Marija Bezbradica

Urban enterprise: A review of Smart City frameworks from an Enterprise Architecture perspective

With the increasing interest towards the concept of Smart Cities from the city governments world-wide there is a need for useful and Information Systems oriented approach to understand Smart City propositions. In this paper we review a Smart City from an Enterprise Architecture (EA) perspective. We adapt TOGAF Architecture Development Method (ADM) to derive the concept of Enterprise Concerns. These concerns will subsequently be used to review the Smart City literature. Finally, we summarize our findings and propose the concept of the Urban Enterprise composed of Urban Enterprise Components.

Parallelisation strategies for large scale cellular automata frameworks in pharmaceutical modelling

Cellular Automata (CA) properties facilitate the detail required for the bottom-up approach to modelling and simulation of a broad range of physico-chemical reactions. In pharmaceutical applications, CA models use a combination of discrete-event rules based on probabilistic distributions and fundamental physical laws to predict the behaviour of active substances (drug molecules) and structural changes in Drug Dissolution Systems (DDS) over time. Several models of this type have been described so far in the scientific literature. Yet, practical applications are lacking in the context of large-scale, high-precision, high-fidelity simulations. The key obstacle to parallelisation of such models is not only the amount of data involved, but also the fact that many of these models incorporate agent-like behaviour within the CA framework in order to describe pharmaceutical components. This makes communication across process boundaries expensive. In this paper, we apply different parallelisation strategies to a large scale CA framework, used to model coated drug spheres. We use two parallel-computing application programming interfaces (APIs), namely OpenMP and MPI, to partition the simulation space. We analyse the applicability of each API to the problem individually, as well as in the hybrid solution. We examine speedup potential and overhead for local and global communication for simulation speed and solution scalability. For these types of problems, our results show that performance is much improved for appropriate combinations of parallelisation solutions.

Comparative analysis of asynchronous cellular automata in stochastic pharmaceutical modelling

In pharmaceutical modelling, cellular automata have been used as an established tool to represent molecular changes through discrete structural interactions. The data quality provided by such modelling is found suitable for the early drug design phase where flexibility is paramount. While both synchronous (CA) and asynchronous (ACA) types of automata have been used, analysis of their nature and comparative influence on model outputs is lacking. In this paper, we outline a representative probabilistic CA for modelling complex controlled drug formulations and investigate its transition from synchronous to asynchronous update algorithms. The key investigation points include quantification of model dynamics through three distinct scenarios, parallelisation performance and the ability to describe different release phenomena, namely erosion, diffusion and swelling. The choice of the appropriate update mechanism impacts the perceived realism of the simulation as well as the applicability of large-scale simulations.

Probabilistic Pharmaceutical Modelling: A Comparison Between Synchronous and Asynchronous Cellular Automata

The field of pharmaceutical modelling has, in recent years, benefited from using probabilistic methods based on cellular automata, which seek to overcome some of the limitations of differential equation based models. By modelling discrete structural element interactions instead, these are able to provide data quality adequate for the early design phases in drug modelling. In relevant literature, both synchronous (CA) and asynchronous (ACA) types of automata have been used, without analysing their comparative impact on the model outputs. In this paper, we compare several variations of probabilistic CA and ACA update algorithms for building models of complex systems used in controlled drug delivery, analysing the advantages and disadvantages related to different modelling scenarios. Choosing the appropriate update mechanism, besides having an impact on the perceived realism of the simulation, also has practical benefits on the applicability of different model parallelisation algorithms and their performance when used in large-scale simulation contexts.

Modelling Impact of Morphological Urban Structure and Cognitive Behaviour on Pedestrian Flows

A novel, discrete space-time model of pedestrian behaviour in real urban networks is presented. An agent-based approach is used to define characteristics of individual pedestrians, based on spatial awareness and cognition theories, combined with preferential choices of different social groups. Behaviour patterns are considered incorporating rules of movement along pedestrian routes and for intermediate decision and conflict points. The model utilises dynamic volunteered geographic information system data allowing analysis of arbitrary city networks and comparison of the effect of grid structure and amenity distribution. As an example, two distinctive social groups are considered, namely ’directed’ and ’leisure’, and their interaction, together with the way in which flow congestion and changes in network morphology affect route choice in central London areas. The resulting stress and flow characteristics of the urban network simulations as well as the impact on individual agent paths and travel times, are discussed.

Bitcoin Currency Fluctuation.

Predicting currency prices remains a difficult endeavour. Investors are continually seeking new ways to extract meaningful information about the future direction of price changes. Recently, cryptocurrencies have attracted huge attention due to their unique way of transferring value as well as its value as a hedge. A method proposed in this project involves using data mining techniques: mining text documents such as news articles and tweets try to infer the relationship between information contained in such items and cryptocurrency price direction. The Long Short-Term Memory Recurrent Neural Network (LSTM RNN) assists in creating a hybrid model which comprises of sentiment analysis techniques, as well as a predictive machine learning model. The success of the model was evaluated within the context of predicting the direction of Bitcoin price changes. Findings reported here reveal that our system yields more accurate and real-time predictions of Bitcoin price fluctuations when compared to other existing models in the market.

Generic Refactoring Methodology for Cloud Migration - Position Paper.

Cloud migration has attracted a lot of attention in both industry and academia due to the on-demand, high availability, dynamic scalable nature. Organizations choose to move their on-premise applications to adapt to the virtualized environment of the cloud where the services are accessed remotely over the internet. These applications need to be re-engineered to completely exploit the cloud infrastructure such as performance and scalability improvements over the on-premise infrastructure. This paper proposes a re-engineering approach called architectural refactoring for restructuring on-premise application components to adopt to the cloud environment with the aim of achieving significant increase in non-functional quality attributes such as performance, scalability and maintainability of the cloud architectures. This paper proposes, when needed to migrate to cloud, the application is divided into smaller components, converted into services and deployed to cloud. The paper discusses existing issues faced by software developers and engineers during cloud migration, introduces architectural refactoring as a solution and explains the generic refactoring process at an architectural level.

Cities as Enterprises: A Comparison of Smart City Frameworks Based on Enterprise Architecture Requirements

There is a significant challenge in smart cities implementations. One challenge is to align smart city strategies with the impact on quality of life. Stakeholders’ concerns are multiple and diverse, and there is a high interdependency and heterogeneity of technologies and solutions. To tackle this challenge, researchers have suggested to view cities as enterprises and apply an Enterprise Architecture (EA) approach. This approach specifies core requirements on business, information, and technology domains, which are essential to model architecture components and to establish relations between these domains. Existing smart cities frameworks describe different components and domains. However, the main domain requirements and the relations between them are still missing. This paper identifies essential requirements of enterprise architecture in smart cities. These requirements will be used to review and compare current smart city frameworks.

A Usage-based Data Extraction Framework for Cloud-based Application - An Human-Computer Interaction Approach

Features or functionalities provided by cloud-based applications are accessed by users through various interfaces such as web browser, mobile app, and command line interface. Yet for monitoring cloud-based applications, software developers and researchers have focused on web browsers. Software updates are provided for such applications based on the data acquired from the cloud monitoring components but usage data of the cloud application features are difficult to extract in a cloud environment as the usage data is spread across the interfaces on the front-end and the back-end. In this paper, we focus on the usage of the cloud application features from the user perspective and how to extract these data in a cloud environment. We define six criteria for the user-level usage data, analyse the existing usage data extraction techniques and propose a usage data extraction framework adhering to the defined criteria.

High-Performance Computing for Data Analytics

One of the main challenges in data analytics is that discovering structures and patterns in complex datasets is a computer-intensive task. Recent advances in high-performance computing provide part of the solution. Multicore systems are now more affordable and more accessible. In this paper, we investigate how this can be used to develop more advanced methods for data analytics. We focus on two specific areas: model-driven analysis and data mining using optimisation techniques.