Obelix searches Internet using customer data

One major problem with purchasing through the Web is locating reliable suppliers that offer the exact product or service you need. In the usual approach, you access an indexing based search engine, specify keywords for the purchase, and initiate the search. The outcome is typically a list ranked according to keyword matches; useful, but not always helpful. Keyword matches provide only one ingredient to finding the right Web sites. The ranking should also consider the satisfaction of previous customers purchasing from those sites, customer profiles, and customer behavior. The Obelix search engine uses reconfigurable technology to apply customer satisfaction data obtained from the Internet service provider infrastructure to refine its search criteria. The Obelix system collects data about customer activities, calculates a customer satisfaction index, and updates the search engines with its findings     

Information searches on the internet: Conducting thematic searches

This paper describes a procedure for thematic searches on the Internet, including query generalization, restriction, and decomposition. Query decomposition using semantic indexation schemes is considered.     

Ways to improve the quality of textual data searches on the internet

It is argued that the quality of a textual data search on the Internet is low due to century-long misconceptions about the content structure of texts. Methods of improving the quality of Internet searches are suggested.     

Evaluation of customer impressions using virtual prototypes in the internet environment

This study aims to investigate a cost effective and efficient way of analyzing customer impressions on design alternatives by incorporating the benefits of virtual prototyping into the Internet-based experimental environment. It is hypothesized that the results of the Internet-based experiment using the images of virtual prototypes are comparable to those of the virtual reality-based environment using virtual prototypes. Two experiments were conducted. In the virtual reality environment, participants were employed to evaluate virtual prototypes while in the Internet environment participants evaluated images of the same virtual prototypes from their own places. For each experiment, 16 male participants were employed to evaluate 32 different virtual prototypes generated from the combination of 17 design elements of automobile interior. The results of the experiments indicated that there were no significant differences between the two experimental methods while the Internet environment-based method could save considerable time and efforts for experimentation. This study concludes that the Internet-based evaluation method using the images of virtual prototypes could be a cost effective and efficient way of analyzing customer impressions on design alternatives.

Relevance to Industry

This study showed that the results of the Internet-based evaluation method using the images of virtual prototypes are comparable to those of the virtual reality-based method using real virtual prototypes. The Internet-based evaluation method could be used as a cost effective and efficient way of collecting and analyzing various customers’ impressions on design alternatives at the early stage of product development process.     

Web data retrieval: solving spatial range queries using k-nearest neighbor searches

As Geographic Information Systems (GIS) technologies have evolved, more and more GIS applications and geospatial data are available on the web. Spatial objects in a given query range can be retrieved using spatial range query − one of the most widely used query types in GIS and spatial databases. However, it can be challenging to retrieve these data from various web applications where access to the data is only possible through restrictive web interfaces that support certain types of queries. A typical scenario is the existence of numerous business web sites that provide their branch locations through a limited “nearest location” web interface. For example, a chain restaurant’s web site such as McDonalds can be queried to find some of the closest locations of its branches to the user’s home address. However, even though the site has the location data of all restaurants in, for example, the state of California, it is difficult to retrieve the entire data set efficiently due to its restrictive web interface. Considering that k-Nearest Neighbor (k-NN) search is one of the most popular web interfaces in accessing spatial data on the web, this paper investigates the problem of retrieving geospatial data from the web for a given spatial range query using only k-NN searches. Based on the classification of k-NN interfaces on the web, we propose a set of range query algorithms to completely cover the rectangular shape of the query range (completeness) while minimizing the number of k-NN searches as possible (efficiency). We evaluated the efficiency of the proposed algorithms through statistical analysis and empirical experiments using both synthetic and real data sets.

Analyzing customer behavior from shopping path data using operation edit distance

Radio frequency identification (RFID) technology has been successfully applied to gather customers’ shopping habits from their motion paths and other behavioral data. The customers’ behavioral data can be used for marketing purposes, such as improving the store layout or optimizing targeted promotions to specific customers. Some data mining techniques, such as clustering algorithms can be used to discover customers’ hidden behaviors from their shopping paths. However, shopping path data has peculiar challenges, including variable length, sequential data, and the need for a special distance measure. Due to these challenges, traditional clustering algorithms cannot be applied to shopping path data. In this paper, we analyze customer behavior from their shopping path data by using a clustering algorithm. We propose a new distance measure for shopping path data, called the Operation edit distance, to solve the aforementioned problems. The proposed distance method enables the RFID customer shopping path data to be processed effectively using clustering algorithms. We have collected a real-world shopping path data from a retail store and applied our method to the dataset. The proposed method effectively determined customers’ shopping patterns from the data.     

A data structure for fast region searches

Bucketing, also known as fixed cells, is a data structure that is especially suitable for queries that cover small windows. However, this technique is not efficient if objects are long and narrow, such as wires. Also, as more objects are added, the efficiency of the query process may suffer. The authors describe a dynamic bucketing structure that maintains a two-level director structure and uses a directory doubting and merging technique called extendible hashing. This technique allows directories to expand without sacrificing performance as more objects are inserted into the structure. They report the results of tests comparing the performance of dynamic bucketing with the performance of the quad-tree algorithms     

Development of an automatic customer service system on the internet

Most existing network-based customer services heavily rely on manpower in replying e-mails or on-line requests from customers, which not only increases the service cost, but also delay the time for responding the service requests. To cope with these problems, this paper proposes a customer service system, which can automatically handle customer requests by analyzing the contents of the requests and finding the most feasible answers from the frequently asked question (FAQ) database. In the situation that a customer is not satisfied with the reply, the system will forward the request to the appropriate service personnel for further processing. An assistance mechanism has been developed to help the service personnel in finding potential answers from existing FAQ data or creating more appropriate answers. Experimental results on practical applications showed that over 87.3% of users were satisfied with the replies given by the system; therefore, we conclude that the system can significantly reduce the service cost and provide more efficient and effective customer service.     

Spatially enabled customer segmentation using a data classification method with uncertain predicates

Spatial attributes are important factors for predicting customer behavior. However, thorough studies on this subject have never been carried out. This paper presents a new idea that incorporates spatial predicates describing the spatial relationships between customer locations and surrounding objects into customer attributes. More specifically, we developed two algorithms in order to achieve spatially enabled customer segmentation. First, a novel filtration algorithm is proposed that can select more relevant predicates from the huge amounts of spatial predicates than existing filtration algorithms. Second, since spatial predicates fundamentally involve some uncertainties, a rough set-based spatial data classification algorithm is developed to handle the uncertainties and therefore provide effective spatial data classification. A series of experiments were conducted and the results indicate that our proposed methods are superior to existing methods for data classification.     

Visualization method for customer targeting using customer map

An important issue in customer-oriented marketing area is target selection which enables the determination of potential customers. In this paper, we suggest a novel customer targeting method: customer map for a service industry. The customer map is the visualization method for customer targeting. It depicts value distribution across customer needs and customer characteristics. To develop the customer map, we integrate numerous customer data from various data sources, perform data analyses using data mining techniques, and finally visualize the information derived by the former analyses. The customer map helps decision makers to build customer-oriented strategy under the unified goal of customer targeting. It also affords to monitor and perceive real time state and the change of customer value distribution based on their information without preconception. We apply the customer map to a credit card company, build web-based prototype system for the customer map and acquire managerial implications from this study.     

Bioinformatic searches using a single-chip shared-memory multiprocessor

A single-chip shared-memory multiprocessor architecture is introduced which is particularly well suited to common bioinformatic computing tasks. The architecture uses asynchronous bus interfaces to create an integrated circuit design methodology allowing for scaling of the multiprocessor with very little design effort. A key aspect of this design methodology is that it is not necessary to expend significant design resources and chip area on the clock tree. An analysis of the Smith–Waterman alignment algorithm running on this architecture shows that the performance penalty due to increased bus latency compared to a fully synchronous architecture is negligible.     

Enhanced customer requirement classification for product design using big data and improved Kano model

The classification of customer requirements (CRs) has a significant impact on the solution of product design. Existing CRs classification methods such as the Kano model and IPA model are time-consuming and inaccurate. This paper proposes a CRs classification method for product design using big data of online customer reviews of products to classify CRs accurately and efficiently. Comments of customer reviews are matched to CRs using a hierarchical semantic similarity method. Customer satisfaction degrees are defined based on emotional levels of adjectives and adverbs of customer comments using word vectors. The function implementation degree of each product is determined by specifications crawled from online products. Fitting curves are formed by defined customer satisfaction and function implementation of CRs using polynomial modeling and least square methods. Based on the slope of the fitted curves, CRs are classified to provide the minimum and maximum function implementations of CRs in each CR group to guide a product design process. The proposed method is applied in a case study of defining CRs classifications for design of upper limb rehabilitation devices. For verifying the proposed method, CRs defined by the existing methods are compared with CRs from the proposed method in design of an upper limb rehabilitation device.     

A customer service process innovation using the integration of data base and case base

Customer service is one of the critical processes that bring competitive advantage in today's business environment. By analyzing the current flow of the customer service process, we identify that lack of datalinformation sharing is the main problem. Therefore, all data and knowledge related to the customer service process need to be accumulated and shared. In this research, we develop an enabler for customer service process innovation by integrating data base technology and case-based reasoning technology. In particular, a case-based system for equipment malfunction diagnosis, which we developed in this research, is described and evaluated in detail.     

考虑客户满意度的网络零售配送时隙定价策略

为研究网络零售配送时隙的定价问题,结合客户满意度和收益双重目标,根据预期不确认理论和层次分析法给出了基于价格、交付期长度和宽度的客户满意度模型。基于收益管理理论,引入0-1变量,给出了考虑机会成本及额外成本的收益模型。根据零售商不同的收益偏好给出了双目标条件下的时隙定价模型。通过算例模拟分析了不同的零售商收益偏好对于时隙定价以及收益的影响;根据客户在线时隙选择随机性的特点,分析了在不同交付期长度及敏感度下的时隙定价策略,以及不同的客户到达率对于收益、客户满意度的影响。分析结果得出了考虑客户满意度和不同影响因素下的时隙定价策略,以及一系列比较有指导意义的性质和方法。     

Effective image annotation for searches using multilevel semantics

There is an increasing need for automatic image annotation tools to enable effective image searching in digital libraries. In this paper, we present a novel probabilistic model for image annotation based on content-based image retrieval techniques and statistical analysis. One key difficulty in applying statistical methods to the annotation of images is that the number of manually labeled images used to train the methods is normally insufficient. Numerous keywords cannot be correctly assigned to appropriate images due to lacking or missing information in the labeled image databases. To deal with this challenging problem, we also propose an enhanced model in which the annotated keywords of a new image are defined in terms of their similarity at different semantic levels, including the image level, keyword level, and concept level. To avoid missing some relevant keywords, the model labels the keywords with the same concepts as the new image. Our experimental results show that the proposed models are effective for annotating images that have different qualities of training data.     

Partially specified nearest neighbor searches using k-d trees

A partially specified nearest neighbor query is a nearest neighbor search in which only some of the possible keys are specified. An algorithm that uses k-d trees to perform such searching is described. The expected time complexity is O(N^{1-$\text{j}\text{k}$}). where k is the total number of keys and j the number of keys specified in the query. Experimental results, which are consistent with the theoretical predictions, are also presented.     

Fast feature selection aimed at high-dimensional data via hybrid-sequential-ranked searches

We address the feature subset selection problem for classification tasks. We examine the performance of two hybrid strategies that directly search on a ranked list of features and compare them with two widely used algorithms, the fast correlation based filter (FCBF) and sequential forward selection (SFS). The proposed hybrid approaches provide the possibility of efficiently applying any subset evaluator, with a wrapper model included, to large and high-dimensional domains. The experiments performed show that our two strategies are competitive and can select a small subset of features without degrading the classification error or the advantages of the strategies under study.     

互联网金融数据抓取方法研究

介绍了金融数据采集工具软件中用到的数据抓取方法.该方法通过微软公司MSXML组件中的XMLHTTP对象获取网页文本,并将待提取数据文本划分为3个层次:数据块、数据行和数据字段,逐层截取.对每个数据层次给出了7种不同的标识类型,以方便对各层次数据进行标识.为达到灵活和精确提取数据的目的,正则表达式被引入.以新浪网的金融网...     

一种互联网数据联合查询的优化方案

介绍了在Internet上数据联合查询的相关概念,以及简单联合查询的基本思想。针对于传统的简单联合查询,在实际操作中存在的执行效率较低的问题,结合目标数据集合中的数据在各个数据源上的分布不均匀的特点,将数据源划分为大、小数据源,在进行数据联合查询的过程中,优先查询大数据源中的数据,提出了一种新的互联网数据联合查询的优化方案,并论证了此方案的有效性和可行性。     

Are customer satisfaction and customer loyalty drivers of customer lifetime value in mobile data services: a comparative cross-country study

Many research papers have been published on the effect of customer satisfaction and customer loyalty on customer profitability which is related to customer lifetime value (CLV). However, there is limited research on the impact of cross-cultural factors on the effect of customer satisfaction and customer loyalty on CLV. This study aims to fill this gap. Focusing on the usage of mobile data services, 846 samples from China and 689 from the US are obtained. Data analysis suggests that customer loyalty is a driver of CLV, while customer satisfaction is not. This research has important implications for firms about how to enhance CLV in mobile data services.