Value-added Knowledge layer for the Context-aware Personalized Services in the Next Generation Networks

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1 Value-added layer for the -aware Persalized s in the Next Generati Networks Yoo-mi Park, Aekyung Mo, Young-il Choi ETRI, 138, Kajg-no, Yusg-gu, Daej, ROK, and Sang-ha Kim Chungnam Natial University, 79, Daehak-no, Yusg-gu, Daej, ROK, ABSTRACT This paper proposes the new knowledge layer that can be easily overlaid the service layer or the ctrol layer of the existing networks to support ctext-aware persalized service in the next generati networks. Since the traditial communicati services have been generally designed for large user groups, they d t have sufficient capabilities to provide persalized services csidering individual situati, intenti, and preferences. To overcome the limitati, the proposed knowledge layer collects informati from the underlying network and makes them value-added knowledge with knowledge processing technologies. In this paper, we design a network knowledge model to be manipulated the knowledge layer and service architecture for handling the network knowledge. We also give a couple of examples of providing network knowledge through the knowledge layer for the ctext-aware persalized services. It is shown that ctextaware persalized services can be more efficiently provided through the knowledge layer. We expect that the proposed layer will be an essential compent in next generati networks. Keywords:, Network knowledge, -aware persalized service, Next-generati communicati service, Persalizati, ualizati 1. INTRODUCTION There have been a couple of big waves in the area of communicati services. In the 1990s, the idea of the intelligent network service makes an innovati of the provisi of valueadded call service beyd the cvential ccept of the eto-e voice call. The intelligent network service was the result of the new paradigm adding the intelligence to the basic call service as shown in the figure 1. Informati type Locati Video Data Text Voice Intelligence Basic Call e.g. voice call Openness Cvergence -aware Persalized Open e.g. Parlay/OSA API, Parlay X web service,.. Intelligent Network e.g. Toll free calls, Prepaid calling, Call screening,.. ualizati Persalizati Year Figure 1.Paradigm Shift of Communicati s This work was supported by the IT R&D program of the MKE and IITA in Korea [2008-S ], Research and Development of Persalized Platform based Network-wide. Around 2000, the open service architecture was introduced to the telecommunicati. This architecture allows network operators to open their network capabilities to the third party service providers by means of providing standard interfaces between service layer and network ctrol layer. The introducti of the open service architecture has the important meaning in which it was the first attempt for the service cvergence between telecommunicati and IT. Lately various attempts for providing the ctext-aware persalized service, which csidering a situati and preference of a user, are leading a new paradigm of the nextgenerati communicati services. It originally comes from the basic requirement of the next-generati communicati services, i.e. should be provided for the end-user csidering his/her preference at the given situati and seamless service has to be guaranteed in any envirment of the user. However, the traditial communicati service infrastructure has limits to support the new service paradigm as follows [1]; It is impossible to provide services which can be dynamically adapted to a user s situati. Since the traditial communicati services have been designed for large user groups, they didn t csider individual situati, intenti, or preferences. The user by eself has to select the device which is suitable for a service and network envirment. If the device or the network envirment for getting a service is changed, a service is unable to be ctinuously provided. That is, it doesn't appropriately adapt with the heterogeneous network envirment and the multimodality of devices. Since the user is able to use ly subscribed service at the moment, he/she cannot use the unsubscribed service when it is necessary. In other words, it is impossible to provide the active service for the users by csidering their current situati regardless of the subscripti In the existing network, the soluti which integrates individual static data and dynamic ctextual informati is not csidered. Therefore, it doesn t have the capability providing the knowledge-based service yet. To overcome the limitati of the existing network and to meet the requirements of the next-generati communicati services, a new knowledge processing layer has to be introduced in the existing network with minimum changes of the existing network architecture. The knowledge processing layer provides applicatis in the service layer with the knowledge about the user. It includes the ctextual informati ccerning the user to be required for the service persalizati such as devices which he/she own or can use, service preferences, basic informati, etc. This paper proposes the knowledge processing layer overlaid the existing network to efficiently support ctext-aware persalized service for the next generati networks. We

2 named it Layer as shown in the Figure 2. We also propose a knowledge tology model to be dealt with in this layer. For modeling, we define that what is network knowledge and which informati can be applicable for service persalizati, and describe how to classify their types and how to get them from the underlying network. Applicati Layer Ctrol Layer or Layer Transport Layer Access Layer User Device Layer -aware Persalized -aware Persalized -aware Persalized Figure 2. layer in the existing network This paper is organized as follows. Secti 2 surveys the related projects. In Secti 3, we describe the model of network knowledge and classificati criteria. Secti 4 proposes how to evolve the raw data into network knowledge. Secti 5 ccludes this paper with further issues. 2. RELATED WORK Recently, the studies of the visi of the next generati of communicatis services and the development of prototypes to verify the feasibility of the technologies have been performed in some of European projects. This secti introduces the leading projects for service persalizati and we briefly compares these projects with the knowledge layer proposed in this paper. WWRF [2], which is an open research forum, proposed the visi and strategy of the future telecommunicati services of the I-Centric in The main purpose of I-Centric is to csider the human communicati behavior, not the technologies that support communicati, as the starting point for the design of telecommunicati systems. The individual user, I, has to be put in the center of service provisiing. They offered the reference network model and described the requirements circumstantially as each technology necessary for providing I-Centric service. However, they didn t csider the modeling of the knowledge that can be used for future service platform architecture. IST-MobiLife project [3] carried out by Nokia, Siemens, etc from 2004 to 2006 proposed service architecture of I-Centric Communicatis and made the outstanding results which were next-generati mobile communicatis service scenarios and requirements. The results of this project had an influence the following related projects SPICE. However, it was insufficient to accommodate the requirements defined in the existing network because it didn't develop the service architecture they suggested. By the year 2008 the SPICE project [4] has been performed around FT and Orange from They have focused the research of the knowledge-based service platform to provide a seamless delivery of variety of ctent and easy way to develop a new persalized service in B3G. It could be an important meaning that a knowledge-based service platform proposed by SPICE was implemented physically and was demstrated to the public. They also had built 5 kinds of tologies, physical space (locati), service ctext, user profiles, recommendati and learning, presence. However, the data model reflecting the network informati is limited to the presence in IMS. IST-DAIDALOS project [5] which studied B3G service infrastructure and the pervasive network technology for usercentric service provider, has mainly researched the integrati of network technologies from 2003 to DAIDALOS developed the platform for seamless services and ctents delivery in the heterogeneous network envirment. DAIDALOS proposed how to realize the requirements of network ctrol layer and service layer from a practical point of view of research. However, the further research of knowledge modeling is the relatively insufficient. From 2004 to 2005, IST-Simplicity project [6] has researched device-free service framework in order to adapt the services to the device s capabilities and user s preference. However, they had not focused the knowledge structure of the network. We analyze the requirements of the knowledge layer and modeled the network informati manipulated in the knowledge layer as the first step of the research the persalized service infrastructure for the next generati network. 3. NETWORK KNOWLEDEG MODELING In this secti, we define the network knowledge and extract useful informati for service persalizati from data stored in the network. The extracted informati is modeled as the network knowledge tology. What is network knowledge? Network knowledge csists of the network informati itself and the manipulated network informati by knowledge processing technologies such as reasing, learning, and predicti. Network informati is a raw data stored in the network-operator domain. Network informati is basic and passive informati to be used for providing telecom service, while network knowledge is active and value-added informati to be invented for the service persalizati and ctextualizati. Alberthal and Fleming said that a collecti of informati is not knowledge in [7] and [8]. When a pattern relati exists amidst the data and informati, the pattern has the potential to represent knowledge. It ly becomes knowledge, however, when e is able to realize and understand the patterns and their implicatis. Therefore, network knowledge has to implicate some patterns of informati to find appropriate service which a user is likely to want to use at the given situati. Network knowledge could be the inferred informati, the learnt informati, or predict informati according as which knowledge processing technology is applied to the network informati. Classificati of network knowledge In this secti, we describe two kinds of network knowledge. As shown in the figure 3, network knowledge csists of low-level knowledge and high-level knowledge. Low-level User Network Device learning Usage Behavior Terminal Interred (pattern, situati,..) reasing User PCS predicti Rule/Policy Envirment Network Device Figure 3. Classificati of the Network High-level

3 Low-level network knowledge All network informati is included in a low-level knowledge. The low-level knowledge is classified into three ccepts: s are a collecti of structured data that describe the static properties of an object, which are required for specific needs. A. User profile: User s informati which can identify the user, such as social security id, name, age, gender, job, etc. B. Device profile: Device s informati generated by manufacturer such as device model, type, capabilities (input/output Modality), etc A. Network profile: static informati about network capabilities in which the user is interested from the point of using services, such as operator, coverage, bandwidth, access technologies, etc. B. profile: service informati that describes the features of the service including service category, service fees, service provider, locati where the service is available, etc. s are user s cditial choices of service characteristics of an object depending ctext and ambient informati. User preference csists of a set of policies {cditi, actis} in order to apply that it can be dynamically changed according to the user s situati. A. Network preference: a set of policies that a user prefers to use service the specific network the particular cditis B. Device preference: a set of policies that a user prefers to use special device some cditis C. preference: a set of policies that a user prefers a service that is likely to appropriate for him/her. : any informati that can be used to characterize the situati of an entity. An entity is a pers, place, or object that is csidered relevant to the interacti between a user and an applicati, including the user and applicatis themselves [9]. A. User ctext: dynamically changed status about a user such as presence including mood, activity, schedule, and locati, etc. B. Device ctext: the ctextual informati which a user device can have such as device s locati & status, memory csumpti, battery power, RSS (Received Signal Strength) C. Network ctext: The dynamically changed ctextual informati which a network can have including current bandwidth, call log, charging informati D. Envirment ctext: the envirmental informati including the weather, a date, and etc. High-level network knowledge To define high-level network knowledge, we first illustrate questis that are likely to occur in the next-generati mobile communicatis services. For example, the user s nearby available devices or nearby friends. In another example, which service does the user usually use when she/he is doing something at locati time with computing entities (device/network). The former is an example of the inferred knowledge based the locati and the latter is an example of the learnt knowledge which has been acquired by keeping the user s usage behaviors for a lg time. How to get network informati Table 1 shows how to get the network informati from the underlying network or envirment. In the future, some informati can be taken implicitly and automatically using knowledge processing technologies such as reasing and learning. We describe reasing and learning process of the knowledge layer at the given examples in the next secti. type user ctext device ctext network ctext Table 1. Network informati ctext How to get it Source now In the future presence user User input Reasing locati user MLP* Server, IMS HSS ** schedule user User input Learning device s HSS in detected locati & IMS automatically status HLR *** in cellular system memory device detected utilizati automatically battery device detected power automatically current device detected bandwidth automatically call log network OSS/BS S *** network OSS/BS S device or web device charging informati date envirmental ctext time or web weather web * MLP : Mobile Locati Protocol ** HSS : Home Subscriber Server *** OSS/BSS : Operatis and Billing Support Systems Network Model Reasing, Learning Reasing, Learning Figure 4 shows graph illustrati of cceptual model of the network knowledge at the user-centric. This model is upper level model to provide persalized services. That is to say, the proposed models can be extended according to specific applicati domains. Each class is represented by network informati and has relatiship with other classes. User is located in the special locati and has devices more than e. And at the sometime, user does special activity and can use some services. 0,1 availbleon Time 1,* Schedule use 1,* requires 1,1 haveplan availableat 0,* knows 1,* 1,1 User 0,1 locatedin do 1,* owns Locati locatedin Resource Device Network Envirment Figure 4. Cceptual Model of the Network 0,1 1.* Activity 1.* 1.*

4 Table 2 shows the definiti of the user who is provided with user-centric services according to user s ctext. Table 2. Cceptual Model of User User userid.xsd:anyuri Π 1userId Π 1userId Π locatedin.locati Π ( T 1locatedIn) Π owns.device Π 1owns Π uses. Π 1uses Π knows.user Π 1knows Π do.activity Π 1do Π hasplan.schedule Π has. 1has In the table 2, user has preference informati of services to recommend services with highest user preference. In the case of rating functi R (R: Users Items Ratings), it is selected the item i u with the highest rating or a set of k highest-rated items for user u and recommend that item(s) to the user [10-11]: u Users, i ' u = argmaxr( u, i), i items. The previous recommendati approach to implement R can be categorized into two major classes: ctent-based approaches and collaborative approaches. Ctent-based approaches attempt to match items with the user preference and then recommend items with the highest user preference. In the case of collaborative filtering, it matches the user pattern with those of other users having the same tastes and then predicts the items the user will find most interesting. Note that it is difficult to acquisiti of other s behavior and preferences informati due to privacy and abundance problems; hence, these limitatis need to be csidered of algorithms design. As a result, we deal with ctent-based filtering at the first step. We match the user s preference to the service category and recommends service lists with highest rated. After designing a cceptual model for network knowledge to be implemented in the knowledge layer, we built an tology model for them. Since it is widely accepted that tology model is good for ctext reasing recently and ctext reasing is a necessary process in the knowledge layer that we suggested, we applied it for modeling of the network knowledge. In figure 5, top level classes of the tology were shown. We built 56 OWL classes, 31 data properties and 16 object properties finally. 4. SERVICE ARCHITECTURE OF KNOWLEDGE LAYER In this secti, we analyze the requirement of functiality of layer for data processing and show how to support persalized service by the examples. Requirements and Functialities of Layer layer has the following requirements of functiality. It should not require altering the existing network architectures so that the knowledge layer could be easily overlaid the service layer or the ctrol layer. and data processing technologies should be supported for collecting informati from the underlying network and evolving them into the value-added knowledge. High level knowledge can be generated from the raw data through knowledge and data processing technologies such as accessing, reasing, and learning. It has to provide a set of standard interfaces for applicati service developers to develop the ctextaware persalized communicati services. The services can utilize the knowledge about the user generated through this layer from communicati network. Functialities of the layer to meet the requirements are as follows: reasing :We classified the reasing process into two types: ctext reasing and situati reasing. reasing is the capability deducing new informati from available informati using a predefined schema, model or knowledge base. It includes collecting ctext from underlying network or user s envirment. And situati reasing is the capability interpreting the situati which a user takes based the informati deduced by ctext reasing. learning : Learning is the capability to keep the history of usage behavior to extract rules and patterns out of massive data sets. It is respsible to set up and maintain the situati model as patterns. usage behavior pattern of a user can be used in preferentially recommending the service when the user is in the situati which is similar to the situati in which a user is learnt. Figure 5. Ontology Model of the Network

5 predicti : Predicti is an ability to predict the services which the user is likely to want to use in the near future. It is based the user s situati and his/her service usage behavior patterns. The result of predicti can be notified to the service applicatis that subscribe the event. Use cases The first use case shows that the call to the user in a meeting is automatically handled with pre-defined policy by aware Call Forwarding. The knowledge layer supports that the service can get persal ctextual informati in order to decide if the call should be forwarded or not. 6 -aware Set Rule for forwarding Call Forwarding a call to X 7 Feedback Notify X is in a meeting Locati(X)=room 5 Activity (X) = meeting 1 Nearby(x)=people 2 Inferred Reaser Trigger 3 Access 4 Predictor Persal informati Learnt Learner 8 1 Layer Presence.Activity (x)=busy Presence.PlaceType(x) = Office Presence server Figure 6. -aware Call Forwarding The inference rule for reasing his current activity (1-2) in table 3 is described as the tology model depicted in figure 5. After this rule is triggered by Reaser(3-4), the service can be notified that user s activity is a meeting at the moment from the Predictor ( 5 ). Table 3. Inference rule for ctext reasing x,y,z. (rdf:type(x, User) locatedin (x, y) rdf:type(y, locati:room) Presence.placeType(y, Office) nearby(x,z) (rdf:type(z,user) ) Presence (x,busy) Inferred.Activity (x, meeting) The secd e shows the example of ctext-aware persalized advertisement service. In figure 7, we assume that Music Recommendati is e of ctext-aware persalized advertisement service. 3 7 Send Music Advertisement 8 Send feedback Locati(X)=Bus Schedule(X)=ne Time=5 pm Reaser Predictor HSS IMS IMS Figure 7. -aware Persalized Advertisement Suppose that a student usually listens to music when he gets the bus after school. His music service usage behaviors are stored increasingly in the usage history kept in the knowledge layer. Learner can make a learnt rule with behavior pattern from the usage history(1-2). When the usage pattern became mature and Reaser catch his current situati that he is the bus after school(3), Reaser triggers the inference rule HSS IMS Music Recommendati Notify X may want to listen to music Learner Layer Presence server LCS server Activity (X) = AfterSchool Inferred 5 LCS server feedback User s usage Usage history 2 Learnt AfterSchool (x) & OnBus(x) Listen to Music described in table 3(4). The result of the inference rule is applied to the learnt rule by Predictor to predict his next acti(5). Predictor notifies to the service applicati that he usually likes listening to music at this moment (6). After notificati, the service can send new music advertisement, receive feedback from the user and send it back to store in the usage history of the knowledge layer (7-12). Table 4. Inference rule for ctext reasing x,y,z. (rdf:type(x, User) locatedin (x, y) rdf:type(z, device:mobilephe) status(z, device:idle) rdf:type (y,locati:bus) ^ schedule (x, ne) Inferred.Activity (x, AfterSchool) Table 5. Learnt rule for predicti If (Activity of x = AfterSchool) and (Locati of x = Bus) and (Device of x = mobilephe) then Learnt.Activity (x, listentomusic) 5. CONCLUSIONS We proposed the knowledge layer that can be easily overlaid the service layer or the ctrol layer to support ctext-aware persalized service in the next generati networks. Through this knowledge layer, network operators can provide more intelligent knowledge about the user for the service providers and the service providers can develop value added service applicatis to be suitable for each user. On the other hand, we have still lots problems to be solved to realize the knowledge layer with regard to user privacy, accurate ctext acquisiti, performance of the implementati of knowledge layer, etc. However, it is shown that ctext-aware persalized services can be more efficiently provided through the knowledge layer. We expect that the proposed layer will be an essential compent in next generati networks. For further study, we have a plan to provide a couple of implementati approaches of the knowledge layer and compare performance evaluatis for each implementati. 6. 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