Implementation of industry 4.0 technologies in the mining industry : a case study
- Sishi, M. N., Telukdarie, A.
- Authors: Sishi, M. N. , Telukdarie, A.
- Date: 2017
- Subjects: Industry 4.0 , IoT , Big data
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/255995 , uj:26866 , Citation: Sishi, M.N. & Telukdarie, A. 2017. Implementation of industry 4.0 technologies in the mining industry : a case study.
- Description: Abstract: In modern mining, it is imperative to have a real-time flow of information between enterprise level systems (ERP, CRM, SCM) and shop floor systems. The gaps that exist between the two spheres make it difficult for managers to have timely information for optimum decision making. A mining company needs instantaneous visibility on production, quality, cycle times, machine status, and other important operational variables in order to achieve optimum and effective operations. With the implementation of Industry 4.0 technologies fragmented shop floor systems and the enterprise level systems communicate seamlessly in delivering optimum operations. The research demonstrates Industry 4.0 technologies as the mechanisms for integrating business systems and processes. The methods researched are deployed in a uranium mining company to integrate all shop floor systems with SAP ERP. The results introduce a semi-smart Mine with real-time visibility of overall mining status.
- Full Text:
- Authors: Sishi, M. N. , Telukdarie, A.
- Date: 2017
- Subjects: Industry 4.0 , IoT , Big data
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/255995 , uj:26866 , Citation: Sishi, M.N. & Telukdarie, A. 2017. Implementation of industry 4.0 technologies in the mining industry : a case study.
- Description: Abstract: In modern mining, it is imperative to have a real-time flow of information between enterprise level systems (ERP, CRM, SCM) and shop floor systems. The gaps that exist between the two spheres make it difficult for managers to have timely information for optimum decision making. A mining company needs instantaneous visibility on production, quality, cycle times, machine status, and other important operational variables in order to achieve optimum and effective operations. With the implementation of Industry 4.0 technologies fragmented shop floor systems and the enterprise level systems communicate seamlessly in delivering optimum operations. The research demonstrates Industry 4.0 technologies as the mechanisms for integrating business systems and processes. The methods researched are deployed in a uranium mining company to integrate all shop floor systems with SAP ERP. The results introduce a semi-smart Mine with real-time visibility of overall mining status.
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The extent to which a financial services institution uses big data : a marketing perspective
- Authors: Smart, Cindy
- Date: 2015
- Subjects: Financial services industry , Big data , Web usage mining , Marketing - Management
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/83379 , uj:19085
- Description: Abstract: The financial services industry is known to be a competitive one, and literature suggests that it has an abundance of data, otherwise known as big data (SAS, 2012a). The industry not only makes a large contribution to the national GDP, but also has the most potential to embrace big data in order to have a competitive advantage over the various other industries contributing to the national GDP. However, in South Africa, this industry is currently perceived not to be leveraging its data optimally, particularly from a marketing perspective, with more than 50% of marketers stating that using data was last on their list of priorities when making decisions (Spenner & Bird, 2012). Literature suggests that South African marketers currently have a very vague formulation of who their customer is. In order for the financial services industry to gain competitive advantage from a marketing perspective, it needs to use data to better profile and understand their customer. This will lead to more personalised relationship with the customer, and will ultimately cement the relationship between the customer and the institution. The primary objective of this study is therefore to discern the extent to which data is used in a financial services institution from a marketing perspective. First, literature is addressed which introduces an adapted model which was initially developed by Byrom, Bennison, Hernández and Hooper (2001:336), which is used to guide the study. The empirical study is qualitative in nature, using a case study approach in order to meet primary and secondary objectives. A financial services institution was chosen wherein employees working with big data from a marketing perspective were identified through snowball sampling. In-depth personal interviews were conducted with these employees, using a discussion guide which was based on the model mentioned above. The Morse and Field approach was used to analyse the data whereby when the findings indicated that the institution analysed in this study is using various types of data sources, some more comprehensively than others. The institution identifies the importance of integrating various data sources, however this is not being done to the fullest extent. The institution currently uses big data from a market perspective for better customer profiling. The findings also revealed that the institution was highly dependent on using big data to make decisions at an operational level. , M.Com. (Marketing Management)
- Full Text:
- Authors: Smart, Cindy
- Date: 2015
- Subjects: Financial services industry , Big data , Web usage mining , Marketing - Management
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/83379 , uj:19085
- Description: Abstract: The financial services industry is known to be a competitive one, and literature suggests that it has an abundance of data, otherwise known as big data (SAS, 2012a). The industry not only makes a large contribution to the national GDP, but also has the most potential to embrace big data in order to have a competitive advantage over the various other industries contributing to the national GDP. However, in South Africa, this industry is currently perceived not to be leveraging its data optimally, particularly from a marketing perspective, with more than 50% of marketers stating that using data was last on their list of priorities when making decisions (Spenner & Bird, 2012). Literature suggests that South African marketers currently have a very vague formulation of who their customer is. In order for the financial services industry to gain competitive advantage from a marketing perspective, it needs to use data to better profile and understand their customer. This will lead to more personalised relationship with the customer, and will ultimately cement the relationship between the customer and the institution. The primary objective of this study is therefore to discern the extent to which data is used in a financial services institution from a marketing perspective. First, literature is addressed which introduces an adapted model which was initially developed by Byrom, Bennison, Hernández and Hooper (2001:336), which is used to guide the study. The empirical study is qualitative in nature, using a case study approach in order to meet primary and secondary objectives. A financial services institution was chosen wherein employees working with big data from a marketing perspective were identified through snowball sampling. In-depth personal interviews were conducted with these employees, using a discussion guide which was based on the model mentioned above. The Morse and Field approach was used to analyse the data whereby when the findings indicated that the institution analysed in this study is using various types of data sources, some more comprehensively than others. The institution identifies the importance of integrating various data sources, however this is not being done to the fullest extent. The institution currently uses big data from a market perspective for better customer profiling. The findings also revealed that the institution was highly dependent on using big data to make decisions at an operational level. , M.Com. (Marketing Management)
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Parasitic effect on reduced latency of SoC-based big data
- Olokede, Seyi Stephen, Paul, Babu Sena
- Authors: Olokede, Seyi Stephen , Paul, Babu Sena
- Date: 2018
- Subjects: Big data , Insertion loss , Integrated circuit
- Language: English
- Type: Conference proceeding
- Identifier: http://hdl.handle.net/10210/290816 , uj:31577 , Citation: Olokede, S.S. & Paul, B.S. 2018. Parasitic effect on reduced latency of SoC-based big data.
- Description: Abstract: Big data technology sustainability is contingent on the availability of interconnections of large scale, ultra-high-speed, densely integrated big data heterogeneous server platforms. For highly densified servers to be attainable, semiconductors technologies upon which these servers are predicated must further be miniaturized. It is recently not uncommon to implement bandgap reduction engineering of SiGe HBT in a bid to attain highly densified integrated circuit for large scale servers. Unfortunately, the parasitic effects become significant, in particular as these integrated circuits are targeted for high frequency of operations due to the interconnections links between the chip and the transceivers. Insertion loss |S21| becomes considerable, and both the signal level as well as noise figure depreciate substantially as a result. In this work therefore, we investigate the effect of parasitic effect on the roundtrip latency of system-on-chip (SoC).
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- Authors: Olokede, Seyi Stephen , Paul, Babu Sena
- Date: 2018
- Subjects: Big data , Insertion loss , Integrated circuit
- Language: English
- Type: Conference proceeding
- Identifier: http://hdl.handle.net/10210/290816 , uj:31577 , Citation: Olokede, S.S. & Paul, B.S. 2018. Parasitic effect on reduced latency of SoC-based big data.
- Description: Abstract: Big data technology sustainability is contingent on the availability of interconnections of large scale, ultra-high-speed, densely integrated big data heterogeneous server platforms. For highly densified servers to be attainable, semiconductors technologies upon which these servers are predicated must further be miniaturized. It is recently not uncommon to implement bandgap reduction engineering of SiGe HBT in a bid to attain highly densified integrated circuit for large scale servers. Unfortunately, the parasitic effects become significant, in particular as these integrated circuits are targeted for high frequency of operations due to the interconnections links between the chip and the transceivers. Insertion loss |S21| becomes considerable, and both the signal level as well as noise figure depreciate substantially as a result. In this work therefore, we investigate the effect of parasitic effect on the roundtrip latency of system-on-chip (SoC).
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Miniaturized microwave sensor for internet of things wireless connectivity
- Olokede, Seyi Stephen, Paul, Babu Sena
- Authors: Olokede, Seyi Stephen , Paul, Babu Sena
- Date: 2018
- Subjects: Antenna-on-chip/antenna-in-package , Big data , Integrated circuit
- Language: English
- Type: Article
- Identifier: http://ujcontent.uj.ac.za8080/10210/374488 , http://hdl.handle.net/10210/290534 , uj:31542 , Citation: Olokede, S.S. & Paul, B.S. 2018. Miniaturized microwave sensor for internet of things wireless connectivity.
- Description: Abstract: A miniaturized microwave sensor for internet of things (IoTs) is presented. The proposed sensor though a periodic structure exhibits two intrinsic resonances namely: the spatial wavelength due to its periodic geometric structure, and the radiation wavelength due to applied voltage source to the microwave sensor. The wavelength differential between the spatial and radiation wavelengths is employed for the sensing based on the electrical impedance tomography of the sensed material. The miniaturized capability of the proposed sensor is investigated based on some available formulae using Matlab code for parameter extraction, and also with finite integration technique electromagnetic (EM) codes. A proof-of-concept prototyped sensor is fabricated on a printed circuit microwave laminate board in order to validate the miniaturized capability of the proposed sensor. Findings indicate a superior impedance match, substantial impedance bandwidth, robust gain, cost effectiveness, compared to AoC/AiPs with associated losses due to the silicon substrate.
- Full Text:
- Authors: Olokede, Seyi Stephen , Paul, Babu Sena
- Date: 2018
- Subjects: Antenna-on-chip/antenna-in-package , Big data , Integrated circuit
- Language: English
- Type: Article
- Identifier: http://ujcontent.uj.ac.za8080/10210/374488 , http://hdl.handle.net/10210/290534 , uj:31542 , Citation: Olokede, S.S. & Paul, B.S. 2018. Miniaturized microwave sensor for internet of things wireless connectivity.
- Description: Abstract: A miniaturized microwave sensor for internet of things (IoTs) is presented. The proposed sensor though a periodic structure exhibits two intrinsic resonances namely: the spatial wavelength due to its periodic geometric structure, and the radiation wavelength due to applied voltage source to the microwave sensor. The wavelength differential between the spatial and radiation wavelengths is employed for the sensing based on the electrical impedance tomography of the sensed material. The miniaturized capability of the proposed sensor is investigated based on some available formulae using Matlab code for parameter extraction, and also with finite integration technique electromagnetic (EM) codes. A proof-of-concept prototyped sensor is fabricated on a printed circuit microwave laminate board in order to validate the miniaturized capability of the proposed sensor. Findings indicate a superior impedance match, substantial impedance bandwidth, robust gain, cost effectiveness, compared to AoC/AiPs with associated losses due to the silicon substrate.
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Tweets and Facebook posts, the novelty techniques in the creation of origin-destination models
- Authors: Malema, H. K. , Musakwa, W.
- Date: 2016
- Subjects: Geolocation based services , Big data , Social media , Pattern analysis , Network movements , Origin-Destination models , Kriging , Transportation planning
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/122345 , uj:20641 , Citation: Malema, H. K. & Musakwa, W. 2016. Tweets and Facebook posts, the novelty techniques in the creation of origin-destination models.
- Description: Abstract: Social media and big data have emerged to be a useful source of information that can be used for planning purposes, particularly transportation planning and trip-distribution studies. Cities in developing countries such as South Africa often struggle with out-dated, unreliable and cumbersome techniques such as traffic counts and household surveys to conduct origin and destination studies. The emergence of ubiquitous crowd sourced data, big data, social media and geolocation based services has shown huge potential in providing useful information for origin and destination studies. Perhaps such information can be utilised to determine the origin and destination of commuters using the Gautrain, a high-speed railway in Gauteng province South Africa. To date little is known about the origins and destinations of Gautrain commuters. Accordingly, this study assesses the viability of using geolocation-based services namely Facebook and Twitter in mapping out the network movements of Gautrain commuters. Explorative Spatial Data Analysis (ESDA), Echo-social and ArcGis software were used to extract social media data, i.e. tweets and Facebook posts as well as to visualize the concentration of Gautrain commuters. The results demonstrate that big data and geolocation based services have the significant potential to predict movement network patterns of commuters and this information can thus, be used to inform and improve transportation planning. Nevertheless use of crowd sourced data and big data has privacy concerns that still need to be addressed.
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- Authors: Malema, H. K. , Musakwa, W.
- Date: 2016
- Subjects: Geolocation based services , Big data , Social media , Pattern analysis , Network movements , Origin-Destination models , Kriging , Transportation planning
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/122345 , uj:20641 , Citation: Malema, H. K. & Musakwa, W. 2016. Tweets and Facebook posts, the novelty techniques in the creation of origin-destination models.
- Description: Abstract: Social media and big data have emerged to be a useful source of information that can be used for planning purposes, particularly transportation planning and trip-distribution studies. Cities in developing countries such as South Africa often struggle with out-dated, unreliable and cumbersome techniques such as traffic counts and household surveys to conduct origin and destination studies. The emergence of ubiquitous crowd sourced data, big data, social media and geolocation based services has shown huge potential in providing useful information for origin and destination studies. Perhaps such information can be utilised to determine the origin and destination of commuters using the Gautrain, a high-speed railway in Gauteng province South Africa. To date little is known about the origins and destinations of Gautrain commuters. Accordingly, this study assesses the viability of using geolocation-based services namely Facebook and Twitter in mapping out the network movements of Gautrain commuters. Explorative Spatial Data Analysis (ESDA), Echo-social and ArcGis software were used to extract social media data, i.e. tweets and Facebook posts as well as to visualize the concentration of Gautrain commuters. The results demonstrate that big data and geolocation based services have the significant potential to predict movement network patterns of commuters and this information can thus, be used to inform and improve transportation planning. Nevertheless use of crowd sourced data and big data has privacy concerns that still need to be addressed.
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Spatial array of microwave sensors for IoT-based wireless connectivity
- Olokede, Seyi Stephen, Paul, Babu Sena
- Authors: Olokede, Seyi Stephen , Paul, Babu Sena
- Date: 2018
- Subjects: Big data , Insertion loss , Integrated circuit
- Language: English
- Type: Conference proceeding
- Identifier: http://hdl.handle.net/10210/290807 , uj:31576 , Citation: Olokede, S.S. & Paul, B.S. 2018. Spatial array of microwave sensors for IoT-based wireless connectivity.
- Description: Abstract: Spatial array of microwave sensors for IoT-based wireless connectivity is presented. The traditional challenges of poor input impedance matching associated with small antenna is analytically characterized using the many available formulae based on a novel 2 × 2 excitation network. Alternative microwave sensor solution designed at originally known low data throughput IEEE 802.11x standard, was previously investigated to support multi-channel bandwidth capacity, now examined for robust link budget to provide complementary leverage for IoT-based applications.
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- Authors: Olokede, Seyi Stephen , Paul, Babu Sena
- Date: 2018
- Subjects: Big data , Insertion loss , Integrated circuit
- Language: English
- Type: Conference proceeding
- Identifier: http://hdl.handle.net/10210/290807 , uj:31576 , Citation: Olokede, S.S. & Paul, B.S. 2018. Spatial array of microwave sensors for IoT-based wireless connectivity.
- Description: Abstract: Spatial array of microwave sensors for IoT-based wireless connectivity is presented. The traditional challenges of poor input impedance matching associated with small antenna is analytically characterized using the many available formulae based on a novel 2 × 2 excitation network. Alternative microwave sensor solution designed at originally known low data throughput IEEE 802.11x standard, was previously investigated to support multi-channel bandwidth capacity, now examined for robust link budget to provide complementary leverage for IoT-based applications.
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The use of big data in marketing : an emerging market financial services industry perspective
- Smart, C., De Meyer-Heydenrych, C.F.
- Authors: Smart, C. , De Meyer-Heydenrych, C.F.
- Date: 2018
- Subjects: Big data , Business intelligence , Marketing
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/274419 , uj:29277 , Citation: Smart, C. & De Meyer-Heydenrych, C.F. 2018. The use of big data in marketing : an emerging market financial services industry perspective.
- Description: Abstract: As a result of the competitive nature of financial services, this industry is positioned to leverage big data to gain a competitive advantage. The financial services industry, in South Africa and in emerging markets, has been known not to leverage big data optimally. Creating personalized customer relationships through big data has been found to be a source of competitive advantage. This study aims to identify the extent to which big data is used in a financial institution. This will be done from a marketing perspective, using a model developed by Byrom, Bennison, Hernandez and Hooper (2001, p.336). Qualitative research was conducted, including in-depth personal interviews with marketers in the financial services industry. The Morse and Field approach was used for data analysis. Findings indicated that although big data was being utilized, it was not optimally exploited. The study identified gaps in the usage of big data for marketing decision making.
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- Authors: Smart, C. , De Meyer-Heydenrych, C.F.
- Date: 2018
- Subjects: Big data , Business intelligence , Marketing
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/274419 , uj:29277 , Citation: Smart, C. & De Meyer-Heydenrych, C.F. 2018. The use of big data in marketing : an emerging market financial services industry perspective.
- Description: Abstract: As a result of the competitive nature of financial services, this industry is positioned to leverage big data to gain a competitive advantage. The financial services industry, in South Africa and in emerging markets, has been known not to leverage big data optimally. Creating personalized customer relationships through big data has been found to be a source of competitive advantage. This study aims to identify the extent to which big data is used in a financial institution. This will be done from a marketing perspective, using a model developed by Byrom, Bennison, Hernandez and Hooper (2001, p.336). Qualitative research was conducted, including in-depth personal interviews with marketers in the financial services industry. The Morse and Field approach was used for data analysis. Findings indicated that although big data was being utilized, it was not optimally exploited. The study identified gaps in the usage of big data for marketing decision making.
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Data management considerations in the design of Internet of Things applications
- Authors: Sikwela, J. G.
- Date: 2020
- Subjects: Computer networks - Technological innovations , Database management , Computer networks - Management , Network performance (Telecommunication) - Reliability , Big data
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/417757 , uj:35393
- Description: M.Ing. (Engineering Management) , Abstract: In the last decade, there has been an increase in the number of IoT devices and applications. It is estimated that by 2020 more than 2 billion devices will be connected to the internet. With the increase in the number of devices, more data will be transferred to the servers. These huge amounts of data will require extraction, processing, and storage for future use. The current data management solutions cannot accommodate the increase in the data generated by these devices. The purpose of this work is to do research on data management considerations in IoT by asking the question of how we can design IoT networks that take into consideration data management and what solutions are available to address this increase in data. The aim of the research is to identify the key principles of data management, investigate techniques that can be used for data management, investigate the best possible frameworks that can be used for data management in IoT, and investigate data storage systems that would be suitable for use in IoT applications. The scope of the research is to study peer-reviewed articles on IoT and data management. This includes studying the different frameworks that exist currently, identifying their limitations and doing an analysis based on IoT design primitives to find a framework that attempts to meet all the desired requirements for an IoT data management framework.
- Full Text:
- Authors: Sikwela, J. G.
- Date: 2020
- Subjects: Computer networks - Technological innovations , Database management , Computer networks - Management , Network performance (Telecommunication) - Reliability , Big data
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/417757 , uj:35393
- Description: M.Ing. (Engineering Management) , Abstract: In the last decade, there has been an increase in the number of IoT devices and applications. It is estimated that by 2020 more than 2 billion devices will be connected to the internet. With the increase in the number of devices, more data will be transferred to the servers. These huge amounts of data will require extraction, processing, and storage for future use. The current data management solutions cannot accommodate the increase in the data generated by these devices. The purpose of this work is to do research on data management considerations in IoT by asking the question of how we can design IoT networks that take into consideration data management and what solutions are available to address this increase in data. The aim of the research is to identify the key principles of data management, investigate techniques that can be used for data management, investigate the best possible frameworks that can be used for data management in IoT, and investigate data storage systems that would be suitable for use in IoT applications. The scope of the research is to study peer-reviewed articles on IoT and data management. This includes studying the different frameworks that exist currently, identifying their limitations and doing an analysis based on IoT design primitives to find a framework that attempts to meet all the desired requirements for an IoT data management framework.
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The use of social media big data within South African hotels and lodges
- Authors: Gutfreund, Sebastian
- Date: 2019
- Subjects: Management - Data processing , Hospitality industry - Customer services , Online social networks , Data mining , Big data
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/421222 , uj:35895
- Description: Abstract: Big data is a revolutionary and disruptive technology that is used to identify behavioural patterns and track customer preferences. It has several advantages for the hospitality industry, where customer loyalty is integral for brand performance. However, big data is greatly underutilised. Therefore, a study was conducted to look at the use of social media big data within South African hotels and lodges The aim of this study was to focus on the general understanding of big data and the link it shares with social media. There was a further focus on the analytical tools that hotels and lodges make use of, as well as the benefits and challenges which social media big data elucidates for these sectors. This information provides an overall image of how South African hotels and lodges are wielding this technology, giving a future viewpoint on the progression and improvements that need to be undertaken. A comparison concerning the key similarities and differences between the lodge and hotel sector was also provided. This gave an overall picture on how South African hotels and lodges are using this technology, thus giving a future outlook on the progression and improvements that need to be taken into consideration. In order to fully grasp and appreciate big data, a literature review was provided in order to understand the relationship big data has with social media, and the impact it has within the hospitality industry, playing closer attention to hotels and lodges. The methodological approach of the study focused on the qualitative research method, where ten participants in total were interviewed - five being marketing managers in hotels and five marketing managers in lodges. The key findings of the study revealed that the South African hospitality industry is presently only at the genesis when it comes to the use of social media big data. This was revealed through the marketing manager’s generic understanding of the phenomenon. Furthermore, the data predominantly illustrated that only basic analytical tools were used, which indicates that there is a shortage of internal specialists who are capable of handling more advanced tools to further their findings. However, the benefits established were primarily related to the identification of behavioural patterns and preferences of both future and current customers, as well as the marketability of certain promotions that are placed on various platforms. In summary, the data is essentially used to enhance the guests experience through targeting their likes and dislikes. The primary challenges within both sectors of the industry emphasised areas such as education and training, the lack of advanced technology, and the security and privacy concerns pertaining to guest data. .. , M.Com. (Tourism and Hospitality Management)
- Full Text:
- Authors: Gutfreund, Sebastian
- Date: 2019
- Subjects: Management - Data processing , Hospitality industry - Customer services , Online social networks , Data mining , Big data
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/421222 , uj:35895
- Description: Abstract: Big data is a revolutionary and disruptive technology that is used to identify behavioural patterns and track customer preferences. It has several advantages for the hospitality industry, where customer loyalty is integral for brand performance. However, big data is greatly underutilised. Therefore, a study was conducted to look at the use of social media big data within South African hotels and lodges The aim of this study was to focus on the general understanding of big data and the link it shares with social media. There was a further focus on the analytical tools that hotels and lodges make use of, as well as the benefits and challenges which social media big data elucidates for these sectors. This information provides an overall image of how South African hotels and lodges are wielding this technology, giving a future viewpoint on the progression and improvements that need to be undertaken. A comparison concerning the key similarities and differences between the lodge and hotel sector was also provided. This gave an overall picture on how South African hotels and lodges are using this technology, thus giving a future outlook on the progression and improvements that need to be taken into consideration. In order to fully grasp and appreciate big data, a literature review was provided in order to understand the relationship big data has with social media, and the impact it has within the hospitality industry, playing closer attention to hotels and lodges. The methodological approach of the study focused on the qualitative research method, where ten participants in total were interviewed - five being marketing managers in hotels and five marketing managers in lodges. The key findings of the study revealed that the South African hospitality industry is presently only at the genesis when it comes to the use of social media big data. This was revealed through the marketing manager’s generic understanding of the phenomenon. Furthermore, the data predominantly illustrated that only basic analytical tools were used, which indicates that there is a shortage of internal specialists who are capable of handling more advanced tools to further their findings. However, the benefits established were primarily related to the identification of behavioural patterns and preferences of both future and current customers, as well as the marketability of certain promotions that are placed on various platforms. In summary, the data is essentially used to enhance the guests experience through targeting their likes and dislikes. The primary challenges within both sectors of the industry emphasised areas such as education and training, the lack of advanced technology, and the security and privacy concerns pertaining to guest data. .. , M.Com. (Tourism and Hospitality Management)
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Intelligent scheduling for stratosphere cloud platforms
- Periola, A. A., Alonge, A. A., Ogudo, K. A.
- Authors: Periola, A. A. , Alonge, A. A. , Ogudo, K. A.
- Date: 2020
- Subjects: PUE , Stratosphere Cloud Platforms , Big data
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/450350 , uj:39584 , Citation: Periola, A.A., Alonge, A.A. & Ogudo, K.A. 2020. Intelligent scheduling for stratosphere cloud platforms.
- Description: Abstract: Data centres play an important role in developing machine learning algorithms and in wireless networks. The large scale use of data centres increases the operational costs for cloud computing service providers. The operational costs can be reduced by siting data centres in locations such as the stratosphere. The siting of data centres in stratosphere reduces cooling costs and enhances the power usage effectiveness (PUE). This paper examines the PUE improvement obtainable with data centres located in the stratosphere. Performance evaluation shows that the stratosphere’s cooling capacity increases with the proportion of green house gases. The PUE is enhanced by at least 27% on average when 33.3% of data centres in a cloud platform are located in the stratosphere. The PUE is enhanced by up to 40.1% on average when 50% of data centres in a cloud platform are located in the stratosphere.
- Full Text:
- Authors: Periola, A. A. , Alonge, A. A. , Ogudo, K. A.
- Date: 2020
- Subjects: PUE , Stratosphere Cloud Platforms , Big data
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/450350 , uj:39584 , Citation: Periola, A.A., Alonge, A.A. & Ogudo, K.A. 2020. Intelligent scheduling for stratosphere cloud platforms.
- Description: Abstract: Data centres play an important role in developing machine learning algorithms and in wireless networks. The large scale use of data centres increases the operational costs for cloud computing service providers. The operational costs can be reduced by siting data centres in locations such as the stratosphere. The siting of data centres in stratosphere reduces cooling costs and enhances the power usage effectiveness (PUE). This paper examines the PUE improvement obtainable with data centres located in the stratosphere. Performance evaluation shows that the stratosphere’s cooling capacity increases with the proportion of green house gases. The PUE is enhanced by at least 27% on average when 33.3% of data centres in a cloud platform are located in the stratosphere. The PUE is enhanced by up to 40.1% on average when 50% of data centres in a cloud platform are located in the stratosphere.
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The digital University : of March hares and tortoises
- Authors: Desai, Ashwin
- Date: 2020
- Subjects: Online learning , Knowledge , Big data
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/463849 , uj:41411 , Citation: Desai, A. 2020. The digital University : of March hares and tortoises.
- Description: Abstract: The learning environment of universities is changing dramatically with the coming of Covid-19. Universities were summarily evacuated and plans put in place to ensure online teaching. In some senses, this was the quickening of a trend that was already unfolding, while for others it signalled new territory. This article explores the coming of online education by highlighting the experiences of lecturers who have already taught courses while raising questions about disciplinary boundaries and knowledge production. It situates this discussion by exploring the challenges to the traditional notions of the role of universities and the changing orientations of the academy against the backdrop of the global juggernaut of privatised higher education.
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- Authors: Desai, Ashwin
- Date: 2020
- Subjects: Online learning , Knowledge , Big data
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/463849 , uj:41411 , Citation: Desai, A. 2020. The digital University : of March hares and tortoises.
- Description: Abstract: The learning environment of universities is changing dramatically with the coming of Covid-19. Universities were summarily evacuated and plans put in place to ensure online teaching. In some senses, this was the quickening of a trend that was already unfolding, while for others it signalled new territory. This article explores the coming of online education by highlighting the experiences of lecturers who have already taught courses while raising questions about disciplinary boundaries and knowledge production. It situates this discussion by exploring the challenges to the traditional notions of the role of universities and the changing orientations of the academy against the backdrop of the global juggernaut of privatised higher education.
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