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Готові списки джерел за темами / Data Product

Добірка наукової літератури з теми "Data Product"

Автор: Grafiati

Опубліковано: 7 липня 2024

Оновлено: 7 липня 2024

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Статті в журналах з теми "Data Product":

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 (March 1, 2017): 80–88. http://dx.doi.org/10.7315/cde.2017.080.

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2

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 (March 2002): 289–98. http://dx.doi.org/10.1016/s0166-3615(01)00152-x.

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3

Do, Namchul. "Integration of Social Media with Product Data Management for Collaborative Product Design." Journal of Korean Institute of Industrial Engineers 42, no. 1 (February 15, 2016): 50–56. http://dx.doi.org/10.7232/jkiie.2016.42.1.050.

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4

Krisnanto, Umbas, J. Juharsah, Purnama Putra, Andini Dani Achmad, and Elkana Timotius. "Utilizing Apriori Data Mining Techniques on Sales Transactions." Webology 19, no. 1 (January 28, 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 priori approach to the promotion of customer product recommendations by association rules on product sales transaction datasets to aid in the formation of applications between product items. The dataset represents a sample of sales of products for 2020. The application used for analyzing is RapidMiner, where a support value of > 20% and confidence of > 60% is determined. Each product package promoted is made up of 2 products from the calculation results. The two best rules that have value confidence is combined with 2 items (Cre1Cre2), (Cre1Cre12) and (Cre9Cre10). Based on the minimum support and confidence values that have been set, the results of the a priori method can produce association rules that can be used as a reference in product promotion and decision support in providing product recommendations to consumers.

5

Shaw, N. K., M. Susan Bloor, and A. de Pennington. "Product data models." Research in Engineering Design 1, no. 1 (March 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 (August 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

Werder, Karl, Stefan Seidel, Jan Recker, Nicholas Berente, John Gibbs, Nouredine Abboud, and Yossef Benzeghadi. "Data-Driven, Data-Informed, Data-Augmented: How Ubisoft’s Ghost Recon Wildlands Live Unit Uses Data for Continuous Product Innovation." California Management Review 62, no. 3 (April 27, 2020): 86–102. http://dx.doi.org/10.1177/0008125620915290.

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To stay ahead of the competition, firms must continuously learn from their customers and swiftly adopt those lessons to improve their products. A unit at Ubisoft, a leading game publisher headquartered in Paris, has established a three-pronged approach to drive product innovation based on three practices: data-driven exploration, data-augmented ideation, and data-informed validation. By establishing processes and capabilities for these practices and blending them in a portfolio approach to product design, they maximize the value generation potential of the data at their disposal. Product development in a variety of industries can benefit from the lessons of these data-oriented innovation practices.

9

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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10

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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Дисертації з теми "Data Product":

1

Morshedzadeh, Iman. "Data Classification in Product Data Management." Thesis, Högskolan i Skövde, Institutionen för teknik och samhälle, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-14651.

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This report is about the product data classification methodology that is useable for the Volvo Cars Engine (VCE) factory's production data, and can be implemented in the Teamcenter software. There are many data generated during the life cycle of each product, and companies try to manage these data with some product data management software. Data classification is a part of data management for most effective and efficient use of data. With surveys that were done in this project, items affecting the data classification have been found. Data, attributes, classification method, Volvo Cars Engine factory and Teamcenter as the product data management software, are items that are affected data classification. In this report, all of these items will be explained separately. With the knowledge obtained about the above items, in the Volvo Cars Engine factory, the suitable hierarchical classification method is described. After defining the classification method, this method has been implemented in the software at the last part of the report to show that this method is executable.

2

Čvančarová, Lenka. "MDM of Product Data." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-150246.

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This thesis is focused on Master Data Management of Product Data. At present, most publications on the topic of MDM take into account customer data, and a very limited number of sources focus solely on product data. Some resources actually do attempt to cover MDM in full-depth. Even those publications are typically are very customer oriented. The lack of Product MDM oriented literature became one of the motivations for this thesis. Another motivation was to outline and analyze specifics of Product MDM in context of its implementation and software requirements for a vendor of MDM application software. For this I chose to create and describe a methodology for implementing MDM of product data. The methodology was derived from personal experience on projects focused on MDM of customer data, which was applied on findings from the theoretical part of this thesis. By analyzing product data characteristics and their impacts on MDM implementation as well as their requirements for application software, this thesis helps vendors of Customer MDM to understand the challenges of Product MDM and therefore to embark onto the product data MDM domain. Moreover this thesis can also serve as an information resource for enterprises considering adopting MDM of product data into their infrastructure.

3

Parviainen, A. (Antti). "Product portfolio management requirements for product data management." Master's thesis, University of Oulu, 2014. http://urn.fi/URN:NBN:fi:oulu-201409021800.

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In large organisations today the amount of products is numerous and it is challenging for senior management to have proper control and understanding over all products. As the product is the most important aspect for an organisation to consider, senior management must have ability to manage investments on products and follow development of product related indicators. Managing products as investment on portfolio level, where products are divided into a limited amount of portfolios is a solution for achieving decent control over product investments on senior management level. Product portfolio management is decision making oriented, where the goal is to make the best possible strategic and financial decisions when allocating constraint resources across the entire product portfolio. The product portfolio management aims to increase strategic fit of chosen new product projects, balance in product portfolio and maximizing value of the products. The product portfolio management is constantly ongoing, cross-functional decision making function which is present in all lifecycle states of the portfolios. In this research the product portfolios are seen as investments for mainly internal use of a decision making process. The product portfolios are items that are embodied into the case company’s product data management system and the product portfolios have own lifecycle states. Approach in this research is constructive, where a current state of the case company is analysed and based on the analysis and the literature review a construction is established. The Research questions are: 1) What are the required product structures in product data management systems to support product portfolio management practices? 2) What are the information elements and their lifecycle states and what they should be in product data management systems to support product portfolio decisions? Results of this research are the current state analysis committed in the case company and the construction of product portfolio management structure and lifecycle states. In the construction a portfolio package is defined. The portfolio package is the item used for embodying portfolios into the information systems. An information model for implementing the portfolio packages into the product data management system is introduced. The construction also presents product structure for implementing the portfolio package into the product data management system. Relation of lifecycle states between the portfolio package and other items in a product hierarchy is assessed in a nested lifecycle model. Two models, required and recommended, are suggested for the company to consider for managing the lifecycle of the portfolio package item. All the results are validated from several perspectives.

4

Flankegård, Filip. "The use of data within Product Development of manufactured products." Thesis, Mälardalens högskola, Innovation och produktrealisering, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-35084.

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5

Antonov, Anton. "Product Information Management." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-150108.

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Product Information Management (PIM) is a field that deals with the product master data management and combines into one base the experience and the principles of data integration and data quality. Product Information Management merges the specific attributes of products across all channels in the supply chain. By unification, centralization and standardization of product information into one platform, quality and timely information with added value can be achieved. The goal of the theoretical part of the thesis is to construct a picture of the PIM, to place the PIM into a broader context, to define and describe various parts of the PIM solution, to describe the main differences in characteristics between the product data and data about clients and to summarize the available information on the administration and management of knowledge bases of the PIM data quality relevant for solving practical problems. The practical part of the thesis focuses on designing the structure, the content and the method of filling the knowledge base of the Product Information Management solution in the environment of the DataFlux software tools from SAS Institute. The practical part of the thesis further incorporates the analysis of the real product data, the design of definitions and objects of the knowledge base, the creation of a reference database and the testing of the knowledge base with the help of specially designed web services.

6

Hannila, H. (Hannu). "Towards data-driven decision-making in product portfolio management:from company-level to product-level analysis." Doctoral thesis, Oulun yliopisto, 2019. http://urn.fi/urn:isbn:9789526224428.

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Abstract Products and services are critical for companies as they create the foundation for companies’ financial success. Twenty per cent of company products typically account for some eighty per cent of sales volume. Nevertheless, the product portfolio decisions — how to strategically renew company product offering — tend to involve emotions, pet products and who-shout-the-loudest mentality while facts, numbers, and quantitative analyses are missing. Profitability is currently measured and reported at a company level, and firms seem unable to measure product-level profitability in a constant way. Consequently, companies are unable to maintain and renew their product portfolio in a strategically or commercially balanced way. The main objective of this study is to provide a data-driven product portfolio management (PPM) concept, which recognises and visualises in real-time and based on facts which company products are concurrently strategic and profitable, and what is the share of them in the product portfolio. This dissertation is a qualitative study to understand the topical area by the means combining literature review, company interviews, observations, and company internal material, to take steps towards data-driven decision-making in PPM. This study indicates that company data assets need to be combined and governed company-widely to realise the full potential of company strategic assets — the DATA. Data must be governed separately from business IT technology and beyond it. Beyond data and technology, the data-driven company culture must be adopted first. The data-driven PPM concept connects key business processes, business IT systems and several concepts, such as productization, product lifecycle management and PPM. The managerial implications include, that the shared understanding of the company products is needed, and the commercial and technical product structures are created accordingly, as they form the backbone of the company business as the skeleton to gather all product-related business-critical information for product-level profitability analysis. Also, product classification for strategic, supportive and non-strategic is needed, since the strategic nature of the product can change during the entire product lifecycle, e.g. due to the technology obsolescence, disruptive innovations by competitors, or for any other reason
Tiivistelmä Tuotteet ja palvelut ovat yrityksille kriittisiä, sillä ne luovat perustan yritysten taloudelliselle menestykselle. Kaksikymmentä prosenttia yrityksen tuotteista edustaa tyypillisesti noin kahdeksaakymmentä prosenttia myyntimääristä. Siitä huolimatta tuoteporfoliopäätöksiin — kuinka strategisesti uudistetaan yrityksen tuotetarjoomaa — liittyy tunteita, lemmikkituotteita ja kuka-huutaa-kovimmin -mentaliteettia faktojen, numeroiden ja kvantitatiivisten analyysien puuttuessa. Kannattavuutta mitataan ja raportoidaan tällä hetkellä yritystasolla, ja yritykset eivät näyttäisi pystyvän mittaamaan tuotetason kannattavuutta johdonmukaisesti. Tämä estää yrityksiä ylläpitämästä ja uudistamasta tuotevalikoimaansa strategisesti tai kaupallisesti tasapainoisella tavalla. Tämän tutkimuksen päätavoite on tarjota dataohjattu (data-driven) tuoteportfoliohallinnan konsepti, joka tunnistaa ja visualisoi reaaliajassa ja faktapohjaisesti, mitkä yrityksen tuotteet ovat samanaikaisesti strategisia ja kannattavia ja mikä on niiden osuus tuoteportfoliossa. Tämä väitöskirja on laadullinen tutkimus, jossa yhdistyy kirjallisuuskatsaus, yrityshaastattelut, havainnot ja yritysten sisäinen dokumentaatio, joiden pohjalta pyritään kohti dataohjautuvaa päätöksentekoa tuoteportfolion hallinnassa. Tämä tutkimus osoittaa, että yrityksen data assettit on yhdistettävä ja hallittava yrityksenlaajuisesti, jotta yrityksen strategisten assettien — DATAN — potentiaali voidaan hyödyntää kokonaisuudessaan. Data on hallittava erillään yrityksen IT-teknologiasta ja sen yläpuolella. Ennen dataa ja teknologiaa on omaksuttava dataohjattu yrityskulttuuri. Dataohjatun tuoteportfolionhallinnan konsepti yhdistää keskeiset liiketoimintaprosessit, liiketoiminnan IT-järjestelmät ja useita konsepteja, kuten tuotteistaminen, tuotteen elinkaaren hallinta ja tuoteportfolion hallinta. Yhteisymmärrys yrityksen tuotteista ja sekä kaupallisen että teknisen tuoterakenteet luominen vastaavasti on ennakkoedellytys dataohjatulle tuoteportfolion hallinnalle, koska ne muodostavat yrityksen liiketoiminnan selkärangan, joka yhdistää kaikki tuotteisiin liittyvät liiketoimintakriittiset tiedot tuotetason kannattavuuden analysoimiseksi. Lisäksi tarvitaan tuotteiden kategorisointi strategisiin, tukeviin ja ei-strategisiin tuotteisiin, koska tuotteen strateginen luonne voi muuttua tuotteen elinkaaren aikana, johtuen esimerkiksi teknologian vanhenemisesta, kilpailijoiden häiritsevistä innovaatioista tai mistä tahansa muusta syystä

7

Hooi, Leng Lee. "Application of product data management within the product development process." Thesis, University of Huddersfield, 2002. http://eprints.hud.ac.uk/id/eprint/14688/.

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Manufacturing companies need to be able to respond to customer demand quickly and accurately. This requires the capability to manage product data effectively. Product Data Management (PDM) systems have been identified as a solution to deliver this capability by providing the right information to the right people at the right time and in the right format. The foundation of this research is that the concept of PDM is relevant and important within the product development process. This research focuses upon how the PDM concept is applied in practice to define and configure products and how it can be integrated with other major information systems to enable an enterprise wide information system. To enable the research aim, an extensive review of literature was undertaken to investigate the effectiveness of PDM in enhancing the product definition process and in creating an interface between different business functional areas. A survey ofPDM system usage was undertaken aimed at identifying the current level of PDM usage within manufacturing enterprises in the UK. This was followed up by three industrial case studies to provide some degrees of validation of the results obtained. A need for effective one time order capture was identified from the three case studies which led to the development of a model specification for a late product configuration tool. A prototype system was produced to validate the design specification and was successfully demonstrated to a collaborating company. During the submission of this thesis, the collaborating company and the university are working on funding a project to pursue with its implementation. The work undertaken has firmly established the relevance ofPDM within the product development process and the importance of effective interfaces between PDM and other manufacturing information systems. The research will be of interest to small and medium sized manufacturing companies searching solutions for improving the management of their product data to enhance product definition and configuration.

8

Sun, Tao. "Product Context Analysis with Twitter Data." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13526.

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Context. For the product manager, the product context analysis, which aims to align their products to the market needs, is very important. By understanding the market needs, the product manager knows the product context information about the environment the products conceived and the business the products take place. The product context analysis using the product context information helps the product manager find the accurate position of his/her products and support the decision-making of the products. The product context information generally could be found in the user feedbacks. And the traditional techniques of acquiring the user feedbacks can be replaced by collecting the existed online user feedbacks with a cheaper cost. Therefore, researchers did studies on the online user feedbacks and the results showed those user feedbacks contain the product context information. Therefore, in this study, I tried to elicit the product context information from the user feedbacks posted on Twitter. Objectives. Objectives of this study are 1. I investigated what kinds of Apps can be used to collect more related Tweets, and 2. I investigated what kinds of product context information can be elicited from the collected Tweets. Methods. To achieve the first objective, I designed unified criteria for selecting Apps and collecting App-related Tweets, and then conduct the statistical analysis to find out what is/are the factor(s) affect (s) the Tweets collection. To achieve the second objective, I conducted the directed content analysis on the collected Tweets with an indicator for identifying the product context information, and then make a descriptive statistical analysis of the elicited product context information. Results. I found the top-ranked Apps or Apps in few themes like “Health and Fitness” and “Games” have more and fresher App-related Tweets. And from my collected Tweets, I can elicit at least 15 types of product context information, the types include “user experience”, “use case”, “partner”, “competitor”, “platforms” and so on. Conclusions. This is an exploratory study of eliciting product context information from the Tweets. It presented the method of collecting the App-related Tweets and eliciting product context information from the collected Tweets. It showed what kinds of App are suitable to do so and what types of product context information can be elicited from the Tweets. This study let us be aware of that the Tweets can be used for the product context analysis, and let us know the appropriate condition to use the Tweets for the product context analysis.

9

Romaniuk, Helena. "Analysis of product usage panel data." Thesis, University of Southampton, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326798.

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10

Hines, Erisa K. (Erisa Kimberly). "Lifecycle perspectives on product data management." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/34141.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2005.
Includes bibliographical references (p. 106-109).
Implementing a new IT system often requires the enterprise to transform in order to maximally leverage the capabilities generated by the new system. The challenge in using IT as an enabler to change arises from the need to synergistically redesign processes, develop and implement a solution using internal talent and external suppliers, and establish adoption by users. Product Data Management (PDM) technology represents a substantial portion of large industry IT investment over the last decade. The ability to manage and deliver product data throughout the lifecycle has become increasingly important to the aerospace enterprise as products become more complex, cost and development cycles shorten, and customer, partner, and supplier relationships evolve. Currently, the aerospace community does not have capability to provide traceability from requirements and design through field maintenance. While initially an attempt to understand the application of PDM in product development, what emerged was a study in how PDM affects and enables lean enterprise transformation. The selection, development, and deployment of PDM solutions were studied in the aerospace industry in order to enable better implementation decisions in varying complex environments. Organizational, technical, and cultural factors were considered as they contribute to a PDM's effectiveness. .
(cont.) A current-state observation of nine aerospace company sites highlights the difficulty in reaching the technology's full potential to deliver customer value. Data show that PDMs are being used primarily to manage design engineering data and are not tightly integrated with other business systems. The data also show a distinct difference between prime and supplier companies' spending on and capability of their respective data management systems. While the value of PDM to product development includes better data quality, traceability and transparency, value to the enterprise is also found beyond the traditional role of PDM. Looking horizontally across the lifecycle and vertically through the hierarchical relationships, PDM provides opportunities for organizational and process change and stakeholder involvement, both important tenets for evolving into a lean enterprise. This conclusion is supported by both the site interviews and the two case studies
by Erisa K. Hines.
S.M.

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Книги з теми "Data Product":

1

CIMdata. Product-data management. London: Department of Trade and Industry, 1994.

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2

Bloor, M. Susan. Product data exchange. London: UCL Press, 1995.

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3

Corporation, Dallas Semiconductor. Product data book. Dallas, Tex: Dallas Semiconductor Corporation., 1987.

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4

(Firm), Harris Semiconductor. Digital product data book. Florida: Harris Semiconductor, 1988.

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5

Limited, IQD. Crystal product data book. Crewkerne: IQD Limited, 1994.

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6

Limited, IQD. Crystal product data book. Crewkerne: IQD Limited, 1995.

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7

Corporation, Harris. Analog product data book. [Boston, Mass.]: Harris, 1986.

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8

Corporation, ILC Data Device. Data converters product catalog. Bohemia, N.Y: ILC DDC, 1989.

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9

(Firm), Shell UK Oil. Product safety data sheet. London: Shell UK oil, 1985.

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10

Ohio-Kentucky-Indiana Regional Council of Governments and Ohio-Kentucky-Indiana Regional Council of Governments. Decision Resources Center, eds. Regional data product catalog. Cincinnati, Ohio: Ohio-Kentucky-Indiana Regional Council of Governments, 1988.

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Частини книг з теми "Data Product":

1

Stark, John. "Product Data." In Decision Engineering, 115–45. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-546-0_7.

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2

Stark, John. "Product Data." In Product Lifecycle Management (Volume 2), 145–80. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24436-5_8.

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3

Yin, Yong, Ikou Kaku, Jiafu Tang, and JianMing Zhu. "Product Architecture and Product Development Process for Global Performance." In Data Mining, 133–55. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-84996-338-1_8.

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4

Mechlinski, Thomas, and Andreas Schreiber. "Product Data Management." In IFIP Advances in Information and Communication Technology, 233–40. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-0-387-35577-1_15.

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5

Sheth, Sharad. "Product Data Management." In CAD/CAM Robotics and Factories of the Future, 446–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-52320-5_75.

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Kerkhove, Louis-Philippe. "Managing Product Returns." In Data-driven Retailing, 161–75. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12962-9_6.

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7

Ball, Robert, and Brian Rague. "Final Product." In The Beginner's Guide to Data Science, 241–48. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07865-1_11.

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8

Anderl, Reiner, and Martin Momberg. "Authentication of product data and product components." In Information Infrastructure Systems for Manufacturing II, 275–86. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-0-387-35385-2_19.

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9

Paffrath, Rainer. "Mining Product Configurator Data." In Modern Concepts of the Theory of the Firm, 110–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-08799-2_7.

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Åsebø, Olav. "Product Data Management Systems." In Enterprise Modeling, 201–16. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4475-3_13.

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Тези доповідей конференцій з теми "Data Product":

1

"GATHERING PRODUCT DATA FROM SMART PRODUCTS." In 10th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001700302520257.

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2

Attokurov, Ulukbek, Okan Kaya, and Mehmet Selman Sezgin. "Product Recommendation Based on Embeddings: People Who Viewed This Product Also Viewed These Products." In 2022 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2022. http://dx.doi.org/10.1109/bigcomp54360.2022.00063.

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3

Vrampas, George, Corey Kado, Xiaoou Yang, Elisabeth Kames, and Beshoy Morkos. "Collecting Product Design Data: Examining the Applicability of Utilizing Gaze Data to Obtain Customer Feedback on Product Designs." In ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/detc2023-117130.

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Анотація:

Abstract Studies have shown gaze path data to be helpful in gaining consumer feedback. This study examines the applicability of utilizing eye tracking in collecting consumer preference data on a product’s design. During the study, the subjects are asked to indicate their preferences textually and verbally; concurrently, eye-tracking software collects gaze data from the test subjects. Both datasets are compared for correlation. The robustness of the study is tested against three variables: (1) product type; (2) design fidelity; and (3) product features. The study determines whether gaze data can be applied to a range of different products. The specific sample products used are cars, cellular phones, and microwaves. This tests viability when applied to a variety of products. The study also investigates preference data on various design fidelities: a sketch generated early in the design process (low fidelity), a drawing at an intermediate stage in the process (medium fidelity), or a picture/rendering of the final product (high fidelity). The last variable in the study examines the impact of the number of features that could be altered on each of the products. Due to the diversity of the product set, many features can be changed and analyzed. By comparing the preference data to the gaze data collected for these variables, we find that all product types can be examined; however, it is important to note that lower interest products presented as sketches (lower fidelity) with two changed features most accurately predicted preferences data of consumers.

4

Mitik, Merve, Ozan Korkmaz, Pinar Karagoz, Ismail Hakki Toroslu, and Ferhat Yucel. "Data Mining Based Product Marketing Technique for Banking Products." In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW). IEEE, 2016. http://dx.doi.org/10.1109/icdmw.2016.0085.

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5

Entezari, Negin, Evangelos E. Papalexakis, Haixun Wang, Sharath Rao, and Shishir Kumar Prasad. "Tensor-based Complementary Product Recommendation." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671938.

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6

Micol, Alberto, Magda Arnaboldi, Nausicaa A. R. Delmotte, Laura Mascetti, and Joerg Retzlaff. "ESO science data product standard for 1D spectral products." In SPIE Astronomical Telescopes + Instrumentation, edited by Alison B. Peck, Robert L. Seaman, and Chris R. Benn. SPIE, 2016. http://dx.doi.org/10.1117/12.2232655.

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7

Yoshimura, Masataka, Tsutomu Nishimura, and Kazuhiro Izui. "Acquisition of Product Design Guidelines Considering User Kansei Data Pertaining to Product Environments." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85160.

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Анотація:

Recently, almost all industrially manufactured consumer goods have a high level of engineering excellence, and product designers face an increasingly difficult task of creating products that will stand out in a competitive marketplace. At present, users tend to base their purchasing decisions on the product’s degree of fitness to their preferences, not the degree of functional fulfillment that the product offers. The development of products that are more attractive to users requires the consideration of human preferences and sensibilities, so-called “Kansei,” as well as the skillful application of these factors to the design sequence. The process of identifying and clarifying Kansei suggests that personal preferences concerning a given product are strongly influenced by both the person’s environment and the circumstances in which the product will be used. Analyzing both of these clarifies the influence that subconscious desires and human nature have on the expression of Kansei. This paper proposes a method for extracting the Kansei of potential customers and applying it to product designs that aim to maximize their human appeal, rather than their technical superiority.

8

Storch, Tobias, Martin Bachmann, Hans-Peter Honold, Hermann Kaufmann, Harald Krawczyk, Rupert Muller, Bernhard Sang, Mathias Schneider, Karl Segl, and Christian Chlebek. "ENMAP data product standards." In IGARSS 2014 - 2014 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2014. http://dx.doi.org/10.1109/igarss.2014.6947002.

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9

Fu, Xin, and Hernán Asorey. "Data-Driven Product Innovation." In KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2783258.2789994.

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10

Grote, Karl-H., and Soeren Schumann. "Intelligent Product Data Exchange." In ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/eim-9010.

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Анотація:

Abstract The computer based engineering design process today is characterized by a large variety of (specialized) systems. This and the ongoing globalization and outsourcing of engineering services and competencies causes an increased need for data exchange over the borders of the numerous CAx-systems. Under these circumstances, data exchange has been playing an important role for time and cost sensitive development and manufacturing in every field of industry. This paper presents actual problems and solutions of data exchange over the borders of modern software platforms. It includes the description of possible influences on a product data model and introduces the latest data exchange concepts.

Звіти організацій з теми "Data Product":

1

Smith, Bradford M. Product data exchange:. Gaithersburg, MD: National Institute of Standards and Technology, 1989. http://dx.doi.org/10.6028/nist.ir.89-4165.

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2

Altemueller, Jeffrey, Kelly Chi, Gary Baldridge, Lori Davis, and Phillip Dorr. Product Definition Data Interface (PDDI) Product Specification. Fort Belvoir, VA: Defense Technical Information Center, July 1991. http://dx.doi.org/10.21236/ada239256.

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3

Abeye, Binyam, Edward Barkmeyer, and Peter Denno. Product data sheet ontology. Gaithersburg, MD: National Institute of Standards and Technology, December 2014. http://dx.doi.org/10.6028/nist.ir.8035.

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4

ENSR CONSULTING AND ENGINEERING FLORENCE AL. RAMP Product Data Translation System Printed Wiring Assembly Product Data Definition Document. Fort Belvoir, VA: Defense Technical Information Center, January 1992. http://dx.doi.org/10.21236/ada251560.

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5

Brei, Marylouise. Exchange Standards for Electronic Product Data. Fort Belvoir, VA: Defense Technical Information Center, October 1988. http://dx.doi.org/10.21236/ada205729.

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6

Smith, Bradford M. External representation of product definition data. Gaithersburg, MD: National Institute of Standards and Technology, 1989. http://dx.doi.org/10.6028/nist.ir.89-4166.

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7

Carver, Gary P., and Howard M. Bloom. Concurrent engineering through product data standards. Gaithersburg, MD: National Institute of Standards and Technology, 1991. http://dx.doi.org/10.6028/nist.ir.4573.

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8

Parks, Curtis. Internet commerce for manufacturing product data. Gaithersburg, MD: National Institute of Standards and Technology, 1999. http://dx.doi.org/10.6028/nist.ir.6320.

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9

Flater, David, and KC Morris. Testability of product data management interfaces. Gaithersburg, MD: National Institute of Standards and Technology, 1999. http://dx.doi.org/10.6028/nist.ir.6429.

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10

Mitchell, Mary, Yuhwei Yang, Steve Ryan, and Bryan Martin. Data model development and validation for product data exchange. Gaithersburg, MD: National Institute of Standards and Technology, 1990. http://dx.doi.org/10.6028/nist.ir.90-4241.

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