TAXONOMY DEVELOPMENT IN INFORMATION SYSTEMS: DEVELOPING A TAXONOMY OF MOBILE APPLICATIONS

The complexity of the information systems field often lends itself to classification schemes, or taxonomies, which provide ways to understand the similarities and differences among objects under study. Developing a taxonomy, however, is a complex process that is often done in an ad hoc way. This research-in-progress paper uses the design science paradigm to develop a systematic method for taxonomy development in information systems. The method we propose uses an indicator or operational level model that combines both empirical to deductive and deductive to empirical approaches. We evaluate this method by using it to develop a taxonomy of mobile applications, which we have chosen because of their ever-increasing number and variety. The resulting taxonomy contains seven dimensions with fifteen characteristics. We demonstrate the usefulness of this taxonomy by analyzing a range of current and proposed mobile applications. From the results of this analysis we identify combinations of characteristics where applications are missing and thus are candidates for new and potentially useful applications.taxonomy, design science, mobile application

The emergence of a new form of IS offshore enterprise - The modern heterarchy

The complexity of the information systems field often lends itself to classification schemes, or taxonomies, which provide ways to understand the similarities and differences among objects under study. Developing a taxonomy, however, is a complex process that is often done in an ad hoc way. This research-in-progress paper uses the design science paradigm to develop a systematic method for taxonomy development in information systems. The method we propose uses an indicator or operational level model that combines both empirical to deductive and deductive to empirical approaches. We evaluate this method by using it to develop a taxonomy of mobile applications, which we have chosen because of their ever-increasing number and variety. The resulting taxonomy contains seven dimensions with fifteen characteristics. We demonstrate the usefulness of this taxonomy by analyzing a range of current and proposed mobile applications. From the results of this analysis we identify combinations of characteristics where applications are missing and thus are candidates for new and potentially useful applications

A taxonomy of network threats and the effect of current datasets on intrusion detection systems

As the world moves towards being increasingly dependent on computers and automation, building secure applications, systems and networks are some of the main challenges faced in the current decade. The number of threats that individuals and businesses face is rising exponentially due to the increasing complexity of networks and services of modern networks. To alleviate the impact of these threats, researchers have proposed numerous solutions for anomaly detection; however, current tools often fail to adapt to ever-changing architectures, associated threats and zero-day attacks. This manuscript aims to pinpoint research gaps and shortcomings of current datasets, their impact on building Network Intrusion Detection Systems (NIDS) and the growing number of sophisticated threats. To this end, this manuscript provides researchers with two key pieces of information; a survey of prominent datasets, analyzing their use and impact on the development of the past decade’s Intrusion Detection Systems (IDS) and a taxonomy of network threats and associated tools to carry out these attacks. The manuscript highlights that current IDS research covers only 33.3% of our threat taxonomy. Current datasets demonstrate a clear lack of real-network threats, attack representation and include a large number of deprecated threats, which together limit the detection accuracy of current machine learning IDS approaches. The unique combination of the taxonomy and the analysis of the datasets provided in this manuscript aims to improve the creation of datasets and the collection of real-world data. As a result, this will improve the efficiency of the next generation IDS and reflect network threats more accurately within new datasets

A taxonomy of network threats and the effect of current datasets on intrusion detection systems

As the world moves towards being increasingly dependent on computers and automation, building secure applications, systems and networks are some of the main challenges faced in the current decade. The number of threats that individuals and businesses face is rising exponentially due to the increasing complexity of networks and services of modern networks. To alleviate the impact of these threats, researchers have proposed numerous solutions for anomaly detection; however, current tools often fail to adapt to ever-changing architectures, associated threats and zero-day attacks. This manuscript aims to pinpoint research gaps and shortcomings of current datasets, their impact on building Network Intrusion Detection Systems (NIDS) and the growing number of sophisticated threats. To this end, this manuscript provides researchers with two key pieces of information; a survey of prominent datasets, analyzing their use and impact on the development of the past decade's Intrusion Detection Systems (IDS) and a taxonomy of network threats and associated tools to carry out these attacks. The manuscript highlights that current IDS research covers only 33.3% of our threat taxonomy. Current datasets demonstrate a clear lack of real-network threats, attack representation and include a large number of deprecated threats, which together limit the detection accuracy of current machine learning IDS approaches. The unique combination of the taxonomy and the analysis of the datasets provided in this manuscript aims to improve the creation of datasets and the collection of real-world data. As a result, this will improve the efficiency of the next generation IDS and reflect network threats more accurately within new datasets

A survey of text representation methods and their genealogy

In recent years, with the advent of highly scalable artificial-neural-network-based text representation methods the field of natural language processing has seen unprecedented growth and sophistication. It has become possible to distill complex linguistic information of text into multidimensional dense numeric vectors with the use of the distributional hypothesis. As a consequence, text representation methods have been evolving at such a quick pace that the research community is struggling to retain knowledge of the methods and their interrelations. We contribute threefold to this lack of compilation, composition, and systematization by providing a survey of current approaches, by arranging them in a genealogy, and by conceptualizing a taxonomy of text representation methods to examine and explain the state-of-the-art. Our research is a valuable guide and reference for artificial intelligence researchers and practitioners interested in natural language processing applications such as recommender systems, chatbots, and sentiment analysis

Developing a taxonomy for the understanding of business and it alignment paradigms and tools

The alignment of information technology with business objectives tends to be a managerial priority in modern organisations. Thus, practitioners and researchers have proposed different approaches to assess this relationship, some following similar approaches whilst others proposing different ones. The variety of approaches proposed, however, has created confusion about the applicability and context in which these approaches can be used. Thus, aiming to tackle this challenge, this paper proposes a taxonomy that organises and compares studies of alignment assessment in terms of their theoretical constructors and their practical use. The taxonomy is build around two research sources: a) a review of the literature of alignment and b) a framework for comparing IS methodologies. The structure of the taxonomy permits insights into studies by means of six theoretical (objective, nature of strategy, paradigm, dimension, type of measurement, model) and six practical constructors (audience, scope, output, techniques, product, target). The taxonomy is then applied to six assessment studies. The benchmarking analysis of these helped to identify their theoretical basis and its practical use, and confirms the need for more practical mechanisms to assess alignment. Additionally, it becomes apparent that process perspectives and social understanding of alignment are the two main paradigms for alignment

Business Grid Services

Grid services have come to represent the synthesis of web services and grid computing paradigms. Web services provide the means to modularize software, enabling loosely coupled and novel synthesis. Grid computing removes the binding between functional software components and specific hosting hardware, enabling software to be deployed dynamically over a network (e.g. intra-, extra- or inter-net). Applying the constructs of grid computing to the service orientation of enterprise software will allow business service networks to utilize more specialized services. An upper service ontology that enables business grid services to be described and then related to the grid hosting platform is presented. Explicit knowledge is required for enterprise software, hosting servers and the domain that can then be utilized by both SLA and reservation systems. The ontology presented is derived from and validated using a collection of web services taken from leading investment banks

A Survey and Taxonomy of Sequential Recommender Systems for E-commerce Product Recommendation

E-commerce recommendation systems facilitate customers’ purchase decision by recommending products or services of interest (e.g., Amazon). Designing a recommender system tailored toward an individual customer’s need is crucial for retailers to increase revenue and retain customers’ loyalty. As users’ interests and preferences change with time, the time stamp of a user interaction (click, view or purchase event) is an important characteristic to learn sequential patterns from these user interactions and, hence, understand users’ long- and short-term preferences to predict the next item(s) for recommendation. This paper presents a taxonomy of sequential recommendation systems (SRecSys) with a focus on e-commerce product recommendation as an application and classifies SRecSys under three main categories as: (i) traditional approaches (sequence similarity, frequent pattern mining and sequential pattern mining), (ii) factorization and latent representation (matrix factorization and Markov models) and (iii) neural network-based approaches (deep neural networks, advanced models). This classification contributes towards enhancing the understanding of existing SRecSys in the literature with the application domain of e-commerce product recommendation and provides current status of the solutions available alongwith future research directions. Furthermore, a classification of surveyed systems according to eight important key features supported by the techniques along with their limitations is also presented. A comparative performance analysis of the presented SRecSys based on experiments performed on e-commerce data sets (Amazon and Online Retail) showed that integrating sequential purchase patterns into the recommendation process and modeling users’ sequential behavior improves the quality of recommendations

Predicting pilot error on the flight deck: Validation of a new methodology and a multiple methods and analysts approach to enhancing error prediction sensitivity

The Human Error Template (HET) is a recently developed methodology for predicting designed induced pilot error. This article describes a validation study undertaken to compare the performance of HET against three contemporary Human Error Identification (HEI) approaches when used to predict pilot errors for an approach and landing task and also to compare individual analyst error predictions to an approach to enhancing error prediction sensitivity: the multiple analysts and methods approach, whereby multiple analyst predictions using a range of HEI technique are pooled. The findings indicate that, of the four methodologies used in isolation, analysts using the HET methodology offered the most accurate error predictions, and also that the multiple analysts and methods approach was more successful overall in terms of error prediction sensitivity than the three other methods but not the HET approach. The results suggest that when predicting design induced error, it is appropriate to use domain specific approaches and also a toolkit of different HEI approaches and multiple analysts in order to heighten error prediction sensitivity