Conjoint Mining of Data and Content with Applications in Business, Bio-medicine, Transport Logistics and Electrical Power Systems

Dillon, Tharam S.; Chen, Yi‐Ping Phoebe; Chang, Elizabeth; Mohania, Mukesh

doi:10.1007/978-3-662-44654-6_1

Cited by 2 publications

(4 citation statements)

References 65 publications

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“…In particular, existing machine learning either doesn't consider domain knowledge during classification or if it does, then this knowledge holds for the whole domain. Such approaches ignore any specific information or situation that may apply to some small set of chosen learning examples 6 . Therefore, it is intended to enhance the current generation of planning system with argumentation driven machine learning techniques.…”

Section: Discussionmentioning

confidence: 99%

“…In the last decade, the focus of SC trading partners was on Systems of Records (SoR) i.e. structured data about sales, customer and product information, inventory forecasts and so, and it was used for planning and decision-making [6]. As a result, centralized enterprise information systems such as data warehousing systems exclusively dealt with record-oriented data that was carefully mapped using schema-centric mediation approaches by knowledge experts to support planning decisions.…”

Section: Motivationmentioning

confidence: 99%

“…In the proposed technique UIMA (Unstructured Information Management Architecture) [8] is a fundamental framework and act as a back-end engine that uses both statistical and rule-based annotators for text annotation. As pointed out in the literature [6], this research will take into account some important considerations duration semantic annotation which are: entity extraction; identification of Figure 4: UIMA Pipleline's Moudles a relationship between two entities; a network of relationships between entities; associations between several entities; and associations between groups of entities such as ones arranged in sections and/or subsections. The UIMA pipeline consists of five functional processing namely; Linguistic Processing, Information Extraction, Named Entity Recognition, Open Annotation, and Triplification.…”

Section: Semantic Annotation Of Unstructured Information Using Domainmentioning

confidence: 99%

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Conjoint utilization of structured and unstructured information for planning interleaving deliberation in supply chains

Janjua

Hussain

Chang

et al. 2017

Proceedings of the International Conference on Web Intelligence

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Effective business planning requires seamless access and intelligent analysis of information in its totality to allow the business planner to gain enhanced critical business insights for decision support. Current business planning tools provide insights from structured business data (i.e. sales forecasts, customers and products data, inventory details) only and fail to take into account unstructured complementary information residing in contracts, reports, user's comments, emails etc. In this article, a planning support system is designed and developed that empower business planners to develop and revise business plans utilizing both structured data and unstructured information conjointly. This planning system activity model comprises of two steps. Firstly, a business planner develops a candidate plan using planning template. Secondly, the candidate plan is put forward to collaborating partners for its revision interleaving deliberation. Planning interleaving deliberation activity in the proposed framework enables collaborating planners to challenge both a decision and the thinking that underpins the decision in the candidate plan. The planning system is modeled using situation calculus and is validated through a prototype development.

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Section: Discussionmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Semantic Annotation Of Unstructured Information Using Domainmentioning

confidence: 99%

See 1 more Smart Citation

Conjoint utilization of structured and unstructured information for planning interleaving deliberation in supply chains

Janjua

Hussain

Chang

et al. 2017

Proceedings of the International Conference on Web Intelligence

Self Cite

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“…32 Thus, there has been a steady rise in the applications of text mining in the logistics sector. Dillon et al 33 discussed the importance of cojoint and content mining of data for "Intelligent tracking" in the transport logistics industry. Chae 34 studied the role of social media data, particularly Twitter data, in the supply chain context, and stressed the significance of using such data for research and development.…”

Section: Real-time Extraction Of Issues To Maximize Delivery and Demand Fulfillment Through A Vrp During Disruptionmentioning

confidence: 99%

Multiobjective fuzzy vehicle routing using Twitter data: Reimagining the delivery of essential goods

2021

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The world faced a major disruption in the form of the coronavirus disease (COVID-19) pandemic, which caused many countries to impose severe restrictions on movement, popularly known as "lockdown." These lockdowns impacted transportation adversely, leading to massive disruptions in global and local supply chains. As the local markets were shut down, more people started turning to e-commerce logistics platforms offering doorstep deliveries of essential items (food and medicines). This resulted in an explosion in demand for such services, and businesses struggled to complete their deliveries. Additionally, the volume of real-time text data suddenly increased, as these customers started sharing their feedback on social media platforms. The availability of real-time raw text data and its popularity for solving complex business problems motivated the development of the approach proposed herein to address last-mile delivery issues.Thus, this paper suggests the use of Twitter data to identify the various grievances of customers about e-commerce logistics platforms. Natural language processing, a popular tool for text analytics, is employed to extract consumer tweets from the Twitter profiles of such businesses and subsequently to clean, process, and analyse them. Issues are categorized and used as objectives in a multiobjective fuzzy vehicle routing problem (VRP). An integrated hybrid fuzzy VRP is developed and coded to solve last-mile delivery issues. Experimental results and comparative analyses highlight the benefits of the novel approach. Managerial insights and scope for future research assist in the further development of the idea.

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Conjoint Mining of Data and Content with Applications in Business, Bio-medicine, Transport Logistics and Electrical Power Systems

Cited by 2 publications

References 65 publications

Conjoint utilization of structured and unstructured information for planning interleaving deliberation in supply chains

Conjoint utilization of structured and unstructured information for planning interleaving deliberation in supply chains

Multiobjective fuzzy vehicle routing using Twitter data: Reimagining the delivery of essential goods

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