Kamran Soomro

Towards cloud based big data analytics for smart future cities

A large amount of land-use, environment, socio-economic, energy and transport data is generated in cities. An integrated perspective of managing and analysing such big data can answer a number of science, policy, planning, governance and business questions and support decision making in enabling a smarter environment. This paper presents a theoretical and experimental perspective on the smart cities focused big data management and analysis by proposing a cloud-based analytics service. A prototype has been designed and developed to demonstrate the effectiveness of the analytics service for big data analysis. The prototype has been implemented using Hadoop and Spark and the results are compared. The service analyses the Bristol Open data by identifying correlations between selected urban environment indicators. Experiments are performed using Hadoop and Spark and results are presented in this paper. The data pertaining to quality of life mainly crime and safety & economy and employment was analysed from the data catalogue to measure the indicators spread over years to assess positive and negative trends.

ICT enabled participatory urban planning and policy development: The UrbanAPI project

Purpose – The aim of this paper is to present the effectiveness of participatory information and communication technology (ICT) tools for urban planning, in particular, supporting bottom-up decision-making in urban management and governance. Design/methodology/approach – This work begins with a presentation on the state of the art literature on the existing participatory approaches and their contribution to urban planning and the policymaking process. Furthermore, a case study, namely, the UrbanAPI project, is selected to identify new visualisation and simulation tools applied at different urban scales. These tools are applied in four different European cities – Vienna, Bologna, Vitoria-Gasteiz and Ruse – with the objective to identify the data needs for application development, commonalities in requirements of such participatory tools and their expected impact in policy and decision-making processes. Findings – The case study presents three planning applications: three-dimensional Virtual Reality at neig...

A framework for cloud-based context-aware information services for citizens in smart cities

BackgroundIn the context of smart cities, public participation and citizen science are key ingredients for informed and intelligent planning decisions and policy-making. However, citizens face a practical challenge in formulating coherent information sets from the large volumes of data available to them. These large data volumes materialise due to the increased utilisation of information and communication technologies in urban settings and local authorities’ reliance on such technologies to govern urban settlements efficiently. To encourage effective public participation in urban governance of smart cities, the public needs to be facilitated with the right contextual information about the characteristics and processes of their urban surroundings in order to contribute to the aspects of urban governance that affect them such as socio-economic activities, quality of life, citizens well-being etc. The cities on the other hand face challenges in terms of crowd sourcing with quality data collection and standardisation, services inter-operability, provisioning of computational and data storage infrastructure.FocusIn this paper, we highlight the issues that give rise to these multi-faceted challenges for citizens and public administrations of smart cities, identify the artefacts and stakeholders involved at both ends of the spectrum (data/service producers and consumers) and propose a conceptual framework to address these challenges. Based upon this conceptual framework, we present a Cloud-based architecture for context-aware citizen services for smart cities and discuss the components of the architecture through a common smart city scenario. A proof of concept implementation of the proposed architecture is also presented and evaluated. The results show the effectiveness of the cloud-based infrastructure for the development of a contextual service for citizens.

Providing traceability for neuroimaging analyses.

INTRODUCTION With the increasingly digital nature of biomedical data and as the complexity of analyses in medical research increases, the need for accurate information capture, traceability and accessibility has become crucial to medical researchers in the pursuance of their research goals. Grid- or Cloud-based technologies, often based on so-called Service Oriented Architectures (SOA), are increasingly being seen as viable solutions for managing distributed data and algorithms in the bio-medical domain. For neuroscientific analyses, especially those centred on complex image analysis, traceability of processes and datasets is essential but up to now this has not been captured in a manner that facilitates collaborative study. PURPOSE AND METHOD Few examples exist, of deployed medical systems based on Grids that provide the traceability of research data needed to facilitate complex analyses and none have been evaluated in practice. Over the past decade, we have been working with mammographers, paediatricians and neuroscientists in three generations of projects to provide the data management and provenance services now required for 21st century medical research. This paper outlines the finding of a requirements study and a resulting system architecture for the production of services to support neuroscientific studies of biomarkers for Alzheimers disease. RESULTS The paper proposes a software infrastructure and services that provide the foundation for such support. It introduces the use of the CRISTAL software to provide provenance management as one of a number of services delivered on a SOA, deployed to manage neuroimaging projects that have been studying biomarkers for Alzheimers disease. CONCLUSIONS In the neuGRID and N4U projects a Provenance Service has been delivered that captures and reconstructs the workflow information needed to facilitate researchers in conducting neuroimaging analyses. The software enables neuroscientists to track the evolution of workflows and datasets. It also tracks the outcomes of various analyses and provides provenance traceability throughout the lifecycle of their studies. As the Provenance Service has been designed to be generic it can be applied across the medical domain as a reusable tool for supporting medical researchers thus providing communities of researchers for the first time with the necessary tools to conduct widely distributed collaborative programmes of medical analysis.

From Grid Middleware to a Grid Operating System

Grid computing has made substantial advances during the last decade. Grid middleware such as globus has contributed greatly in making this possible. There are, however, significant barriers to the adoption of grid computing in other fields, most notably day-to-day user computing environments. We will demonstrate in this paper that this is primarily due to the limitations of the existing grid middleware which does not take into account the needs of everyday scientific and business users. In this paper we will formally advocate a grid operating system and propose an architecture to migrate grid computing into a grid operating system which we believe would help remove most of the technical barriers to the adoption of grid computing and make it relevant to the day-to-day user. We believe this proposed transition to a grid operating system will drive more pervasive grid computing research and application development and deployment in future

Provenance Management for Neuroimaging Workflows in neuGrid

An increased amount of large scale, collaborative biomedical research has recently been conducted on e-Science infrastructures. Such research typically involves conducting comparative analysis on large amounts of data to search for biomarkers for diseases. Running these analysis manually can often be quite cumbersome, labour-intensive and error-prone. Significant work has been invested into automating such analysis with appropriately configured workflows. It is also important for biomedical researchers to validate analysis outcomes, to ensure the reproducibility of the results and to ascertain the ownership of specific scientific results. The detailed, traceable information required for this is often referred to as provenance data. Developing suitable methods and approaches to managing provenance data in large-scale distributed e-Science environments is another important area of research currently being investigated. We present an approach that has been adopted in the neu GRID project, which aims to develop an infrastructure to facilitate research into neurodegenerative disease studies such as Alzheimers. To facilitate the automation of complex, large-scale analysis in neu GRID, we have adapted CRISTAL, a workflow and provenance tracking solution. The use of CRISTAL has provided a rich environment for neuroscientists to track and manage the evolution of both data and workflows in the neu GRID infrastructure.

Developing Knowledge-Based Citizen Participation Platform to Support Smart City Decision Making: The Smarticipate Case Study

Citizen participation for social innovation and co-creating urban regeneration proposals can be greatly facilitated by innovative IT systems. Such systems can use Open Government Data, visualise urban proposals in 3D models and provide automated feedback on the feasibility of the proposals. Using such a system as a communication platform between citizens and city administrations provides an integrated top-down and bottom-up urban planning and decision-making approach to smart cities. However, generating automated feedback on citizens’ proposals requires modelling domain-specific knowledge i.e., vocabulary and rules, which can be applied on spatial and temporal 3D models. This paper presents the European Commission funded H2020 smarticipate project that aims to achieve the above challenge by applying it on three smart cities: Hamburg, Rome and RBKC-London. Whilst the proposed system architecture indicates various innovative features, a proof of concept of the automated feedback feature for the Hamburg use case ‘planting trees’ is demonstrated. Early results and lessons learned show that it is feasible to provide automated feedback on citizen-initiated proposals on specific topics. However, it is not straightforward to generalise this feature to cover more complex concepts and conditions which require specifying comprehensive domain languages, rules and appropriate tools to process them. This paper also highlights the strengths of the smarticipate platform, discusses challenges to realise its different features and suggests potential solutions.

Participatory governance in smart cities: the urbanAPI case study

This paper presents some results from an EU FP7 RTD project urbanAPI, in which three ICT applications target different aspects of participatory urban governance. The 3D Scenario Creator allows urban planners to visualise urban development proposals in three dimensions, share them with different stakeholders and obtain their feedback. The Mobility Explorer allows urban land use and transport planners to visualise and analyse population distribution and mobility patterns in the city. Finally, the Urban Development Simulator simulates socio-economic activity in response to alternative planning scenarios. These urbanAPI ICT applications are implemented in four pilot cities; Vienna (Austria), Vitoria-Gasteiz (Spain), Bologna (Italy) and Ruse (Bulgaria), and evaluated with respect to their various requirements. Results show that the applications are useful tools, enhancing spatial planning assessements, and enabling public participation, communicating proposed plans to different stakeholders and identifying key development issues which can provide crucial inputs in planning and decision making processes.

From top-down land use planning intelligence to bottom-up stakeholder engagement for smart cities - a case study: DECUMANUS service products

Intelligence delivered by earth observation (EO) satellites performs a vital role in supporting ICT enabled urban governance, and the creation of decision making tools delivering integrated urban planning. This paper reviews the DECUMANUS project experience, detailing the development of the EO derived tools, and evaluating the service products that facilitate the deployment of top-down expertise in land use planning. The central purpose of the paper is to assess the potential for use of these DECUMANUS high resolution EO images and data, also to support bottom-up participatory planning, promoting co-design. It is concluded: 1) EO derived images and associated data offer great opportunity to deliver top-down decision making tools, which combined with auxiliary data, including participatory sensing data, effectively support integrated urban planning; 2) EO derived images also offer substantial potential as communication tools, enabling citizens to make more informed and responsible choices and participate in co-designed urban planning.

Towards provisioning of real-time smart city services using clouds

ICT is becoming an enabler for smart city applications by making effective use of various data resources generated daily in an urban environment. Mostly this data is utilised by city authorities for city planning purposes and often citizens become indirect beneficiaries of such applications. In this paper we present an algorithm for real-time processing of streaming data from multiple sources. We also present the design and proof of concept of an application that performs mining and analysis of open data available through city portals and social networks and generates an information service in real time for use by city administrations. The prototype utilises streaming data from Twitter and open data from Bristol to demonstrate a hypothetical scenario using Apache Storm. The output is presented in the form of visual maps using OpenStreetMaps as a backend and the prototype highlights various challenges which are discussed in detail.