Social media big data: a diary study of ten pharmaceutical firms
- Authors: Baker, Nadia Samantha
- Date: 2020
- Subjects: Big data , Internet in medicine , Social media in medicine , Internet marketing -- Evaluation , Pharmacy management -- South Africa
- Language: English
- Type: text , Thesis , Masters , MBA
- Identifier: http://hdl.handle.net/10962/140737 , vital:37914
- Description: Purpose: The goal of the research was to demonstrate how firms can use social media big data, to make strategic business decisions, through the lens of Resource Based Theory (RBT) and Dynamic Capability Theory (DCT), that could lead to a sustained competitive advantage. In and of its own, big data, does not constitute a competitive advantage. It may hold value for the firm, but lacks rarity, inimitability, and is not substitutable (Braganza, et al. 2017; Mata, Fuerst and Barney, 1995; Delmonte, 2003). It is in the analysis of this data, through RBT and DCT, that will turn the information into useful business intelligence (Amit and Schoemaker, 1993; Barney, 1991; 1995; Marr, 2015; Gupta and George, 2016; Kurtmollaiev, et al., 2018). Most importantly, firms must constantly reconfigure their resources in line with the dynamic business environment to ensure superior performance (Teece, Pisano and Shuen, 1997; Helfat, et al., 2007; Teece, 2014; 2018). Method: In this study, a qualitative approach was used to examine the RBT (Value, Rarity, Inimitability and Non-Substitutable - VRIN Framework) and DCT, to describe and understand the relevant theories and to build upon the quantitative results. While a quantitative approach was used to analyse the social media sentiment as depicted by Social Mention metrics. A novel technique, Chernoff Faces, was used to analyse and visualize the data (de Vos, Strydom, Fouche and Delport, 2011). Results and Findings: The research results show that, while the 10 firms in the study all have a presence on social media, it is on selective platforms. The content that is posted, is on very specific topics (Narayan, 2017; Cornejo, 2018). The Chernoff Faces indicate that the firms’ Social Mention metrics, over the 30 day period, was at low values. Since strength of social mention is depicted by the face line, the thin, long, generally sad looking faces implies that more than 70 percent of the firms’ social media strength over the study period, was weak. Conclusion: The literature indicates that the true value of big data and big data analytics can only be realised if firms make sound business decisions and act upon it swiftly.
- Full Text:
- Date Issued: 2020
- Authors: Baker, Nadia Samantha
- Date: 2020
- Subjects: Big data , Internet in medicine , Social media in medicine , Internet marketing -- Evaluation , Pharmacy management -- South Africa
- Language: English
- Type: text , Thesis , Masters , MBA
- Identifier: http://hdl.handle.net/10962/140737 , vital:37914
- Description: Purpose: The goal of the research was to demonstrate how firms can use social media big data, to make strategic business decisions, through the lens of Resource Based Theory (RBT) and Dynamic Capability Theory (DCT), that could lead to a sustained competitive advantage. In and of its own, big data, does not constitute a competitive advantage. It may hold value for the firm, but lacks rarity, inimitability, and is not substitutable (Braganza, et al. 2017; Mata, Fuerst and Barney, 1995; Delmonte, 2003). It is in the analysis of this data, through RBT and DCT, that will turn the information into useful business intelligence (Amit and Schoemaker, 1993; Barney, 1991; 1995; Marr, 2015; Gupta and George, 2016; Kurtmollaiev, et al., 2018). Most importantly, firms must constantly reconfigure their resources in line with the dynamic business environment to ensure superior performance (Teece, Pisano and Shuen, 1997; Helfat, et al., 2007; Teece, 2014; 2018). Method: In this study, a qualitative approach was used to examine the RBT (Value, Rarity, Inimitability and Non-Substitutable - VRIN Framework) and DCT, to describe and understand the relevant theories and to build upon the quantitative results. While a quantitative approach was used to analyse the social media sentiment as depicted by Social Mention metrics. A novel technique, Chernoff Faces, was used to analyse and visualize the data (de Vos, Strydom, Fouche and Delport, 2011). Results and Findings: The research results show that, while the 10 firms in the study all have a presence on social media, it is on selective platforms. The content that is posted, is on very specific topics (Narayan, 2017; Cornejo, 2018). The Chernoff Faces indicate that the firms’ Social Mention metrics, over the 30 day period, was at low values. Since strength of social mention is depicted by the face line, the thin, long, generally sad looking faces implies that more than 70 percent of the firms’ social media strength over the study period, was weak. Conclusion: The literature indicates that the true value of big data and big data analytics can only be realised if firms make sound business decisions and act upon it swiftly.
- Full Text:
- Date Issued: 2020
A framework for scoring and tagging NetFlow data
- Authors: Sweeney, Michael John
- Date: 2019
- Subjects: NetFlow , Big data , High performance computing , Event processing (Computer science)
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/65022 , vital:28654
- Description: With the increase in link speeds and the growth of the Internet, the volume of NetFlow data generated has increased significantly over time and processing these volumes has become a challenge, more specifically a Big Data challenge. With the advent of technologies and architectures designed to handle Big Data volumes, researchers have investigated their application to the processing of NetFlow data. This work builds on prior work wherein a scoring methodology was proposed for identifying anomalies in NetFlow by proposing and implementing a system that allows for automatic, real-time scoring through the adoption of Big Data stream processing architectures. The first part of the research looks at the means of event detection using the scoring approach and implementing as a number of individual, standalone components, each responsible for detecting and scoring a single type of flow trait. The second part is the implementation of these scoring components in a framework, named Themis1, capable of handling high volumes of data with low latency processing times. This was tackled using tools, technologies and architectural elements from the world of Big Data stream processing. The performance of the framework on the stream processing architecture was shown to demonstrate good flow throughput at low processing latencies on a single low end host. The successful demonstration of the framework on a single host opens the way to leverage the scaling capabilities afforded by the architectures and technologies used. This gives weight to the possibility of using this framework for real time threat detection using NetFlow data from larger networked environments.
- Full Text:
- Date Issued: 2019
- Authors: Sweeney, Michael John
- Date: 2019
- Subjects: NetFlow , Big data , High performance computing , Event processing (Computer science)
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/65022 , vital:28654
- Description: With the increase in link speeds and the growth of the Internet, the volume of NetFlow data generated has increased significantly over time and processing these volumes has become a challenge, more specifically a Big Data challenge. With the advent of technologies and architectures designed to handle Big Data volumes, researchers have investigated their application to the processing of NetFlow data. This work builds on prior work wherein a scoring methodology was proposed for identifying anomalies in NetFlow by proposing and implementing a system that allows for automatic, real-time scoring through the adoption of Big Data stream processing architectures. The first part of the research looks at the means of event detection using the scoring approach and implementing as a number of individual, standalone components, each responsible for detecting and scoring a single type of flow trait. The second part is the implementation of these scoring components in a framework, named Themis1, capable of handling high volumes of data with low latency processing times. This was tackled using tools, technologies and architectural elements from the world of Big Data stream processing. The performance of the framework on the stream processing architecture was shown to demonstrate good flow throughput at low processing latencies on a single low end host. The successful demonstration of the framework on a single host opens the way to leverage the scaling capabilities afforded by the architectures and technologies used. This gives weight to the possibility of using this framework for real time threat detection using NetFlow data from larger networked environments.
- Full Text:
- Date Issued: 2019
Categorising Network Telescope data using big data enrichment techniques
- Authors: Davis, Michael Reginald
- Date: 2019
- Subjects: Denial of service attacks , Big data , Computer networks -- Security measures
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/92941 , vital:30766
- Description: Network Telescopes, Internet backbone sampling, IDS and other forms of network-sourced Threat Intelligence provide researchers with insight into the methods and intent of remote entities by capturing network traffic and analysing the resulting data. This analysis and determination of intent is made difficult by the large amounts of potentially malicious traffic, coupled with limited amount of knowledge that can be attributed to the source of the incoming data, as the source is known only by its IP address. Due to the lack of commonly available tooling, many researchers start this analysis from the beginning and so repeat and re-iterate previous research as the bulk of their work. As a result new insight into methods and approaches of analysis is gained at a high cost. Our research approaches this problem by using additional knowledge about the source IP address such as open ports, reverse and forward DNS, BGP routing tables and more, to enhance the researcher's ability to understand the traffic source. The research is a BigData experiment, where large (hundreds of GB) datasets are merged with a two month section of Network Telescope data using a set of Python scripts. The result are written to a Google BigQuery database table. Analysis of the network data is greatly simplified, with questions about the nature of the source, such as its device class (home routing device or server), potential vulnerabilities (open telnet ports or databases) and location becoming relatively easy to answer. Using this approach, researchers can focus on the questions that need answering and efficiently address them. This research could be taken further by using additional data sources such as Geo-location, WHOIS lookups, Threat Intelligence feeds and many others. Other potential areas of research include real-time categorisation of incoming packets, in order to better inform alerting and reporting systems' configuration. In conclusion, categorising Network Telescope data in this way provides insight into the intent of the (apparent) originator and as such is a valuable tool for those seeking to understand the purpose and intent of arriving packets. In particular, the ability to remove packets categorised as non-malicious (e.g. those in the Research category) from the data eliminates a known source of `noise' from the data. This allows the researcher to focus their efforts in a more productive manner.
- Full Text:
- Date Issued: 2019
- Authors: Davis, Michael Reginald
- Date: 2019
- Subjects: Denial of service attacks , Big data , Computer networks -- Security measures
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/92941 , vital:30766
- Description: Network Telescopes, Internet backbone sampling, IDS and other forms of network-sourced Threat Intelligence provide researchers with insight into the methods and intent of remote entities by capturing network traffic and analysing the resulting data. This analysis and determination of intent is made difficult by the large amounts of potentially malicious traffic, coupled with limited amount of knowledge that can be attributed to the source of the incoming data, as the source is known only by its IP address. Due to the lack of commonly available tooling, many researchers start this analysis from the beginning and so repeat and re-iterate previous research as the bulk of their work. As a result new insight into methods and approaches of analysis is gained at a high cost. Our research approaches this problem by using additional knowledge about the source IP address such as open ports, reverse and forward DNS, BGP routing tables and more, to enhance the researcher's ability to understand the traffic source. The research is a BigData experiment, where large (hundreds of GB) datasets are merged with a two month section of Network Telescope data using a set of Python scripts. The result are written to a Google BigQuery database table. Analysis of the network data is greatly simplified, with questions about the nature of the source, such as its device class (home routing device or server), potential vulnerabilities (open telnet ports or databases) and location becoming relatively easy to answer. Using this approach, researchers can focus on the questions that need answering and efficiently address them. This research could be taken further by using additional data sources such as Geo-location, WHOIS lookups, Threat Intelligence feeds and many others. Other potential areas of research include real-time categorisation of incoming packets, in order to better inform alerting and reporting systems' configuration. In conclusion, categorising Network Telescope data in this way provides insight into the intent of the (apparent) originator and as such is a valuable tool for those seeking to understand the purpose and intent of arriving packets. In particular, the ability to remove packets categorised as non-malicious (e.g. those in the Research category) from the data eliminates a known source of `noise' from the data. This allows the researcher to focus their efforts in a more productive manner.
- Full Text:
- Date Issued: 2019
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