1.
Al-Serafi, Ayman, et al.
(författare)
Visual trajectory pattern mining : An exploratory study in baggage handling systems
2014
Ingår i: Advances in data mining. - Cham : Encyclopedia of Global Archaeology/Springer Verlag. - 9783319089751 - 9783319089768 ; , s. 159-173
Konferensbidrag (refereegranskat) abstract
There is currently a huge amount of data being collected about movements of objects. Such data is called spatiotemporal data and paths left by moving-objects are called trajectories. Recently, researchers have been targeting those trajectories for extracting interesting and useful knowledge by means of pattern analysis and data mining. But, it is difficult to analyse huge datasets of trajectories without summarizing them and visualizing them for the knowledge seeker and for the decision makers. Therefore, this research paper focuses on utilizing visual techniques and data mining analysis of trajectory patterns in order to help extract patterns and knowledge in an interactive approach. The research study proposes a research framework which integrates multiple data analysis and visualization techniques in a coherent architecture in support of interactive trajectory pattern visualization for the decision makers. An application case-study of the techniques is conducted on an airport's baggage movement data within the Baggage Handling System (BHS). The results indicate the feasibility of the approach and its methods in visually analysing trajectory patterns in an interactive approach which can support the decision maker. © 2014 Springer International Publishing Switzerland.
2.
Amin, Marian H., et al.
(författare)
Corporate Disclosure via Social Media : A Data Science Approach
2020
Ingår i: Online information review (Print). - : Emerald Group Publishing Limited. - 1468-4527 .- 1468-4535. ; 44:1, s. 278-298
Tidskriftsartikel (refereegranskat) abstract
Purpose - The aim of this paper is to investigate corporate financial disclosure via Twitter among the top listed 350 companies in the UK as well as identify the determinants of the extent of social media usage to disclose financial information.Design/methodology/approach – This study applies an unsupervised machine learning technique, namely, Latent Dirichlet Allocation (LDA) topic modeling to identify financial disclosure tweets. Panel, Logistic, and Generalized Linear Model Regressions are also run to identify the determinants of financial disclosure on Twitter focusing mainly on board characteristics.Findings – Topic modeling results reveal that companies mainly tweet about 12 topics, including financial disclosure, which has a probability of occurrence of about 7 percent. Several board characteristics are found to be associated with the extent of Twitter usage as a financial disclosure platform, among which are board independence, gender diversity, and board tenure.Originality/value – Extensive literature examines disclosure via traditional media and its determinants, yet this paper extends the literature by investigating the relatively new disclosure channel of social media. This study is among the first to utilize machine learning, instead of manual coding techniques, to automatically unveil the tweets’ topics and reveal financial disclosure tweets. It is also among the first to investigate the relationships between several board characteristics and financial disclosure on Twitter; providing a distinction between the roles of executive versus non-executive directors relating to disclosure decisions.
3.
4.
Amin, Marian Hany, et al.
(författare)
Financial Disclosure on Twitter by Top Listed UK Companies : A Data Science Approach
2018
Konferensbidrag (refereegranskat) abstract
Ongoing advancements in technology have changed dramatically the disclosure media that companies adopt. Such disclosure media have evolved from the traditional paper-based ones, to the internet as the new platform to disclose information via companies’ designated websites. However, currently the new media for disclosures are the social media. The aim of this paper is to investigate corporate social media accounts for financial disclosure, as well as, identify its determinants. The sample of the study is comprised of the tweets posted on the Twitter accounts belonging to the FTSE 350 constituents. Topic modeling is applied to identify financial disclosure tweets and logistic regression is run to identify the determinants of financial disclosure on Twitter. Results show that companies use Twitter to make corporate disclosures and some board characteristics are found to have a significant relationship with financial disclosure.
5.
Amin, Marian Hany, et al.
(författare)
Twitter : An emerging media for corporate disclosure
2018
Konferensbidrag (refereegranskat) abstract
Ongoing advancements in technology have changed dramatically the disclosure media that companies adopt. Such disclosure media have evolved from the traditional paper-based ones, to the internet as the new platform to disclose information via companies’ designated websites. However, currently the new media for disclosures are the social media. The aim of this paper is to investigate corporate social media accounts for financial disclosure, as well as, identify its determinants. The sample of the study is comprised of the tweets posted on the Twitter accounts belonging to the FTSE 350 constituents. Topic modeling is applied to identify financial disclosure tweets and logistic regression is run to identify the determinants of financial disclosure on Twitter. Results show that companies use Twitter to make corporate disclosures and some board characteristics are found to have a significant relationship with financial disclosure.
6.
Badawy, Shaimaa Ali, et al.
(författare)
Multivariate similarity-based conformity measure (MSCM : An outlier detection measure for data mining applications
2008
Ingår i: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications Machine Learning. - Anaheim, Calif : ACTA Press. - 9780889867093 ; , s. 314-320
Konferensbidrag (refereegranskat) abstract
Outliers, the odd objects in the dataset, can be viewed from two different perspectives; the outliers as undesirable objects that should be treated or deleted in the data preparation step of the data mining process, and the outliers as interesting objects that are identified for their own interest in the data mining step of the mining process. In the latter case, outliers shouldn't be removed, that's why one of the main categories of tasks performed by data mining techniques is outlier detection. Applications that make use of such detection include credit card fraud detection and network intrusion detection. Most of the available outlier detection techniques rely in a distance measure to compare the objects in the dataset which imposed the restriction of dealing with numeric data. In this paper a new multivariate similarity-based conformity measure (MSCM) is suggested to be used to detect outliers in datasets that contain attributes of different data types. The MSCM satisfies two other desirable features; being a multivariate measure and giving ranking instead of a binary judgment of the object. The measure has been applied on three different datasets in order to be evaluated; the measure has shown good results in these experiments.
7.
Basuony, Mohamed A.K., et al.
(författare)
Big data analytics of corporate internet disclosures
2022
Ingår i: Accounting Research Journal. - : Emerald Group Publishing Limited. - 1030-9616 .- 1839-5465. ; 35:1, s. 4-20
Tidskriftsartikel (refereegranskat) abstract
Purpose: This study aims to investigate the extent and characteristics of corporate internet disclosure via companies’ websites as well via social media and networks sites in the four leading English-speaking stock markets, namely, Australia, Canada, the UK and the USA.Design/methodology/approach: A disclosure index comprising a set of items that encompasses two facets of online disclosure, namely, company websites and social media sites, is used. This paper adopts a data science approach to investigate corporate internet disclosure practices among top listed firms in Australia, Canada, the UK and the USA.Findings: The results reveal the underlying relations between the determining factors of corporate disclosure, i.e. profitability, leverage, liquidity and firm size. Profitability in its own has no great effect on the degree of corporate internet disclosure whether via company websites or social media sites. Liquidity has an impact on the degree of disclosure. Firm size and leverage appear to be the most important factors driving better disclosure via social media. American companies tend to be on the cutting edge of technology when it comes to corporate disclosure.Practical implications: This paper provides new insights into corporate internet disclosure that will benefit all stakeholders with an interest in corporate reporting. Social media is an influential means of communication that can enable corporate office to get instant feedback enhancing their decision-making process.Originality/value: To the best of the authors’ knowledge, this study is amongst few studies of corporate disclosure via social media platforms. This study has adopted disclosure index incorporating social media as well as applying data science approach in disclosure in an attempt to unfold how accounting could benefit from data science techniques.
8.
El-Telbany, Ola, et al.
(författare)
Decision 2.0 : An exploratory case study
2012
Ingår i: 2012 45th Hawaii International Conference on System Sciences. - Piscataway, NJ : IEEE Communications Society. - 9780769545257 ; , s. 432-443
Konferensbidrag (refereegranskat) abstract
The emergence of the Enterprise 2.0 technologies indicates that they can provide value to different types of users and potentially different types of value. Many published research explored what these E2.0 tools and applications can offer to organizations, such as collaboration platforms, social networking and user-created content, enhancing their productivity and management among employees. However, little research was devoted to study the effect these tools and applications have on the decision making process. Decision 2.0 has received little attention in literature, especially from the standpoint of making use of the "crowd". Therefore, this paper focuses on this research gap with a case study in an attempt to elucidate and extract knowledge to answer this question "How does decision 2.0 make use of the crowd to support the traditional decision making process and hence add value to organizations through collaboration and collective intelligence?"
9.
El-Telbany, Ola, et al.
(författare)
Gamification of Enterprise Systems: A Lifecycle Approach
2017
Ingår i: Procedia Computer Science. - : Elsevier. - 1877-0509. ; 121, s. 106-114
Tidskriftsartikel (refereegranskat) abstract
Introducing an Enterprise Resource Planning (ERP) system within an organization can bring many benefits and paybacks, yet an effective implementation of a fully functioning ERP system is still a challenge, the odds are high the costly investment might turn into an implementation failure or even lead to bankruptcy. To prevent such situations, organizations need to go through several changes, and carefully manage the critical success factors affecting each stage of the ERP implementation lifecycle, respectively. Previous studies have observed that the majority of the challenges faced during the implementation of ERP systems arise from social and organizational aspects rather than technical ones. This is where gamification comes to the rescue. This research adopts a design science paradigm in an attempt to develop a gamified process for the ERP lifecycle, to ease and enhance the ERP implementation process. The objective of this research is threefold. Firstly, to explore the benefits ERP systems can render via the gamification of the ERP lifecycle. Secondly, to pinpoint the ERP lifecycle phases that are most likely to benefit from gamification. Thirdly, to gamify these formerly identified phases; that were found to be mostly affected by gamification, and test for the impact of gamification on them.
10.
Elgendy, Nada, et al.
(författare)
Big data analytics : A literature review paper
2014
Ingår i: Advances in data mining. - Cham : Encyclopedia of Global Archaeology/Springer Verlag. - 9783319089768 ; , s. 214-227
Konferensbidrag (refereegranskat) abstract
In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets. Furthermore, decision makers need to be able to gain valuable insights from such varied and rapidly changing data, ranging from daily transactions to customer interactions and social network data. Such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper aims to analyze some of the different analytics methods and tools which can be applied to big data, as well as the opportunities provided by the application of big data analytics in various decision domains. © 2014 Springer International Publishing Switzerland.
