a framework for context sensitive services: a knowledge discovery based approach

Decision Support Systems 48 (2009) 158–168

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Decision Support Systems

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A framework for context sensitive services: A knowledge discovery based approach

Indranil Bose a, Xi Chen b,⁎a School of Business, The University of Hong Kong, Hong Kongb School of Management, Zhejiang University, China

⁎ Corresponding author. Management School, Zijingsity, Hangzhou, China. Tel.: +86 571 88206827; fax: +

E-mail addresses: [email protected] (I. Bose), ch

0167-9236/$ – see front matter © 2009 Elsevier B.V. Adoi:10.1016/j.dss.2009.07.009

a b s t r a c t
a r t i c l e i n f o
Article history:Received 5 November 2008Received revised form 16 April 2009Accepted 26 July 2009Available online 5 August 2009

Keywords:Business understandingCase studiesContextContext sensitive servicesFrameworkKnowledge discoveryMobile servicesModelingSimulation

Advances in mobile technologies have made the collection of customers' context information feasible.Appropriate customer centric strategies that make use of customers' context data are eagerly awaited bymobile service providers. To address this need, a framework is proposed in this paper that can be used foranalyzing customers' context based behavioral data to provide suitable services to customers. Six casestudies of context sensitive services are discussed to illustrate how the proposed framework can be used toimprove them. Furthermore, simulated experiments are conducted using customers' behavioral dataincluding location and time and it is found that the use of context related data leads to the discovery ofdeeper and improved knowledge of customers' behavior.

© 2009 Elsevier B.V. All rights reserved.

1. Introduction

Due to the fast penetration of mobile phones, mobile services andcommerce are becoming more and more popular. According to asurvey conducted by AuthenTec [20], 58% of the survey respondentspurchased handphones that can handle mobile services such aswireless banking; nearly 47% of them are contemplating the use ofmobile services; and about 30% of them are going to upgrade theirhandphones so that they can use mobile services. The mobile datamarket is growing in terms of number of users and generated revenue.According to a report published by A.C. Nielsen, nearly 5 million of the40 million active users of mobile Web in the US accessed mobileshopping and auctionWeb sites in April 2008 in contrast to 2.9 millionusers in 2007 [8]. At the same time, the revenue gained fromworldwide mobile data services exceeded US$49 billion in the firstquarter of 2008 and is expected to exceed US$200 billion by the end ofthe year [9].With thematurity of mobile technologies, more andmoredata about the environment that the customers are in can be collectedeasily. The existence of this type of data can help service providersmatch the context of services and context of customers so as tounderstand customers' demands better. These types of services, whichtake into consideration the context data of customers such as time,location etc., are called context sensitive services (CSS). However, in

ang Campus, Zhejiang Univer-86 571 8820 [email protected] (X. Chen).

ll rights reserved.

spite of the attractiveness of CSS, a few solutions have been providedto help the service providers deliver context sensitive and customercentric services to customers. Some frameworks related to thedelivery of such services exist but they are more conceptual ratherthan operational. In the context of mobile telecommunications, it iscritical to understand customers' preferences in order to provideeffective services that cater to their needs. Understanding customers'behavior is possible by amassing data on customer demographics andtransactions. A knowledge discovery approach that can analyze suchvoluminous data, deliver usable knowledge, and lead to theformulation of customer focused business strategy is needed by themobile service providers. In this paper, our objective is to proposesuch a knowledge discovery based operational framework for CSS thatlays down the steps to be followed by mobile service providers foroffering such services to customers. We discuss six real life casestudies of CSS and illustrate how the proposed framework canenhance the delivery of these existing services. Then we use acombination of experimental and simulated data to show how theframework can be used for deeper understanding of customers'behavior and provisioning of services that are aligned with thecustomers' preferences.

We start with a review of related literature in Section 2. In thissection we define the key elements of context for CSS, discuss theimportance of CSS, and review some of the existing frameworksrelated to CSS. We also describe six real life case studies that make useof context data for operational purposes in this section. We propose anew framework for CSS based on a knowledge discovery approach in

159I. Bose, X. Chen / Decision Support Systems 48 (2009) 158–168

the third section. In the same section, the six case studies arediscussed once again to show how the proposed framework can helpin improving current practice. In the fourth section, we describe thenumerical experiments and corresponding results to illustrate howcontext data can help gain a thorough understanding of customers'preferences for a mobile service provider of Hong Kong. Conclusionand directions for future research wrap up the paper.

2. Literature review

2.1. Context sensitive services (CSS)

Location based services take location data into consideration and isdefined as “services that integrate a mobile device's location orposition with other information so as to provide added value to auser” [26]. However, there are other elements in the customers'environment besides location, such as time. Dey et al. [11] provided alist of components of context and remarked that “context is typicallythe location, identification, and the state of people, groups, andcomputational and physical objects”. However, this definition is notgeared towards mobile services. Zhang [30] mentioned that contextshould includemobile services users' preferences, mobile devices, andwireless network. Rao and Minakakis [23] suggested that time, reasoncustomers are at a location, means by which customers arrived at thelocation, and preferences of customers are also important context databesides location. Abowd and Mynatt [2] pointed out specifically thatcontext should include the ‘five W’: Who, What, Where, When, andWhy. By ‘Who’, for example, it's not enough to identify a person as acustomer. The person's past actions and service related backgroundshould also be identified for better service provision. ‘What’ referredto the activities conducted by the people involved in the context andinteractions between them. ‘Where’ represented the location data.‘When’ was related to time. ‘Where’ and ‘When’ were closely relatedto each other. ‘Why’ specified the reason for ‘Who’ did ‘What’. ‘Why’represented a complicated notion and acted as the driving force forthe context sensitive information system.

Mobile services are services delivered to customers via mobileartifacts such as mobile networks and mobile devices. One charac-teristic of mobile services is that they can be delivered to customersanytime and anywhere. It is believed that mobile services are highlyrelated to the environment that customers are in at the time theservices are needed. Location may be one of the most importantfactors used to describe the environment. People may have differentneeds when they are at different places. For example, when someoneis driving (s)he wants to know the most convenient way to get to his/her destination, whereas when (s)he is in the office (s)he needsinformation related to his/her work. Based on the above idea, severalelements are considered to be important components of context forCSS. Location is the first element. Providing location based services isthe first task of CSS. Time is the second element of CSS because peopleare mobile and locations changewith time. The third element of CSS ismobile technology. By mobile technology we refer to the hardwareand the software that allows transmission of data between mobiledevices or mobile devices and other devices wirelessly. These

Table 1Comparison of existing frameworks for CSS.

Authors Components of framework Context informatio

Lankhorst et al. (2002) [18] Services YesVarshney (2003) [27] Technical YesYuan and Tsao (2003) [29] Services YesLiao et al. (2004) [19] Knowledge discovery YesAalto et al. (2004) [1] Services Yes but only locatiXu et al. (2008) [28] Services Yes

elements are inter-related to each other. Mobile technology worksas an enabler of CSS. Mobile technology enables CSS not only byproviding mobile devices but also by connecting these devicestogether so that services can be provided to people anywhere andanytime. The characteristics of themobile technology are also a part ofthe context. Services delivered via mobile networks should bedisplayable or executable on customers' mobile devices [14,17].

2.2. Frameworks for CSS

Many researchers have proposed frameworks related to CSS. Someof them are more focused on technical issues, such as how thenetwork should be designed to support the services. For example,Varshney [27] compared three types of mobile services: mobileadvertising, location aware services, and mobile financial services, interms of their requirements such as the location precision, responsetime, network coverage, and wireless dependability etc. He proposedan integrated network architecture that considered the above factorsfor different services. Xu et al. [28] used a Bayesian network to buildup conceptual relationships between different components andevaluated the importance of those components in an applicationrelated to mobile advertising. The components included in theirresearch were context, content, demographics, and preferences ofusers. Their framework determined the importance of those compo-nents in CSS. Liao et al. [19] proposed a layered framework for contextmanagement that consisted of data, information, and knowledge asthe three layers. This framework provided guidelines on how a CSSsystem could be operated. The framework focused mainly on contextand did not discuss the other components of CSS in detail. Theframework proposed by Yuan and Tsao [29] listed the functions to beincluded in a system that recommend context sensitive advertisingsuch as advertising representation in vector space, user profilelearning, and recommendation. However, no details were providedon the process used for manipulation of data. Aalto et al. [1] proposeda system based on a similar idea that could provide location awareservices information to customers via Bluetooth and WAP technolo-gies. But personalization was not indicated as a part of their system.Contrary to their approach, Lankhorst et al. [18] proposed apersonalized services environment which could solve three keyissues: profiles management, services discovery, and services adap-tation. User profile, services profile, and context profile (such asnetwork condition, location, and format etc.) were all considered to beimportant components of profile management. This was a serviceoriented framework but it did not detail the process of knowledgegeneration and knowledge utilization for CSS. In Table 1, wesummarize the characteristics of the different frameworks that wereproposed for CSS. From Table 1, we can observe some problems ofcurrent frameworks. First, none of the frameworks are designed basedon the knowledge discovery perspective in spite of the importance ofanalysis of customer data. Second, some of the frameworks do notconsider the links that exist between customers' behavior, products,and context and only devote their attention to the importance ofcontext. Third, most of the proposed frameworks are conceptual innature and do not provide specific guidelines about what steps are to

n User information Service information Applications

Yes Yes Video mail messageNo No Multiple applicationsYes Yes Mobile advertisingNo No Campus services

on No No Mobile advertisingYes Yes Mobile advertising

Table 2Comparison of the case studies related to CSS.

Applications Contextinformation

Userinformation

Serviceinformation

Kista mobile city [4] Location Yes YesMyCampus [25] Location Yes Yesi-area [21] Location No YesHospital [6] Location No NoGUIDE [10] Location No YesActiveCampus [13] Location No No

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be followed by the mobile service providers [1,18,27]. It seems that aframework that focuses on knowledge discovery in CSS and whichincludes profiles of context, user, and services is needed.

2.3. Cases of CSS

Several projects related to CSS have been reported in literature butmost of them are conceptual in nature. In this section, we identifyprojects related to CSS that are currently in operation.

Kista mobile city is a case of context aware services implementedin a shopping mall by Appear Context Company [4]. The services thatare provided to consumers in Kista mobile city are aware of thecontext and profile of customers. As a result, customers are alertedabout offers of products or services that are available during a specificperiod of time, reachable in places close to their physical location, andmatch their desires.

Another example of CSS is Mycampus [25] which is a systemdeveloped and implemented by Carnegie Mellon University. There areseveral different types of services provided by the system: contextaware recommender services, context aware message filteringservices, context aware reminder applications, context sensitivecrime alerts, collaboration applications, and community applications.For example, the system can recommend nearby movies and places toeat; it can remind users about things they need to purchase when theyare close to a store; and it can send messages to users when (s)he isnot busy.

Another prominent provider of CSS is NTT DoCoMo [21] — theJapanese telecom company. The i-mode service of NTT DoCoMoincludes a location based service called i-area. i-area provides usefulinformation about services that are close to the location of the users.For example, users can check the availability at a restaurant, locationof the nearest ATM, or movies that have tickets available and arerunning at nearby entertainment multiplexes etc.

Bardram et al. [6] reported a unique implementation of a CSS at anoperating ward of a medium-sized European hospital. The system hasfour components: a context awareness infrastructure, a locationtracking system, the AwareMedia application, and the AwarePhoneapplication. The system can track the location of both physicians andpatients, and monitor activities or events that are going on in thehospital. For example, whether there is a surgery being conducted inthe operating room, how many people are attending, and what is thenext surgery to be performed in the same room etc.

In the area of tourism, the GUIDE system [10] has been developedto assist visitors in navigating foreign areas with the help of handhelddevices. For example, a list of attractions that is open and close to thelocation of visitors is shown on Web pages together with any specialevents taking place at these locations. Visitors can choose to view theinformation about the place they are interested on the list. The GUIDEsystem can also help visitors create tour routes and recommendsuitable sequences for visiting the attractions, access to interactiveservices such as booking movie tickets, and send and receivemessages.

Finally, the ActiveCampus project [13] provides location basedservices for the educational environment to enrich the learningcommunity. ActiveCampus includes two types of applications:ActiveClass and ActiveCampus Explorer. Students can post questionsanonymously and instantly for classes that they are enrolled inthrough an online forum. From the forum, professors can know aboutstudents' needs. Students are also able to find out whether a friend isin the vicinity and whether a professor who can answer questions isavailable.

We compared the six case studies in Table 2. From Table 2, it can beobserved that all of these services only included location as a part ofcontext. For Kista mobile city and MyCampus, the services areprovided in order to match customers' characteristics with services'characteristics and location of customers and services. In comparison,

i-area and GUIDE services are provided to customers based on thematching between location of customers and the services. For thehospital system and ActiveCampus, it can be seen that the services areprovided when two customers are close to each other and there is noneed for personalization of services for customers.

3. A knowledge discovery based framework for CSS

Personalization as a key characteristic of CSS requires a sophisti-cated approach based on understanding of customers' behaviors, or inother words knowledge about customers. Knowledge of customerscomes from data collected from customers. With the advancement indatabase technology, service providers can collect more and moredata about customers. However, raw data are not knowledge. It onlyserves as the raw material needed to produce knowledge. Sophisti-cated processing is needed to convert data into information and theninto knowledge. Service providers need to have as completeknowledge about customers as possible so that they can providebetter services. In spite of the importance of knowledge aboutcustomers for CSS, no such framework exists that considers thedesign and implementation of CSS. In this paper, we propose aframework for CSS from the perspective of knowledge discovery. Inthe proposed framework, shown in Fig. 1, there are five phases: datacollection, contextual information acquisition, information generationand contextual information association, modeling-evaluation-deploy-ment, and business understanding. The five phases constitute aniterative process from which knowledge about customers' contextsensitive behaviors can be generated. The framework is furtherdescribed and details about how it is relevant for understanding thesix case studies on CSS are provided in the following sub-sections.

3.1. Data collection and contextual information acquisition

The acquisition of contextual data depends on two sources:customers' transaction records and context monitor. Transactionrecords list data related to products or services consumed bycustomers and the context monitor lists data such as the time ofoccurrence of those services, and also the location of such transac-tions. Context monitor uses various advanced wireless technologiessuch as RFID, Bluetooth, and GPS to collect time and location datawhenever there is any transaction by the customers. Other types ofcustomer data such as their demographics can be collected by meansof survey or by purchasing it from a third party. These data can bereferred to as external data. At the same time, the service providerusually maintains databases about their services or products and thisconstitutes the internal data.

For Kista mobile city, the aim of the project is improving theshopping experience of customers, enhancing the interactionsbetween retailers and customers, and raising the efficiency of themaintenance and security of the shopping malls. As a result, relateddata on customers' shopping needs, schedule of staffs, facilities in theshopping malls is collected. In the case of Kista mobile city, couponsare distributed to customers when they are shopping. Since thecoupons have bar codes on them for identification, it is possible to

Fig. 1. A framework for CSS from the perspective of knowledge discovery.


track what customers have bought as well as where and when theyhave made the purchases. For MyCampus and ActiveCampus, dataabout students, professors, education facilities, and course schedulesneed to be collected. For i-area, detailed location data of variousfacilities that can provide services that users might need are collected.For the context awareness systemused in the hospital, data about staffand patient locations, related context information about the hospital'sfacilities, and job schedules need to be collected. For GUIDE, the aim ofthe project is to help tourists in the city and therefore, data related toattractions in the city and traffic need to be collected because theseprovide information about the services available to the users.

3.2. Information generation and contextual information association

Rawdata cannot be used directly due to the presence of errors, noise,and duplicates. Also the data may be too large and need someconsolidation. Raw data need to be preprocessed to generate valuableinformation and this makes up the second phase of the framework.Various approaches can be used in this phase. These range from basicoperations such as data cleaning to more sophisticated operations suchasderivingofnewdatabymeansof aggregationor specificmathematical

formulae, and transformation of data. The main purpose of this phase isto generate information that can describe customers, contexts, andservices appropriately and lead to the creation of customer profile,context profile, and services profile. Customerprofile andservices profileneed to be created for providing personalized services [12]. Contextprofile helps to provide context sensitive personalized services. In orderto make an intelligent match between context, customer, and possibleservices, it is very important to relate the context datawith the customerprofile and the services profile. This means the mobile service providerneeds to understand what the customer wants and at what time andlocation (s)he wants it, and also what appropriate services they haveavailable at that time and location, that can be provided to satisfy thecustomer's needs. This phase is carried out under the guidance of serviceproviders andmakes use of their knowledge about customers, contexts,and services.

The introduction of this phase in the framework can also help toimprove the quality of the information. Ballou et al. [5] consideredfour attributes of information in their paper: timeliness, quality, cost,and value. Cappiello et al. [7] defined a mathematical model toevaluate time-related measures of information quality and proposedthat data can bemeasured along four dimensions: data relevance, data


accuracy, data format or ease of interpretation, and data privacy,security and ownership. They focus their research on three qualitydimensions: currency, accuracy, and completeness. The operationsrequired in the phase of information generation such as cleaning,transformation, and derivation can improve the completeness andaccuracy of information. Further, the process of constructing contextprofile, user profile, and services profile can increase the relevance orinterpretability of information.

Mycampus and ActiveCampus aim to improve the everyday life ofpeople on campus. Consequently, data related to course schedule,time tables of students and professors, their privacy preferences, andalso their interests are collected and stored in the e-Wallets system.Ontology and semantic Web technologies are used to develop generalunderstanding of various users' preferences from those detailedspecifications input by the users themselves. Different users will havedifferent ways of expressing their needs for the same objects.Preprocessing is needed to convert those different expressionsabout the same objects to a unique phrase. For Kista mobile city andi-mode, the services used by the users can be stored, summarized, andanalyzed to construct the context profile and the services profile of theusers. In these systems there are some common problems incustomers' data. First, there are duplicated data about customersbecause customers register their personal data multiple times.Second, some data need to be consolidated in order to reduce thenumber of values. For example, attributes about customers' addressusually have many different values and cannot be processed by anyanalytical tool. Third, customers' may not provide all the data andtherefore, there may be missing data. The service providers can usemean or median values of the attributes of the population to fill in themissing data. It is also possible to use data mining techniques such asdecision trees to fill in the missing data intelligently. For GUIDE andthe hospital system, the main purpose is to provide accurate,complete, and useful information about attractions or staff andpatients in the hospital. It is worth mentioning that for these systemspeople, facilities, and locations all need to have unique identificationnumbers in order to aid subsequent analysis.

3.3. Modeling, evaluation, and implementation

During this phase, appropriate knowledge discovery technologiesare selected and evaluated until the performance is acceptable. Datamining techniques can be used as knowledge discovery technologybecause they have been found to be useful for analyzing customer datafor better customer relationship management [24]. Once the appropri-ate techniques are identified they are deployed as-is or with somemodification. Service providers often set goals for modeling and criteriafor evaluation. Often this goal is to recommend personalized services tothe right customer at the right context. The criteria used for evaluationcan be accuracy of the recommendation or the users' satisfaction. Oncethe goal is achieved, new knowledge about customers, contexts, andservices is generated and added to the service providers' repository toenhance their understanding.

In the system of Mycampus, case based reasoning (CBR) is used toinfer the users' ‘live’ preferences for message filtering from thepreferences stored in the users' e-Wallet. CBR is a type of data miningtechnique that uses experiences from past cases to help decisionmaking in new situations. This is evaluated by how accurately themodule can filter messages that are undesirable for users. SinceActiveCampus is also an application of CSS on campus, CBR can beused to accomplish similar tasks. Association rule mining can beapplied for i-area and Kista mobile city to identify what types ofproducts and services customers like. Prediction or classificationmodels such as decision trees and neural networks can be used toidentify the right targets for marketing activities. Also, GUIDE systemcan make use of data mining techniques like clustering to find outwhat type of services visitors want most or which routes visitors

follow most frequently so that value-added services such as tourgroups can be organized. Finally, the hospital system can useclustering techniques to group appropriate resources (i.e., personnel,software, and hardware) of the hospital more efficiently.

In addition to the matching of customers' preferences and servicescharacteristics, it is also important to find the match of context. Thatmatch of context can be between a customer and a service or betweentwo different customers. It may be possible to identify communitiesthat consist of several customers with similar context and similarinterests. Services for such communities can be recommended in thissituation. Thematching of context with services requires some uniquetechniques. The status of a location often changes dynamically overtime, such as the traffic situation near a busy shopping mall. It is possi-ble to use cameras connected to the Internet to monitor the status of alocation by continuously taking photographs and broadcasting them.Imagepatternanalysis techniques can thenbeusedon thosephotographsto discover patterns in images on a real-time basis. Such an analysis canreveal useful information about changes taking place at a location overtime and can lead to appropriate services provision for the customers.

Of the six cases, Kista mobile city and Mycampus showed signs ofimplementation of recommendation functions in their systems. In oneof the applications in Kista mobile city, a customer went out forshopping while on vacation. When she arrived at the shoppingdestination, she received a SMSwhich offered her special discounts oncertain products that were in a store just in front of her. The SMScontained promotional information and a bar code as the identifica-tion. This application was accomplished by matching the customer'sphysical location with that of the stores and the customer's shoppinginterests with the products on sale at the store. In Mycampus, acommunity application called InfoBridge is provided. By usingInfoBridge services, students can publish virtual posters online. Notonly students who have similar interests as the publisher, but alsostudents who have similar context as that of the author can receivethe posters. Both of these application make good use of customers'interests and location to recommend a service to a customer.

3.4. Business understanding

Business understanding is of tremendous importance to serviceproviders. The aim of all the earlier phases of the framework is toprovide better understanding of the business by addressing questionslike: what are the expectations of the customers; what types of servicescan be provided to customers by keeping track of their context; howmuch revenue can be earned by providing such CSS; and how can CSSprovision be improved to better satisfy the needs and preferences ofcustomers and generate higher revenues at the same time.

Kista mobile city and i-area provide services with the purpose ofselling more products to customers. For example, business under-standingmay lead to finding outwhat types of products customers arelikely to purchase; when is a good time to attract customers to theshopping mall; and what type of information customers need whenmaking shopping decisions. MyCampus and ActiveCampus aim toprovide suitable services to students, professors, and other staff sothat they can lead a more convenient life on campus. Services such asbooks recommendation, courses recommendation, and updates onlatest classroom bookings will help in understanding what servicesare most demanded by customers and why. The goal of GUIDE is tohelp tourists have a good travel experience in a city. Consequently, theservice providersmay provide services such as hotel room reservationand recommendation, booking of tickets for attractions, and travelroute recommendation and the user response to these services willenable the creation of typical tourist profiles and preferences. The goalof the hospital system is to make the operation of the hospital moreefficient. Services such as staff location tracking, patient locationtracking, facility utilization tracking, and dynamic updates of surgeryscheduling can be provided and their usage tracked to find out which

Table 3Current practices and possible future improvements.

Projects Data collection Information generation Modeling Business understanding

Kista mobile city [4] Current Data about customers, services, andcontext were collected.

Only location information. Query based. What does a customer need?

Improvement Data about other context factors such asweather condition, device characteristics,and time need to be collected.

Profiling other context factors such astime and device.Ensuring information quality based onbusiness understanding.

Intelligent matching of products andcustomers using prediction techniques suchas decision trees and neural networks.

What does a customer need, when,and where?What is the best way to deliver theservices to customers?How can the customers travel toreceive the services on their way?

Mycampus [25] Current Input from users. Location information and user profileinput by users themselves.

Query based. What does a student or a professorwant in his/her everyday campus life?

Improvement The system will record users browsingbehaviors automatically.

Profiling other context factors such as timeand devices.Automatically generate user profiles fromdata recorded by the system.

Clustering techniques can be used to identify thecommunities among students and professors.Prediction techniques can be used torecommend books and courses to students.

When and where do they want it?Who will have similar interests to formcommunities?

i-area [21] Current Data on location and characteristicsof customers.

Only location information. Query based. What does a customer need?

Improvement Data about other context factors besideslocation need to be collected.

Profiling of other context factorssuch as time and device.Profiling of customers and services.

Clustering techniques can be used to identifycustomers' behavioral patterns about servicesusage and shopping.Prediction techniques for what a customer islikely to purchase on a trip.

What does a customer need? When andwhere does (s)he need it? What is the bestway to deliver the services to customers?

Hospital [6] Current Only location data. Only location information. Query based. Where are the staff and patients?Improvement Data on context, hospital staff, patients,

and services need to be collected.Profiling other context factors such astime and devices.Profiling hospital staff and patients.Profiling services.

Clustering techniques can be used to identifythe current locations of hospital staff.Association rules mining can be used to identifythe rules for scheduling of surgeries.

More efficient scheduling of surgeries atthe hospital.

GUIDE [10] Current Only data about location and attractions. Only location information. Query based. Providing complete information aboutattractions.

Improvement Data about tourists' behavior, anddemographics need to be collected.

Profiling other context factors such as timeand device.Profiling tourists.

Clustering can be used to identify common travelpatterns of tourists.Prediction can be used to predict where and whentourists may go.

Providing complete and personalizedtourism services to customers.

ActiveCampus [13] Current Only data about location. Only location information. Query based. Teaching and learning facilitation.Improvement Data about students, professors, and their

behaviors need to be collected.Profiling other context factors such astime and device.Profiling courses.Profiling students, professors and otheruniversity staff.

Clustering can be used to identify communitiesamong students and professors. Predictiontechniques can be used to recommend booksand courses to them.

Providing personalized and contextsensitive teaching and learning servicesto students.

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Table 4Description of attributes.

Attributes Distribution

GPRS Average size of General Packet Ratio Services usage (bytes)MIDD International direct dial usage (minutes of use)PHS Personal handyphone usage (minutes of use)PM Average size of picture mail usage (bytes)Roaming Roaming usage (minutes of use)SMS Total number of short message service callsLatitudeM Latitude of customers' position in morningLongitudeM Longitude of customers' position in morningLatitudeN Latitude of customers' position in afternoonLongitudeN Longitude of customers' position in afternoonLatitudeE Latitude of customers position in eveningLongtitudeE Longitude of customers' position in evening


services are most desirable for customers, how frequently they usesuch services, and in what sequence they use them. This will lead to abetter understanding of the needs and preferences of various partiesin a hospital.

3.5. Improvements to current practice

From the above discussion, we can see that the proposedframework can be applied to improve current practice. We makesuch recommendations that are shown in Table 3. In the phase of datacollection, the framework suggests that more context related dataneed to be collected. Currently, most of the applications only collectdata about location. Additional effort is needed to extract time andlocation data from customers' transaction records besides theirpreferences. With the help of wireless technologies such as RFID,Bluetooth, and GPRS, it is possible to collect more data about context,such as weather conditions, mobile devices, time of transactions, etc.

In the phase of information generation, the problem faced bycurrent practice is either incomplete profiling of context, absence ofprofiling of users and services, or the lack of association betweencustomers, services, and context. Profiling of context requires iden-tification of key features such as location, time, and device relatedcharacteristics to describe the context efficiently. The context profilewill be useful in revealing needs of communities of customers who arein similar context. Similarly, services or products can also be profiled toidentify their similarities and dissimilarities. After all, the profiling ofcustomer and services must be associated with context information.We can identify or design newservices specialized for a certain contextsuch as reserving seats at the last moment at a popular restaurant inadvance of customers' arrival at the location. If the services are similarto those that have been consumed by customers in a similar context,service providers can promote those services to customers in order toenable cross-selling of services. This may include prompts aboutwhether seats are needed at the restaurant, which is frequented by thecustomer, when (s)he is in the vicinity of the restaurant.

In the phase ofmodeling,most of the applicationswe have discusseduse a query based approach. Decidingwhich attributes and criteria to beincluded in the query requires extensive knowledge of the related taskswhich sometimes may not be available. Further, a query based systemcannot predict the customers' behavior. As a result, service providershave towait until users submit their query requests to knowmore abouttheir preferences. In contrast, sophisticated analytical models can beused to discover interesting behavioral patterns and predict customers'future behavior. Data mining techniques such as association rulesmining [3], decision trees [22] and clustering [16] can be used to identifyusers' preferences. It is alsoworth noting that information quality can beimproved by applying this framework and including a richer definitionof context for those applications.

In the phase of business understanding, the framework suggeststhat instead of knowing what the customers want, it is also importantto know when and where the customers want such services. Theproposed framework emphasized an iterative and evolving process ofknowledge creation and use. Customers are the most important assetof service providers. Service providers need to maintain them as longas possible. Customers will change their preferences over time. As aresult, the service providers need to update their knowledge base ofcustomers from time to time so that they can align their strategiesaccordingly.

4. Numerical study

The six case studies did not include details about how knowledgediscovery techniques can provide better understanding of customers'preferences. In order to illustrate the complete process of a project ofcustomer analysis using context data and making use of the proposedframework, we conducted a study on customers of mobile services

using datamining techniques to analyze customers' behavioral data aswell as context data. The behavioral data are collected from real lifeoperations data provided by a mobile service provider of Hong Kong.Due to the lack of real data, the context data on location and time areobtained by simulation. The goal of this numerical study is to showthat use of context data leads to deeper understanding of customers'behavior. This can be followed by better provisioning of services tocustomers that suitably match their needs.

4.1. Phase 1: data collection

For this study, data are collected from a major mobile telecommu-nications service provider of Hong Kong. The data consist ofmore than50,000 records of customers. Each of these records represents a userand is characterized by more than 200 attributes. The set of attributesis a mixture of numerical and categorical variables. The attributes canbe divided into four categories: usage, revenue contribution, servicessubscription, and users' personal information. The usage informationis recorded in minutes of usage (MOU). Some of the attributes arecontinuous variables, while the other variables are discrete. Themobile telecommunication data are collected from September 2004 toSeptember 2005, over a period of 12 months. The raw data sufferedfrom several problems such as high missing rate and highly skeweddistribution of attributes. Besides, attributes had different scales.

4.2. Phase 2: informationgenerationand contextual informationassociation

In this stage, attributes with missing rate higher than 70% areremoved from further consideration. Since customers' behaviorreflected customers' interests most efficiently, we selected attributesrelated to customers' usage ofmobile services and revenue contribution.By doing this, only the most relevant attributes are retained. Theattributes used in this study are listed in Table 4. Furthermore, some ofthe attributes cannot be used in analytical tools directly. For example,attributes whose distributions are highly skewed, such as InternationalDirect Dial (IDD) and roaming, needed to be normalized to increase theinterpretability of the data. Correlation analysis is conducted to removeattributes that are highly correlated with others. Usage information isaggregated for the last six months. The purpose of aggregation ofattributes is to eliminate unwanted variations recorded in the raw dataso thatmore stablepatternsof services usage of customers are observed.All the usage attributes are standardized to keep their value within thesame scale. Correlation analysis, aggregation, and standardizationhelped improve the accuracy and relevance of the data. In short, thequality of information is enhanced in the second phase.

Since the data available from the mobile service provider did notcontain any context data, we simulated six new attributes related tocustomers' location at three different times of the day: morning,afternoon, and evening. We assumed that customers could only movebetween four different regions in Hong Kong: Sai Wan, Causeway Bay,Tsim Sha Tsui, and Central. For each region, we specified the maximum

Fig. 2. Usage clusters of customers.


value of latitude, minimum value of latitude, maximum value oflongitude, and minimum value of longitude. In order to decide thelocation of customers at each time of the day in the simulation, we firstused a dummy variable which randomly assigned an integer (1 to 4) toeach customer to decide which area (s)he resided in. Then for eachcustomer, we generated the location in terms of the value of longitudeand latitude of his or her position using uniform distribution in theinterval of the maximum and minimum value of longitude and latitudefor the area in Hong Kong. We generated 50 data sets using the aboveapproach.

4.3. Phase 3: modeling

We grouped the customers according to their usage attributes andthen clustered them according to their locations at different times ofthe day. Finally, we conducted inter-cluster analysis to examine therelationship between customers' location and their usage of services.The clustering of customers had two steps. We first used BIRCH todecide the appropriate number of clusters. BIRCH [31] belongs to the

Fig. 3. Location cluste

family of hierarchical clustering algorithms and has been found to beefficient for large data sets. It can automatically identify the optimalnumber of clusters during clustering. Then, we used K-means tocluster customers according to the number of clusters identified byBIRCH.

The clustering result using usage information is shown in Fig. 2.The x-axis represents the variety of services that are enjoyed by thecustomers and are listed in Table 4. The y-axis represents thestandardized value of usage for the different services. The lines inthe figure indicate how the usage changes for the different services.The higher the line, the more is the usage. Three clusters are identifiedrepresenting different levels of usage of mobile services and these areindicated by three different lines in Fig. 2. Cluster USE1 had relativelylow usage for all six types of services. USE2 had high usage for GPRS,PM, and SMS services. USE3 had high usage for PHS and roamingservices. Customers in USE1 are low end users who only made phonecalls or sent SMS. USE2 and USE3 represented customers withdifferent needs for mobile services. Customers in USE2 surfed theInternet on their handphone frequently and shared pictures andmusic via the wireless network and also sent SMS. Customers in USE3seemed to care more about talking to someone they knew byhandphones and also used roaming frequently.

In Fig. 3, the results of clustering using location data are shown. It isdiscovered that the locations of customers in the afternoon andlocations of customers in the evening can be divided into four clusters.These clusters can be identified as four different areas of Hong Kong:Sai Wan, Central, Causeway Bay, and Tsim Sha Tsui. Sai Wan is aresidential area. Central is known as an area for doing business. TsimSha Tsui and Causeway Bay are areas for entertainment, dining, andshopping.

4.4. Phase 4: business understanding

If we only look at the usage clusters, we can know what types ofservices customers used but we cannot know why they used these

rs of customers.

Table 5Inter-cluster analysis for location clusters and usage clusters.

Locations USE1 USE2 USE3 Average

Central 297 188 150 212Tsim Sha Tsui 306 164 150 207Causeway Bay 297 177 149 208Sai Wan 300 172 150 207Average 300 175 150 209


services. In order to understand customers' needs better, context dataare essential. In Table 5, we used inter-cluster analysis to link thelocation of customers in the afternoon with the customers' usage anddiscovered some interesting patterns. The value of each cellrepresented the average number of customers in the 50 simulateddata sets that belonged to the corresponding usage clusters andlocation clusters at the same time. From this we are able tomake somemeaningful inferences about customers' behavior. For customers inUSE1 and located in SaiWan, it seemed that they aremostly in need ofbasic services for communication. For customers belonging to USE2and located in Tsim Sha Tsui, it seemed that they used mobile servicesfor searching products and services information on the Internet.Customers that belonged to USE2 and are located in Central, mainlyused mobile services to search or share information with theircolleagues or business partners. For customers located in Central thatbelonged to USE3 frequently traveled abroad for business purposes.Customers located in Causeway Bay that belonged to USE3 likedtalking to their friends during shopping trips.

In Table 6, we included a new column called ‘Movement’ thatrecorded the sequence of travel locations for a customer. The number ineach cell represented the number of customers that belonged to theusage cluster and movement cluster at the same time and are averagevalues recorded over 50 simulation runs. Table 4 presented a staticrepresentation of customers' behaviors whereas Table 6 provided adynamic representation. In Table 6, we observed three types ofcustomers. No travelers always stayed at the same place. Light travelersmoved sparingly.Heavy travelersmoved fromoneplace to another overtime. Different strategies should beused for different types of customersas shown in Table 7. It might be reasonable to assume that none of these

Table 6Customers' movement and mobile services usage.

Type Movement (morning-to-afternoon-to-evening) USE1 USE2 USE3

Statictraveler

Causeway Bay-to-Causeway Bay-to-CausewayBay

19 10 9

Central-to-Central-to-Central 15 10 10Sai Wan-to-Sai Wan-to-Sai Wan 18 12 9Tsim Sha Tsui-to-Tsim Sha Tsui-to-Tsim Sha Tsui 22 13 8

Lighttraveler

Causeway Bay-to-Causeway Bay-to-Central 22 11 8Central-to-Central-to-Tsim Sha Tsui 17 13 9Sai Wan-to-Causeway Bay-to-Causeway Bay 18 13 9Sai Wan-to-Tsim Sha Tsui-to-Tsim Sha Tsui 19 10 7Tsim Sha Tsui-to-Causeway Bay-to-CausewayBay

21 9 7

Tsim Sha Tsui-to-Central-to-Central 24 10 12Causeway Bay-to-Sai Wan-to-Causeway Bay 20 14 9Central-to-Tsim Sha Tsui-to-Central 17 9 8Sai Wan-to-Causeway Bay-to-Sai Wan 22 12 10Tsim Sha Tsui-to-Sai Wan-to-Tsim Sha Tsui 19 12 9

Heavytraveler

Causeway Bay-to-Central-to-Sai Wan 20 12 8Causeway Bay-to-Central-to-Tsim Sha Tsui 19 9 5Central-to-Tsim Sha Tsui-to-Sai Wan 19 9 11Central-to-Tsim Sha Tsui-to-Causeway Bay 20 10 11Sai Wan-to-Causeway Bay-to-Central 15 9 10Sai Wan-to-Causeway Bay-to-Tsim Sha Tsui 19 11 10Sai Wan-to-Central-to-Causeway Bay 20 12 13Tsim Sha Tsui-to-Sai Wan-to-Causeway Bay 18 10 9Tsim Sha Tsui-to-Sai Wan-to-Central 17 10 11

travelers are less attractive tomobile service providers. A safe strategy isto maintain usage of current services for all types of travelers. For lighttravelers, service providers can identify the time they moved. It can beobserved from Table 6 that some of them moved in the afternoon andsome in the evening. Service providers can deliver necessary mobileservices, such as MMS promotions, WAP surfing with limited GPRScapabilities, to those customers when they are in transit. For example,peoplewhoaremoving around in the eveningmaywant to knowwhichmovie is runningwhereas people who are moving in themorningwantto readnews. Althoughheavy travelersmaybequite attractive tomobileservice providers because they always move around, we can observethat some of those heavy travelers belonged to USE1 whichmeant theyonly used basic calling services. These customers had the potential needfor services they could use.We recommend that service providers selectthem as targets for promotion of mobile services such as instant stockquotes, mobile television, and mobile internet access. Sophisticatedcustomer retention should be done for this type of customers and caninclude forecasting their behavior and preferences using data miningtechniques and determining appropriate opportunities for cross-sellingof products or services.

Including time factors can help service providers understandcustomers' behaviors more accurately. For example, if we only lookedat the location of a customer in the afternoon, we found that (s)hestayed in SaiWan at that time. Meanwhile, we also discovered that (s)he belonged to USE1. This may lead the service providers tomisunderstand that (s)he lived in Sai Wan and only needed basiccalling services. However, after including the time factor andobserving the movement of that customer, the service provider canrealize that (s)he moved around during the day and Sai Wan was justone location in his/her journey. Because the customer spent a lot oftime on his/her way to somewhere in the city, (s)he may needadvanced mobile services instead of basic calling services. Serviceproviders can use this important knowledge by suggesting a moreappropriate calling plan for this customer.

From the above example, it can be seen that data miningtechniques and context data can help discover interesting userbehavioral patterns. By integrating context data such as location, weare able to know not only what the customers want but also why theywant the products or services. The knowledge discovered in the inter-cluster analysis can be used to update the knowledge base to enhancethe understanding about customers. Based on such understanding,service providers can use push strategy to send personalized mobileadvertising or mobile coupons for personalized services to customers.For example, for customers belonging to USE1 that stayed in Sai Wan,discounted tariff plans can be offered because their needs are relatedto basic services of mobile telecommunications. They seemed to caremore about the prices than the services. For customers belonging toUSE3 that moved frequently to Causeway Bay or Tsim Sha Tsui,services such as mobile instant messaging or multimedia down-loading may be suitable and may serve their needs better.

Table 7Suggested strategies for different types of customers.

Type of customers Strategy

Static traveler Ordinary tariff plan, economic package.Light traveler Basic mobile services, such as MMS promotions,

limited GPRS coverage.Regular contact to maintain relationship.

Heavy traveler Advanced mobile services, such as, push mail,mobile TV, unlimited GPRS coverage, and variousinformation services (stocks, weather, sports, news, etc.).Maintain relationship carefully. Forecast behavior andpreferences for cross-sell.


5. Conclusion

In this paper, weproposed a framework for CSS from the perspectiveof knowledge discovery. The framework included four iterative phases:data collection, information generation, modeling-evaluation-deploy-ment, and business understanding. Most of the current studies on CSSfocused on services and techniques but did not address the creation,management, and usage of knowledge generated from CSS. Ourframework filled this gap and provided a holistic view of knowledgediscovery in CSS, including the profiling of context, user, and services asneeded. The framework introduced an iterative process of collection ofnew data, generation of new information, building of new data miningmodels, and discovery of new knowledge with the goal to improve thebusiness understanding of service providers. The hope is that onceservice providers better understand their business and the contextsensitive needs of the customers that take part in the business, theywillbe able to improve business related operations and generate higherrevenues by better satisfying the needs of their customers.

We analyzed six real life cases of CSS and explained how theproposed knowledge discovery based framework can be applied toimprove their operation. We found that due to the limited understand-ing of context, the service providers of the six cases did not collectcomplete data about context. Our framework canhelp services operatorsunderstand themeaning of contextmuchbetter so that amore completeprofile of customers can be established. Second, after the data arecollected, there is either no or very limited actions taken by theprovidersfor information generation. This can be due to two reasons. The firstreason is that the data they collect are not complete. The second reasonmay be that the current practice may not be capable of understandingthe difference between information and data, and may not know howinformation can be generated from raw data. Our framework can helpservice providers convert user behavioral data into meaningful userpreference information. Third,most of the case studies showed theuse ofa query based system for matching users' preferences and mobileservices. In our framework, we introduced the use of data miningtechniques that can determine an intelligent match between users'preferences and services and make such recommendations. Finally andmost importantly, the current practice depicted in the six cases did notpay much attention to business understanding. Following the iterativeprocess of the framework, the service providers should be able to obtainand update their knowledge about customers, services, and context on acontinuous basis. As a result, they will be able to use this enhancedbusiness understanding to design and provide personalized CSS to theirvalued customers that are able to satisfy their needs better.

A simulated case study is conducted in this research to show howclustering techniques can make use of simulated context data toidentify customers' preferences. Our analysis compared the knowl-edge obtainedwhen context data are not includedwith the knowledgeobtained when context data are included and showed that use ofcontext data led to deeper understanding of customers' behavior. Thecontext data provided a more vivid understanding of customers'preferences. The analysis followed the steps of the knowledgediscovery based framework and answered the following questions:what types of customer data should be collected by service providers;what type of information should be generated from these data; whattype of modeling techniques should be used to analyze and interpretthis information; and what knowledge should be obtained aboutbehavior of customers so that more targeted services can be planned.

The use of simulated data is one limitation of this paper. Simulateddata may not reflect the real situation accurately. The results ofexperiments using simulated data are best used for scenario analysis.More research needs to be done with actual context data from the fieldcollected frommobile serviceproviders. Thiswill enhance the validationof this research.

Future research can be conducted to enhance the framework. Forexample, in themodelingphase, hybrid dataminingmodels can beused

that cluster customers, services, and context, make predictions aboutfuture context and services consumption patterns of customers, andmake recommendations about specific products at specific times andlocations to customers. Different clustering techniques are based ondifferent grouping principles [16] and as such they are suitable fordifferent types of data. However, the various attributes that can be usedto describe context often belong to different types of data. Combiningseveral clustering techniques in a hybrid fashion can take advantage ofthe beneficial features of each of the techniques for a specific type ofdata and can improve the overall performance of clustering. Anotherway to enhance this research will be to conduct a longitudinal study ofcustomer behavior over time and to seek out if customer behaviorchanges dynamically over time and if context plays an important role indetermining such changes in behavior. Finally, contextual data areclosely related to personal privacy and is likely to reveal more privatebehavior of customers than any other types of usage data collected bythe mobile service providers. Therefore, mobile service providers needto take the responsibility to closely safeguard customers' privacy byavoiding leakage, abuse, and loss of any contextual data related tocustomers. This further implies that research on methods that can helpprevent privacy violation through appropriate handling and usage ofcustomers' contextual data (e.g. [15]) will become very important infuture.

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Indranil Bose is an associate professor of Information Systems at the School ofBusiness, The University of Hong Kong. He holds a B. Tech. from the Indian Institute ofTechnology, MS from the University of Iowa, MS and Ph.D. from Purdue University. Hisresearch interests are in telecommunications, data mining, information security, andsupply chain management. His publications have appeared in Communications of theACM, Communications of AIS, Computers and Operations Research, Decision SupportSystems, Ergonomics, European Journal of Operational Research, Information & Manage-ment, Journal of Organizational Computing and Electronic Commerce, Journal of theAmerican Society for Information Science and Technology, and Operations Research Letters.He is listed in the International Who's Who of Professionals 2005–2006, Marquis Who'sWho in the World 2006, Marquis Who's Who in Asia 2007, Marquis Who's Who in Scienceand Engineering 2007, and Marquis Who's Who of Emerging Leaders 2007. He serves onthe editorial board of Information & Management, Communications of AIS, and severalother IS journals.

Xi Chen is a lecturer of Information Systems at the School of Management, Zhejiang,China. He obtained BS (Management Information Systems) from Fudan University, MS(Information Systems) from the National University of Singapore, and Ph.D. (Informa-tion Systems) from the University of Hong Kong. His research interests are in the areasof data mining, mobile services, and churn management. His research has appeared oris forthcoming in European Journal of Operational Research, Journal of OrganizationalComputing and Electronic Commerce, Journal of the American Society for InformationScience and Technology, and in the proceedings of several international conferences.

a framework for context sensitive services: a knowledge discovery based approach

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