5,544 research outputs found
Improving Support Ticket Systems Using Machine Learning: A Literature Review
Processing customer support requests via a support ticket system is a key-element for companies to provide support to their customers in an organized and professional way. However, distributing and processing such tickets is much work, increasing the cost for the support providing company and stretching the resolution time. The advancing potential of Machine Learning has led to the goal of automating those support ticket systems. Against this background, we conducted a Literature Review aiming at determining the present state-of-the-art technology in the field of automated support ticket systems. We provide an overview about present trends and topics discussed in this field. During the Literature Review, we found creating an automated incident management tool being the majority topic in the field followed by request escalation and customer sentiment prediction and identified Random Forrest and Support Vector Machine as best performing algorithms for classification in the field
Closing the loop: assisting archival appraisal and information retrieval in one sweep
In this article, we examine the similarities between the concept of appraisal, a process that takes place within the archives, and the concept of relevance judgement, a process fundamental to the evaluation of information retrieval systems. More specifically, we revisit selection criteria proposed as result of archival research, and work within the digital curation communities, and, compare them to relevance criteria as discussed within information retrieval's literature based discovery. We illustrate how closely these criteria relate to each other and discuss how understanding the relationships between the these disciplines could form a basis for proposing automated selection for archival processes and initiating multi-objective learning with respect to information retrieval
Automated analysis of feature models: Quo vadis?
Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.Ministerio de Economía y Competitividad TIN2015-70560-RJunta de Andalucía TIC-186
ProcessGPT: Transforming Business Process Management with Generative Artificial Intelligence
Generative Pre-trained Transformer (GPT) is a state-of-the-art machine
learning model capable of generating human-like text through natural language
processing (NLP). GPT is trained on massive amounts of text data and uses deep
learning techniques to learn patterns and relationships within the data,
enabling it to generate coherent and contextually appropriate text. This
position paper proposes using GPT technology to generate new process models
when/if needed. We introduce ProcessGPT as a new technology that has the
potential to enhance decision-making in data-centric and knowledge-intensive
processes. ProcessGPT can be designed by training a generative pre-trained
transformer model on a large dataset of business process data. This model can
then be fine-tuned on specific process domains and trained to generate process
flows and make decisions based on context and user input. The model can be
integrated with NLP and machine learning techniques to provide insights and
recommendations for process improvement. Furthermore, the model can automate
repetitive tasks and improve process efficiency while enabling knowledge
workers to communicate analysis findings, supporting evidence, and make
decisions. ProcessGPT can revolutionize business process management (BPM) by
offering a powerful tool for process augmentation, automation and improvement.
Finally, we demonstrate how ProcessGPT can be a powerful tool for augmenting
data engineers in maintaining data ecosystem processes within large bank
organizations. Our scenario highlights the potential of this approach to
improve efficiency, reduce costs, and enhance the quality of business
operations through the automation of data-centric and knowledge-intensive
processes. These results underscore the promise of ProcessGPT as a
transformative technology for organizations looking to improve their process
workflows.Comment: Accepted in: 2023 IEEE International Conference on Web Services
(ICWS); Corresponding author: Prof. Amin Beheshti ([email protected]
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