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Research Papers

A New User Implicit Requirements Process Method Oriented to Product Design PUBLIC ACCESS

[+] Author and Article Information
Qi Guo

School of Mechanical Engineering,
Southeast University,
Nanjing 210000, China
e-mail: 230169630@seu.edu.cn

Chengqi Xue

School of Mechanical Engineering,
Southeast University,
Nanjing 210000, China
e-mail: ipd_xcq@seu.edu.cn

Mingjiu Yu

School of Mechanical Engineering,
Northwestern Polytechnical University,
Xi'an 710000, China
e-mail: 395458317@qq.com

Zhangfan Shen

ASME Member
School of Mechanical Engineering,
Southeast University,
Nanjing 210000, China
e-mail: shenzhangfan@163.com

1Corresponding author.

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received January 8, 2018; final manuscript received August 28, 2018; published online November 22, 2018. Assoc. Editor: Ying Liu.

J. Comput. Inf. Sci. Eng 19(1), 011010 (Nov 22, 2018) (11 pages) Paper No: JCISE-18-1014; doi: 10.1115/1.4041418 History: Received January 08, 2018; Revised August 28, 2018

User requirements play an important role in product design activities. Customer satisfaction has a direct bearing on the acquisition of user requirements for product design. However, these implicit requirements are equipped with the attributes of potentiality, fuzziness, and subjectivity. In this paper, a new implicit user requirement processing method based on a cloud service platform is proposed to resolve the difficulty of acquiring implicit requirements. Initially, this method collects user requirement data via a metaphor extraction technique using a cloud service platform. Then, the requirement data are clustered and mapped with product attributes. Finally, the mapping results are visualized to intuitively instruct product design and optimization. Overall, the method is a user-centered innovation paradigm implemented on a cloud service platform to realize collaborative design and resource sharing. Finally, an application case is presented to illustrate the method, and the results indicate that the method is effective and could serve as a reference for product design.

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User requirements play an important role in the product design process and perform the function of connecting the designer and user. Therefore, user requirement acquisition is the premise of product design. As people's living standards improve, they expect product designs to be updated more rapidly, and their user requirements tend to exhibit more diversification, fuzzification, and individuality [1]. Therefore, the accurate and timely acquisition and processing of user requirements is a key factor in determining whether the product design is successful or not. Additionally, as competition becomes increasingly fierce, an enterprise wishing to dominate its market needs to accurately determine customer requirements and predict consumption trends. In general, user requirements can be divided into explicit requirements and implicit requirements [2]. The explicit user requirements can be easily illustrated and acquired by the designer. However, because implicit user requirements consist of fuzzy intentions, the designer cannot acquire the requirements directly. In addition, the users themselves cannot describe their requirements in a crystal-clear fashion. Normally, implicit requirements reflect user potential requirements [3], and they affect users' perceptions of and satisfaction with the product. Hence, when the implicit requirements are not fully extracted and considered in the process of product design, the final products may not be recognized and accepted by both the users and the market.

How can one ensure that implicit user requirements are fully and precisely acquired? To unwind this problem, a novel product design method is proposed. The conventional product design method spearheaded by designers completes all design procedures without involving users and is known as the top-down design method [4]. Inevitably, this practice may not fully meet the expectations and requirements of the users. User-centered design methods have recently become increasingly popular and achieved wide recognition [5]. The open source concept, which is represented by resource sharing and free thinking, has given rise to spill-over effects from the software industry to other industries [6]. The open-source concept has inspired a new bottom-up product design method that is spearheaded by users and presents a more open and transparent nature. Because of the direct user participation in the design process, their requirements could be well reflected and properly addressed in the final products. Thus, fuzzy implicit requirements are no longer problematic.

A method of implementing bottom-up product design practices is urgently required. Cloud service platforms oriented to product design have been proposed for a new mode of product design in which numerous target users will be gathered voluntarily to participate in the product design. The requirements of these users can be collected and processed via cloud computing, thus allowing the designer to optimize the product design. Ultimately, the mature product developed by this process will be able to meet the expectations and requirements of users. Moreover, the cloud service platform integrates Internet resources, including the designer, user, manufacturer, investor, seller, and other social resources, to facilitate collaborative design and resource sharing.

Based on the above introduction, this paper proposes a new user requirement processing method based on a cloud service platform to resolve the difficulty of acquiring implicit requirements. The method initially collects user requirement data using the metaphor extraction technique on the cloud service platform, and then, the requirement data are clustered and mapped with the product attributes. Eventually, the mapping results are visualized to instruct the product design and optimization processes. This method is a user-centered innovation paradigm implemented on a cloud service platform to realize collaborative design and resource sharing. Thus, this approach could process implicit user requirements in an effective and thorough manner. In general, explicit requirements are easier to capture than implicit requirements from users. Explicit requirements can be acquired by conventional investigation methods, including questionnaire surveys, focus groups, and face-to-face interviews. Therefore, a method of processing explicit user requirements is not presented separately in this paper.

Research on User Requirements Processing Methods.

A processing method to obtain explicit user requirements has been the subject of a wide range of research, and a questionnaire survey method and quality function deployment method [7] have been combined to acquire explicit user requirements and transform product attributes [8]. In addition, the TRIZ theory [9], Kano model [10], and fuzzy comprehensive evaluation method [11,12] have been used to acquire explicit user requirements. Additionally, researchers have processed explicit requirements by constructing user requirement acquisition models with different methods [1315]. Product online reviews contain a plethora of useful information that can provide user requirements and preferences to the designer [16,17]. Studies have focused on online review extraction and clustering, using helpful online reviews to identify user requirements to support product design and optimization [18,19]. Generally, online reviews written by customers include honest praise or complaints, which provide the specific requirements for the product and belong to explicit user requirements. Thus, the requirement processing method using online reviews adopts explicit requirements.

Research on implicit user requirement processing methods includes three stages. The first stage studies the mechanism underlying implicit requirements, the second stage studies the explicit expression of implicit requirements, and the third stage focuses on the transformation between implicit requirements and product attributions.

Stage 1: Researchers have investigated the principles and associations between user knowledge and user cognition. The semantic differential method is introduced to analyze users' implicit requirements by reflecting their requirements on a Likert scale and calculating the semantics of product and color [20]. This approach can distinguish implicit user knowledge and design knowledge. Furthermore, the previous research has explored the transformation of implicit requirements from the perspective of cognition. From a psychological perspective, implicit knowledge is action oriented and procedural and can be obtained without external assistance. Implicit knowledge represents the capacity to obtain new knowledge from experience. Based on Sternberg's research, a formal conceptual analysis method is introduced to model and test implicit knowledge [21]. Thus, the previous research has discovered the features of implicit knowledge, which represent the foundation of making implicit knowledge explicit.

Stage 2: Achievements have been made on implicit user requirement acquisition and processing, with the approaches of Emotional Design, Experience Design, and Emphatic Design standing out as product design methods specializing in meeting implicit user requirements [2224]. The semiology method is adopted to resolve unstructured and fuzzy descriptions in the management of requirements [25]. In addition, the transfer model and the spiral model are proposed to transform implicit knowledge to explicit knowledge [26]. Correspondingly, the Zaltman metaphor elicitation technique (ZMET) can solve the problem of extra guidance [27], and it stimulates users via pictures and vocal cues, and users can express their feelings without being interrupted. Thus, although implicit requirements can be precisely acquired, ZMET is not equipped with the function to transform requirements into product attributions.

Stage 3: From the perspective of cognition, encoding and decoding theories are used to transform knowledge between the designer and the user. Kansei Engineering, proposed by Nagamachi, Mitsuo, provides an innovative alternative idea to process implicit requirements by combining sensibility and engineering to construct the relationship between product features and user intentions [28]. However, this process may compromise the expression of user feelings because the selection of adjectives might cause an extra guidance issue and the acquired implicit requirements may not reflect the original user requirement.

Research on Cloud Service Platform.

The concept of a public service platform was first introduced in 1995 [29]. This platform has a free and open interface where all the interested users can upload their information. Hence, information and resources can be shared among users. Research on a public service platform subsequently increased in popularity, and then, cloud computing was defined and the architecture was developed to support clouds with market-oriented resource allocation by leveraging various technologies, such as virtual machines. Additionally, a meta-negotiation infrastructure is introduced to establish global cloud exchanges and markets [30]. Taking stock of the cloud computing technology, a cloud-based infrastructure has been developed to support data mining applications, which consist of a storage cloud and a computing cloud [31]. This research illustrates the technological base for implementing a cloud platform. Enterprises have applied various cloud technology from cloud computing to cloud manufacturing as a platform to transform the business models of traditional manufacturing with the goal of aligning product innovation with business strategies and creating intelligent factory networks that encourage effective collaboration [32].

Two types of cloud computing platforms have been recommended in the manufacturing sector, and studies have also focused on cloud platforms in specific fields. For instance, a vehicular data analytics platform has been developed to identify unusual driving behaviors, monitor the health status of vehicles, and track fleets of vehicles [33]. A cloud innovation platform for the shaping design of industrial products has been created based on a service model, technical support, and an operating mechanism [34]. In consideration of cost effectiveness, the Republic of Kazakhstan e-Government has selected a cloud platform as well as an information and communication architecture framework [35]. The implementation of cloud technology could cut the overhead and improve the efficiency of equipment applications; however, only 10% of the applications in the public sector are cloud-based technologies [36].

The construction techniques for cloud computing technologies and cloud service platforms are relatively mature; however, the previous studies have not focused on a comprehensive cloud service platform oriented to product design for the entire industry chain. The cloud service platform oriented to product design proposed in this paper is a new product innovation paradigm cultivated and developed in the context of cloud computing. Moreover, the platform is underpinned by the principle of cloud computing [3739]. The essence of the mode is to provide a platform for users so that they can record and share creative ideas or requirements free of spatial or time constraints. This platform is dependent on the Internet community, which allows people with common interests to share their design ideas with the community via the cloud service platform at anytime and anywhere. In addition, users can also discuss and comment on the design ideas in the community to obtain individualized and differentiated product designs to meet their own requirements. Moreover, the cloud service platform enables the rapid integration of the product's designer, user, manufacturer, investor, seller, and other social resources via the Internet platform, thus facilitating collaborative design and resource sharing and providing timely services based on individualized user requirements. This platform is also highly efficient in modifying and updating information.

The cited research provided a theoretical foundation for studies on implicit user requirements and their application in the process of product design. This paper proposes a new implicit requirement processing method, namely, an online requirement acquisition and transformation approach implemented via a cloud service platform. This approach encompasses several key technologies, including target user recruitment, requirement extraction and clustering, and requirement and product attribute mapping, all of which have the potential to provide new ideas to acquire and process implicit requirements.

New product design models aimed at improving the processing efficiency of user requirements will guide research trends and emphasize product design in the future. In particular, the bottom-up product design method can easily address the fuzziness and complexity of implicit user requirements. Furthermore, the cloud service platform serves as the best medium to execute the new bottom-up design practice in a user-centered design approach

Implicit requirements constitute an important element of user requirements and play a vital role in the process of product design. Because of the fuzzy, elusive, and unpredictable attributes of implicit requirements, accurately and efficiently acquiring these requirements remains a vexing challenge for designers. The Metaphor Elicitation Technique provides a practical option addressing this challenge [40,41] because it transforms fuzzy elusive user language to explicit formalities so that implicit user requirements can be treated as explicit elements in the product design. In addition, the information expressed by metaphors always represents potential user requirements, such as potential cultural attributes, product connotation, and brand value. Thus, the ability of designers to obtain a new understanding of user requirements is essential and allows them to recalibrate the design with the user requirements.

Zaltman metaphor elicitation technique is an effective method of acquiring the true thoughts and feelings of users. First, this method provides users with specific pictures to provide for visual stimulation, which is then combined with in-depth interviews. The implicit function of the visual elements in the pictures will induce users' deep feelings and thoughts. Furthermore, we can excavate users' implicit and potential requirements accurately and rapidly. However, the traditional ZMET method is not suitable for acquiring user requirements via a cloud service platform. The reasons are chiefly as follows. First, cloud service platforms have the characteristics of rapid updating and instant information sharing, although in the traditional ZMET method, the interviewees' recruitment and guided interviews are conducted offline. Thus, the requirement collection cycle will generally be very long, which cannot meet the requirements of cloud service platforms for real-time data. Second, the traditional ZMET method requires the research analyst to master all aspects of the collected knowledge and has a high level of interview skills. In addition, the subjectivity of the research analyst may also affect the interviewee's real requirements. Third, the number of interviewees is restricted because the method is difficult to widely implement. Therefore, the samples are limited, which hinder the method's ability to represent the opinions of most users.

Based on the processing characteristics of the user requirements on the cloud service platform, we modified and optimized the traditional ZMET by comparing the methods, processes, and tools of implicit requirement acquisition in the current design field. In this paper, the principles of the ZMET method are used as a guide, and the method of identifying user requirements is summarized in five steps:

Step 1. Defining product attributes;

Step 2. Recruiting and filtering users;

Step 3. Extracting user requirements;

Step 4. Clustering user requirements;

Step 5. Mapping of user requirements and product attributes;

Step 6. Visualizing the mapping results.

The designer is required to structure the model of product attributes by extracting the product attributes from the initial design scheme on the cloud service platform. Then, a number of users will be recruited and filtered as the target user, and they will be asked to upload the pictures representing their perceptual intentions and describe them textually on the platform. The cloud service platform will then collect the pictures and text, extract the information on the users' requirements, and cluster the information to form data sets of user implicit requirements. In the next step, the data sets will be mapped with the model of product attributes. Finally, the mapping result will be visualized to guide the informed product design process.

Definition of Product Attributes.

At the initial phase of product design, designers tend to express their ideas via sketches or other graphical tools, which are not only the media connecting users, although they provide an explicit representation of the designer's professional knowledge and experience. Additionally, the graphical presentation of information is ideal for users to evaluate the design scheme, which they can use to select a suitable design.

After submitting the initial designing scheme, the product designer extracts the product elements that might moderate the product's intention expression, such as the profile, surface treatment, and color. Then, the elements are divided into various product attributes, which are used to formulate the database of product attributes. For instance, the profile could be divided into the fillet edge, right-angle edge, and diversiform edge attributes. In addition, the designer should provide correlative pictures to reflect the product attributes. The process of defining product attributes is shown in Fig. 1.

Recruiting and Filtering Users.

To identify user requirements, target users must be efficiently and effectively recruited so that authentic user requirements can be acquired. If the recruits who provide their requirements lack the freedom to make their own choices or present subjective biases, then untrustworthy results and misinformed design decisions will occur. To safeguard against inauthentic user requirements, the method of real-time recruitment is presented to find and filter the target users. Real-time recruitment collects information submitted voluntarily by users through the Internet and then rapidly analyzes this information to determine suitable users. Compared with questionnaire surveys and other conventional approaches, the major benefit of real-time recruitment is manifested in the skipping of a user reservation step. If the users participate in the project through the Internet, their requirements could be acquired anytime and anywhere. For example, to find users interested in a reading lamp, such information can be obtained when the potential users browse or search reading lamps through the Internet and these users can be targeted using Real-time Recruitment. Thus, target users can be located. Users who are truly motivated to complete the task are more likely to provide valuable information for requirements.

To recruit more target users, a recruitment and filtering module is inserted in the design websites, shopping websites, and community forum. When the visitors browse the webpage, the recruitment information will show up with a floating window on the page. Then, the potential users will enter the website by clicking the recruitment and filtering module link. The recruitment and filter module can be created using HTML form creation tools, such as Wufoo and Google Doc, and the convenience of such tools offers an optimal range of target users, thereby ensuring the authenticity and reliability of the users.

To ensure that the platform can recruit sufficient target users, the number of page views cannot be overlooked. The recruitment rate of the pop-up recruitment and filtering step can be estimated by a sketchy principle. Approximately 1.5–2% of users will fill in the form when they take note of the pop-up recruitment and filtering window. The percentage of users who accept the interview is higher, and approximately 65% is able to participate in the project successfully. Therefore, to obtain a stable user recruitment flow, at least 1000 page views are needed.

User Requirements Extracting.

Originally, user thoughts were processed by a pictorial form rather than by a textual description. The visceral feelings of the users can be explored by certain associated pictures, thus allowing their deep thoughts, ideas, and cognitions on certain issues to be obtained. Therefore, based on ZMET, user implicit requirements can be extracted by analyzing the pictures uploaded by the users themselves.

After publishing tasks to the cloud service platform, the recruited target users may develop their own thoughts pertaining to the design tasks, and they can upload to the platform pictures that reflect their visceral feelings as well as descriptive vocabularies. In general, these responses are the explicit forms of the implicit user requirements. Then, the information on all of these requirements is integrated and stored on the cloud service platform. This step is known as the process of user intention expression. Because deep feelings and thoughts may be difficult to express via pictures, the users can also describe their requirements textually as a supplementary input, and all of these responses are collected and stored on the platform as well.

User Requirements Clustering.

After extracting the user requirements, the information of a great number of associated vocabularies and pictures reflecting the requirements of the users is stored on the cloud service platform; this information forms the raw data of the user requirements and requires further processing. Because different users may express themselves via different language customs, a number of the extracted vocabularies may illustrate one requirement. In addition, a massive amount of scattered vocabularies is not appropriate for analyzing user implicit requirements. Therefore, the requirement vocabularies must be clustered to limit the data dimensions. Moreover, several core product requirement subjects are developed in the final step. The semantic similarity calculation and clustering technique are used to process the raw data of the user requirements, and the detailed procedure is shown in Fig. 2.

All of the user requirements are collected on the cloud service platform to form the original requirements set PG which includes all core requirements GDisplay Formula

(1)PG={P1,P2.PT}

T is the number of requirements vocabularies that G eventually corresponds to.

The relationship between requirements vocabularies is very complex, including synonymy, antisense, and hyponymy, and semantic similarity is the quantitative measurement of complex relationships between requirements vocabularies. It mainly refers to the similarity degree of two vocabularies that can be replaced with each other without changing the semantic structure of the text. In this study, the calculation method based on HowNet is used to calculate semantic similarity. HowNet [42]2 is common sense knowledge base aiming to reveal the attribution relationships of different concepts; it describes the concepts represented by Chinese and English vocabularies.

There are two basic terms in HowNet, concept and primitive [43], concept is the description of semantic vocabulary, each vocabulary can be expressed as multiple concepts, each concept corresponds to a part of speech, but the part of speech of different concept in the same vocabulary can be identical or not. Primitive is the minimum unit to describe concept, all of the primitive constitute a tree layer system [44]. The basic definition is as shown below:

Definition one-common node number:C ( q1,q2) refers to the number of parent nodes owned by the two primitive q1 and primitive q2 in the semantic hierarchy tree.

Definition two-primitive distance: Dis( q1,q2) is the routine distance between primitive q1 and primitive q2 in the semantic hierarchy tree.

Definition three-primitive depth: Dep(q) refers to the path length of the root node in the semantic hierarchy tree to the q node.

Based on the above definition, the calculation of semantic similarity between requirements vocabularies follows the three steps:

  1. (1)To calculate primitive similarity Simq1,q2 : Display Formula
    (2)Simq1,q2=β*C(q1,q2)β*C(q1,q2)+Dis(q1,q2)*Dis(q1,q2)1
  2. (2)To calculate the concept similarity SimC1,C2 :

In HowNet, the description of concepts can be divided into four categories: the original primitive description, the extended primitive description, the relational primitive description, and the relational symbol description. Supposing that the concept C1 and C2 have i and j primitives, respectively, Display Formula

(3)C1=q11,q12,q1iC2=q21,q22,q2j

where the original primitive similarity is recorded as Sim1q1,q2, the extended primitive similarity is recorded as Sim2q1,q2, the relational primitive similarity is recorded as Sim3q3,q3, and the relational symbol primitive similarity is recorded as Sim4q1,q2. Then, the similarity between the two concepts can be calculated as follows: Display Formula

(4)Sim(C1,C2)=m=14(αin=1mSimn(q1,q2))αi=γi/γ1in

where αi is the variable coefficient, γi is the number of the primitive category i, and γ is the sum of four classes of primitive.

  1. (1)To calculate the semantic similarity between vocabularies Display Formula
    (5)Simv1,v2=maxi=1f,j=1kSimC1i,C2j+aA1+aA

where a/A is adjustment coefficient and a/A (0,1).

As for the new emerged vocabularies which are not included in Hownet, we defined the semantic similarity of same vocabulary as 1 and the semantic similarity of different vocabularies as 0. Hence, the equation to calculate semantic similarity of v1 and v2 is Display Formula

(6)Sim(v1,v2)maxi=1f,j=1kSimC1i,C2j+aA1+aAw1,w2inHownet0w1,w2Hownet,w1w21w1,w2Hownet,w1=w2

In the process of clustering user requirements, single vocabulary is clustered to a small group, then small groups are clustered to big groups, finally all vocabularies are clustered to one group. Thus, the process of clustering requirements is not just calculating semantic similarity of signal vocabulary, but also calculating semantic similarity of all vocabulary groups.

V1 and V2 are two vocabulary groups, if V1 has m vocabularies: v11, v12, …, v1m, V2 has n vocabularies: v21, v22, …,v2m, the semantic similarity of group V1 and group V2is the minimum semantic similarity of each vocabulary couple Display Formula

(7)SimV1,V2=mini=1,2,.,mj=1,2,,ns(V1i,V2j)

For special vocabularies, corresponding methods are needed to calculate the semantic similarity. Certain vocabularies are not indexed in HowNet, and thus, are out of vocabulary. Thus, concept segmentation and automatic semantic generation are used to integrate the out of vocabularies into the semantic computation. Moreover, certain vocabularies have more than one implication, which is called a polysemy, and certain vocabularies have the same implications, which are called synonyms. For polysemies and synonyms, the concept features are extracted from the vocabularies that need to be clustered, and the semantic similarity between the polysemy or synonym and the concept features will be calculated to distinguish the true meaning of the polysemies and synonyms.

This paper clustered the user requirements via the improved K-means method. Compared with the traditional K-means algorithm, the improved K-means algorithm will obtain the clustering center by mining the maximum length frequent vocabulary sets and calculating the semantic similarity of vocabularies, which effectively overcome the sensitivity of the K-means clustering algorithm to the initial clustering center and solve the problem of the comprehensibility of clusters.

The vocabulary set V includes all vocabularies which can represent user requirements. The process of clustering the user requirements with improved K-means algorithm Display Formula

(8)V=V1,V2,,Vn

The original cluster centers are obtained by mining the maximum frequent vocabulary sets. The themes of the vocabulary set will be obtained by mining the maximum length frequent vocabulary sets, and each theme can be defined as one cluster center. The maximum frequent vocabulary set F={fnfn=w1,w2,,wn} can be obtained by the FP_tree algorithm [45].

According to the above formula, the semantic similarity between fn and wn is obtained, and then, the vocabularies in which the semantic similarities are greater than a given threshold are classified as ci. Then, the cluster center will be set as C=cici=wi1,wi2,,win}.Subsequently,fn and the vocabularies in which the semantic similarity with fn is greater than the threshold u will be deleted.

Because many cluster centers ci only include one or two vocabularies, they cannot represent the theme. These cluster centers are not the real original cluster centers, and they should be deleted. In this paper, we assume that the cluster center ci that includes more than three vocabularies is the real original cluster center.

Then, we calculate the semantic similarity between each vocabulary wi and each cluster center ci.

First, sw1,c1 can be obtained according to formula (8). If sw1,c1>δ ( δ is the semantic distance threshold), we calculate the weight distance D1 between vocabulary w1 and cluster center c1 according to the Euclidean distance formula (7)Display Formula

(9)DWi,Cj=wiCj2

Then, we calculate the weighted distance D between w1 and ci, which meets semantic distance threshold δ. If Dj=minD,w1 belongs to cluster j. Then, we process w2,w3,,wn with the same method. Each cluster center is calculated as follows: Display Formula

(10)cjI=1ni=1njwi(j)

If WIWI1<ε ( ε is the distance threshold of cluster center), then the algorithm is ended; otherwise, I=I+1, and repeat steps (2) and (3) until WIWI1<ε.

The output includes the number of clusters and the vocabularies included in each cluster. Then, we can capture the key user requirement categories, which include the total number of user requirements.

Mapping of User Requirements and Product Attributes.

After extracting and clustering the user requirements, the user core requirement themes are obtained. However, these themes are not sufficient to reveal the users' potential feelings and cannot be used to build contacts with product attributes. Thus, a mapping method is proposed to connect the implicit requirements with the product attributes.

Based on psychology research [46], free association is the advanced function of a human and represents the thoughts from an unconscious mind. Since the content of free association generally represents deep aspirations and ideas, free association is often used in the process of mapping technology. A cue is presented to the user usually in the form of a vocalization or visual picture, and the users are asked to identify the vocabularies that immediately came to mind. During the mapping process, implicit requirements and product attributes are separately projected into adjective vocabularies, which can then be used to reflect the users' potential intentions.

In this paper, after extracting and clustering the user requirements, the implicit requirements are visualized via pictures, and then, the target users are asked to state the vocabularies that immediately came to mind upon viewing the pictures. Similarly, users will be asked to state the vocabularies that immediately came to mind when they see the pictures that illustrate product attributes. The process aims to separately extract the users' psychological metaphor for the implicit requirements and product attributes. Finally, using the theory of Gestalt's metaphor logic, we can establish the connections between the user requirements and the product attributes. The schematic diagram of the mapping process is shown in Fig. 3.

For the metaphor technology, a method based on the concept of pool space replacing possible worlds is used to realize the metaphorical understanding and mapping issue when transforming implicit requirements. This method is proposed to solve the problem in which the possible words model proposed by Fitting [47] cannot describe inconsistent concepts. Therefore, the modal term Up, the relation character “ ”, and Gestalt principles are introduced to establish the metaphor logic system.

Suppose that there are two propositions, a and b ; “ “is introduced as a relation character, and they are expressed with the logic form based on Gestalt principles [48].

Then,ab means that a and b are the same. Meanwhile, if ab=1, then “ a is the same as b ” is true; and if ab=0, “ a is the same as b ” is false; and if the value of ab is between 0 and 1, then the value indicates the semantic connectivity of a and b.

The definition of Up is as follows: the pool space is a set composed of specific attributes and propositions, Upα means that the user understands or accepts proposition α.

The details about the inference rule, the properties and semantic symbol of the metaphor logic system, and the processing method for different metaphor forms (verbal, adjective, substantival metaphor, etc.) are based on the work of Rickheit [49,50] said.

Certain explanations must be provided for the logical expression. Each proposition can only deduce one conclusion. If you accept a, then you cannot deny b. For example, if fashion is a requirement for a user's mobile phone, then the user will not deny a fashion phone.

If a user understands ab and understands a, then the user cannot deny b. For example, if the user thinks that the large-display mobile phone is fashionable and fashion is a user's requirement, the user will not deny a large-display phone.

For the logical expression of a metaphor, if a user thinks that a metaphor is true, then the metaphor does not have to be true in reality; rather, it is just the user's subjective idea.

All pool spaces are logically consistent, and there is no case in which the user thinks that a is not only true but also false. That is (UpαUpα).

The expression vector of user requirements include two sides: the set of metaphorical adjective called adjPijq and the set of metaphorical product attributes called adjFmqDisplay Formula

(11)adjpijq=adjijq1,adjijq2,,adjijqeijq
Display Formula
(12)adjOmq=adjmq1,adjmq2,,adjmqeimq

Cq is the target user, Fm is the product attributes, Pij is the picture which can represent user requirement, eijq is the number of adjectives which reflect the psychological metaphor of Pij, and emq is the number of adjectives which reflect the psychological metaphor of Fm.

Establishing the mapping relation between Pij and Fm according to the metaphorical logic system [51] Display Formula

(13)Z=ZCq,adjPijqadjFmq

If Z=Ø, the mapping degree between Pij and Fm is 0; if ZØ, gestalt equation is established according to metaphor logic system, is introduced as comparison operators Display Formula

(14)UadjpijqadjOmqPijFm

It can be derived that the mapping degree of Pij and Fm is 1, so the mapping degree between images Pij and product attributes Fm is shown below [52]: Display Formula

(15)ZC,Pij,Fm=freqZØcountC

The higher mapping degree between Pij and Fm indicates that the product attributes are closer to user's real psychological requirements.

Visualization of Mapping Results.

After mapping of user requirements and product attributes, we can obtain the relationship between user requirements and product attributes. As user requirements have the feature of mass data on the cloud service platform, the technology of visualization is introduced to express and understand the mapping results, so that it can conveniently instruct product design and optimization. With the technology of visualization, we can understand the data of requirements more in-depthing and more intuitive, in addition, the designer could analyze user requirements exactly and completely.

Data visualization is an effective way to express and understand data information with the form of information graphics [53]. The information graphics have strong visual impact, it can bring out resonance and attract users easily, and meanwhile, the information graphics could transmit the information of requirements intuitively to all participants of the platform with a simplified and generalized form.

In the form of data display tools, chart types are more diverse and richer. In addition to the traditional histogram, pie chart, line chart, other common graphics, and even GIS map, these various graphics can meet different needs of display and analysis.

So, according to the data size, type, relationship, information display interface features, and the visual cognitive characteristics, the visual data display form which accords with the visual cognition is built.

The Construction of a Cloud Service Platform Oriented to Product Design.

A cloud service platform is presented to illustrate the implicit user requirement process. The platform is a socialization and networking platform of an industrial design intended for resource sharing and public innovation, and it can efficiently integrate design resources and collect the maximal user requirements. Then, the platform transforms the requirements into design elements and allows the users to realize their ideas and creativity. The platform eliminates the space limitations associated with collecting decentralized manufacturing resources, and the description, packaging, publication, and storage processes can be implemented on the platform in virtualized forms. Moreover, requirements, information, and resources can be accumulated orderly, and the many-to-many service pattern of the cloud platform facilitates a wider range of promotion and application. The platform integrated all types of resources via the network, and it can promote resource sharing throughout the product's full life-cycle, including the design, manufacturing, management, and sales processes. Based on cloud computing and Internet technology, the platform can provide for advanced services the rapid release and dynamic updating of information, the optimal distribution of supply and demand, the secure protection of transaction processes, etc. The operating process of the cloud service platform is shown in Fig. 4.

The cloud service platform is constructed based on the J2EE technology framework [54], which includes many editable components and is different from the traditional system development framework [55,56]. J2EE can improve the system's portability, security, and reusability by simplifying and regulating the system development process and deployment structure [57]. In addition, the technology and the tools of velocity, mybatis, mysql, Javabean, and tomcat will be used to build the cloud service platform system. The whole design flow follows software engineering methods. After the overall process design, data analysis, documentation, coding, module testing, and system implementation steps have been complemented, the platform system will be able to operate normally.

User Requirements Acquisition Process.

In this paper, an air cleaner is designed as an application case to illustrate the proposed user implicit requirement processing method on the cloud service platform.

First, the designer needs to register and log in to access the cloud service platform. Subsequently, the designer uploads the initial design scheme of the air cleaner to the platform by clicking “Submit Ideas.” The design elements of the air cleaner are extracted and then divided into product attributes. On the platform, the product attributes are named “Labels,” and the labels for the air cleaner are “fashion,” “special,” and “simple.” The submission page is shown in Fig. 5.

Large amounts of data are required to ensure the scientific soundness of user requirement research; therefore, as many target users as possible are needed to acquire the implicit requirements. The recruitment and filtering module “Wufoo” will be used to recruit target users. Recruitment information and links can be placed in recruitment and filtering modules on the websites, which have a large number of page views. When users browse these pages and notice the recruitment information, they can access the recruitment and filter module by clicking “Continue” if they are interested in the information.

If the users are interested in the specific creative ideas, they can access the cloud service platform to participate in the process of product design and optimization. These users will be the target users to collect the user requirements. After performing simple registration and login tasks, the target users could reach the “Inspiration” pages. The pages show all types of creative design schemes submitted by the professional designers or ordinary users, and the schemes could be presented in many styles, such as sketches, renderings, and engineering models. Target users select their ideas of interest and access the pages with detailed information on the ideas, including the specific description and different tags provided by the designers. If users are not interested in being interviewed or if they have a strong desire and ingenious ideas to modify or optimize the original design scheme, the cloud service platform can address these different users. Users can optimize and upload new design schemes to the cloud service platform, and the design scheme will also be further optimized by other target users via a cocreation process until the final scheme becomes a mature, marketable commercial product for more users. This process provides a good complement to the traditional methods of acquiring explicit user requirements. The “Inspiration” page is shown in Fig. 6.

The target users who are interested in the air cleaner could see the specific information about the design scheme, if they want to express their own ideas about the design scheme, they can access the page of requirements acquisition by click “Participating Innovation.” In this page, target users could express their ideas and feelings about the scheme freely by uploading the related pictures which are described with some vocabularies according to the page's hints, at most ten pictures are allowed to upload each time. When users cannot find the appropriate pictures to reflect their true ideas, some describing words could be added in the textbox to replenish their ideas. After finishing collecting ideas, the requirements data will be uploaded to the platform server to process.

The design task of air cleaner is published on the platform for one month, there are 352 target users who are participating the design task, and totally, we have 2382 requirements vocabularies. After filtering and process with the technology of data mining, 14 vocabularies which appeared frequently are selected as final requirements vocabularies set R for further analysis

R=PersonalizedConciseUniqueSpecialStableLightDelicatePortableBeautifulElegantSafeTexturedSimplyFashion

Furthermore, all the requirements vocabularies will be clustered according to the method in Sec. 3.4. In the process of clustering, we can obtain the semantic similarity between every two vocabularies based on Hownet by Eq. (6), besides, the semantic similarity between single vocabulary and vocabulary group or two vocabularies groups can be calculated by Eq. (7). Then, we can get the semantic similarity of requirement vocabularies sets, as shown in Table 1.

For example, according to equations in Sec. 3.5, we can obtain the semantic similarity of cluster groups between ConciseSimple and BeautifulFashion

SConciseSimpleBeautifulFashion=minConciseBeautifulConciseFashionSimpleFashion=min0.403,0.396,0.380,0.645=0.380

Clustering all the vocabulary from bottom to top in accordance with the semantic similarity until the final one class is established, and then, the subclass vector space of requirements formed by user requirement vector space will be ranked ordering as shown in Table 2.

According to the rank of cumulative coverage ratio, C1, C2, C3, C4, C5, and C6 are selected as the ultimate theme of key requirements, k = 6, α = 0.018.

Taking the core requirements of “Concise” in C1 as an example to illustrate the process of mapping user requirements and product attributes. First, the pictures uploaded by target users would be selected as the resource of free association; the selected pictures can represent the core requirements of Concise

Pelpelegantgoblet,pelegantjade.pelegantn

Mapping the representative vocabularies extracted from collected pictures with product attributes extracted from the product information uploaded by designer

adjPelegantgoblet1=smooth,shiny,limpid,clean,brightadjFprofilebevel1=soft,smooth,elasticadjFprofilebevel1adjPelegantgoblet1=smooth

Gestalt equation is established according to metaphor logic system: Usmoothelegantprofilebevel

Based on the above analysis, the mapping results of all user requirements are collected on the cloud service platform. To generate a more precise and intuitive understanding of the requirements information, visualization technology is applied to present the mapping results. In this application, the data visualization uses the concept of the hue circle. Different product attributes are shown in different colors, and different product elements are shown in different color brightnesses. Furthermore, the sizes of the color blocks represent the importance of the product attributes and elements. When the designer needs to improve the contour of the design product, “Contour” is selected. Then, all the product attributes related to the contour will be displayed with different colors, brightnesses, and block sizes as shown in Fig. 7.

In this paper, a new processing approach for user requirement data based on a cloud service platform is proposed that combines cloud service technology and a conventional metaphor elicitation technique. This method serves as a novel alternative to processing implicit user requirements.

This method is implemented via a cloud service platform, which is a connected public innovative platform underpinned by cloud computing. The platform promotes the collaborative design and resource sharing of products throughout the full lifecycle. In terms of the cloud service platform, the target users are accurately recruited, and the users' implicit requirements are extracted in the form of pictures and vocabularies. Moreover, the semantic similarities of the vocabularies are calculated to cluster all requirement data. Then, a metaphor logic system is applied to map the implicit requirements with product attributes. This process allows the designer to incorporate the user requirements into the product attributes during the product design phase. Furthermore, visualization technology is introduced to display the mapping results, which is an effective technique that facilitates product design and optimization. Finally, an application case is presented to illustrate how user implicit requirements are processed on the cloud service platform, and the results demonstrate the feasibility and effectiveness of this processing method.

  • The National Natural Science Foundation of China (under Grant Nos. 71471037 and 71271053).

  • Science and Technology on Avionics Integration Laboratory and Aeronautical Science Fund (No. 20165569019).

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References

Qi, G. N. , Gu, X. J. , Yang, Q. H. , and Yu, J. H. , 2003, “ Principles and Key Technologies of Mass Customization,” Comput. Integr. Manuf. Syst., 9(9), pp. 776–783.
Münte, T. F. , Brack, M. , Grootheer, O. , Wieringa, B. M. , Matzke, M. , and Johannes, S. , 1997, “ Event-Related Brain Potentials to Unfamiliar Faces in Explicit and Implicit Memory Tasks,” Neurosci. Res., 28(3), pp. 223–233. [CrossRef] [PubMed]
Luo, Y. , and Lu, Z. , 2006, “ Analysis on Demand and Definition of Implicit Demand,” Nankai Bus. Rev., 9(3), pp. 22–27.
Mantyla, M. , 2010, “ A Modeling System for Top-down Design of Assembled Products,” J. Res. Develop., 34(5), pp. 636–659.
McKeown, C. , 2013, “ Designing for Situation Awareness: An Approach to User-Centered Design,” Ergonom., 56(4), pp. 727–728. [CrossRef]
Valkov, S. , 2008, “ Innovative Concept of Open Source Enterprise Resource Planning (ERP) System,” Ninth International Conference on Computer Systems and Technologies and Workshop for Ph.D. Students in Computing (CompSysTech'08), Gabrovo, Bulgaria, June 12–13, Article No. 11. https://dl.acm.org/citation.cfm?id=1500893
LaiKow, Chan. , and MingLu, Wu. , 2002, “ Quality Function Deployment: A Comprehensive Review of Its Concepts and Methods,” Qual. Eng., 15(1), pp. 23–35. [CrossRef]
Karsak, E. E. , 2004, “ Fuzzy Multiple Objective Programming Framework to Prioritize Design Requirements in Quality Function Deployment,” Comput. Ind. Eng., 47(2–3), pp. 149–163. [CrossRef]
Chai, K. H. , Zhang, J. , and Tan, K. C. , 2005, “ A TRIZ-Based Method for New Service Design,” J. Service Res., 8(1), pp. 48–66. [CrossRef]
Chen, C. C. , and Chuang, M. C. , 2008, “ Integrating the Kano Model Into a Robust Design Approach to Enhance Customer Satisfaction With Product Design,” Int. J. Prod. Econ., 114(2), pp. 667–681. [CrossRef]
Kong, Z. J. , and Hao, Y. J. , 2001, “ Determine the Importance of Customer Requirement in QFD Using Importance-Probability Comprehensive Coefficient Method,” Comput. Integr. Manuf. Syst., 7(2), pp. 65–72.
Guo, C. , Liu, Y. , Tian, P. , and Hou, S. , 2010, “ Fuzzy Comprehensive Evaluation Method of the Importance Ratings of Customers' Requirements,” Sixth CIRP-Sponsored International Conference on Digital Enterprise Technology, pp. 361–373.
Yamashina, H. , Ito, T. , and Kawada, H. , 2002, “ Innovative Product Development Process by Integrating QFD and TRIZ,” Int. J. Prod. Res., 40(5), pp. 1031–1050. [CrossRef]
Borgianni, Y. , and Rotini, F. , 2015, “ Towards the Fine-Tuning of a Predictive Kano Model for Supporting Product and Service Design,” Total Qual. Manage. Bus. Excellence, 26(3–4), pp. 263–283. [CrossRef]
Hong, G. , Hu, L. , Xue, D. , Tu, Y. L. , and Xiong, Y. L. , 2008, “ Identification of the Optimal Product Configuration and Parameters Based on Individual Customer Requirements on Performance and Costs in One-of-a-Kind Production,” Int. J. Prod. Res., 46(12), pp. 3297–3326. [CrossRef]
Jin, J. , Ji, P. , and Gu, R. , 2016, “ Identifying Comparative Customer Requirements From Product Online Reviews for Competitor Analysis,” Eng. Appl. Artif. Intell., 49, pp. 61–73. [CrossRef]
Liu, Y. , Jin, J. , Ji, P. , Harding, J. A. , and Fung, R. Y. K. , 2013, “ Identifying Helpful Online Reviews: A Product Designer's Perspective,” Comput. Aided Des., 45(2), pp. 180–194. [CrossRef]
Qi, J. , Zhang, Z. , Jeon, S. , and Zhou, Y. , 2016, “ Mining Customer Requirements From Online Reviews: A Product Improvement Perspective,” Inf. Manage., 53(8), pp. 951–963. [CrossRef]
Zhang, H. , Rao, H. , and Feng, J. , 2018, “ Product Innovation Based on Online Review Data Mining: A Case Study of Huawei Phones,” Electron. Commerce Res., 18(1), pp. 3–22. [CrossRef]
Osgood, C. E. , and Luria, Z. , 1954, “ A Blind Analysis of a Case of Multiple Personality Using the Semantic Differential,” J. Abnormal Psychol., 49(4), p. 579. [CrossRef]
Busch, P. , and Richards, D. , 2004, “ Modelling Tacit Knowledge Via Questionnaire Data,” Concept Lattices, Second International Conference on Formal Concept Analysis, ICFCA 2004, pp. 321–328.
Zhang, T. , 2009, “ Emotional Design Bring New Opportunity to Creative Product Design,” Packag. Eng., 30(7), pp. 119–121.
Hassenzahl, M. , 2010, “ Experience Design: Technology for All the Right Reasons,” Synth. Lectures Human-Centered Inf., 3(1), pp. 1–11. [CrossRef]
Wright, P. , and Mccarthy, J. , 2008, “ Empathy and Experience in HCI,” Conference on Human Factors in Computing Systems, CHI 2008, Florence, Italy, pp. 637–646.
Wu, J. H. , and Gan, R. C. , 2005, “ The Application and Research of Semiotics in Demand Engineering,” Comput. Eng. Des., 26(12), pp. 3291–3294.
Luo, S. J. , Pan, Y. H. , and ahd Zhu, S. H. , 2007, “ Patterns of Tacit Knowledge Based on Graphic Thinking in Product Design,” Chin. J. Mech. Eng., 43(6), pp. 93–98. [CrossRef]
Coulter, R. A. , Zaltman, G. , and Coulter, K. S. , 2001, “ Interpreting Consumer Perceptions of Advertising: An Application of the Zaltman Metaphor Elicitation Technique,” J. Advertising, 30(4), pp. 1–21. [CrossRef]
Nagamachi, M. , 1995, “ Kansei Engineering: A New Ergonomic Consumer-Oriented Technology for Product Development,” Int. J. Ind. Ergonom., 15(1), pp. 3–11. [CrossRef]
Collinge, B. , 1995, “ New Consumer Online Services,” Electron. Libr., 13(2), pp. 116–126. [CrossRef]
Buyya, R. , Yeo, C. S. , Venugopal, S. , Broberg, J. , and Brandic, I. , 2009, “ Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility,” Future Gener. Comput. Syst., 25(6), pp. 599–616. [CrossRef]
Grossman, R. L. , Gu, Y. , Sabala, M. , and Zhang, W. , 2008, “ Compute and Storage Clouds Using Wide Area High Performance Networks,” Future Gener. Comput. Syst., 25(2), pp. 179–183. [CrossRef]
Xu, X. , 2012, “ From Cloud Computing to Cloud Manufacturing,” Rob. Comput. Integr. Manuf., 28(1), pp. 75–86. [CrossRef]
Muramudalige, S. R. , and Bandara, H. M. N. D. , 2016, “ Demo: Cloud-Based Vehicular Data Analytics Platform,” International Conference on Mobile Systems, Applications, and Services Companion, Singapore, June 25–30, pp. 1–1. https://dl.acm.org/citation.cfm?id=2948849&dl=ACM&coll=DL
Yang, Q. , Luo, W. , Jiang, L. J. , and Li, Z. L. , 2014, “ Research on Cloud Innovation Platform for Innovative Design of Industrial Products,” Mech. Des., 9(2), pp. 97–101.
Aubakirov, M. , and Nikulchev, E. , 2016, “ Development of System Architecture for e-Government Cloud Platforms,” Int. J. Adv. Comput. Sci. Appl., 7(2), pp. 253–258.
Liang, J. , 2012, “ Government Cloud: Enhancing Efficiency of E-Government and Providing Better Public Services,” International Joint Conference on Service Sciences (IJCSS2012), Shanghai, China, May 24–26, pp. 261–265.
Studer, R. , Benjamins, V. R. , and Fensel, D. , 1998, “ Knowledge Engineering: Principles and Methods,” Data Knowl. Eng., 25(1–2), pp. 161–197. [CrossRef]
Hayes, B. , 2008, “ Cloud Computing,” Commun. ACM, 51(7), pp. 9–11. [CrossRef]
Zhang, L. , Luo, Y. L. , Tao, F. , Ren, L. , and Guo, H. , 2010, “ Key Technologies for the Construction of Manufacturing Cloud,” Comput. Integr. Manuf. Syst., 16(11), pp. 2510–2520.
Christensen, G. L. , and Olson, J. C. , 2002, “ Mapping Consumers' Mental Models With Zmet,” Psychol. Mark., 19(6), pp. 477–501. [CrossRef]
Forr, J. , Derosia, E. D. , and Christensen, G. L. , 2008, “ Forecasting Deep Consumer Resonance: An Application of the Zaltman Metaphor Elicitation Technique (ZMET),” Advances in Business and Management Forecasting (Advances in Business and Management Forecasting, Vol. 5), K. D. Lawrence and M. D. Geurts , eds., Emerald Group Publishing Limited, Bingley, UK, pp. 133–156.
Feng, L. I. , and Fang, L. I. , 2007, “ A New Approach Measuring Semantic Similarity in Hownet 2000,” J. Chin. Inf. Process., 21(3), pp. 99–105.
Dai, L. , Xia, Y. , Liu, B. , and Wu, S. , 2008, “ Measuring Semantic Similarity Between Words Using HowNet,” International Conference on Computer Science and Information Technology (ICCSIT), Singapore, Aug. 29–Sept. 2, pp. 601–605.
Liu, M. Y. , 2012, “ Research on Semantic Based Word Similarity Algorithm,” Res. Dev. Sci. Technol. World, 34(9), pp. 617–620.
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Figures

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Fig. 1

Process of defining product attributes

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Fig. 2

Flowchart of the user requirements cluster

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Fig. 3

Schematic diagram of the mapping process

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Fig. 4

Operating process of the cloud service platform

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Fig. 5

Design scheme submission page

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Fig. 6

“Inspiration” page

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Fig. 7

Mapping results visualization

Tables

Table Grahic Jump Location
Table 1 The semantic similarity of requirement vocabularies sets
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Table 2 The rank of user requirement vector space

Errata

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