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business</title> <meta name="description" content="Search results for: data driven business"> <meta name="keywords" content="data driven business"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="data driven business"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 27993</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: data driven business</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27993</span> Data-driven Decision-Making in Digital Entrepreneurship</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abeba%20Nigussie%20Turi">Abeba Nigussie Turi</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiangming%20Samuel%20Li"> Xiangming Samuel Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data-driven business models are more typical for established businesses than early-stage startups that strive to penetrate a market. This paper provided an extensive discussion on the principles of data analytics for early-stage digital entrepreneurial businesses. Here, we developed data-driven decision-making (DDDM) framework that applies to startups prone to multifaceted barriers in the form of poor data access, technical and financial constraints, to state some. The startup DDDM framework proposed in this paper is novel in its form encompassing startup data analytics enablers and metrics aligning with startups' business models ranging from customer-centric product development to servitization which is the future of modern digital entrepreneurship. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=startup%20data%20analytics" title="startup data analytics">startup data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=data-driven%20decision-making" title=" data-driven decision-making"> data-driven decision-making</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20acquisition" title=" data acquisition"> data acquisition</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20generation" title=" data generation"> data generation</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20entrepreneurship" title=" digital entrepreneurship"> digital entrepreneurship</a> </p> <a href="https://publications.waset.org/abstracts/145802/data-driven-decision-making-in-digital-entrepreneurship" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145802.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">329</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27992</span> To Handle Data-Driven Software Development Projects Effectively</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shahnewaz%20Khan">Shahnewaz Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine learning (ML) techniques are often used in projects for creating data-driven applications. These tasks typically demand additional research and analysis. The proper technique and strategy must be chosen to ensure the success of data-driven projects. Otherwise, even exerting a lot of effort, the necessary development might not always be possible. In this post, an effort to examine the workflow of data-driven software development projects and its implementation process in order to describe how to manage a project successfully. Which will assist in minimizing the added workload. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data" title="data">data</a>, <a href="https://publications.waset.org/abstracts/search?q=data-driven%20projects" title=" data-driven projects"> data-driven projects</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20science" title=" data science"> data science</a>, <a href="https://publications.waset.org/abstracts/search?q=NLP" title=" NLP"> NLP</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20project" title=" software project"> software project</a> </p> <a href="https://publications.waset.org/abstracts/163467/to-handle-data-driven-software-development-projects-effectively" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163467.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">84</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27991</span> Big Data-Driven Smart Policing: Big Data-Based Patrol Car Dispatching in Abu Dhabi, UAE</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Oualid%20Walid%20Ben%20Ali">Oualid Walid Ben Ali </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Big Data has become one of the buzzwords today. The recent explosion of digital data has led the organization, either private or public, to a new era towards a more efficient decision making. At some point, business decided to use that concept in order to learn what make their clients tick with phrases like ‘sales funnel’ analysis, ‘actionable insights’, and ‘positive business impact’. So, it stands to reason that Big Data was viewed through green (read: money) colored lenses. Somewhere along the line, however someone realized that collecting and processing data doesn’t have to be for business purpose only, but also could be used for other purposes to assist law enforcement or to improve policing or in road safety. This paper presents briefly, how Big Data have been used in the fields of policing order to improve the decision making process in the daily operation of the police. As example, we present a big-data driven system which is sued to accurately dispatch the patrol cars in a geographic environment. The system is also used to allocate, in real-time, the nearest patrol car to the location of an incident. This system has been implemented and applied in the Emirate of Abu Dhabi in the UAE. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data%20analytics" title=" big data analytics"> big data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=patrol%20car%20allocation" title=" patrol car allocation"> patrol car allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=dispatching" title=" dispatching"> dispatching</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS" title=" GIS"> GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=intelligent" title=" intelligent"> intelligent</a>, <a href="https://publications.waset.org/abstracts/search?q=Abu%20Dhabi" title=" Abu Dhabi"> Abu Dhabi</a>, <a href="https://publications.waset.org/abstracts/search?q=police" title=" police"> police</a>, <a href="https://publications.waset.org/abstracts/search?q=UAE" title=" UAE"> UAE</a> </p> <a href="https://publications.waset.org/abstracts/18580/big-data-driven-smart-policing-big-data-based-patrol-car-dispatching-in-abu-dhabi-uae" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18580.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">491</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27990</span> Emerging Technology for Business Intelligence Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hsien-Tsen%20Wang">Hsien-Tsen Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Business Intelligence (BI) has long helped organizations make informed decisions based on data-driven insights and gain competitive advantages in the marketplace. In the past two decades, businesses witnessed not only the dramatically increasing volume and heterogeneity of business data but also the emergence of new technologies, such as Artificial Intelligence (AI), Semantic Web (SW), Cloud Computing, and Big Data. It is plausible that the convergence of these technologies would bring more value out of business data by establishing linked data frameworks and connecting in ways that enable advanced analytics and improved data utilization. In this paper, we first review and summarize current BI applications and methodology. Emerging technologies that can be integrated into BI applications are then discussed. Finally, we conclude with a proposed synergy framework that aims at achieving a more flexible, scalable, and intelligent BI solution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20intelligence" title="business intelligence">business intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20web" title=" semantic web"> semantic web</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title=" cloud computing"> cloud computing</a> </p> <a href="https://publications.waset.org/abstracts/162726/emerging-technology-for-business-intelligence-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162726.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">98</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27989</span> Framework for Integrating Big Data and Thick Data: Understanding Customers Better</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nikita%20Valluri">Nikita Valluri</a>, <a href="https://publications.waset.org/abstracts/search?q=Vatcharaporn%20Esichaikul"> Vatcharaporn Esichaikul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the popularity of data-driven decision making on the rise, this study focuses on providing an alternative outlook towards the process of decision-making. Combining quantitative and qualitative methods rooted in the social sciences, an integrated framework is presented with a focus on delivering a much more robust and efficient approach towards the concept of data-driven decision-making with respect to not only Big data but also 'Thick data', a new form of qualitative data. In support of this, an example from the retail sector has been illustrated where the framework is put into action to yield insights and leverage business intelligence. An interpretive approach to analyze findings from both kinds of quantitative and qualitative data has been used to glean insights. Using traditional Point-of-sale data as well as an understanding of customer psychographics and preferences, techniques of data mining along with qualitative methods (such as grounded theory, ethnomethodology, etc.) are applied. This study’s final goal is to establish the framework as a basis for providing a holistic solution encompassing both the Big and Thick aspects of any business need. The proposed framework is a modified enhancement in lieu of traditional data-driven decision-making approach, which is mainly dependent on quantitative data for decision-making. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20behavior" title=" customer behavior"> customer behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20experience" title=" customer experience"> customer experience</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=qualitative%20methods" title=" qualitative methods"> qualitative methods</a>, <a href="https://publications.waset.org/abstracts/search?q=quantitative%20methods" title=" quantitative methods"> quantitative methods</a>, <a href="https://publications.waset.org/abstracts/search?q=thick%20data" title=" thick data"> thick data</a> </p> <a href="https://publications.waset.org/abstracts/99833/framework-for-integrating-big-data-and-thick-data-understanding-customers-better" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99833.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">163</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27988</span> The Disposable Identities; Enabling Trust-by-Design to Build Sustainable Data-Driven Value</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lorna%20Goulden">Lorna Goulden</a>, <a href="https://publications.waset.org/abstracts/search?q=Kai%20M.%20Hermsen"> Kai M. Hermsen</a>, <a href="https://publications.waset.org/abstracts/search?q=Jari%20Isohanni"> Jari Isohanni</a>, <a href="https://publications.waset.org/abstracts/search?q=Mirko%20Ross"> Mirko Ross</a>, <a href="https://publications.waset.org/abstracts/search?q=Jef%20Vanbockryck"> Jef Vanbockryck</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article introduces disposable identities, with reference use cases and explores possible technical approaches. The proposed approach, when fully developed as an open-source toolkit, enables developers of mobile or web apps to employ a self-sovereign identity and data privacy framework, in order to rebuild trust in digital services by providing greater transparency, decentralized control, and GDPR compliance. With a user interface for the management of self-sovereign identity, digital authorizations, and associated data-driven transactions, the advantage of Disposable Identities is that they may also contain verifiable data such as the owner’s photograph, official or even biometric identifiers for more proactive prevention of identity abuse. These Disposable Identities designed for decentralized privacy management can also be time, purpose and context-bound through a secure digital contract; with verification functionalities based on tamper-proof technology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dentity" title="dentity">dentity</a>, <a href="https://publications.waset.org/abstracts/search?q=trust" title=" trust"> trust</a>, <a href="https://publications.waset.org/abstracts/search?q=self-sovereign" title=" self-sovereign"> self-sovereign</a>, <a href="https://publications.waset.org/abstracts/search?q=disposable%20identity" title=" disposable identity"> disposable identity</a>, <a href="https://publications.waset.org/abstracts/search?q=privacy%20toolkit" title=" privacy toolkit"> privacy toolkit</a>, <a href="https://publications.waset.org/abstracts/search?q=decentralised%20identity" title=" decentralised identity"> decentralised identity</a>, <a href="https://publications.waset.org/abstracts/search?q=verifiable%20credential" title=" verifiable credential"> verifiable credential</a>, <a href="https://publications.waset.org/abstracts/search?q=cybersecurity" title=" cybersecurity"> cybersecurity</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20driven%20business" title=" data driven business"> data driven business</a>, <a href="https://publications.waset.org/abstracts/search?q=PETs" title=" PETs"> PETs</a>, <a href="https://publications.waset.org/abstracts/search?q=GDPRdentity" title=" GDPRdentity"> GDPRdentity</a>, <a href="https://publications.waset.org/abstracts/search?q=trust" title=" trust"> trust</a>, <a href="https://publications.waset.org/abstracts/search?q=self-sovereign" title=" self-sovereign"> self-sovereign</a>, <a href="https://publications.waset.org/abstracts/search?q=disposable%20identity" title=" disposable identity"> disposable identity</a>, <a href="https://publications.waset.org/abstracts/search?q=privacy%20toolkit" title=" privacy toolkit"> privacy toolkit</a>, <a href="https://publications.waset.org/abstracts/search?q=decentralised%20identity" title=" decentralised identity"> decentralised identity</a>, <a href="https://publications.waset.org/abstracts/search?q=verifiable%20credential" title=" verifiable credential"> verifiable credential</a>, <a href="https://publications.waset.org/abstracts/search?q=cybersecurity" title=" cybersecurity"> cybersecurity</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20driven%20business" title=" data driven business"> data driven business</a>, <a href="https://publications.waset.org/abstracts/search?q=PETs" title=" PETs"> PETs</a>, <a href="https://publications.waset.org/abstracts/search?q=GDPRI" title=" GDPRI"> GDPRI</a> </p> <a href="https://publications.waset.org/abstracts/136294/the-disposable-identities-enabling-trust-by-design-to-build-sustainable-data-driven-value" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136294.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">219</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27987</span> Data Quality as a Pillar of Data-Driven Organizations: Exploring the Benefits of Data Mesh</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marc%20Bachelet">Marc Bachelet</a>, <a href="https://publications.waset.org/abstracts/search?q=Abhijit%20Kumar%20Chatterjee"> Abhijit Kumar Chatterjee</a>, <a href="https://publications.waset.org/abstracts/search?q=Jos%C3%A9%20Manuel%20Avila"> José Manuel Avila</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data quality is a key component of any data-driven organization. Without data quality, organizations cannot effectively make data-driven decisions, which often leads to poor business performance. Therefore, it is important for an organization to ensure that the data they use is of high quality. This is where the concept of data mesh comes in. Data mesh is an organizational and architectural decentralized approach to data management that can help organizations improve the quality of data. The concept of data mesh was first introduced in 2020. Its purpose is to decentralize data ownership, making it easier for domain experts to manage the data. This can help organizations improve data quality by reducing the reliance on centralized data teams and allowing domain experts to take charge of their data. This paper intends to discuss how a set of elements, including data mesh, are tools capable of increasing data quality. One of the key benefits of data mesh is improved metadata management. In a traditional data architecture, metadata management is typically centralized, which can lead to data silos and poor data quality. With data mesh, metadata is managed in a decentralized manner, ensuring accurate and up-to-date metadata, thereby improving data quality. Another benefit of data mesh is the clarification of roles and responsibilities. In a traditional data architecture, data teams are responsible for managing all aspects of data, which can lead to confusion and ambiguity in responsibilities. With data mesh, domain experts are responsible for managing their own data, which can help provide clarity in roles and responsibilities and improve data quality. Additionally, data mesh can also contribute to a new form of organization that is more agile and adaptable. By decentralizing data ownership, organizations can respond more quickly to changes in their business environment, which in turn can help improve overall performance by allowing better insights into business as an effect of better reports and visualization tools. Monitoring and analytics are also important aspects of data quality. With data mesh, monitoring, and analytics are decentralized, allowing domain experts to monitor and analyze their own data. This will help in identifying and addressing data quality problems in quick time, leading to improved data quality. Data culture is another major aspect of data quality. With data mesh, domain experts are encouraged to take ownership of their data, which can help create a data-driven culture within the organization. This can lead to improved data quality and better business outcomes. Finally, the paper explores the contribution of AI in the coming years. AI can help enhance data quality by automating many data-related tasks, like data cleaning and data validation. By integrating AI into data mesh, organizations can further enhance the quality of their data. The concepts mentioned above are illustrated by AEKIDEN experience feedback. AEKIDEN is an international data-driven consultancy that has successfully implemented a data mesh approach. By sharing their experience, AEKIDEN can help other organizations understand the benefits and challenges of implementing data mesh and improving data quality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20culture" title="data culture">data culture</a>, <a href="https://publications.waset.org/abstracts/search?q=data-driven%20organization" title=" data-driven organization"> data-driven organization</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mesh" title=" data mesh"> data mesh</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20quality%20for%20business%20success" title=" data quality for business success"> data quality for business success</a> </p> <a href="https://publications.waset.org/abstracts/165331/data-quality-as-a-pillar-of-data-driven-organizations-exploring-the-benefits-of-data-mesh" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165331.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">137</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27986</span> Social Business: Opportunities and Challenges</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Mustafizur%20Rahaman">Muhammad Mustafizur Rahaman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Social business is a new concept in the field of Business Economics and Capitalist Economy. It has increased the importance in economic and social development in emerging economies. Professor Muhammad Yunus is the founding father of the notion. While conventional business underscores profit maximization as a core business principle, social business calls for addressing social problems at the expense of profit. This underlying principle gives social business advantageous position over conventional businesses to serve those who live at the bottom of the pyramid. It also poses grave challenges to the social business because social business sacrifices profit at one hand and seeks financial sustainability on the other. For the sake of its financial sustainability, the social business might increase the price of its product or service which might lower its social impact, thus, makes the business self-defeating. Therefore, social business should be more innovative in every business process including production, marketing, and management. Otherwise, the business is unlikely to be driven out from the society. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=innovativeness" title="innovativeness">innovativeness</a>, <a href="https://publications.waset.org/abstracts/search?q=self-defeat" title=" self-defeat"> self-defeat</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20business" title=" social business"> social business</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20problem" title=" social problem "> social problem </a> </p> <a href="https://publications.waset.org/abstracts/19929/social-business-opportunities-and-challenges" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19929.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">620</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27985</span> Data-Driven Market Segmentation in Hospitality Using Unsupervised Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rik%20van%20Leeuwen">Rik van Leeuwen</a>, <a href="https://publications.waset.org/abstracts/search?q=Ger%20Koole"> Ger Koole</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Within hospitality, marketing departments use segmentation to create tailored strategies to ensure personalized marketing. This study provides a data-driven approach by segmenting guest profiles via hierarchical clustering based on an extensive set of features. The industry requires understandable outcomes that contribute to adaptability for marketing departments to make data-driven decisions and ultimately driving profit. A marketing department specified a business question that guides the unsupervised machine learning algorithm. Features of guests change over time; therefore, there is a probability that guests transition from one segment to another. The purpose of the study is to provide steps in the process from raw data to actionable insights, which serve as a guideline for how hospitality companies can adopt an algorithmic approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20cluster%20analysis" title="hierarchical cluster analysis">hierarchical cluster analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=hospitality" title=" hospitality"> hospitality</a>, <a href="https://publications.waset.org/abstracts/search?q=market%20segmentation" title=" market segmentation"> market segmentation</a> </p> <a href="https://publications.waset.org/abstracts/151668/data-driven-market-segmentation-in-hospitality-using-unsupervised-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151668.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">108</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27984</span> Business Domain Modelling Using an Integrated Framework</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Hasan%20Salahat">Mohammed Hasan Salahat</a>, <a href="https://publications.waset.org/abstracts/search?q=Stave%20Wade"> Stave Wade</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an application of a “Systematic Soft Domain Driven Design Framework” as a soft systems approach to domain-driven design of information systems development. The framework combining techniques from Soft Systems Methodology (SSM), the Unified Modeling Language (UML), and an implementation pattern knows as ‘Naked Objects’. This framework have been used in action research projects that have involved the investigation and modeling of business processes using object-oriented domain models and the implementation of software systems based on those domain models. Within this framework, Soft Systems Methodology (SSM) is used as a guiding methodology to explore the problem situation and to develop the domain model using UML for the given business domain. The framework is proposed and evaluated in our previous works, and a real case study ‘Information Retrieval System for Academic Research’ is used, in this paper, to show further practice and evaluation of the framework in different business domain. We argue that there are advantages from combining and using techniques from different methodologies in this way for business domain modeling. The framework is overviewed and justified as multi-methodology using Mingers Multi-Methodology ideas. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SSM" title="SSM">SSM</a>, <a href="https://publications.waset.org/abstracts/search?q=UML" title=" UML"> UML</a>, <a href="https://publications.waset.org/abstracts/search?q=domain-driven%20design" title=" domain-driven design"> domain-driven design</a>, <a href="https://publications.waset.org/abstracts/search?q=soft%20domain-driven%20design" title=" soft domain-driven design"> soft domain-driven design</a>, <a href="https://publications.waset.org/abstracts/search?q=naked%20objects" title=" naked objects"> naked objects</a>, <a href="https://publications.waset.org/abstracts/search?q=soft%20language" title=" soft language"> soft language</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20retrieval" title=" information retrieval"> information retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=multimethodology" title=" multimethodology"> multimethodology</a> </p> <a href="https://publications.waset.org/abstracts/32073/business-domain-modelling-using-an-integrated-framework" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32073.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">560</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27983</span> Domain Driven Design vs Soft Domain Driven Design Frameworks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Salahat">Mohammed Salahat</a>, <a href="https://publications.waset.org/abstracts/search?q=Steve%20Wade"> Steve Wade</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents and compares the SSDDD “Systematic Soft Domain Driven Design Framework” to DDD “Domain Driven Design Framework” as a soft system approach of information systems development. The framework use SSM as a guiding methodology within which we have embedded a sequence of design tasks based on the UML leading to the implementation of a software system using the Naked Objects framework. This framework has been used in action research projects that have involved the investigation and modelling of business processes using object-oriented domain models and the implementation of software systems based on those domain models. Within this framework, Soft Systems Methodology (SSM) is used as a guiding methodology to explore the problem situation and to develop the domain model using UML for the given business domain. The framework is proposed and evaluated in our previous works, a comparison between SSDDD and DDD is presented in this paper, to show how SSDDD improved DDD as an approach to modelling and implementing business domain perspectives for Information Systems Development. The comparison process, the results, and the improvements are presented in the following sections of this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=domain-driven%20design" title="domain-driven design">domain-driven design</a>, <a href="https://publications.waset.org/abstracts/search?q=soft%20domain-driven%20design" title=" soft domain-driven design"> soft domain-driven design</a>, <a href="https://publications.waset.org/abstracts/search?q=naked%20objects" title=" naked objects"> naked objects</a>, <a href="https://publications.waset.org/abstracts/search?q=soft%20language" title=" soft language"> soft language</a> </p> <a href="https://publications.waset.org/abstracts/53604/domain-driven-design-vs-soft-domain-driven-design-frameworks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53604.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">298</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27982</span> Procedure Model for Data-Driven Decision Support Regarding the Integration of Renewable Energies into Industrial Energy Management </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Graus">M. Graus</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Westhoff"> K. Westhoff</a>, <a href="https://publications.waset.org/abstracts/search?q=X.%20Xu"> X. Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The climate change causes a change in all aspects of society. While the expansion of renewable energies proceeds, industry could not be convinced based on general studies about the potential of demand side management to reinforce smart grid considerations in their operational business. In this article, a procedure model for a case-specific data-driven decision support for industrial energy management based on a holistic data analytics approach is presented. The model is executed on the example of the strategic decision problem, to integrate the aspect of renewable energies into industrial energy management. This question is induced due to considerations of changing the electricity contract model from a standard rate to volatile energy prices corresponding to the energy spot market which is increasingly more affected by renewable energies. The procedure model corresponds to a data analytics process consisting on a data model, analysis, simulation and optimization step. This procedure will help to quantify the potentials of sustainable production concepts based on the data from a factory. The model is validated with data from a printer in analogy to a simple production machine. The overall goal is to establish smart grid principles for industry via the transformation from knowledge-driven to data-driven decisions within manufacturing companies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20analytics" title="data analytics">data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=green%20production" title=" green production"> green production</a>, <a href="https://publications.waset.org/abstracts/search?q=industrial%20energy%20management" title=" industrial energy management"> industrial energy management</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=renewable%20energies" title=" renewable energies"> renewable energies</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a> </p> <a href="https://publications.waset.org/abstracts/62395/procedure-model-for-data-driven-decision-support-regarding-the-integration-of-renewable-energies-into-industrial-energy-management" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62395.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">436</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27981</span> Perception-Oriented Model Driven Development for Designing Data Acquisition Process in Wireless Sensor Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Indra%20Gandhi">K. Indra Gandhi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wireless Sensor Networks (WSNs) have always been characterized for application-specific sensing, relaying and collection of information for further analysis. However, software development was not considered as a separate entity in this process of data collection which has posed severe limitations on the software development for WSN. Software development for WSN is a complex process since the components involved are data-driven, network-driven and application-driven in nature. This implies that there is a tremendous need for the separation of concern from the software development perspective. A layered approach for developing data acquisition design based on Model Driven Development (MDD) has been proposed as the sensed data collection process itself varies depending upon the application taken into consideration. This work focuses on the layered view of the data acquisition process so as to ease the software point of development. A metamodel has been proposed that enables reusability and realization of the software development as an adaptable component for WSN systems. Further, observing users perception indicates that proposed model helps in improving the programmer's productivity by realizing the collaborative system involved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20acquisition" title="data acquisition">data acquisition</a>, <a href="https://publications.waset.org/abstracts/search?q=model-driven%20development" title=" model-driven development"> model-driven development</a>, <a href="https://publications.waset.org/abstracts/search?q=separation%20of%20concern" title=" separation of concern"> separation of concern</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20sensor%20networks" title=" wireless sensor networks"> wireless sensor networks</a> </p> <a href="https://publications.waset.org/abstracts/63714/perception-oriented-model-driven-development-for-designing-data-acquisition-process-in-wireless-sensor-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63714.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">435</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27980</span> Exploring the Intersection of Accounting, Business, and Economics: Bridging Theory and Practice for Sustainable Growth</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Stephen%20Acheampong%20Amoafoh">Stephen Acheampong Amoafoh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In today's dynamic economic landscape, businesses face multifaceted challenges that demand strategic foresight and informed decision-making. This abstract explores the pivotal role of financial analytics in driving business performance amidst evolving market conditions. By integrating accounting principles with economic insights, organizations can harness the power of data-driven strategies to optimize resource allocation, mitigate risks, and capitalize on emerging opportunities. This presentation will delve into the practical applications of financial analytics across various sectors, highlighting case studies and empirical evidence to underscore its efficacy in enhancing operational efficiency and fostering sustainable growth. From predictive modeling to performance benchmarking, attendees will gain invaluable insights into leveraging advanced analytics tools to drive profitability, streamline processes, and adapt to changing market dynamics. Moreover, this abstract will address the ethical considerations inherent in financial analytics, emphasizing the importance of transparency, integrity, and accountability in data-driven decision-making. By fostering a culture of ethical conduct and responsible stewardship, organizations can build trust with stakeholders and safeguard their long-term viability in an increasingly interconnected global economy. Ultimately, this abstract aims to stimulate dialogue and collaboration among scholars, practitioners, and policymakers, fostering knowledge exchange and innovation in the realms of accounting, business, and economics. Through interdisciplinary insights and actionable recommendations, participants will be equipped to navigate the complexities of today's business environment and seize opportunities for sustainable success. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=financial%20analytics" title="financial analytics">financial analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20performance" title=" business performance"> business performance</a>, <a href="https://publications.waset.org/abstracts/search?q=data-driven%20strategies" title=" data-driven strategies"> data-driven strategies</a>, <a href="https://publications.waset.org/abstracts/search?q=sustainable%20growth" title=" sustainable growth"> sustainable growth</a> </p> <a href="https://publications.waset.org/abstracts/185080/exploring-the-intersection-of-accounting-business-and-economics-bridging-theory-and-practice-for-sustainable-growth" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185080.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">55</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27979</span> Data-Driven Dynamic Overbooking Model for Tour Operators</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kannapha%20Amaruchkul">Kannapha Amaruchkul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We formulate a dynamic overbooking model for a tour operator, in which most reservations contain at least two people. The cancellation rate and the timing of the cancellation may depend on the group size. We propose two overbooking policies, namely economic- and service-based. In an economic-based policy, we want to minimize the expected oversold and underused cost, whereas, in a service-based policy, we ensure that the probability of an oversold situation does not exceed the pre-specified threshold. To illustrate the applicability of our approach, we use tour package data in 2016-2018 from a tour operator in Thailand to build a data-driven robust optimization model, and we tested the proposed overbooking policy in 2019. We also compare the data-driven approach to the conventional approach of fitting data into a probability distribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=applied%20stochastic%20model" title="applied stochastic model">applied stochastic model</a>, <a href="https://publications.waset.org/abstracts/search?q=data-driven%20robust%20optimization" title=" data-driven robust optimization"> data-driven robust optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=overbooking" title=" overbooking"> overbooking</a>, <a href="https://publications.waset.org/abstracts/search?q=revenue%20management" title=" revenue management"> revenue management</a>, <a href="https://publications.waset.org/abstracts/search?q=tour%20operator" title=" tour operator"> tour operator</a> </p> <a href="https://publications.waset.org/abstracts/125929/data-driven-dynamic-overbooking-model-for-tour-operators" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/125929.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">134</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27978</span> Data-Driven Decision Making: A Reference Model for Organizational, Educational and Competency-Based Learning Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emanuel%20Koseos">Emanuel Koseos</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data-Driven Decision Making (DDDM) refers to making decisions that are based on historical data in order to inform practice, develop strategies and implement policies that benefit organizational settings. In educational technology, DDDM facilitates the implementation of differential educational learning approaches such as Educational Data Mining (EDM) and Competency-Based Education (CBE), which commonly target university classrooms. There is a current need for DDDM models applied to middle and secondary schools from a concern for assessing the needs, progress and performance of students and educators with respect to regional standards, policies and evolution of curriculums. To address these concerns, we propose a DDDM reference model developed using educational key process initiatives as inputs to a machine learning framework implemented with statistical software (SAS, R) to provide a best-practices, complex-free and automated approach for educators at their regional level. We assessed the efficiency of the model over a six-year period using data from 45 schools and grades K-12 in the Langley, BC, Canada regional school district. We concluded that the model has wider appeal, such as business learning systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=competency-based%20learning" title="competency-based learning">competency-based learning</a>, <a href="https://publications.waset.org/abstracts/search?q=data-driven%20decision%20making" title=" data-driven decision making"> data-driven decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=secondary%20schools" title=" secondary schools"> secondary schools</a> </p> <a href="https://publications.waset.org/abstracts/130911/data-driven-decision-making-a-reference-model-for-organizational-educational-and-competency-based-learning-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130911.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">174</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27977</span> Energy Efficient Assessment of Energy Internet Based on Data-Driven Fuzzy Integrated Cloud Evaluation Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chuanbo%20Xu">Chuanbo Xu</a>, <a href="https://publications.waset.org/abstracts/search?q=Xinying%20Li"> Xinying Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Gejirifu%20De"> Gejirifu De</a>, <a href="https://publications.waset.org/abstracts/search?q=Yunna%20Wu"> Yunna Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Energy Internet (EI) is a new form that deeply integrates the Internet and the entire energy process from production to consumption. The assessment of energy efficient performance is of vital importance for the long-term sustainable development of EI project. Although the newly proposed fuzzy integrated cloud evaluation algorithm considers the randomness of uncertainty, it relies too much on the experience and knowledge of experts. Fortunately, the enrichment of EI data has enabled the utilization of data-driven methods. Therefore, the main purpose of this work is to assess the energy efficient of park-level EI by using a combination of a data-driven method with the fuzzy integrated cloud evaluation algorithm. Firstly, the indicators for the energy efficient are identified through literature review. Secondly, the artificial neural network (ANN)-based data-driven method is employed to cluster the values of indicators. Thirdly, the energy efficient of EI project is calculated through the fuzzy integrated cloud evaluation algorithm. Finally, the applicability of the proposed method is demonstrated by a case study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy%20efficient" title="energy efficient">energy efficient</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20internet" title=" energy internet"> energy internet</a>, <a href="https://publications.waset.org/abstracts/search?q=data-driven" title=" data-driven"> data-driven</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20integrated%20evaluation" title=" fuzzy integrated evaluation"> fuzzy integrated evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20model" title=" cloud model"> cloud model</a> </p> <a href="https://publications.waset.org/abstracts/109039/energy-efficient-assessment-of-energy-internet-based-on-data-driven-fuzzy-integrated-cloud-evaluation-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/109039.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">203</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27976</span> Mission Driven Enterprises in Ecosystems as Drivers for Sustainable System Change</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Monique%20de%20Ritter">Monique de Ritter</a>, <a href="https://publications.waset.org/abstracts/search?q=Annemieke%20Roobeek"> Annemieke Roobeek</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study takes a holistic multi-layered systems approach on entrepreneurship, innovation, and sustainability. Concretely we looked how mission driven entrepreneurs (level 1) employ new business models and launch innovative products and/or ideas in their enterprises, which are (level 2) operating in entrepreneurial ecosystems (level 3), and how these in turn may generate higher level sustainable change (level 4). We employed a qualitative grounded research approach in which our aim is to contribute to theory. Fourteen in-depth semi-structured interviews were conducted with mission driven entrepreneurs in the Netherlands in which their individual drives, business models, and ecosystems were discussed. Interview transcripts were systematically coded and analysed and the ecosystems were visually mapped. Most important patterns include 1) entrepreneurs have a clear sustainable mission and regard this mission as de raison d’être of their enterprise; 2) entrepreneurs employ new business models with a focus on collaboration for innovation; the business model supports or enhances the sustainable mission of the enterprise, 3) entrepreneurs collaborate in ecosystems in which a) they also regard suppliers as partners for innovation and clients as ambassadors for the sustainable mission, b) would like to improve their relationships with financial institutions as they are in the entrepreneurs’ perspective often lagging behind with their innovative ideas and models, c) they collaborate for knowledge and innovation with several parties, d) personal informal connections are very important, and e) in which the higher sustainable mission is not a point of competition but of collaboration. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sustainability" title="sustainability">sustainability</a>, <a href="https://publications.waset.org/abstracts/search?q=entrepreneurship" title=" entrepreneurship"> entrepreneurship</a>, <a href="https://publications.waset.org/abstracts/search?q=innovation" title=" innovation"> innovation</a>, <a href="https://publications.waset.org/abstracts/search?q=ecosystem" title=" ecosystem"> ecosystem</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20models" title=" business models "> business models </a> </p> <a href="https://publications.waset.org/abstracts/27692/mission-driven-enterprises-in-ecosystems-as-drivers-for-sustainable-system-change" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27692.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">376</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27975</span> Business Intelligence for Profiling of Telecommunication Customer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rokhmatul%20Insani">Rokhmatul Insani</a>, <a href="https://publications.waset.org/abstracts/search?q=Hira%20Laksmiwati%20Soemitro"> Hira Laksmiwati Soemitro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Business Intelligence is a methodology that exploits the data to produce information and knowledge systematically, business intelligence can support the decision-making process. Some methods in business intelligence are data warehouse and data mining. A data warehouse can store historical data from transactional data. For data modelling in data warehouse, we apply dimensional modelling by Kimball. While data mining is used to extracting patterns from the data and get insight from the data. Data mining has many techniques, one of which is segmentation. For profiling of telecommunication customer, we use customer segmentation according to customer’s usage of services, customer invoice and customer payment. Customers can be grouped according to their characteristics and can be identified the profitable customers. We apply K-Means Clustering Algorithm for segmentation. The input variable for that algorithm we use RFM (Recency, Frequency and Monetary) model. All process in data mining, we use tools IBM SPSS modeller. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20intelligence" title="business intelligence">business intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20segmentation" title=" customer segmentation"> customer segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20warehouse" title=" data warehouse"> data warehouse</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a> </p> <a href="https://publications.waset.org/abstracts/46969/business-intelligence-for-profiling-of-telecommunication-customer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46969.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">485</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27974</span> A Study of Relational Factors Associated with Online Celebrity Business and Consumer Purchase Intention</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sixing%20Chen">Sixing Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Shuai%20Yang"> Shuai Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Online celebrity business, also known as Internet celebrity business (or Wanghong business in Chinese), is an emerging relational C2C business model, and an alternative to traditional C2C transactional business models. There are already millions of these consumers, and this number is growing. In this model, consumer purchase decisions are driven by recommendations and endorsements in videos posted online by celebrities. The purpose of this paper is to determine the relational constructs within consumer relationships in the Internet celebrity business model and to investigate relationships between the constructs and consumer purchase intention. A questionnaire-based study was conducted with consumers who had an awareness of, or prior purchase experience with online celebrities. The results of exploratory factor analysis (EFA) and multiple regression analysis revealed three valid relational constructs: product experience sharing, lifestyle association, and real-time interaction. This study indicated that these constructs had the direct effect on consumer preference and purchase intention. The findings of this study provide insight into a business model in which online shopping is driven by celebrities. They suggest that online celebrities should pay more attention to product experience sharing, life style association and real-time interaction for managing their product promotions. These are the most salient factors with respect to the relational constructs identified in this study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=customer%20relationship" title="customer relationship">customer relationship</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20to%20customer" title=" customer to customer"> customer to customer</a>, <a href="https://publications.waset.org/abstracts/search?q=Internet%20celebrity" title=" Internet celebrity"> Internet celebrity</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20celebrity" title=" online celebrity"> online celebrity</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20marketing" title=" online marketing"> online marketing</a>, <a href="https://publications.waset.org/abstracts/search?q=purchase%20intention" title=" purchase intention"> purchase intention</a> </p> <a href="https://publications.waset.org/abstracts/68605/a-study-of-relational-factors-associated-with-online-celebrity-business-and-consumer-purchase-intention" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68605.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">319</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27973</span> Customer Data Analysis Model Using Business Intelligence Tools in Telecommunication Companies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Monica%20Lia">Monica Lia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article presents a customer data analysis model using business intelligence tools for data modelling, transforming, data visualization and dynamic reports building. Economic organizational customer’s analysis is made based on the information from the transactional systems of the organization. The paper presents how to develop the data model starting for the data that companies have inside their own operational systems. The owned data can be transformed into useful information about customers using business intelligence tool. For a mature market, knowing the information inside the data and making forecast for strategic decision become more important. Business Intelligence tools are used in business organization as support for decision-making. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=customer%20analysis" title="customer analysis">customer analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20intelligence" title=" business intelligence"> business intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20warehouse" title=" data warehouse"> data warehouse</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=decisions" title=" decisions"> decisions</a>, <a href="https://publications.waset.org/abstracts/search?q=self-service%20reports" title=" self-service reports"> self-service reports</a>, <a href="https://publications.waset.org/abstracts/search?q=interactive%20visual%20analysis" title=" interactive visual analysis"> interactive visual analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=and%20dynamic%20dashboards" title=" and dynamic dashboards"> and dynamic dashboards</a>, <a href="https://publications.waset.org/abstracts/search?q=use%20cases%20diagram" title=" use cases diagram"> use cases diagram</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20modelling" title=" process modelling"> process modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=logical%20data%20model" title=" logical data model"> logical data model</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mart" title=" data mart"> data mart</a>, <a href="https://publications.waset.org/abstracts/search?q=ETL" title=" ETL"> ETL</a>, <a href="https://publications.waset.org/abstracts/search?q=star%20schema" title=" star schema"> star schema</a>, <a href="https://publications.waset.org/abstracts/search?q=OLAP" title=" OLAP"> OLAP</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20universes" title=" data universes"> data universes</a> </p> <a href="https://publications.waset.org/abstracts/39914/customer-data-analysis-model-using-business-intelligence-tools-in-telecommunication-companies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39914.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">434</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27972</span> A Goal-Oriented Social Business Process Management Framework</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Ehson%20Rangiha">Mohammad Ehson Rangiha</a>, <a href="https://publications.waset.org/abstracts/search?q=Bill%20Karakostas"> Bill Karakostas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Social Business Process Management (SBPM) promises to overcome limitations of traditional BPM by allowing flexible process design and enactment through the involvement of users from a social community. This paper proposes a meta-model and architecture for socially driven business process management systems. It discusses the main facets of the architecture such as goal-based role assignment that combines social recommendations with user profile, and process recommendation, through a real example of a charity organization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20process%20management" title="business process management">business process management</a>, <a href="https://publications.waset.org/abstracts/search?q=goal-based%20modelling" title=" goal-based modelling"> goal-based modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20recommendation%20social%20collaboration" title=" process recommendation social collaboration"> process recommendation social collaboration</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20BPM" title=" social BPM"> social BPM</a> </p> <a href="https://publications.waset.org/abstracts/9192/a-goal-oriented-social-business-process-management-framework" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9192.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">494</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27971</span> Data-Driven Decision Making: Justification of Not Leaving Class without It</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Denise%20Hexom">Denise Hexom</a>, <a href="https://publications.waset.org/abstracts/search?q=Judith%20Menoher"> Judith Menoher </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Teachers and administrators across America are being asked to use data and hard evidence to inform practice as they begin the task of implementing Common Core State Standards. Yet, the courses they are taking in schools of education are not preparing teachers or principals to understand the data-driven decision making (DDDM) process nor to utilize data in a much more sophisticated fashion. DDDM has been around for quite some time, however, it has only recently become systematically and consistently applied in the field of education. This paper discusses the theoretical framework of DDDM; empirical evidence supporting the effectiveness of DDDM; a process a department in a school of education has utilized to implement DDDM; and recommendations to other schools of education who attempt to implement DDDM in their decision-making processes and in their students’ coursework. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data-driven%20decision%20making" title="data-driven decision making">data-driven decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=institute%20of%20higher%20education" title=" institute of higher education"> institute of higher education</a>, <a href="https://publications.waset.org/abstracts/search?q=special%20education" title=" special education"> special education</a>, <a href="https://publications.waset.org/abstracts/search?q=continuous%20improvement" title=" continuous improvement"> continuous improvement</a> </p> <a href="https://publications.waset.org/abstracts/13576/data-driven-decision-making-justification-of-not-leaving-class-without-it" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13576.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">388</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27970</span> Value Chain Based New Business Opportunity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seonjae%20Lee">Seonjae Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Sungjoo%20Lee"> Sungjoo Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Excavation is necessary to remain competitive in the current business environment. The company survived the rapidly changing industry conditions by adapting new business strategy and reducing technology challenges. Traditionally, the two methods are conducted excavations for new businesses. The first method is, qualitative analysis of expert opinion, which is gathered through opportunities and secondly, new technologies are discovered through quantitative data analysis of method patents. The second method increases time and cost. Patent data is restricted for use and the purpose of discovering business opportunities. This study presents the company's characteristics (sector, size, etc.), of new business opportunities in customized form by reviewing the value chain perspective and to contributing to creating new business opportunities in the proposed model. It utilizes the trademark database of the Korean Intellectual Property Office (KIPO) and proprietary company information database of the Korea Enterprise Data (KED). This data is key to discovering new business opportunities with analysis of competitors and advanced business trademarks (Module 1) and trading analysis of competitors found in the KED (Module 2). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=value%20chain" title="value chain">value chain</a>, <a href="https://publications.waset.org/abstracts/search?q=trademark" title=" trademark"> trademark</a>, <a href="https://publications.waset.org/abstracts/search?q=trading%20analysis" title=" trading analysis"> trading analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=new%20business%20opportunity" title=" new business opportunity"> new business opportunity</a> </p> <a href="https://publications.waset.org/abstracts/43595/value-chain-based-new-business-opportunity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43595.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">374</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27969</span> Data Mining and Knowledge Management Application to Enhance Business Operations: An Exploratory Study </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zeba%20Mahmood">Zeba Mahmood</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The modern business organizations are adopting technological advancement to achieve competitive edge and satisfy their consumer. The development in the field of Information technology systems has changed the way of conducting business today. Business operations today rely more on the data they obtained and this data is continuously increasing in volume. The data stored in different locations is difficult to find and use without the effective implementation of Data mining and Knowledge management techniques. Organizations who smartly identify, obtain and then convert data in useful formats for their decision making and operational improvements create additional value for their customers and enhance their operational capabilities. Marketers and Customer relationship departments of firm use Data mining techniques to make relevant decisions, this paper emphasizes on the identification of different data mining and Knowledge management techniques that are applied to different business industries. The challenges and issues of execution of these techniques are also discussed and critically analyzed in this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=knowledge" title="knowledge">knowledge</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management" title=" knowledge management"> knowledge management</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery%20in%20databases" title=" knowledge discovery in databases"> knowledge discovery in databases</a>, <a href="https://publications.waset.org/abstracts/search?q=business" title=" business"> business</a>, <a href="https://publications.waset.org/abstracts/search?q=operational" title=" operational"> operational</a>, <a href="https://publications.waset.org/abstracts/search?q=information" title=" information"> information</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a> </p> <a href="https://publications.waset.org/abstracts/82255/data-mining-and-knowledge-management-application-to-enhance-business-operations-an-exploratory-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82255.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">538</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27968</span> AI-Driven Solutions for Optimizing Master Data Management</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Srinivas%20Vangari">Srinivas Vangari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the era of big data, ensuring the accuracy, consistency, and reliability of critical data assets is crucial for data-driven enterprises. Master Data Management (MDM) plays a crucial role in this endeavor. This paper investigates the role of Artificial Intelligence (AI) in enhancing MDM, focusing on how AI-driven solutions can automate and optimize various stages of the master data lifecycle. By integrating AI (Quantitative and Qualitative Analysis) into processes such as data creation, maintenance, enrichment, and usage, organizations can achieve significant improvements in data quality and operational efficiency. Quantitative analysis is employed to measure the impact of AI on key metrics, including data accuracy, processing speed, and error reduction. For instance, our study demonstrates an 18% improvement in data accuracy and a 75% reduction in duplicate records across multiple systems post-AI implementation. Furthermore, AI’s predictive maintenance capabilities reduced data obsolescence by 22%, as indicated by statistical analyses of data usage patterns over a 12-month period. Complementing this, a qualitative analysis delves into the specific AI-driven strategies that enhance MDM practices, such as automating data entry and validation, which resulted in a 28% decrease in manual errors. Insights from case studies highlight how AI-driven data cleansing processes reduced inconsistencies by 25% and how AI-powered enrichment strategies improved data relevance by 24%, thus boosting decision-making accuracy. The findings demonstrate that AI significantly enhances data quality and integrity, leading to improved enterprise performance through cost reduction, increased compliance, and more accurate, real-time decision-making. These insights underscore the value of AI as a critical tool in modern data management strategies, offering a competitive edge to organizations that leverage its capabilities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=master%20data%20management" title=" master data management"> master data management</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20governance" title=" data governance"> data governance</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20quality" title=" data quality"> data quality</a> </p> <a href="https://publications.waset.org/abstracts/190016/ai-driven-solutions-for-optimizing-master-data-management" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/190016.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">20</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27967</span> SeCloudBPMN: A Lightweight Extension for BPMN Considering Security Threats in the Cloud</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Somayeh%20Sobati%20Moghadam">Somayeh Sobati Moghadam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Business processes are crucial for organizations and help businesses to evaluate and optimize their performance and processes against current and future-state business goals. Outsourcing business processes to the cloud becomes popular due to a wide varsity of benefits and cost-saving. However, cloud outsourcing raises enterprise data security concerns, which must be incorporated in Business Process Model and Notation (BPMN). This paper, presents SeCloudBPMN, a lightweight extension for BPMN which extends the BPMN to explicitly support the security threats in the cloud as an outsourcing environment. SeCloudBPMN helps business’s security experts to outsource business processes to the cloud considering different threats from inside and outside the cloud. In this way, appropriate security countermeasures could be considered to preserve data security in business processes outsourcing to the cloud. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BPMN" title="BPMN">BPMN</a>, <a href="https://publications.waset.org/abstracts/search?q=security%20threats" title=" security threats"> security threats</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title=" cloud computing"> cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20processes%20outsourcing" title=" business processes outsourcing"> business processes outsourcing</a>, <a href="https://publications.waset.org/abstracts/search?q=privacy" title=" privacy"> privacy</a> </p> <a href="https://publications.waset.org/abstracts/97274/secloudbpmn-a-lightweight-extension-for-bpmn-considering-security-threats-in-the-cloud" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97274.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">272</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27966</span> Business-Intelligence Mining of Large Decentralized Multimedia Datasets with a Distributed Multi-Agent System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karima%20Qayumi">Karima Qayumi</a>, <a href="https://publications.waset.org/abstracts/search?q=Alex%20Norta"> Alex Norta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The rapid generation of high volume and a broad variety of data from the application of new technologies pose challenges for the generation of business-intelligence. Most organizations and business owners need to extract data from multiple sources and apply analytical methods for the purposes of developing their business. Therefore, the recently decentralized data management environment is relying on a distributed computing paradigm. While data are stored in highly distributed systems, the implementation of distributed data-mining techniques is a challenge. The aim of this technique is to gather knowledge from every domain and all the datasets stemming from distributed resources. As agent technologies offer significant contributions for managing the complexity of distributed systems, we consider this for next-generation data-mining processes. To demonstrate agent-based business intelligence operations, we use agent-oriented modeling techniques to develop a new artifact for mining massive datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agent-oriented%20modeling%20%28AOM%29" title="agent-oriented modeling (AOM)">agent-oriented modeling (AOM)</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20intelligence%20model%20%28BIM%29" title=" business intelligence model (BIM)"> business intelligence model (BIM)</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed%20data%20mining%20%28DDM%29" title=" distributed data mining (DDM)"> distributed data mining (DDM)</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-agent%20system%20%28MAS%29" title=" multi-agent system (MAS)"> multi-agent system (MAS)</a> </p> <a href="https://publications.waset.org/abstracts/44164/business-intelligence-mining-of-large-decentralized-multimedia-datasets-with-a-distributed-multi-agent-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44164.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">432</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27965</span> Artificial Intelligence Ethics: What Business Leaders Need to Consider for the Future</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kylie%20Leonard">Kylie Leonard</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Investment in artificial intelligence (AI) can be an attractive opportunity for business leaders as there are many easy-to-see benefits. These benefits include task completion rates, overall cost, and better forecasting. Business leaders are often unaware of the challenges that can accompany AI, such as data center costs, access to data, employee acceptance, and privacy concerns. In addition to the benefits and challenges of AI, it is important to practice AI ethics to ensure the safe creation of AI. AI ethics include aspects of algorithm bias, limits in transparency, and surveillance. To be a good business leader, it is critical to address all the considerations involving the challenges of AI and AI ethics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence%20ethics" title=" artificial intelligence ethics"> artificial intelligence ethics</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20leaders" title=" business leaders"> business leaders</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20concerns" title=" business concerns"> business concerns</a> </p> <a href="https://publications.waset.org/abstracts/144879/artificial-intelligence-ethics-what-business-leaders-need-to-consider-for-the-future" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144879.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">149</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27964</span> Mathematics Bridging Theory and Applications for a Data-Driven World</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zahid%20Ullah">Zahid Ullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Atlas%20Khan"> Atlas Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In today's data-driven world, the role of mathematics in bridging the gap between theory and applications is becoming increasingly vital. This abstract highlights the significance of mathematics as a powerful tool for analyzing, interpreting, and extracting meaningful insights from vast amounts of data. By integrating mathematical principles with real-world applications, researchers can unlock the full potential of data-driven decision-making processes. This abstract delves into the various ways mathematics acts as a bridge connecting theoretical frameworks to practical applications. It explores the utilization of mathematical models, algorithms, and statistical techniques to uncover hidden patterns, trends, and correlations within complex datasets. Furthermore, it investigates the role of mathematics in enhancing predictive modeling, optimization, and risk assessment methodologies for improved decision-making in diverse fields such as finance, healthcare, engineering, and social sciences. The abstract also emphasizes the need for interdisciplinary collaboration between mathematicians, statisticians, computer scientists, and domain experts to tackle the challenges posed by the data-driven landscape. By fostering synergies between these disciplines, novel approaches can be developed to address complex problems and make data-driven insights accessible and actionable. Moreover, this abstract underscores the importance of robust mathematical foundations for ensuring the reliability and validity of data analysis. Rigorous mathematical frameworks not only provide a solid basis for understanding and interpreting results but also contribute to the development of innovative methodologies and techniques. In summary, this abstract advocates for the pivotal role of mathematics in bridging theory and applications in a data-driven world. By harnessing mathematical principles, researchers can unlock the transformative potential of data analysis, paving the way for evidence-based decision-making, optimized processes, and innovative solutions to the challenges of our rapidly evolving society. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mathematics" title="mathematics">mathematics</a>, <a href="https://publications.waset.org/abstracts/search?q=bridging%20theory%20and%20applications" title=" bridging theory and applications"> bridging theory and applications</a>, <a href="https://publications.waset.org/abstracts/search?q=data-driven%20world" title=" data-driven world"> data-driven world</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20models" title=" mathematical models"> mathematical models</a> </p> <a href="https://publications.waset.org/abstracts/168282/mathematics-bridging-theory-and-applications-for-a-data-driven-world" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168282.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">77</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=data%20driven%20business&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=data%20driven%20business&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=data%20driven%20business&page=4">4</a></li> <li class="page-item"><a class="page-link" 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