28 Facing fierce competition in marketplaces, companies try to determine the optimal settings of design 29 attribute of new products from which the best customer satisfaction can be obtained. To determine 30 the settings, customer satisfaction models relating affective responses of customers to design attributes 31 have to be first developed. Adaptive neuro-fuzzy inference systems (ANFIS) was a...

Many methods have been adopted by shipping operators to evaluate, analyze and select ports of calls. Majority of these methods are generally based on evaluating different port performance criteria involved. The ports performance criteria are interrelated and are of a complex nature. Comparison of ports by different evaluation methods provides a basis for implementing operating strategies for...

Different from explicit customer needs that can be identified directly by analyzing raw data from the customers, latent customer needs are often implied in the semantics of use cases underlying customer needs information. Due to difficulties in understanding semantic implications associated with use cases, typical text mining-based methods can hardly identify latent customer needs, as opposite ...

Organizations spend significant resources tracking customer satisfaction and managing service delivery. Although a great deal of effort is expended in understanding what goes on within each of these areas, little or no effort has been applied to identifying and quantifiing the relationships between the two. The objective of this research is to discover and establish potential relationships betw...

Object Matching (Entity resolution) is a critical data integration task and aims at identifying semantically corresponding objects (records, instances) in one or several data sources. A typical example is the redundant and heterogeneous representation of customers in different enterprise databases. Finding corresponding customer representations is a key task, e.g., for customer relationship man...

Object Matching (Entity resolution) is a critical data integration task and aims at identifying semantically corresponding objects (records, instances) in one or several data sources. A typical example is the redundant and heterogeneous representation of customers in different enterprise databases. Finding corresponding customer representations is a key task, e.g., for customer relationship man...

Object Matching (Entity resolution) is a critical data integration task and aims at identifying semantically corresponding objects (records, instances) in one or several data sources. A typical example is the redundant and heterogeneous representation of customers in different enterprise databases. Finding corresponding customer representations is a key task, e.g., for customer relationship man...

Object Matching (Entity resolution) is a critical data integration task and aims at identifying semantically corresponding objects (records, instances) in one or several data sources. A typical example is the redundant and heterogeneous representation of customers in different enterprise databases. Finding corresponding customer representations is a key task, e.g., for customer relationship man...

Object Matching (Entity resolution) is a critical data integration task and aims at identifying semantically corresponding objects (records, instances) in one or several data sources. A typical example is the redundant and heterogeneous representation of customers in different enterprise databases. Finding corresponding customer representations is a key task, e.g., for customer relationship man...

Object Matching (Entity resolution) is a critical data integration task and aims at identifying semantically corresponding objects (records, instances) in one or several data sources. A typical example is the redundant and heterogeneous representation of customers in different enterprise databases. Finding corresponding customer representations is a key task, e.g., for customer relationship man...