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Journal articles on the topic 'Data Product'

Author: Grafiati

Published: 7 July 2024

Last updated: 31 July 2025

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1

Do, Namchul. "Education and Training of Product Data Analytics using Product Data Management System." Korean Journal of Computational Design and Engineering 22, no. 1 (2017): 80–88. http://dx.doi.org/10.7315/cde.2017.080.

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2

Vineetha Sasikumar. "Product data science: Bridging data and product innovation." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 1813–21. https://doi.org/10.30574/wjarr.2025.26.1.1170.

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The rapid evolution of product data science has transformed the digital landscape, empowering organizations to harness vast volumes of user data for strategic decision-making. By combining advanced analytical techniques with deep domain expertise, Product Data Scientists uncover actionable insights that drive innovation, optimize user experiences, and enhance business outcomes. From behavioral segmentation and interaction mapping to predictive modeling and causal inference, these emerging methodologies enable organizations to navigate complex market dynamics with unprecedented precision. The c

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3

Leong, K. K., K. M. Yu, and W. B. Lee. "Product data allocation for distributed product data management system." Computers in Industry 47, no. 3 (2002): 289–98. http://dx.doi.org/10.1016/s0166-3615(01)00152-x.

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4

Do, Namchul. "Integration of Social Media with Product Data Management for Collaborative Product Design." Journal of Korean Institute of Industrial Engineers 42, no. 1 (2016): 50–56. http://dx.doi.org/10.7232/jkiie.2016.42.1.050.

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5

Shaw, N. K., M. Susan Bloor, and A. de Pennington. "Product data models." Research in Engineering Design 1, no. 1 (1989): 43–50. http://dx.doi.org/10.1007/bf01580002.

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6

Vergeest, JSM. "Product Data Exchange." Computer-Aided Design 28, no. 8 (1996): 665. http://dx.doi.org/10.1016/0010-4485(96)86818-7.

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7

MOCHIZUKI, Tatsuya. "Product Data Management." Journal of the Japan Society for Precision Engineering 72, no. 2 (2006): 161–64. http://dx.doi.org/10.2493/jjspe.72.161.

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8

Silvola, Risto, Arto Tolonen, Janne Harkonen, Harri Haapasalo, and Tarja Mannisto. "Defining one product data for a product." International Journal of Business Information Systems 30, no. 4 (2019): 489. http://dx.doi.org/10.1504/ijbis.2019.099308.

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9

Mannisto, Tarja, Arto Tolonen, Harri Haapasalo, Janne Harkonen, and Risto Silvola. "Defining one product data for a product." International Journal of Business Information Systems 30, no. 4 (2019): 489. http://dx.doi.org/10.1504/ijbis.2019.10020637.

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10

Do, Namchul, HyunChul Kang, and Heechul Bae. "Product Development Processes and Product Data Model for Supporting Development of Smart, Connected Products." Journal of the Korean Institute of Industrial Engineers 46, no. 2 (2020): 82–93. http://dx.doi.org/10.7232/jkiie.2020.46.2.082.

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11

You, Chun-Fong, and Tung-Hua Chan. "Assurance of Product Data." Computer-Aided Design and Applications 3, no. 1-4 (2006): 221–30. http://dx.doi.org/10.1080/16864360.2006.10738459.

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12

Lin, Chen. "Data driven product management." IEEE Engineering Management Review 46, no. 1 (2018): 16–18. http://dx.doi.org/10.1109/emr.2018.2810099.

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13

Zhou, Z. D., Q. S. Ai, Q. Liu, W. Z. Yang, and S. Q. Xie. "A STEP-compliant product data model for injection moulding products." International Journal of Production Research 47, no. 16 (2009): 4497–520. http://dx.doi.org/10.1080/00207540701851780.

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14

MEIJER, A. H. "Electronic data interchange for product structure data." International Journal of Computer Integrated Manufacturing 2, no. 4 (1989): 220–28. http://dx.doi.org/10.1080/09511928908944405.

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15

Burkett, William C. "Product data markup language: a new paradigm for product data exchange and integration." Computer-Aided Design 33, no. 7 (2001): 489–500. http://dx.doi.org/10.1016/s0010-4485(01)00048-3.

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16

Guo, Jingzhi, and Chengzheng Sun. "Transforming ad hoc product data into canonical product representation." International Journal of Internet and Enterprise Management 3, no. 2 (2005): 117. http://dx.doi.org/10.1504/ijiem.2005.007636.

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17

Dong, Jun Hua. "Green Bicycle Product Designation Using Product Data Management System." Advanced Materials Research 630 (December 2012): 483–85. http://dx.doi.org/10.4028/www.scientific.net/amr.630.483.

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Environmental norms and the combination of market mechanism has become an international trend in recent years, therefore green product design is an important research topic. In this paper, we apply product data management system to the R&D of bicycle as a product design management tools, products and components to be established a database in order to generate bill of material to facilitate the assessment, re-use evaluation software inventory of green bicycle main parts, and to provide of the green bicycle industry reference for the design.

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18

Zi-Yang Ye, Zi-Yang Ye, Xuan Ji Zi-Yang Ye, Ming-Zi Ye Xuan Ji, Yu-Tong Shan Ming-Zi Ye, and Xiang-Rong Shi Yu-Tong Shan. "Data Analysis of Amazon Product Based on LSTM and GPR." 電腦學刊 33, no. 4 (2022): 015–27. http://dx.doi.org/10.53106/199115992022083304002.

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<p>In this paper, we propose a method that combines models such as GPR with PSO optimization to predict the time series data. We use LSTM and TOPSIS with entropy weight method modification to process vari-ous types of data from various aspects, taking into account both tabular and textual data, and to mine valuable contents from them. Based on shopping data, we analyze the historical situation and predict the future sales of products. So that we can recommend the most suitable products for customers. At the same time, for merchants, this paper provides directions for product optimization

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19

Mulhall, Douglas, Anne-Christine Ayed, Jeannot Schroeder, Katja Hansen, and Thibaut Wautelet. "The Product Circularity Data Sheet—A Standardized Digital Fingerprint for Circular Economy Data about Products." Energies 15, no. 9 (2022): 3397. http://dx.doi.org/10.3390/en15093397.

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Background. Laws that enable a circular economy (CE) are being enacted globally, but reliable standardized and digitized CE data about products is scarce, and many CE platforms have differing exclusive formats. In response to these challenges, the Ministry of The Economy of Luxembourg launched the Circularity Dataset Standardization Initiative to develop a globalized open-source industry standard to allow the exchange of standardized data throughout the supply cycle, based on these objectives: (a) Provide basic product circularity data about products. (b) Improve circularity data sharing effic

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20

Lewis, Scott, Michael Mateas, Susan Palmiter, and Gene Lynch. "Ethnographic data for product development." Interactions 3, no. 6 (1996): 52–69. http://dx.doi.org/10.1145/242485.242505.

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21

McKay, A., M. S. Bloor, and A. de Pennington. "A framework for product data." IEEE Transactions on Knowledge and Data Engineering 8, no. 5 (1996): 825–38. http://dx.doi.org/10.1109/69.542033.

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22

Skeate, Robert C. "Transfusion medicine data as product." Transfusion 53, no. 6 (2013): 1153–56. http://dx.doi.org/10.1111/trf.12226.

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23

Goldby, M. "Getting agile with product data." Manufacturing Engineer 81, no. 6 (2002): 257–59. http://dx.doi.org/10.1049/me:20020603.

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24

Gott, Brian. "The product data management market." World Class Design to Manufacture 2, no. 4 (1995): 18–22. http://dx.doi.org/10.1108/09642369310091115.

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25

Zubritsky, Elizabeth. "Product Review: Chromatography data systems." Analytical Chemistry 71, no. 9 (1999): 333A—337A. http://dx.doi.org/10.1021/ac990357p.

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26

Guo, Jingzhi, and Chengzheng Sun. "Context representation of product data." ACM SIGecom Exchanges 4, no. 1 (2003): 20–28. http://dx.doi.org/10.1145/844357.844364.

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27

Schröter, Norbert, and Marieke Thome. "Product Data Templates of Repair Products for Building Information Modeling (BIM)." MATEC Web of Conferences 364 (2022): 04009. http://dx.doi.org/10.1051/matecconf/202236404009.

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Building Information Modeling (BIM) describes the digital and collaborative planning, construction and management of buildings by all stakeholders by means of a digital 3D building (data) model. This enables complex analyses, simulations and optimizations of cost, time and quality. For these reasons, BIM is becoming increasingly important in the construction industry and brings changes along the entire “construction value chain”. Such changing and unclear (data) requirements are major challenges for the manufacturers of construction chemicals, e.g. for products for “Concrete Repair and Protect

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28

Hietikko, E., and E. Rajaniemi. "Visualized Data - Tool to Improve Communication in Distributed Product Development Products." IEEE Engineering Management Review 35, no. 4 (2007): 74. http://dx.doi.org/10.1109/emr.2007.4489952.

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29

Yang, Xiaoyu, Philip Moore, and Seng Kwong Chong. "Intelligent products: From lifecycle data acquisition to enabling product-related services." Computers in Industry 60, no. 3 (2009): 184–94. http://dx.doi.org/10.1016/j.compind.2008.12.009.

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30

R., Sangeetha, and A. Geetha Dr. "Effect of Organic Product Label on the Consumer Perception." International Journal of Engineering and Management Research 14, no. 1 (2024): 14–17. https://doi.org/10.5281/zenodo.10628203.

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In the organic market which is saturated in nature, the organic food consumption is seen to be growing to a considerable level. With such a prevalence, the market share for organic products has been seen to be increasing. The main element for the successful growth of the organic sector is the optimistic image which most of the consumers attribute to the organic products. Such an optimistic image is created through the labels possessed in the organic products. In this regard, the current study explores the influence of organic product label over the consumer perception regarding the organic pro

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31

Yu, Suiran. "Product data prediction with uncertainty in product life cycle design." Chinese Journal of Mechanical Engineering (English Edition) 16, no. 03 (2003): 296. http://dx.doi.org/10.3901/cjme.2003.03.296.

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32

Wagner, Anna, Wendelin Sprenger, Christoph Maurer, Tilmann E. Kuhn, and Uwe Rüppel. "Building product ontology: Core ontology for Linked Building Product Data." Automation in Construction 133 (January 2022): 103927. http://dx.doi.org/10.1016/j.autcon.2021.103927.

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33

Chung, Jaemin, Byeongki Jeong, and Janghyeok Yoon. "Dynamic Monitoring of Product Opportunities with Online Product Review Data." Journal of the Korean Institute of Industrial Engineers 45, no. 5 (2019): 387–401. http://dx.doi.org/10.7232/jkiie.2019.45.5.387.

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34

Chessa, Antonio G. "A Product Match Adjusted R Squared Method for Defining Products with Transaction Data." Journal of Official Statistics 37, no. 2 (2021): 411–32. http://dx.doi.org/10.2478/jos-2021-0018.

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Abstract The occurrence of relaunches of consumer goods at the barcode (GTIN) level is a well-known phenomenon in transaction data of consumer purchases. GTINs of disappearing and reintroduced items have to be linked in order to capture possible price changes. This article presents a method that groups GTINs into strata (‘products’) by balancing two measures: an explained variance (R squared) measure for the ‘homogeneity’ of GTINs within products, while the second expresses the degree to which products can be ‘matched’ over time with respect to a comparison period. The resulting product ‘match

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35

DUPLIAK, O., О. KUCHERUK, and R. KUCHERUK. "METHODS OF DATA MINING IN THE PROCESS OF OPTIMIZATION OF PRODUCT ASSISTANCE." Herald of Khmelnytskyi National University. Economic sciences 276, no. 6(1) (2019): 47–50. https://doi.org/10.31891/2307-5740-2019-276-6-50-53.

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The purpose of the study is to present the possibilities of using cluster analysis as the initial stage of optimization of the product range of the enterprise. As for the market economy, competition is intensifying, and the company sticks to finding different ways, negative adjustments to new things and market needs. The updated project of effective planning and management at different stages of the organization's activity is analytical, which checks the information that the management of enterprises is used. This is an especially acute issue in planning that specializes in this field, when th

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36

Wu, Dai, and Guo Fu Yin. "Modeling for Global Management of Complex Product Subject Data Oriented at Big-Data." Key Engineering Materials 621 (August 2014): 725–33. http://dx.doi.org/10.4028/www.scientific.net/kem.621.725.

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Complex-product manufacturers are encountered with heterogeneous data structure in business information systems (BIS) and disconnection between product data management (PDM) and overall corporate demands. In this paper, we aim to overcome these defects. Targeting at the PDM-related business domains, we proposed the concept of subject-based integration and global management of complex product data. We analyzed the advantages of product data resource models built at corporate level and studied the major methods for planning of complex product subject database. We defined a C-U matrix described b

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37

Krisnanto, Umbas, J. Juharsah, Purnama Putra, Andini Dani Achmad, and Elkana Timotius. "Utilizing Apriori Data Mining Techniques on Sales Transactions." Webology 19, no. 1 (2022): 5581–90. http://dx.doi.org/10.14704/web/v19i1/web19376.

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The establishment of a marketing strategy is important for every business actor in the competitive world of business. Business operators must be able to develop sound marketing strategies to influence the attractiveness of consumers and to buy interest in the products provided so that the enterprise they operate can compete and have a market share and to maximize sales sales. To implement marketing strategies, references are required so that promotions can reach the right target, for example by seeking similarities between items. By using data mining techniques, these studies apply the a prior

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38

Preetha, A. S., K. Swathi, Selvi C. Jeya, and G. Elangovan. "An Informational Search for Review through Data Analytics." International Journal of Trend in Scientific Research and Development 2, no. 5 (2018): 38–41. https://doi.org/10.31142/ijtsrd15770.

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Spreading of large unstructured information generated by social networks provide useful results to the effective recommender. However, the high volume of reviews that are typically published for a single product makes harder for individuals as well as manufacturers to locate the best reviews and understand the true underlying quality of a product. This paper suggests an approach to identify product opportunities from customer reviews in social media. Our approach explores to identify multiple reviews and get processed by sentimental analysis. These extracted data may contain positive and negat

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39

Yazawa, Kengo, and Yoshifumi Nishida. "Product Risk Interaction Analysis of Multiple Products Based on Accident Big Data." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2022 (2022): 1P1—N03. http://dx.doi.org/10.1299/jsmermd.2022.1p1-n03.

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40

A.V., Praveen Krishna. "Development of Data Quality Federation for Adaptive Product Search Engine Using Big Data and Fuzzy Logic Algorithms." Revista Gestão Inovação e Tecnologias 11, no. 3 (2021): 1627–36. http://dx.doi.org/10.47059/revistageintec.v11i3.2036.

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41

Siti Maryam, Asep Saepuloh, and Awit Marwati Sakinah. "Eksplorasi Transaksi Pembelian Produk Dengan Metode Asosiasi Apriori Data Mining." Journal of Mathematics, Computations and Statistics 7, no. 2 (2024): 295–303. http://dx.doi.org/10.35580/jmathcos.v7i2.4458.

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In an increasingly digital era and increasingly fierce business competition, businesses must become more innovative to find out how consumers usually buy products. This can be achieved by collecting data from every transaction in which a consumer purchases a product. This research analyzes exploration of product purchase transaction data using the apriori association method in data mining, with primary data obtained from PT employees. Sinar Sosro Tasikmalaya during February 2024. Limitations of this research include a limited data period so the sample size used is small, which can affect the g

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42

Massmann, Melina, Maurice Meyer, Maximilian Frank, Sebastian von Enzberg, Arno Kühn, and Roman Dumitrescu. "Framework for Data Analytics in Data-Driven Product Planning." Procedia Manufacturing 52 (2020): 350–55. http://dx.doi.org/10.1016/j.promfg.2020.11.058.

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43

Moreno, Anna, Francesca Cappellaro, Paolo Masoni, and Anna Amato. "Application of Product Data Technology Standards to LCA Data." Journal of Industrial Ecology 15, no. 4 (2011): 483–95. http://dx.doi.org/10.1111/j.1530-9290.2011.00353.x.

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44

Oyedele, Oyenuga Micahel, and Ekweogwu Fredrick. "Managing Product Features and Customer Satisfaction via Product Development, Product Branding, and Product Packaging: Evidence from CWAY Table Water." Marketing and Branding Research 10, no. 1 (2023): 28–44. https://doi.org/10.32038/mbr.2023.10.01.03.

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Customer satisfaction has become a source of worry to most organizations, including the table water business. The table water industry is one of the most competitive markets in Nigeria today and due to its portability and affordability, it is seen as a lucrative trade to venture into. However, this has also led to the proliferation of mushroom table water companies and has made it difficult for some companies to maintain their customer base. Therefore, this study examines the effect of product features on customer satisfaction of veritas table water. The population of this study comprised of 3

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45

Hidayati, Juliza, Shelvy Riry Gusrina, and Nazaruddin Matondang. "Big data analysis for product design." IOP Conference Series: Materials Science and Engineering 1122, no. 1 (2021): 012064. http://dx.doi.org/10.1088/1757-899x/1122/1/012064.

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46

Shilnikov, P. S. "ONTOLOGIES OF PRODUCT DATA QUALITY MANAGEMENT." Ontology of Designing 7, no. 2 (2017): 216–26. http://dx.doi.org/10.18287/2223-9537-2017-7-2-216-226.

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47

Lee, Dik L., and Yuh-Min Chen. "Integrated Product and Process Data Management." Integrated Computer-Aided Engineering 3, no. 1 (1996): 1–4. http://dx.doi.org/10.3233/ica-1996-3101.

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48

Lawless, J. F. "Statistical Analysis of Product Warranty Data." International Statistical Review / Revue Internationale de Statistique 66, no. 1 (1998): 41. http://dx.doi.org/10.2307/1403656.

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49

Lee, Young Suk, and Sung Heon Shin. "Anthropometric Data Application in Product Design." Japanese journal of ergonomics 40 (2004): 126–27. http://dx.doi.org/10.5100/jje.40.supplement_126.

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50

Wang, Tianyu, Chenye Wu, and Wei Qi. "On Data-Driven Multi-Product Pricing." IEEE Control Systems Letters 5, no. 5 (2021): 1687–92. http://dx.doi.org/10.1109/lcsys.2020.3043591.

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