. However, the expansion of the Internet of Things (IoT) and Knowledge Discovery in Databases (KDD) techniques lead to research this matter. In this paper the EEPSA (Energy Efficiency Prediction Semantic Assistant) process is presented, which leverages the Semantic Web Technologies (SWT) to enhance the KDD process for achieving energy efficiency in tertiary buildings while maintaining comfort levels. This process guides the data analyst through the different KDD phases in a semiautomatic manner and supports prescriptive HVAC control strategies. That is, temperature of a space is predicted simulating the activation of HVAC systems at different times and intensities, so that the facility manager can choose the strategy that best fits both the user's comfort needs and energy efficiency. Furthermore, results show that the proposed solution improves the accuracy of predictions.

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An Autonomic Approach to Real-Time Predictive Analytics Using Open Data and Internet of Things

Cited by 28 publications

References 13 publications

Machine learning for internet of things data analysis: a survey

Machine learning for internet of things data analysis: a survey

Machine Learning Algorithms for Smart Data Analysis in Internet of Things Environment: Taxonomies and Research Trends

Semantic prediction assistant approach applied to energy efficiency in Tertiary buildings

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