Search results

Dr. Gil Jones has been carrying out a research programme into the ways data can be captured, transmitted and used to provide retailers with improved management information. The full results of the research are to be published by the National Computing Centre later this year in a Report entitled “Data Capture in the Retail Environment”. In this two part article, Dr. Jones summarises his findings, identifying the technical options open to the retailer for preparation of management information, the costs involved and the likely benefits more sophisticated solutions can bring. Part I covers the effect the recent economic climate has had on management information strategy, the differing data requirements of various types of retail organisations, and the main options open to retailers for data capture and data processing. In Part II, Dr. Jones will go on to examine the costs of the various systems available and will look at the factors which must be considered in the economic justification of the necessary investment.

The purpose of this research is to develop a generic framework of a digital twin (DT)-based automated construction progress monitoring through reality capture to extended reality (RC-to-XR).

IDEF0 data modeling method has been designed to establish an integration of reality capturing technologies by using BIM, DTs and XR for automated construction progress monitoring. Structural equation modeling (SEM) method has been used to test the proposed hypotheses and develop the skill model to examine the reliability, validity and contribution of the framework to understand the DRX model's effectiveness if implemented in real practice.

The research findings validate the positive impact and importance of utilizing technology integration in a logical framework such as DRX, which provides trustable, real-time, transparent and digital construction progress monitoring.

DRX system captures accurate, real-time and comprehensive data at construction stage, analyses data and information precisely and quickly, visualizes information and reports in a real scale environment, facilitates information flows and communication, learns from itself, historical data and accessible online data to predict future actions, provides semantic and digitalize construction information with analytical capabilities and optimizes decision-making process.

The research presents a framework of an automated construction progress monitoring system that integrates BIM, various reality capturing technologies, DT and XR technologies (VR, AR and MR), arraying the steps on how these technologies work collaboratively to create, capture, generate, analyze, manage and visualize construction progress data, information and reports.

With the rapid development in the internet technologies, the applications of big data in construction have seen considerable attention. Currently, there are many input/output modes of capturing construction knowledge related to all construction stages. On the other hand, building information modelling (BIM) systems have been developed to help in storing various structured data of buildings. However, these systems cannot fully capture the knowledge and unstructured data used in the operation of building systems in a usable format that uses the intelligent capabilities of BIM systems. Therefore, this research aims to adopt the concept of big data and develop a spoken dialogue BIM system to capture buildings operation knowledge, particularly for building maintenance and refurbishment.

The proposed system integrates cloud-based spoken dialogue system and case-based reasoning BIM system.

The system acts as an interactive expert agent that seeks answers from the user for questions specific to building maintenance problems and helps searching for solutions from previously stored knowledge cases. The practices of monitoring and maintaining buildings performance can be more efficient by the retrieval of relevant solutions from the captured knowledge to new problems when maintaining buildings components. The developed system enables easier capture and search for solutions to new problems with a more comprehensive retrieval of information.

Capturing multi-modes data into BIM systems using the cloud-based spoken dialogue systems will help construction teams use the high volume of data generated over building lifecycle and search for the most suitable solutions for maintenance problems. This new area of research also contributes to the current BIM systems by advancing their capabilities to instantly capture and retrieve knowledge of operations instead of only information.

The purpose of this paper is to introduce Digital Methods Initiative Twitter Capture and Analysis Toolset, a toolset for capturing and analyzing Twitter data. Instead of just presenting a technical paper detailing the system, however, the authors argue that the type of data used for, as well as the methods encoded in, computational systems have epistemological repercussions for research. The authors thus aim at situating the development of the toolset in relation to methodological debates in the social sciences and humanities.

The authors review the possibilities and limitations of existing approaches to capture and analyze Twitter data in order to address the various ways in which computational systems frame research. The authors then introduce the open-source toolset and put forward an approach that embraces methodological diversity and epistemological plurality.

The authors find that design decisions and more general methodological reasoning can and should go hand in hand when building tools for computational social science or digital humanities.

Besides methodological transparency, the software provides robust and reproducible data capture and analysis, and interlinks with existing analytical software. Epistemic plurality is emphasized by taking into account how Twitter structures information, by allowing for a number of different sampling techniques, by enabling a variety of analytical approaches or paradigms, and by facilitating work at the micro, meso, and macro levels.

The paper opens up critical debate by connecting tool design to fundamental interrogations of methodology and its repercussions for the production of knowledge. The design of the software is inspired by exchanges and debates with scholars from a variety of disciplines and the attempt to propose a flexible and extensible tool that accommodates a wide array of methodological approaches is directly motivated by the desire to keep computational work open for various epistemic sensibilities.

The purpose of the paper is to identify the multiple types of data that can be collected and analyzed by practitioners across the cold chain, the ICT infrastructure required to enable data capture and how to utilize the data for decision making in cold chain logistics.

Content analysis based literature review of 38 selected research articles, published between 2000 and 2016, was used to create an overview of data capture, technologies used for collection and sharing of data, and decision making that can be supported by the data, across the cold chain and for different types of perishable food products.

There is a need to understand how continuous monitoring of conditions such as temperature, humidity, and vibration can be translated to support real-time assessment of quality, determination of actual remaining shelf life of products and use of those for decision making in cold chains. Firms across the cold chain need to adopt appropriate technologies suited to the specific contexts to capture data across the cold chain. Analysis of such data over longer periods can also unearth patterns of product deterioration under different transportation conditions, which can lead to redesigning the transportation network to minimize quality loss or to take precautions to avoid the adverse transportation conditions.

The findings need to be validated through further empirical research and modeling. There are opportunities to identify all relevant parameters to capture product condition as well as transaction data across the cold chain processes for fish, meat and dairy products. Such data can then be used for supply chain (SC) planning and pricing products in the retail stores based on product conditions and traceability information. Addressing some of the above research gaps will call for multi-disciplinary research involving food science and engineering, information technologies, computer science and logistics and SC management scholars.

The findings of this research can be beneficial for multiple players involved in the cold chain like food processing companies, logistics service providers, ports and wholesalers and retailers to understand how data can be effectively used for better decision making in cold chain and to invest in the specific technologies, which will suit the purpose. To ensure adoption of data analytics across the cold chain, it is also important to identify the player in the cold chain, which will drive and coordinate the effort.

This paper is one of the earliest to recognize the need for a comprehensive assessment for adoption and application of data analytics in cold chain management and provides directions for future research.

The purpose of this paper is to provide an overview of knowledge capture in the biopharmaceutical industry, focusing primarily on the transition from paper‐based to electronic data capture (EDC) systems.

The paper draws on biopharmaceutical industry literature and data from example clinical studies to describe the issues involved in transitioning to EDC in the clinical trials environment.

While electronic data capture systems provide greater efficiencies along the clinical trial supply chain, the industry is still far from achieving wide scale utilization of such technologies. The barriers to successful implementation are multifaceted, involving not only the information technology itself, but also user acceptance issues, lack of interoperability standards, and regulatory compliance. Major shifts in organizational culture and a unified effort within the industry will be necessary in order to derive full benefits from electronic capture systems in the future.

This study was limited in that case data from only one company was used to supplement the literature review. Further research is warranted to better understand the factors that facilitate adoption of electronic knowledge capture systems in the biopharmaceutical industry.

While the need for knowledge management in the healthcare industry is indisputable, there has been remarkably slow progress in this area, and a dearth of research exploring implementation issues. The value of this type of inquiry is profound as it will help us better understand the issues in implementation and adoption, and ultimately to deliver more effective and safe drugs to the public in a more efficient manner.

The purpose of this paper is to develop a conceptual framework to check if an organization is ready to adopt an AI-integrated CRM system. The study also analyzes different situations which can provide a comprehensive check list in the form of indicators that could provide a signal indicating whether the organization is ready to adopt an AI-integrated CRM system by capturing actionable and appropriate data.

The paper is a general review, and appropriate literature has been used to support the conceptual framework.

The key findings of this study are the different indicators that make up the conceptual framework. This framework can help organizations to check at a glance whether they are ready to adopt AI-integrated CRM system in their organizations. Specifically, it has been identified that different approaches are needed to tackle various types of customer data so that those may be made fit and actionable for appropriate utilization of AI algorithms to facilitate business success of an organization.

The paper has elaborately discussed the different approaches to be undertaken to calibrate and reorient the various kinds of actionable data and the contemplated challenges one would face in doing so. This would help the practitioners that how the data so captured can be made fit for action and utilization toward application of AI technologies integrated with existing CRM system in an organization.

This study is claimed to be a unique study to provide a conceptual framework which could help arranging and rearranging of captured data by an organization for making the data fit and ready for use with the help of AI technologies. This successful integration of AI with CRM system can help organizations toward taking quick and automated decision-making without much intervention of human beings.

Until now, the usage and usability factors of construction operation building information exchange (COBie) datasheet has remained largely overlooked. This oversight may be the potential factor in the lower adoption rates as well as effective utilization of COBie datasheet in the architectural, engineering and construction – facilities management industry. Cobie Data drops as a concept has difficulty in adoption pertaining to lengthy process of data capturing with high reliance on manual inputs. Finding from this study will enhance the usability aspects of COBie by looking at the entire process of data assembling in conjuncture with design development and using it to understand the project changes. The paper aims to discuss these issues.

The study is aimed at solving a practical issue in handling COBie datasheets. The study uses iterative steps from design thinking and software development process (SDP) for development of the system. The iterative approach from design thinking helped to understand the problem scenarios, development of rule sets and analysis of various options to tackle this issue. SDP was used for the development and validation of the COBieEvaluator prototype.

Despite the information exchange standards such as COBie is available for adoption for quite some time, its perceived value in the whole chain is less described. Various concepts such as preparing COBie sheets from beginning of project are discussed but hardly adopted due to lengthy process. The study helps in substantiating the need for a continuous data capture and showcase how this continuous data capture can help in tracking various design and equipment changes inside a project, using COBieEvaluator. A comparative view over the data helps in giving fruitful information about the project. The system also verify the quality of data inside the COBie datasheet by not only looking at the cell value inputs but also looking at the entire information linkage and finding the gaps.

COBie has mostly being analyzed as an output and its benefits. However, some important aspects of COBie datasheet such as the process of capturing and verifying it, and understanding the meaning of the changes during incremental building of COBie datasheet, is largely overlooked. This study use the concept behind COBie data drops and devise a system to help track effect of project design changes on COBie datasheet. It also highlights the importance of not looking COBie datasheets only as a FM handover requirement, but a source of information which can help various stakeholders to get useful information about the project development. The study propose a comparative dimension over the COBie sheet to get useful insight over the project development.

Until now, the usage and usability factors of Construction Operation Building information exchange (COBie) datasheet have remained largely overlooked. This oversight may be the potential factor in the lower adoption rates as well as the effective usage of COBie datasheet in the architecture, engineering and construction-facilities management industry. The purpose of this study is to investigate the benefits and key issues associated with COBie datasheet handling and identify the key technological solutions, which can help in mitigating the identified issues.

A literature review was conducted to identify the key benefits of using COBie and issues, which are associated with COBie datasheet handling. This paper has also designed a questionnaire based on a literature review and surveyed professionals who are well versed with handling COBie datasheet. Using responses, the issues are analyzed and discussed using non-parametric statistical analysis.

A total of 9 key benefits and 24 key issues categorized under three groups of usability issues, technical issues and organizational/other issues were identified. The results from the survey agree with all the key issues associated with COBie datasheet handling (with 86 responses). This research also proposes key ideas, that can help in mitigating these issues.

There is a paucity in published literature, which discusses in detail about the various issues associated with COBie datasheet handling. This research study aims to address this gap by identifying key issues by looking at the entire COBie data-capturing process holistically. Finding from this study can help professionals to understand these issues and develop appropriate technological solutions, which can make COBie data capturing and understanding easier. The findings could also assist in increasing the adoption rate of COBie, which could be achieved through mitigation of identified issues.

This paper aims to present a highly accessible and affordable tracking model for earthmoving operations in an attempt to overcome some of the limitations of current tracking models.

The proposed methodology involves four main processes: acquiring onsite terrestrial images, processing the images into 3D scaled cloud data, extracting volumetric measurements and crew productivity estimations from multiple point clouds using Delaunay triangulation and conducting earned value/schedule analysis and forecasting the remaining scope of work based on the estimated performance. For validation, the tracking model was compared with an observation-based tracking approach for a backfilling site. It was also used for tracking a coarse base aggregate inventory for a road construction project.

The presented model has proved to be a practical and accurate tracking approach that algorithmically estimates and forecasts all performance parameters from the captured data.

The proposed model is unique in extracting accurate volumetric measurements directly from multiple point clouds in a developed code using Delaunay triangulation instead of extracting them from textured models in modelling software which is neither automated nor time-effective. Furthermore, the presented model uses a self-calibration approach aiming to eliminate the pre-calibration procedure required before image capturing for each camera intended to be used. Thus, any worker onsite can directly capture the required images with an easily accessible camera (e.g. handheld camera or a smartphone) and can be sent to any processing device via e-mail, cloud-based storage or any communication application (e.g. WhatsApp).