• A novel big data analytics-taliem-ir

    A novel big data analytics framework for smart cities

    تومان

    The emergence of smart cities aims at mitigating the challenges raised due to the continuous urbanization development and increasing population density in cities. To face these challenges, governments and decision makers undertake smart city projects targeting sustainable economic growth and better quality of life for both inhabitants and visitors. Information and Communication Technology (ICT) is a key enabling technology for city smartening. However, ICT artifacts and applications yield massive volumes of data known as big data. Extracting insights and hidden correlations from big data is a growing trend in information systems to provide better services to citizens and support the decision making processes. However, to extract valuable insights for developing city level smart information services, the generated datasets from various city domains need to be integrated and analyzed. This process usually referred to as big data analytics or big data value chain. Surveying the literature reveals an increasing interest in harnessing big data analytics applications in general and in the area of smart cities in particular. Yet, comprehensive discussions on the essential characteristics of big data analytics frameworks fitting smart cities requirements are still needed. This paper presents a novel big data analytics framework for smart cities called “Smart City Data Analytics Panel – SCDAP”. The design of SCDAP is based on answering the following research questions: what are the characteristics of big data analytics frameworks applied in smart cities in literature and what are the essential design principles that should guide the design of big data analytics frameworks have to serve smart cities purposes? In answering these questions, we adopted a systematic literature review on big data analytics frameworks in smart cities. The proposed framework introduces new functionalities to big data analytics frameworks represented in data model management and aggregation. The value of the proposed framework is discussed in comparison to traditional knowledge discovery approaches.

  • Analysis of student behavior-taliem-ir

    Analysis of student behavior in learning management systems through a big data framework

    تومان

     In recent years, learning management systems (LMSs) have played a fundamental role in higher education teaching models. A new line of research has been opened relating to the analysis of student behavior within an LMS, in the search for patterns that improve the learning process. Current e-learning platforms allow for recording student activity, thereby enabling the exploration of events generated in the use of LMS tools. This paper presents a case study conducted at the Catholic University of Murcia, where student behavior in the past four academic years was analyzed according to learning modality  (that is, oncampus, online, and blended), considering the number of accesses to the LMS,tools employed by students and their associated events. Given the difficulty of managing the large volume of  data generated by users in the LMS (up to 70 GB in this study), statistical and association rule techniques were performed using a Big Data framework, thus speeding up the statistical analysis of the data. The obtained results are demonstrated using visual analytic techniques, and evaluated in order to detect trends and deficiencies in the use of the LMS by students.

  • Big data analytics architecture design-taliem-ir

    Big data analytics architecture design—an application in manufacturing systems

    تومان

    Context: The rapid prevalence and potential impact of big data analytics platforms have sparked an interest amongst different practitioners and academia. Manufacturing organisations are particularly well suited to benefit from data analytics platforms in their entire product lifecycle management for intelligent information processing, performing manufacturing activities, and creating value chains. This requires re-architecting their manufacturing  legacy information systems to get integrated with contemporary data analytics platforms. A systematic re-architecting approach is required incorporating careful and thorough evaluation of goals for data analytics  doption. Furthermore, ameliorating the uncertainty of the impact the new big data architecture on system quality goals is needed to avoid cost blowout in implementation and testing phases. Objective: We propose an approach to reason  about goals, obstacles, and to select suitable big data solution architecture that satisfy quality goal preferences and constraints of stakeholders at the presence of the decision outcome uncertainty. The approach  will highlight situations that may impede the goals. They will be assessed and resolved to generate complete requirements of an architectural solution. Method: The approach employs goal-oriented modelling to identify obstacles causing quality goal failure and their corresponding resolution tactics. It combines fuzzy logic to explore uncertainties in solution architectures and to find an optimal set of architectural decisions for the big data enablement process of manufacturing systems. Result: The approach brings two innovations to the state of the art  of big data analytics platform adoption in manufacturing systems: (i) A systematic goal-oriented modelling for exploring goals and obstacles in integrating manufacturing systems with data analytics platforms at the requirement level and (ii) A systematic analysis of the architectural decisions under uncertainty incorporating stakeholders’ preferences. The efficacy of the approach is illustrated with a scenario of reengineering a hyper-connected manufacturing collaboration system to a new big data architecture.

     

  • Big genetic data and its big data protection-taliem-ir

    Big genetic data and its big data protection challenges

    تومان

    The use of various forms of big data have revolutionised scientifc research. This includes research in the feld of genetics in areas ranging from medical research to anthropology. Developments in this area have inter alia been  characterised by the ability to sequence genomewide sequences (GWS) cheaply, the ability to share and combine with other forms of complimentary data and ever more powerful processing techniques that have become possible given tremendous increases in computing power. Given that many if not most of these techniques will make use of personal data it is necessary to take into account data protection law. This article looks at challenges for researchers that will be presented by the EU’s General Data Protection Regulation, which will be in effect from May 2018. The very nature of research with big data in general and genetic data in particular means that in many instances compliance will be onerous, whilst in others it may even be diffcult to envisage how compliance may be possible. Compliance concerns include issues relating to ‘purpose limitation’, ‘data minimisation’ and ‘storage limitation’. Other requirements, including the need to facilitate data subject rights and potentially conduct a Data Protection Impact Assessment (DPIA) may provide further complications for researchers. Further critical issues to consider include the choice of legal base: whether to opt for what is often seen as the ‘default option’ (i.e.  consent) or to process under the so called ‘scientifc research exception’.Each presents its own challenges (including the likely need to gain ethical approval) and opportunities that will have to be considered according to the particular context in question.

     

  • Distributed simulation-taliem-ir

    Distributed simulation: state-of-the-art and potential for operational research

    تومان

    In Operational Research conventional simulation practices typically focus on the conceptualization,  evelopment and use of a single model simulated on a single computer by a single analyst. Since the late  1970s the field of Distributed Simulation has led research into how to speed up simulation and how to compose large-scale simulations consisting of many reusable models running using distributed  computers. There have been significant advances in the theories and technologies underpinning Distributed Simulation and there have been major successes in defence, computer systems design and smart urban environments. However, from an Operational Research perspective, Distributed Simulation has had little impact on mainstream research and practice. To argue the potential benefits of  istributed Simulation for Operational Research, this article gives an overview of Distributed Simulation approaches and technologies as well as discussing the state-of-the-art of Distributed Simulation applications. It will investigate the potential advantages of Distributed Simulation for Operational Research and present a possible sustainable future, based on experiences from e-Science, that will help Operational Research meet future challenges such as those emerging from Big Data Analytics, Cyber-physical systems,  Industry 4.0, Digital Twins and Smart environments.

  • Distributed simulation-taliem-ir

    Distributed simulation: state-of-the-art and potential for operational research

    تومان

    In Operational Research conventional simulation practices typically focus on the conceptualization, development and use of a single model simulated on a single computer by a single analyst. Since the late 1970s the field of Distributed Simulation has led research into how to speed up simulation and how to compose large-scale simulations consisting of many reusable models running using distributed computers. There have been significant advances in the theories and technologies underpinning Distributed Simulation and there have been major successes in defence, computer systems design and smart urban environments. However, from an Operational Research perspective, Distributed Simulation has had little impact on mainstream research and practice. To argue the potential benefits of  istributed Simulation for Operational Research, this article gives an overview of Distributed Simulation approaches and technologies as well as discussing the state-of-the-art of Distributed Simulation applications. It will investigate the potential advantages of Distributed Simulation for Operational Research and present a possible sustainable future, based on experiences from e-Science, that will help Operational Research  meet futurechallenges such as those emerging from Big Data Analytics, Cyber-physical systems, Industry 4.0, Digital Twins and Smart environments.

  • Efficiency evaluation based on data envelopment-taliem-ir

    Efficiency evaluation based on data envelopment analysis in the big data context

    تومان

    Data envelopment analysis (DEA) is a self-evaluation method which assesses the relative efficiency of a particular decision making unit (DMU) within a group of DMUs. It has been widely applied in real-world scenarios, and traditional DEA models with a limited number of variables and linear constraints can be computed easily. However, DEA using big data involves huge numbers of DMUs, which may increase the computational load to beyond what is practical with traditional DEA methods. In this paper, we propose novel algorithms to accelerate the computation process in the big data environment. Specifically, we firstly use an algorithm to divide the large scale DMUs into small scale and identify all strongly efficient DMUs. If the strongly efficient DMU set is not too large, we can use the efficient DMUs as a sample set to evaluate the efficiency of inefficient DMUs. Otherwise, we can identify two reference points as the sample in the situation of just one input and one output. Furthermore, a variant of the algorithm is presented to handle cases with multiple inputs or multiple outputs, in which some of the strongly efficient DMUs are reselected as a reduced-size sample set to precisely measure the efficiency of inefficient DMUs. Last, we test the proposed methods on simulated data in various scenarios.

     

  • Efficiency evaluation based-taliem-ir

    Efficiency evaluation based on data envelopment analysis in the big data context

    تومان

    Data envelopment analysis (DEA) is a self-evaluation method which assesses the relative efficiency of a particular decision making unit (DMU) within a group of DMUs. It has been widely applied in real-world scenarios, and traditional DEA models with a limited number of variables and linear constraints can be computed easily. However, DEA using big data involves huge numbers of DMUs, which may increase the computational load to beyond what is practical with traditional DEA methods. In this paper, we propose novel algorithms to accelerate the computation process in the big data environment. Specifically, we firstly use an algorithm to divide the large scale DMUs into small scale and identify all strongly efficient DMUs. If the strongly efficient DMU set is not too large, we can use the efficient DMUs as a sample set to evaluate the efficiency of inefficient DMUs. Otherwise, we can identify two reference points as the sample in the situation of just one input and one output. Furthermore, a variant of the algorithm is presented to handle cases with multiple inputs or multiple outputs, in which some of the strongly efficient DMUs are reselected as  a reduced-size sample set to precisely measure the efficiency of inefficient DMUs. Last, we test the proposed methods on simulated data in various scenarios.

  • Forecasting tourist arrivals-taliem-ir

    Forecasting tourist arrivals with machine learning and internet search index

    تومان

    Previous studies have shown that online data, such as search engine queries, is a new source of data that can be used to forecast tourism demand. In this study, we propose a forecasting framework that uses machine learning and internet search indexes to forecast tourist arrivals for popular destinations in China and compared its forecasting performance to the search results generated by Google and Baidu, respectively. This study verifies the Granger causality and co-integration relationship between internet search index and tourist arrivals of Beijing. Our experimental results suggest that compared with benchmark models, the proposed kernel extreme learning machine (KELM) models, which integrate tourist volume series with Baidu Index and Google Index, can improve the forecasting performance significantly in terms of both forecasting accuracy and robustness analysis.

     

  • Hydrological Analysis using-taliem-ir

    Hydrological Analysis using Satellite Remote Sensing Big Data and CREST Model

    تومان

    Hydrological modelling significantly contributes to the understanding of catchment water balance and water resource management and mitigates negative impacts of flooding. Considering the advantages of satellite remote sensing big data and the Coupled Routing and Excess Storage (CREST) model, this paper investigates the hydrological modelling in the Shehong basin during 2006-2013. The results show that humid Shehong basin has main rainfalls in summer (From May to September). For the monthly average rainfall and streamflow, there is a remarkable increase (+52%) in discharge and a smaller increase (+18%) in rainfall in the second period (2010-2013) relative to the first period (2006-2009). The CREST model was calibrated using China Gauge-Based Daily Precipitation Analysis (CGDPA) for the period of 2006-2009, followed by a favorable performance with Nash-Sutcliffe coefficient efficiency (NSCE) of 0.77, correlation coefficient (CC) up to 0.88 and -11% Bias. The model validation shows an error metric with NSCE of 0.74, CC of 0.87 and -11.7% Bias. In terms of water balance modeling results at Shehong basin, the runoff and rainfall estimates from CREST model coincide well with the gauge  observations, indicating the model captures the appropriate signature of soil moisture variability. Therefore, the satellite-based precipitation product is feasible in hydrological prediction, and the CREST models the interaction between surface and subsurface water flow process in the Shehong basin.

  • Planning support systems-taliem-ir

    Planning support systems for smart cities

    تومان

    In an era of smart cities, planning support systems (PSS) offer the potential to harness the power of urban big data and support land-use and transport planning. PSS encapsulate data-driven modelling approaches for envisioning alternative future cities scenarios. They are widely available but have limited adoption in the planning profession (Russo, Lanzilotti, Costabile, & Pettit, 2017). Research has identified issues preventing their mainstream adoption to be, among others, the gap between PSS supply and demand (Geertman, 2016), their difficulty of use, a need for greater understanding of PSS capabilities and a lack of awareness of their applications (Russo et al., 2017; Vonk, Geertman, & Schot, 2005). To address this, a review of five PSS is conducted in the context of four vignettes applied in Australia and applicable internationally. A critical review has been undertaken, demonstrating how these PSS provide an evidence basis to understand, model and manage growing cities. The results suggest that PSS can assist in undertaking key tasks associated with the planning process. In addition to supporting planning and decision making, PSS can potentially enable better co-ordination between city, state and federal planning and infrastructure agencies, thus promoting a multi-scaled approach that improves local
    and national data sharing, modelling, reporting and scenario planning. The research demonstrates that PSS can assist in navigating the complexities of rapid multi-faceted urban growth to achieve better-informed planning outcomes. The paper concludes by outlining ways PSS address limitations of the past and can begin to address anticipated future challenges.

     

  • Smart cities with big data-taliem-ir

    Smart cities with big data: Reference models, challenges, and considerations

    تومان

    Cities worldwide are attempting to transform themselves into smart cities. Recent cases and studies show that a key factor in this transformation is the use of urban big data from stakeholders and physical objects in cities. However, the knowledge and framework for data use for smart cities remain relatively unknown. This paper reports findings from an analysis of various use cases of big data in cities worldwide and the authors’ four projects with government organizations toward developing smart cities. Specifically, this paper classifies the urban data use cases into four reference models and identifies six challenges in transforming data into information for smart cities. Furthermore, building upon the relevant literature, this paper proposes five considerations for addressing the challenges in implementing the reference models in real-world applications. The reference models, challenges, and considerations collectively form a framework for data use for smart cities. This paper will contribute to urban planning and policy development in the modern data-rich economy.

  • Action and risk-taliem-ir

    Smart city with Chinese characteristics against the background of big data: Idea, action and risk

    تومان

    Chinese urbanization has generated great impacts on the world since the reform and opening up.
    However, urban problems, e.g., environmental pollution, resources shortage, and traf
    fic jam, have been more and more serious for urban management and development. Smart city has been put forward as an effective approach to achieve better urban management recently. Smart city aims to realize the integration of municipal service, business, transportation, water, energy source and other urban sub-systems through close combination of human wisdom and information communication techniques (ICTs). As a result, the link and synergy of information could be ultimately established with ICTs, e.g., internet, internet of things, cloud computing. Yet, few studies have been conducted to systematically link smart city with big data in China. This paper aims to put forward a development framework of smart city with Chinese characteristics against the background of big data. Key actions, including rational planning of city infrastructures, the establishment and improvement of long-acting mechanism, the effective performance of city managerial function, are proposed to realize the development idea. Meanwhile, this paper also investigates the risks embedded in development of smart city with Chinese  characteristics, e.g.,information safety, weak emergency responding capacity and poor independent research and development capacity of core technology. This study can facilitate Chinese local governments to systematically plan smart city before clinging the hot concept in a rush.

     

  • Towards a systems thinking-taliem-ir

    Towards a systems thinking based view for the governance of a smart city’s ecosystem

    تومان

    Purpose This paper aims to investigate the role of Smart Technologies and Big Data as relevant dimensions in affecting the emerging social and economic dynamics of society with the aim to trace possible guidelines and pathways for decision makers and researchers interested in the governance of the Smart Citys ecosystem. The increasing attention to the domain of technologies and the amazing scenario that is emerging as a consequence of the influence of Smart Technology and Big Data in everyday life require reflection upon the ways in which the world is changing. Design/methodology/approach The paper adopts the interpretative lens provided by the systems thinking to investigate the challenging domain of the Smart City. A qualitative and interpretative approach  is adopted to reflect upon the role of technologies in everyday life. Findings The Smart City ecosystem is defined as a multilevel construct useful for understanding how technical and technological dimensions of the Smart City can be managed not only as supportive instruments but also as key pillars to support, facilitate and ensure an effective cognitive alignment among all the involved actors. Originality/value This paper provides a tangible evidence of the systems thinking contribution in analysing, understanding and managing dimensions and paths of social dynamics. A contribution to previous studies is provided with reference to systems thinking, Big Data and Smart City.

     

  • Unstructured data in marketing-taliem-ir

    Unstructured data in marketing

    تومان

    The rise of unstructured data (UD), propelled by novel technologies, is reshaping markets and the management of marketing activities. Yet these increased data remain mostly untapped by many firms, suggesting the potential for further research developments. The integrative framework proposed in this study addresses the nature of UD and pursues theoretical richness and computational advancements by integrating insights from other disciplines. This article makes three main contributions to the literature by (1) offering a unifying definition and conceptualization of UD in marketing; (2) bridging disjoint literature with an organizing framework that synthesizes various subsets of UD relevant for marketing management through an integrative review; and (3) identifying substantive, computational, and theoretical gaps in extant literature and ways to leverage interdisciplinary knowledge to advance marketing research by applying UD analyses to underdeveloped areas.