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1 – 10 of over 71000
Article
Publication date: 21 September 2015

Moumita Das, Jack C.P. Cheng and Kincho H. Law

The purpose of this paper is to present a framework for integrating construction supply chain in order to resolve the data heterogeneity and data sharing problems in the…

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Abstract

Purpose

The purpose of this paper is to present a framework for integrating construction supply chain in order to resolve the data heterogeneity and data sharing problems in the construction industry.

Design/methodology/approach

Standardized web service technology is used in the proposed framework for data specification, transfer, and integration. Open standard SAWSDL is used to annotate web service descriptions with pointers to concepts defined in ontologies. NoSQL database Cassandra is used for distributed data storage among construction supply chain stakeholders.

Findings

Ontology can be used to support heterogeneous data transfer and integration through web services. Distributed data storage facilitates data sharing and enhances data control.

Practical implications

This paper presents examples of two ontologies for expressing construction supply chain information – ontology for material and ontology for purchase order. An example scenario is presented to demonstrate the proposed web service framework for material procurement process involving three parties, namely, project manager, contractor, and material supplier.

Originality/value

The use of web services is not new to construction supply chains (CSCs). However, it still faces problems in channelizing information along CSCs due to data heterogeneity. Trust issue is also a barrier to information sharing for integrating supply chains in a centralized collaboration system. In this paper, the authors present a web service framework, which facilitates storage and sharing of information on a distributed manner mediated through ontology-based web services. Security is enhanced with access control. A data model for the distributed databases is also presented for data storage and retrieval.

Details

Engineering, Construction and Architectural Management, vol. 22 no. 5
Type: Research Article
ISSN: 0969-9988

Keywords

Article
Publication date: 12 June 2017

Kehe Wu, Yayun Zhu, Quan Li and Ziwei Wu

The purpose of this paper is to propose a data prediction framework for scenarios which require forecasting demand for large-scale data sources, e.g., sensor networks, securities…

Abstract

Purpose

The purpose of this paper is to propose a data prediction framework for scenarios which require forecasting demand for large-scale data sources, e.g., sensor networks, securities exchange, electric power secondary system, etc. Concretely, the proposed framework should handle several difficult requirements including the management of gigantic data sources, the need for a fast self-adaptive algorithm, the relatively accurate prediction of multiple time series, and the real-time demand.

Design/methodology/approach

First, the autoregressive integrated moving average-based prediction algorithm is introduced. Second, the processing framework is designed, which includes a time-series data storage model based on the HBase, and a real-time distributed prediction platform based on Storm. Then, the work principle of this platform is described. Finally, a proof-of-concept testbed is illustrated to verify the proposed framework.

Findings

Several tests based on Power Grid monitoring data are provided for the proposed framework. The experimental results indicate that prediction data are basically consistent with actual data, processing efficiency is relatively high, and resources consumption is reasonable.

Originality/value

This paper provides a distributed real-time data prediction framework for large-scale time-series data, which can exactly achieve the requirement of the effective management, prediction efficiency, accuracy, and high concurrency for massive data sources.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 10 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 3 July 2020

Azra Nazir, Roohie Naaz Mir and Shaima Qureshi

The trend of “Deep Learning for Internet of Things (IoT)” has gained fresh momentum with enormous upcoming applications employing these models as their processing engine and Cloud…

273

Abstract

Purpose

The trend of “Deep Learning for Internet of Things (IoT)” has gained fresh momentum with enormous upcoming applications employing these models as their processing engine and Cloud as their resource giant. But this picture leads to underutilization of ever-increasing device pool of IoT that has already passed 15 billion mark in 2015. Thus, it is high time to explore a different approach to tackle this issue, keeping in view the characteristics and needs of the two fields. Processing at the Edge can boost applications with real-time deadlines while complementing security.

Design/methodology/approach

This review paper contributes towards three cardinal directions of research in the field of DL for IoT. The first section covers the categories of IoT devices and how Fog can aid in overcoming the underutilization of millions of devices, forming the realm of the things for IoT. The second direction handles the issue of immense computational requirements of DL models by uncovering specific compression techniques. An appropriate combination of these techniques, including regularization, quantization, and pruning, can aid in building an effective compression pipeline for establishing DL models for IoT use-cases. The third direction incorporates both these views and introduces a novel approach of parallelization for setting up a distributed systems view of DL for IoT.

Findings

DL models are growing deeper with every passing year. Well-coordinated distributed execution of such models using Fog displays a promising future for the IoT application realm. It is realized that a vertically partitioned compressed deep model can handle the trade-off between size, accuracy, communication overhead, bandwidth utilization, and latency but at the expense of an additionally considerable memory footprint. To reduce the memory budget, we propose to exploit Hashed Nets as potentially favorable candidates for distributed frameworks. However, the critical point between accuracy and size for such models needs further investigation.

Originality/value

To the best of our knowledge, no study has explored the inherent parallelism in deep neural network architectures for their efficient distribution over the Edge-Fog continuum. Besides covering techniques and frameworks that have tried to bring inference to the Edge, the review uncovers significant issues and possible future directions for endorsing deep models as processing engines for real-time IoT. The study is directed to both researchers and industrialists to take on various applications to the Edge for better user experience.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 13 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 24 December 2021

Marsha E. Modeste, Chi Nguyen, Rhoda Nanre Nafziger and Jonathan Hermansen

The purpose of this study is to examine the nature of socially distributed leadership in Denmark and the USA, specifically teacher and staff leadership practices distributed in…

Abstract

Purpose

The purpose of this study is to examine the nature of socially distributed leadership in Denmark and the USA, specifically teacher and staff leadership practices distributed in schools.

Design/methodology/approach

This study used a confirmatory factor analysis and a second-order factor analysis to examine elementary USA and 0–9 Danish school educators’ responses to the Comprehensive Assessment of Leadership for Learning.

Findings

Findings from this analysis of leadership practice demonstrate (1) different approaches to teacher and staff leadership in Denmark and the USA; (2) the importance of a collaborative approach to developing and maintaining professional learning communities in schools in both contexts; and (3) different patterns of leadership practice that broadly reflect the local structure and approach to school leadership while responding to external policy demands.

Originality/value

Drawing on the globalization scholarship, which acknowledges the connection between global policy development and local spaces of implementation, this comparative international study allowed us to examine how policy ideas are parlayed into practice through the use of a shared assessment of leadership practice. The results of this study suggest that while the work of teacher and staff leadership is important and something that educators in Denmark and the USA are engaging in to advance the overall instructional mission of their schools, the approaches taken in each context are different and reflect a local-level negotiation between contextual cultural norms and policy expectations.

Details

Journal of Educational Administration, vol. 60 no. 2
Type: Research Article
ISSN: 0957-8234

Keywords

Article
Publication date: 31 December 2007

Eleftheria Katsiri, Jean Bacon and Alan Mycroft

The event‐driven paradigm is appropriate for context aware, distributed applications, yet basic events may be too low level to be meaningful to users. The authors aim to argue…

Abstract

Purpose

The event‐driven paradigm is appropriate for context aware, distributed applications, yet basic events may be too low level to be meaningful to users. The authors aim to argue that this bottom‐up approach is insufficient to handle very low‐level sensor data or to express all the queries users might wish to make.

Design/methodology/approach

The authors propose an alternative model for querying and subscribing transparently to distributed state in a real‐time, ubiquitous, sensor‐driven environment such as is found in Sentient Computing.

Findings

The framework consists of four components: a state‐based, temporal first‐order logic (TFOL) model that represents the concrete state of the world, as perceived by sensors; an expressive TFOL‐based language, the Abstract Event Specification Language (AESL) for creating abstract event definitions, subscriptions and queries; a higherorder service (Abstract Event Detection Service) that accepts a subscription containing an abstract event definition as an argument and in return publishes an interface to a further service, an abstract event detector; and a satisfiability service that applies classical, logical satisfiability in order to check the satisfiability of the AESL definitions against the world model, in a manner similar to a constraint‐satisfaction problem.

Originality/value

The paper develops a model‐based approach, appropriate for distributed, heterogeneous environments.

Details

International Journal of Pervasive Computing and Communications, vol. 3 no. 4
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 5 December 2017

Darlene García Torres

Singapore is a country with low teacher attrition rates and high performance on international assessments (TIMSS 2011/2015 and PISA 2012/2015). Consequently, its education system…

3107

Abstract

Purpose

Singapore is a country with low teacher attrition rates and high performance on international assessments (TIMSS 2011/2015 and PISA 2012/2015). Consequently, its education system is often considered as a model for other nations. The purpose of this paper is to extend research on teacher job satisfaction in Singapore and provide comparative information for other education systems.

Design/methodology/approach

This paper presents a secondary analysis of data from the Organization for Economic Cooperation and Development’s 2013 Teaching and Learning International Survey with a focus on relationships among teacher and principal perceptions of distributed leadership and teachers’ job satisfaction in Singapore. Hierarchical linear modeling is applied to investigate teacher job satisfaction with principal perceptions and aggregate teacher perceptions of distributed leadership as school-level (level 2) variables and individual teacher perceptions of distributed leadership as a level 1 variable.

Findings

Results indicated that distributed leadership significantly predicted teachers’ work and professional satisfaction; higher distributed leadership scores were associated with higher satisfaction scores.

Originality/value

The significant positive relationship between distributed leadership and both dimensions of job satisfaction after accounting for individual teacher characteristics is a new finding in the Singapore schooling context.

Details

Journal of Educational Administration, vol. 56 no. 1
Type: Research Article
ISSN: 0957-8234

Keywords

Article
Publication date: 13 November 2009

Ling Xuqiang, Huang Xiaodong, Li Bohu and Chai Xudong

Complex system modeling requires not only understanding of modeling framework but also domain knowledge of the system. The purpose of this paper is to present an approach which…

Abstract

Purpose

Complex system modeling requires not only understanding of modeling framework but also domain knowledge of the system. The purpose of this paper is to present an approach which separates the domain knowledge from the modeling framework with different views.

Design/methodology/approach

By establishing the mechanism of association and fusion among the views, the description and characterization of system from different aspect and point of view can form a complete system model. Based on the approach, a modeling and simulation (M&S) platform named SimFaster is developed. Modeling environment and simulation engine are the most important parts of the platform. The modeling environment provides multi‐views and multi‐layers to help the developers to modeling the structure, layers, composition, behavior, and interactions of an application system. The simulation engine provides mechanism of integration and interaction for components and objects, and provides runtime support for the concepts and terms from modeling environment. The simulation engine organizes the objects in the memory of distributed system as reflective object database system, so it is repository centered architecturally.

Findings

Based on the approach of multi‐views modeling, the platform is a flexible framework and supports top‐down design, model reuse and interoperation, dynamic refinement of models, corporative design among different users in different stages, and the rebuilt of application rapidly.

Research limitations/implications

This paper deals with high‐level models of the complex systems.

Practical implications

This platform helps to design, modeling, and simulation complex system (especially for weapon combat system). It can participate into all the stages of the development of complex product/system, and can support the validation, refinement, optimization of models, and systems.

Originality/value

This paper presents a multi‐views modeling approach for the modeling of complex system.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 28 no. 6
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 1 February 2016

Alan H Cron

The purpose of this paper is to examine the leadership practice of an 11-member district team of educators assembled to respond to one of the most comprehensive bullying laws in…

Abstract

Purpose

The purpose of this paper is to examine the leadership practice of an 11-member district team of educators assembled to respond to one of the most comprehensive bullying laws in the nation – the Massachusetts Anti-Bullying Law of 2010. This three-year case study provides school leaders and legislators with an in-depth, fine-grained analysis of how leadership was practiced by a district team of de facto leaders charged with implementing mandatory legislative policy throughout a six-school, 5,000-student, K-12 public school district.

Design/methodology/approach

This three-year case study employed an analytical, distributed leadership framework to identify, categorize, and analyze key artifacts used by a team to design and implement system-wide the comprehensive requirements of legislation. Using Weft qualitative data analysis software and the open, axial, and selective coding guidelines of Strauss and Corbin, data from semi-structured interviews and document analysis revealed a number of hidden structural considerations exerting significant influence on the leadership practice of the team.

Findings

Findings from this study suggest that leadership is perhaps more fluid than previously theorized. Defining leadership as a force that moves between and among organizational stakeholders (as opposed to a person or position), this study identified a number of structural considerations exerting influence on the leadership practice of a team. Furthermore, this study suggests that foreknowledge of these structural considerations may help to foster organizational learning, to leverage preexisting social and intellectual capital, and to more successfully navigate the requirements of complex organizational change such as legislative mandates and standards-based reform.

Research limitations/implications

Because of the chosen research approach, the research results may lack generalizability. Therefore, researchers are encouraged to replicate this study in other school districts or large organizations who are responding to state or federal legislation.

Practical implications

The paper includes implications for state and local educational leaders as they struggle with the increased demands of standards-based educational reform.

Social implications

This study has implications for those seeking to understand how legislation is received and assimilated by schools as well as those seeking a greater understanding of formal and informal leadership.

Originality/value

This paper fulfills an identified need to study how leadership is practiced in response to standards-based state and federal legislation.

Details

Journal of Educational Administration, vol. 54 no. 1
Type: Research Article
ISSN: 0957-8234

Keywords

Article
Publication date: 1 December 1995

Thomas J. Crowe and Edward J. Stahlman

Discusses the movement away from hierarchical organizationalstructures towards flatter, heterarchical, structures which is reflectedin the growing interest in distributed

832

Abstract

Discusses the movement away from hierarchical organizational structures towards flatter, heterarchical, structures which is reflected in the growing interest in distributed manufacturing control systems. Traditional hierarchical control systems are limited by the breadth, quantity and timeliness of information needed for their operation. Distributed, heterarchical, control systems overcome these hierarchical limitations but, concurrently, forfeit advantages of the hierarchy including analytically optimal loading patterns and centralized pristine data tracking. Classifies existing research into four categories and documents a progression of heterarchical control approaches to inject some of the advantages of the traditional hierarchy into new heterarchical frameworks. Concludes that neither hierarchical nor heterarchical control structures are ideal in their pure form and, hence, proposes a modified structure, called the quasi‐heterarchical control system, which is a combination of, and a compromise between, pure hierarchy and pure heterarchy.

Details

Integrated Manufacturing Systems, vol. 6 no. 6
Type: Research Article
ISSN: 0957-6061

Keywords

Article
Publication date: 28 July 2020

Sathyaraj R, Ramanathan L, Lavanya K, Balasubramanian V and Saira Banu J

The innovation in big data is increasing day by day in such a way that the conventional software tools face several problems in managing the big data. Moreover, the occurrence of…

Abstract

Purpose

The innovation in big data is increasing day by day in such a way that the conventional software tools face several problems in managing the big data. Moreover, the occurrence of the imbalance data in the massive data sets is a major constraint to the research industry.

Design/methodology/approach

The purpose of the paper is to introduce a big data classification technique using the MapReduce framework based on an optimization algorithm. The big data classification is enabled using the MapReduce framework, which utilizes the proposed optimization algorithm, named chicken-based bacterial foraging (CBF) algorithm. The proposed algorithm is generated by integrating the bacterial foraging optimization (BFO) algorithm with the cat swarm optimization (CSO) algorithm. The proposed model executes the process in two stages, namely, training and testing phases. In the training phase, the big data that is produced from different distributed sources is subjected to parallel processing using the mappers in the mapper phase, which perform the preprocessing and feature selection based on the proposed CBF algorithm. The preprocessing step eliminates the redundant and inconsistent data, whereas the feature section step is done on the preprocessed data for extracting the significant features from the data, to provide improved classification accuracy. The selected features are fed into the reducer for data classification using the deep belief network (DBN) classifier, which is trained using the proposed CBF algorithm such that the data are classified into various classes, and finally, at the end of the training process, the individual reducers present the trained models. Thus, the incremental data are handled effectively based on the training model in the training phase. In the testing phase, the incremental data are taken and split into different subsets and fed into the different mappers for the classification. Each mapper contains a trained model which is obtained from the training phase. The trained model is utilized for classifying the incremental data. After classification, the output obtained from each mapper is fused and fed into the reducer for the classification.

Findings

The maximum accuracy and Jaccard coefficient are obtained using the epileptic seizure recognition database. The proposed CBF-DBN produces a maximal accuracy value of 91.129%, whereas the accuracy values of the existing neural network (NN), DBN, naive Bayes classifier-term frequency–inverse document frequency (NBC-TFIDF) are 82.894%, 86.184% and 86.512%, respectively. The Jaccard coefficient of the proposed CBF-DBN produces a maximal Jaccard coefficient value of 88.928%, whereas the Jaccard coefficient values of the existing NN, DBN, NBC-TFIDF are 75.891%, 79.850% and 81.103%, respectively.

Originality/value

In this paper, a big data classification method is proposed for categorizing massive data sets for meeting the constraints of huge data. The big data classification is performed on the MapReduce framework based on training and testing phases in such a way that the data are handled in parallel at the same time. In the training phase, the big data is obtained and partitioned into different subsets of data and fed into the mapper. In the mapper, the features extraction step is performed for extracting the significant features. The obtained features are subjected to the reducers for classifying the data using the obtained features. The DBN classifier is utilized for the classification wherein the DBN is trained using the proposed CBF algorithm. The trained model is obtained as an output after the classification. In the testing phase, the incremental data are considered for the classification. New data are first split into subsets and fed into the mapper for classification. The trained models obtained from the training phase are used for the classification. The classified results from each mapper are fused and fed into the reducer for the classification of big data.

Details

Data Technologies and Applications, vol. 55 no. 3
Type: Research Article
ISSN: 2514-9288

Keywords

1 – 10 of over 71000