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18th IFAC Symposium on Information Control Problems in Manufacturing (INCOM)

作者： Novoszel, Lydia Claus, Isabell WU Vienna Univ Econ & Business Welthandelspl 1 A-1020 Vienna Austria Thinkers GmbH INiTS Maria Jacobi Gasse 1 A-1030 Vienna Austria

The advancements and accessibility of AI technologies offer a lot of opportunities for supply chain management (SCM) research to address current challenges, such as environmental, societal and geopolitical needs. While the prominence of e.g., reinforcement learning has been applied for quantitative optimization techniques, the opportunities for qualitative data and approaches have not yet received a lot of emphasis. This new situation (with LLMs) enables joint research approaches to build on capabilities from AI technology together with supply chain management research. To ensure effective, scalable, and trustworthy AI systems in SCM a structured approach is imperative. In this paper, starting from current supply chain AI research, a methodological framework is proposed to build on both AI and SCM capabilities to support qualitative research along the SCM research cycle. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： generative AI qualitative supply chain management research web data analysis

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Geoparsing at web-scale - Challenges and Opportunities 1

Geoparsing at Web-scale - Challenges and Opportunities

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1st Workshop on Geographic Information Extraction from Texts, GeoExT 2023

作者： Farzana, Sheikh Mastura Hecking, Tobias Institute for Software Technology Linder Höhe Cologne51147 Germany

The increasing amount of web data being generated and stored along with geographic information is of great importance to enrich future search applications in science, news, economics, etc.. In addition to location information provided by users or content providers directly, the potential to extract geographic entities from unstructured web content and linking them to geographic coordinates at scale has not been fully exploited. This paper highlights the importance of geoparsing large web archives and associated challenges. Furthermore, this paper evaluates different existing methods with regard to accuracy and scalability to showcase future directions for improving their efficiency. © 2023 Copyright for this paper by its authors.

关键词： Geographic information retrieval Geoparsing web data analysis

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A methodological framework addressing challenges and opportunities in Supply Chain AI

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IFAC-PapersOnLine 2024年第19期58卷 349-354页

作者： Lydia Novoszel Isabell Claus WU (Vienna University of Economics and Business) Welthandelsplatz 1 1020 Vienna Austria thinkers GmbH c/o INiTS Maria-Jacobi-Gasse 1 1030 Vienna Austria

关键词： generative AI qualitative supply chain management research web data analysis

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Metadata analysis Reveals Major Worldwide Trends in Industrial and Academic Geophysics

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ENERGIES 2021年第1期14卷 107页

作者： Eltsov, Timofey Yutkin, Maxim Patzek, Tadeusz W. King Abdullah Univ Sci & Technol Ali I Al Naimi Petr Engn Res Ctr 3220 Bldg 5 Seaside Thuwal 239556900 Saudi Arabia Stepnaya 21 Bolotnoe 633343 Russia

We summarize our metadata analysis of the last 38 well-attended annual conferences, organized by the Society of Exploration Geophysicists. In 2018, Schlumberger and Saudi Aramco had the highest number of publications among service and production companies. In 2019, BGP and PetroChina took the lead. Throughout history, US academics have had the highest number of publications, but in 2019 Chinese academia came close to taking the lead. analysis of the publication activity of oil-producing and oilfield service companies provides insights into the state of geophysical research. The number of publications from industrial companies in the energy sector reflects their financial standing and aspirations for the near future. Publications from academia in different countries tell us about state and private funding of research in each country, and indirectly reflect the geopolitical situation in the world. The changing number of publications over time reflects the dynamics of the transformation of research in geophysics, and allows us to understand better what is happening and make forecasts.

关键词： geophysics web data analysis data mining limited computing power information abundance

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Text analysis Reveals Major Trends in Exploration Geophysics

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ENERGIES 2020年第17期13卷 4550页

作者： Eltsov, Timofey Yutkin, Maxim Patzek, Tadeusz W. King Abdullah Univ Sci & Technol Ali I Al Naimi Petr Engn Res Ctr Thuwal 239556900 Saudi Arabia

Evolution of professional language reveals advances in geophysics: researchers enthusiastically describe new methods of surveying, data processing techniques, and objects of their study. Geophysicists publish their cutting-edge research in the proceedings of international conferences to share their achievements with the world. Tracking changes in the professional language allows one to identify trends and current state of science. Here, we explain our text analysis of the last 30 annual conferences organized by the Society of Exploration Geophysicists (SEG). These conferences are among the largest geophysical gatherings worldwide. We split the 21,864 SEG articles into 52 million words and phrases, and analyze changes in their usage frequency over time. For example, we find that in 2019, the phrase "neural network" was used more often than "field data." The word "shale" became less commonly used, but the term "unconventional" grew in frequency. An analysis of conference materials and metadata allows one to identify trends in a specific field of knowledge and predict its development in the near future.

关键词： SEG proceedings web data analysis data mining data analysis text mining word analysis

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Articulating the Construction of a web Scraper for Massive data Extraction 2

Articulating the Construction of a Web Scraper for Massive D...

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2nd IEEE International Conference on Electrical, Computer and Communication Technologies (IEEE ICECCT)

作者： Upadhyay, Shreya Pant, Vishal Bhasin, Shivansh Pattanshetti, Mahantesh K. Graph Era Deemed Univ Dept Comp Sci & Engn Dehra Dun India Graph Era Hill Univ Dept Comp Sci & Engn Dehra Dun India

ISBN: (纸本)9781509032396

Massive volumes of data are generated by various users, entities, applications and disseminated online. This copious volume of big data is distributed across millions of websites and is available for various applications. Search engines do provide a simple mechanism to access this data. Accessing this data using search engines requires a user to spend time and resources to manually click and download. Clearly, such a manual approach is not scalable for a vast majority of real life applications at the enterprise and organization level. There exist a number of automated approaches to data extraction from the web. Most of these approaches are ad-hoc and domain specific. Therefore, the need for a robust, automated, easy to use framework for extracting content from the web with a minimal human effort across domains appears enticing. The architecture proposed by the authors for a web scraper addresses this gap to harvest data from the web. The proposed web scraping framework offers an easy and feasible approach for parsing and extracting data on a large scale from multiple websites with minimal human intervention. This paper provides an insight into issues relevant to constructing a web scraper and concludes by describing the implementation of a web scraper for harvesting learning objects for an eLearning application.

关键词： web scraping web data extraction web information extraction knowledge-based systems web data analysis

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Preprocessing and Feature Selection Approach for Efficient Sentiment analysis on Product Reviews 5th

Preprocessing and Feature Selection Approach for Efficient S...

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5th International Conference on Frontiers in Intelligent Computing - Theory and Applications (FICTA)

作者： Ghosh, Monalisa Sanyal, Gautam Natl Inst Technol Dept Comp Sci & Engn Durgapur W Bengal India

ISBN: (纸本)9789811031533;9789811031526

In the recent years opinion mining plays an important role by business analyst before launching a product. Opinion mining mainly concerns about detecting and extracting the feature from various opinion rich resources like review sites, discussion forum, blogs and news corpora so on. The data obtained from those are highly unstructured in nature and very large in volume, therefore data preprocessing plays an essential role in sentiment analysis. Researchers are trying to develop newer algorithm. This research paper attempts to develop a better opinion mining algorithm and the performance has been worked out.

关键词： Information retrieval web data analysis Preprocessing Opinion mining Feature selection N-gram model

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Developing a Framework for Analysing web data to Generate Recommendation 1

Developing a Framework for Analysing Web Data to Generate Re...

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INTERNATIONAL CONFERENCE ON COMPUTER & INFORMATION ENGINEERING (ICCIE)

作者： Khanam, Tanjila Aftab Arefin, Mohammad Shamsul Islam, Md. Shahedul CUET Dept CSE Chittagong Bangladesh BSMRMU ICT Ctr Dhaka Bangladesh

ISBN: (纸本)9781467383431

From the advent of the Internet, data contained in the web is increasing exponentially and the number of ecommerce companies is also increasing significantly. Nowadays, we can find that many e-commerce companies selling same products and /or services at different prices. For a customer it is difficult to find an e-commerce company which will be best suited for him. Moreover, it is time consuming and tedious to search for a product in different online sites. For reducing this difficulty, in this paper, we develop a system that can extract web data from different e-commerce sites even though the language of the websites are different. We then analyse the extracted data and recommend best products /services from these sites to the users. For the experimental evaluation of our system, we consider two different languages: English and BangIa and extract books data from difl'erent online book stores considering these two languages and recommend books to the users. From the experimental results, we can find that our system can help the users in selection of their products easily and efficiently. Keywords: web data analysis, data processing, e-commerce

关键词： web data analysis data processing e-commerce

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Developing a framework for analysing web data to generate recommendation

Developing a framework for analysing web data to generate re...

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International Conference on Computer and Information Engineering (ICCIE)

作者： Tanjila Aftab Khanam Mohammad Shamsul Arefin Md. Shahedul Islam Department of CSE CUET Bangladesh ICT Center BSMRMU Bangladesh

From the advent of the Internet, data contained in the web is increasing exponentially and the number of e-commerce companies is also increasing significantly. Nowadays, we can find that many e-commerce companies selling same products and / or services at different prices. For a customer it is difficult to find an e-commerce company which will be best suited for him. Moreover, it is time consuming and tedious to search for a product in different online sites. For reducing this difficulty, in this paper, we develop a system that can extract web data from different e-commerce sites even though the language of the websites are different. We then analyse the extracted data and recommend best products / services from these sites to the users. For the experimental evaluation of our system, we consider two different languages: English and Bangla and extract books data from different online book stores considering these two languages and recommend books to the users. From the experimental results, we can find that our system can help the users in selection of their products easily and efficiently.

关键词： web data analysis data processing e-commerce electronic commerce semantic networks web search engines Consumers Language World Wide web congenital web webs Automatic data processing data processing Frameworks websites Legal Services

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Using statistics, visualization and data mining for monitoring the quality of meta-data in web portals

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INFORMATION SYSTEMS AND E-BUSINESS MANAGEMENT 2013年第4期11卷 569-595页

作者： Domingues, Marcos Aurelio Soares, Carlos Jorge, Alipio Mario INESC Porto INESC Technol & Sci INESC TEC Oporto Portugal Univ Porto INESC Porto INESC TEC P-4100 Oporto Portugal Univ Porto Fac Econ P-4100 Oporto Portugal Univ Porto LIAAD INESC TEC P-4100 Oporto Portugal Univ Porto Fac Sci P-4100 Oporto Portugal

The goal of many web portals is to select, organize and distribute content in order to satisfy its users/customers. This process is usually based on meta-data that represent and describe content. In this paper we describe a methodology and a system to monitor the quality of the meta-data used to describe content in web portals. The methodology is based on the analysis of the meta-data using statistics, visualization and data mining tools. The methodology enables the site's editor to detect and correct problems in the description of contents, thus improving the quality of the web portal and the satisfaction of its users. We also define a general architecture for a system to support the proposed methodology. We have implemented this system and tested it on a Portuguese portal for management executives. The results validate the methodology proposed.

关键词： web data analysis Meta-data quality Quality of process Content management web portals

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