Subscriber data and semantic web for provisioning novel end-user services in telecommunication networks

IEEE Communications Magazine • March 201242 0163-6804/12/$25.00 © 2012 IEEE

INTRODUCTION

A significant amount of subscriber data is avail-able in telecommunication networks. Typicalexamples are subscriber profiles: the list of ser-vices to which a subscriber has subscribed, sub-scriber location, and even (sometimes)subscribers’ contact lists. Additional data couldbe captured — one example is subscribers’behavior (e.g., purchase habits). In this article,we are interested in the use of this data for pro-visioning a wide and varied range of novel value-added services.

We assume a next-generation telecommuni-cations network environment (third generation[3G] and beyond) and a business model withend-users, subscribers, multiple network opera-tors, and multiple service providers. As in anybusiness model, a single entity may play severalroles. A network operator may also be a serviceprovider. It is assumed that the service providershave agreements with the operators to access

and use this data. We further assume that thesubscribers have authorized their operators tocollect (and use) pertinent data.

Let us illustrate our vision with two novel ser-vices, “customer finder” and “my favorite dish.”When Alice goes shopping or dining out, shepays all the bills using her cell phone. The trans-actions she makes (e.g., name and brand of thepurchased items) are then transferred to a repos-itory (in her operator network) as part of herconsumption data. Let us assume that this month,she buys salmon, shrimp, oysters, and tuna (at asupermarket) three times, and she orders aseafood dish five times (at a restaurant).

Let us now assume that a new seafood restau-rant is going to open, and for its opening day,the restaurant owner wants to send an advertise-ment with a coupon to all the seafood loverswithin an area of 2 km. A “seafood lover” isdefined as someone who purchases seafood toprepare at home at least three times, or ordersseafood dishes five times or more in a typicalmonth.

The owner may use the novel “customer find-er” service which allows him to get a list of peo-ple, including Alice. The service relies onsubscribers’ consumption and location data.Alice and the restaurant owner may have differ-ent operators, and the location service may beprovided by a third operator, as allowed by thebusiness model.

In the restaurant, Alice may now use the “myfavorite dish” service to access the menu fromher phone and get a pop-up with her favoritedishes thanks to her consumption data stored inthe network. She may also get other customers’comments about the displayed dishes.

A key challenge for building such novel end-user services is how to organize, link, and utilizethe subscriber data. Provisioning novel value-added services such as “customer finder” and“my favorite dish” requires the combination ofsubscriber data from both location servers and

ABSTRACT

Telecommunication networks already store alarge amount of subscriber data (e.g., subscriberprofile, location data, presence information),and even more data could be captured. Althoughthis data is a key asset of telecommunicationnetwork operators, it is seldom fully exploited.This article proposes a novel architecture toenable the provisioning of new value-added ser-vices, based on subscriber data. This architectureuses semantic web to build a knowledge basefrom the subscriber data available in individualtelecommunication networks. KBs are then fed-erated across telecommunication networks toprovide a single and unified interface to serviceproviders for accessing the different KBs. Thearticle also illustrates the use of the proposedarchitecture in a concrete scenario, and presentsa proof-of-concept prototype.

CONVERGENCE OF APPLICATION SERVICES INNEXT GENERATION NETWORKS

Fatna Belqasmi, Concordia University

Chunyan Fu, Tekelec

Zhongwen Zhu, Ericsson Canada

Roch Glitho, Concordia University

Subscriber Data and Semantic Web forProvisioning Novel End-User Services inTelecommunication Networks

BELQASMI LAYOUT_Layout 1 2/22/12 3:01 PM Page 42

IEEE Communications Magazine • March 2012 43

consumption data servers. The location and con-sumption information for different subscribersmay be maintained by different operators.

Furthermore, data semantics should be cap-tured, and a certain amount of intelligence isrequired at each stage of the service provision-ing; for example, deriving a user’s preference,defining the keywords with which to search (e.g.,“seafood lover”), querying the keywords, andcombining the results. This article proposes anovel architecture that enables the provisioningof new services based on subscriber data storedin next-generation telecommunication networks(i.e., 3G and beyond). The architecture usessemantic web technology to build a knowledgebase (KB) using the stored data. We define aKB as a distributed repository that contains andlinks data residing in different data repositories(e.g., presence server, location server).

In our architecture, KBs are federated acrossnetworks to provide service providers with a sin-gle and unified interface to access the differentKBs. The federation is based on a peer-to-peer(P2P [1]) model, and the unified interface isbased on representational state transfer (REST),an architectural style for designing networkingclient-server applications [2]. P2P provides forthe dynamicity and self-organization expectedfrom such a federation, while REST enablesportability and simplicity.

The rest of the article is organized as follows.We give a synopsis of the semantic web. We pre-sent the requirements and related work. Wedescribe the proposed architecture, including theprocedures and the REST interface. We presentan illustrative scenario. We devote a section tothe validation, including a proof of concept pro-totype. The last section concludes the article.

A SYNOPSIS OF THE SEMANTIC WEBThis section introduces the semantic web, andthen discusses the related standards and othercontributions.

UNDERSTANDING SEMANTIC WEBUnlike a human-readable web of documents, asemantic web is a web of machine-comprehensi-ble data [3]. To better understand what a seman-tic web is, we consider the action of ordering aseafood dish online. Assuming that Alice wishesto find a seafood dish without onion, with today’shuman-readable web, Alice first goes to a restau-rant web page and finds a menu. Then she clickson the link of each seafood dish and checks itslisted ingredients. Each dish’s ingredients aredocumented, and Alice has to read and under-stand the meanings of these documents.

This experience can be much enhanced if asemantic web is used, in which a machine under-stands the relationship between a dish and itsingredients. For example, we can assume that arestaurant web server understands that ‘tunasalad’ is a seafood dish that only has as ingredi-ents tuna, spinach, and mayonnaise. The webserver also understands that onion is an ingredi-ent. Alice can simply do a query for “seafooddish with no onion,” and the web answers “tunasalad” right away. In such a case, the web(machine) knows the reasoning behind concepts,

and is also capable of making a logical relationbetween concepts.

There are three key elements to the function-ing of a semantic web: ontology, data modeling,and query languages. Ontology refers to a for-mal representation of a set of concepts within adomain and the relationships between those con-cepts. A tutorial on ontology and its variousapplications is presented in [4]. The ontologyelement is powerful because it can create rela-tionships and accumulate knowledge from theinferences made from those relationships.

Some sort of data modeling is required toimplement a particular ontology. In other words, amachine-understandable means of data descriptionand interchange must be developed. A straightfor-ward method, similar to today’s object-orientedprogramming, is the abstraction of real-worldobjects into classes and introducing hierarchy andrelationships among these classes. For example, wecan define “seafood dish” as a class and “tunasalad” as a subclass of “seafood dish.”

When the data is properly modeled, storedand understood by a machine, a query languageis required to make use of the data.(e.g., to getAlice’s favorite seafood dish).

STANDARDS AND OTHER CONTRIBUTIONSSimilar to any other technology, the semanticweb cannot be landed without standardization.The World Wide Web Consortium (W3C) is themajor organization for specifications of thesemantic web. The work started as far back as1999, but only in the past five years has it gradu-ally come to the stage of being delivered. Refer-ence [5] summarizes the W3C specificationsrelated to the semantic web. The ResourceDescription Framework (RDF) along with RDFschema (RDFS) and Web Ontology Language(OWL) are the major W3C standards for datamodeling. RDF is a general purpose languagefor representing information (resources) on theweb. It provides simple semantics to describeobjects and the relations between them. RDFSserves as the vocabulary to define the propertiesand classes of RDF resources. It uses UniformResource Identifiers (URIs) for the naming ofresources.

OWL adds more vocabulary for describingthe properties and relations between objects,such as disjointness and cardinality. It facilitatesgreater machine interpretability of web contentthan is possible with RDF. OWL 2 is anenhanced version defined by W3C. It providesfor additional details, such as disjoint union andproperty chains. The relationships betweenRDF, OWL, and OWL 2 are backward-compati-ble (i.e., all the ontologies defined by RDF andOWL are still valid for OWL 2).

Two RDF query languages have been definedby W3C:a simpler and earlier version called RDFData Query Language (RDQL) and a newer andmore comprehensive version called SPARQL.RDQL defines basic query types for RDF triplepatterns. SPARQL includes all of the functionsin RDQL and also allows for a query to consistof conjunctions, disjunctions, and optional pat-terns. In addition, it describes an abstract inter-face independent of any concrete realization,implementation, or binding protocols.

Similar to any other

technology, the

semantic web

cannot be

landed without

standardization.

W3C is the major

organization for the

specifications of the

semantic web. The

work started as far

back as 1999, but

only in the past five

years has it gradually

come to the stage of

being delivered.


IEEE Communications Magazine • March 201244

In addition to the standards work, many stud-ies have been undertaken in academic researchareas. Reference [6] comprehensively comparesthe existing ontology languages, tools, andmethodologies.

A number of web communities and profes-sional groups are involved in semantic web workas well, via the building of metadata vocabular-ies. Some examples are Dbpedia, which coordi-nates efforts to extract structured informationfrom Wikipedia and then makes this informationavailable on the web; the Semantically-Inter-linked Online Communities (SIOC) project,organized to create and leverage a layer ofsemantic data in online communities; and theFriend of a Friend (FOAF) project, which cre-ates a web of machine-readable homepages thatdescribe people, the links between them, as wellas the things they do or create.

REQUIREMENTS AND RELATED WORK

REQUIREMENTSThe requirements can be organized into two cat-egories: KB-building-related and intelligent-search-related.

KB-Building-Related Requirements — Thefirst requirement is that the format of the datatransferred from a subscriber device to a KBshould be value-added service-independent. Thisallows the use of the same data for differentvalue-added services. Second, the KB shouldprovide the necessary intelligence to (re)orga-nize and derive new information from thereceived data (e.g., to get “seafood lovers” fromthe subscribers’ profiles). Third, it should allowthe composition of information from differentsources.

Intelligent-Search-Related Requirements —The first requirement is that the KB should pro-vide a search interface that supports both one-time search and subscriptions (with periodicalnotification). Second, the search interface shouldbe based on existing standard interfaces.

Third, network operators from differentdomains should be able to provide their ownsearch functionalities. This will allow the opera-tors to control the data access in their domains.Fourth, the searching functionalities from differ-ent domains should be federated, thereby pro-viding service providers with a single interface toaccess all the data. This will allow the restaurantowner, for instance, to get a list of potential cus-tomers from different domains using a singlerequest.

Fifth, the federation should be self-organiz-ing. The providers of the searching functionalityshould be able to join/leave the federationdynamically, without interrupting the service.The last requirement is that the system shouldbe scalable in terms of the number of networkoperators.

RELATED WORKTo the best of our knowledge, there is no com-prehensive solution (i.e., a solution that allowsKB building and intelligent search) that canmeet all of our requirements. However, there

have been some interesting studies in the area ofmobile payments, mobile interaction, KB cre-ation, and semantic search on P2P networks.

Reference [7] proposes a solution forenabling mobile phones to extract informationfrom physical objects and use it for service provi-sioning. In our scenario, such a solution can beused to allow mobile phones to collect end users’purchase data and send them to the appropriaterepository in the operator network.

KB creation is not a new area, and muchwork has been done on ontology building. Refer-ence [8] is an example that proposes a solutionfor automatic ontology construction based onmachine learning and natural language process-ing.

However, this solution does not provide thederived concepts (e.g., seafood lover) that arerequired in the value-added services we envision.Furthermore, it does not compose informationfrom different sources. In fact, the derived objectis one of the key concepts proposed in this arti-cle, serving as a magnet for the desired dataavailable in the network.

Reference [9] examines several architecturesfor querying distributed RDF repositories, includ-ing P2P architectures, and pinpoints their limita-tions. It then proposes two architectures for theintegration of semantic-based repositories: a hier-archical mediator architecture and a cooperativemediator architecture. The hierarchical architec-ture is less scalable because it is centralized, andthe root mediator constitutes a single point offailure. The cooperative architecture is morescalable, but each cooperative mediator has tomaintain the global schema, which does limit itsoverall scalability. Furthermore, this architecturedoes not provide self-organization.

THE PROPOSED ARCHITECTUREThe overall architecture is presented first, fol-lowed by the general procedures. The single andunified access interface based on REST is pre-sented last.

OVERALL ARCHITECTUREFigure 1 depicts the architecture. There are fourlayers. The access layer includes access networks,such as wideband code-division multiple access(WCDMA) and Wi-Fi, that are used in next-generation telecommunication networks’ set-tings. The data repository layer hosts the datarepositories. The data repositories may alreadyexist in the telecommunication network (e.g.,presence server, address book), or they may benew, such as consumption data repositories thatmaintain end users’ consumption profiles.

The information layer hides the details andheterogeneity of the data repositories, by meansof information repositories (IRs). An IR is anentity that endows data repositories with seman-tic intelligence and provides an intelligent inter-face to access these repositories. The servicelayer includes intelligent search engines (ISEs)that provide an interface for applications toquery the KB. Each ISE connects to one ormore IRs in the same domain. The ISEs are fed-erated, so they can provide a unified searchfunctionality.

Cooperative

architecture is more

scalable, but each

cooperative mediator

has to maintain the

global schema,

which does limit its

overall scalability.

Furthermore, this

architecture does

not provide

self-organization.



The IR and ISE are new entities we add tothe next-generation network entities. They arethe key entities of the proposed architecture.The IR maintains the ontology for the reposito-ries to which it connects. If a data repositorydoes not support the semantic web, the IR willderive a functional ontology for it. The IR main-tains a merged RDF schema for all of its con-nected repositories. Furthermore, it provides asemantic query interface to the ISE.

In the service layer, each ISE communicateswith its connected IR(s) using SPARQL boundto the protocols that IR(s) support(s). Wechoose SPARQL because it is a standard, it iscomprehensive, and it provides an abstract inter-face. One ISE may support multiple binding pro-tocols.

An ISE can be viewed as a server that pro-vides one special value-added service (i.e., intel-ligent search).

The set of ISEs, IRs, and connected datarepositories inside the same domain make theKB. These KBs are federated across domains via

the ISE federation. The federated KB can beaccessed via any of the ISEs.

GENERAL PROCEDURESISE Federation — We call the P2P protocolused in the federated network a federation pro-tocol. The federation procedures include IRjoining and leaving, ISE joining and leaving, andsearch request processing.

IR joining and leaving — When an IR isadded to the network, it sends a registrationmessage to an ISE to register its capabilities(e.g., it can provide location). The IR is manual-ly configured to know with which ISE to con-nect. The ISE keeps a list of “capability-IR”maps. When an IR leaves, it sends a registrationmessage with the “expires” parameter equal tozero.

ISE joining and leaving — When an ISEjoins the federated network, it publishes a list ofits capabilities to the network. The list consistsof the capabilities supported by the IRs connect-ed to it. The publication message is sent using

Figure 1. Overall architecture.

Access layerAccess network (e.g. WCDMA, Wi-Fi)

Store datarepository

Address book data repository

Consumption profiledata repository

Presence serverdata repositoryLocation server -

data repository

Data repository layer

Service layer

Information layer

ISE

IRIR

IRIR

Information repository - IR

ISE

Intelligent searchengine-ISE

Service provider A Service provider BIf a data repository

does not support

semantic web, the IR

will derive a

functional ontology

for it. The IR

maintains a merged

RDF schema for all

of its connected

repositories.

Furthermore, it

provides a semantic

query interface

to the ISE.



the federation protocol. When an ISE leaves, itun-publishes all its capabilities from the net-work. In addition, an ISE is able to update itscapabilities using Publish/Un-Publish methodsdefined in Table 1.

Search request processing — The procedurerequires two functionalities: finding or discover-ing the ISE(s) that support(s) a specific capabili-ty; and sending a query to the correspondingISE(s). The federation protocol is used for thefirst functionality. Several existing P2P protocolsand middleware that supports the aforemen-tioned functionalities, such as JXTA [10], can beused as the federation protocol. Table 1describes the federated network messages.

Intelligent Search — When an ISE receives asearch request, it first determines a set of theISE capabilities required to answer the request.Then it checks its local ISE table. If the tabledoes not have any ISE for the required capabili-ties, the ISE sends a “Discover” request, definedin Table 1, to the federated network for thosemissing capabilities.

After the ISE obtains all the capability-ISEmap sets, it creates a query plan by splitting themain query in the received request into subqueriesbased on the capabilities. A query plan is a set ofsubqueries, their relevant data sources, and theexecution sequence to answer a given request.

The subqueries are created using SPARQLand sent to the corresponding ISE(s) using the“Query” message of the federation protocol.Each ISE forwards the received “Query” requestto all of the IRs that provide the inherentrequest capability.

When an IR receives a SPARQL from theISE, it first creates a local query plan using thelocal merged RDF schema. It then sends thesubqueries to the appropriate data repositories,gets their responses, merges them, and repliesback to the ISE. The ISE forwards the responseto the requesting ISE, which combines theresponses using the original query plan and thenresponds to the service requestor.

The data repositories comply with the localend users’ privacy when replying to an IR query(e.g., an end user may choose to share his/herconsumption data with service providers inhis/her domain only). The ISEs apply the domainlevel privacy rules (e.g., domain1 shares informa-tion with domain2 but not with domain3).

ISE REST INTERFACEREST models the datasets to operate on asresources and uses a URI to identify eachresource. The resources are then accessed via aunified and standard interface, which consists ofthe HTTP methods GET, POST, PUT, andDELETE. These methods are used to read, cre-ate, update, and delete a resource, respectively.

For the intelligent search service offered bythe proposed architecture, the dataset to bemanaged is composed of the searching requestsand responses. We therefore define one resourceas the “searching resource,” which we expose viathe following URI: http://www.ise.com/{searchid}. Each request has a unique “searchid”identifier.

To initiate a search request, the requestorsends a POST request to the ISE URI (i.e.,http://www.ise.com) and gets the URI of thenewly created resource. The request content(e.g., keywords) is included in the POST body.

To support subscriptions, we use the persis-tent connections defined in HTTP 1.1 to notifythe requestor whenever the requested informa-tion becomes available or changes. The requestorcan still actively ask for new updates on aresource by sending a GET request to theresource URI. This may be needed if the con-nection between the requestor and the ISE isbroken (e.g., due to a network failure).

A created subscription resource is kept untilthe requestor cancels the search request (bysending a DELETE request to the resourceURI), the subscription times out, or the connec-tion with the requestor is broken beyond a givenserver timeout threshold. For a one-time search,the subscription timeout (specified in the POSTmessage body) is set to zero.

SCENARIOThis section studies a simplified version of thescenario presented in the introduction and showshow it can be realized using the proposed archi-tecture. It first describes how a consumptionprofile is built, and then discusses the buildingprocess for the keyword-search database, fol-lowed by the intelligent searching sequence.

CONSUMPTION PROFILE BUILDINGWe assume that the consumption data (contain-ing the name of the purchased items) is collect-ed through the user’s electronic or magneticTable 1. Messages defined in ISE federation.

RegisterDescription: registers the capabilities of IS.Message flow: IS → ISEParameters: The IS capabilities, expires (=0 for deregister).

Publish

Description: publishes the ISE capabilities to the federated net-work.Message flow: ISE → federated network

–Publish message is sent when an ISE joins and when an ISEcapability changes (such as an IS joins)Parameters: a list of capabilities

Un-Publish

Description: unpublishes some ISE capabilities from the feder-ated network.Message flow: ISE → federated network

–Is sent when ISE leaves and ISE capability changes (e.g., anIS leaves)Parameters: a list of capabilities

Discover

Description: discover the ISE(s) that provides a given capabilityand/or provides support for a given key word.Message flow: ISE → federated networkParameters: The IS capability and/or key word to be supportedby the ISE(s)Response content: ISE(s) address(es)

Query

Description: sends a SPARQL queryMessage flow: ISE → ISEParameters: SPARQL queryResponse content: query result (e.g. a list of people, places andobjects)



payment device (e.g., cell phone or credit card)and transferred to the consumption profile datarepository.

We have designed an ontology we term thebehavior building ontology to capture and processuser behavior. The IR uses this ontology to rea-son about the content of the consumption repos-itories that are connected to it. Figure 2a showsthe ontology designed for the scenario. For eachperson, the ontology maintains the records foreach purchased “Merchandise” (i.e., “Pur-chaseRecord”). It also defines the “Dish” thathe/she ordered in a restaurant (i.e., “Order-Record”). A “Merchandise” is either of type“Food” or “NonFood.” “Food” has four sub-cat-egories: “Seafood,” “Meat,” “Vegetable” and“Dairy.”

Each “Dish” can be described through itsmajor ingredient, which is of type “Food.” Theontology also defines a “Seafoodlover” as some-one who purchased seafood more than threetimes, or who ordered a seafood dish in a restau-rant more than five times, between date x anddate y (Fig. 2b). “Seafoodlover” is one of thesearchable keywords in the KB.

The behavior building ontology is implement-ed in each IR and is applied to the data collect-ed from the user’s daily activities (e.g., Alice’sshopping and dining behaviors).

KEYWORD DATABASE BUILDINGFigure 3 shows a network setup for the scenario.The owner of the restaurant has a service agree-ment with the ISE provider in domain1 (i.e.,ISE1), which does not own an IR or a datarepository. ISE1 is federated with ISE2 andISE3 from domains 2 and 3.

IR2 is connected to a presence and a locationdata repository; hence it registers to ISE2 withtwo capabilities: presence and location. IR3 reg-isters to ISE3 with address-book and consump-tion-profile capabilities. Each IR maintains amap between the supported capabilities and theassociated keywords; for example, “presence” isassociated with “mood,” “status,” and “location,”the type of information we can get from thelocal presence-data repository. An IR also pro-vides a SPARQL interface to query the map. Inthis example, it is IR3 that maintains the behav-ior-building ontology since it connects to theconsumption profile data repository.

Each ISE maintains a table in which key-words such as “Seafoodlover” are linked to therelated capabilities and addresses of ISEs. At theinitial stage, the table is empty. The mechanismto build and maintain the table is as follows.When ISE1, for instance, receives a requestfrom the owner of the restaurant, it retrieves thelist of keywords it contains (i.e., “location” and“Seafoodlover”). ISE1 checks its table to see if itknows which ISE(s) can provide the relateddata. If it cannot determine which ISE to query,ISE1 sends a “Discover” request (Table 1) to allthe ISEs in the federation with these keywords.When ISE2 and ISE3 receive the request, theysend SPARQL requests to their associated IRsto check if they support any of the keywords.Each IR replies with the keywords it supportsand the associated capability. ISE2 and ISE3then forward the received capability/keyword

sets to ISE1, which updates its table. ISE1 thenuses the updated table to build and execute thequery plan: it sends the subquery for “location”to ISE2 and “Seafoodlover” to ISE3.

INTELLIGENT SEARCHThe search sequence diagram is given in Fig. 4.The first step shows a data model example thatis used to transfer the records from the end-userdevice (e.g., Alice’s cell phone) to the consump-tion profile data repository. The record isdescribed using an XML document that can beembedded in any underlining transfer service(e.g., SIP messaging).

In the figure, the owner of the Italian restau-rant (“DeliItaly”) first sends an HTTP POSTrequest to its service provider ISE1 (step 2),along with the search query. The request body isshown in the figure. From its local table, ISE1finds out that the queries for “location” and“Seafoodlover” should be forwarded to ISE2and ISE3, respectively. ISE1 creates the queryplan based on this information. Using an inter-nal query optimization algorithm, ISE1 decides

Figure 2. An example of a behaviour building ontology.

a)

b)

Merchandise ≡ Food ∪ NonFoodSeafoodDish ≡ Dish ∩ (hasMajorIngredient ∃ Seafood)

Seafoodlover ≡ Person ∩ (SeafoodPurchaseLover ∪ SeafoodOrderLover)SeafoodPurchaseLover ≡ (hasRecord ≥3 (PurchaseRecord ∩ (hasDate ∃ date [>= xxxx-xx-xx, <= yyyy-yy-yy]) ∩ (hasItem ∃ Seafood)))SeafoodOrderLover ≡ (hasRecord ≥5 (OrderRecord ∩ (hasDate ∃ [>= xxxx-xx-xx, <= yyyy-yy-yy]) ∩ (hasItem ∃ SeafoodDish)))

hasDate

NonFood

Seafood VegetableMeat Dairy

hasItembelongTo

hasRecord

hasDate

hasRecord

belongTo

hasItem

orderpurchase

hasMajorIngredient

OrderRecordPurchaseRecord

Dish

Person

Food

Merchandise



to send the location query first. As a result, ISE1gets three users, including Alice. ISE1 thensends the second query to ISE3 to ask who,among the three users, are Seafoodlovers. ISE3then responds with Alice. ISE1 forwards thatresponse back to the owner of the restaurant.

VALIDATIONThe scenario in the introduction was implement-ed as a proof of concept. Figure 5 depicts theimplementation architecture. Two ISEs, two IRs,and two data repositories are involved. Each ISEconnects with one IR, and each IR connects withone data repository. The presence server datarepository is a Session Initiation Protocol (SIP)presence server that provides location service.The consumption profile data repository is arelational database that stores the end user’sconsumption profile.

The functional entities are described next,followed by the protocols and bindings used.

FUNCTIONAL ENTITIESThe software architectures for the ISE and IRare shown in Fig. 5. The ISE architectureincludes a query handler, a federation handler,and a set of protocol agents. There is an agentfor each supported protocol. The incomingrequests are received by the REST agent, andthen passed to the query handler. The queryhandler instantiates a new query agent, which

will create the query plan, and sends the sub-queries using appropriate protocol agents. Themessage router is used to find the protocol agentfor each destination. The communication withthe federated network is handled by the federa-tion handler.

The IR is composed of a query processor anda converter. The query processor is responsiblefor processing the ISE queries that are receivedthrough the ISE interface. It creates and exe-cutes the query plans. The converter uses thedata repository interface to communicate withthe repositories. It has two functionalities. It getsand merges the schema from the repositorieswith which it connects. Existing tools can beused for this purpose. For example, D2R [11] isused for deriving RDF schema from relationaldatabases. It also translates requests/responsesbetween the query processor (i.e., SPARQL)and the data repository interface (e.g., OpenDataBase Connectivity-ODBC SQL and SIPSubscribe).

PROTOCOLS AND BINDINGSFederated Network Protocol Bindings —The ISE federation architecture is implement-ed using JXTA as middleware. JXTA is a setof open protocols that allow devices on thenetwork to communicate and collaborate in aP2P manner [10]. The JXTA Peer DiscoveryProtocol (PeerDP) is used as the federationprotocol. PeerDP enables both the publication

Figure 3. ISE table creation.

salmon

Locationdata

repository

Presencedata

repository

Address bookdata

repository

Consumption profiledata repository

Restaurant

Keyword

Location

Seafoodlover

Capability

location, presence

consumption-profile

ISE address

ISE2@domain2

ISE2@domain2

IR2@domain2 IR3@domain3

Alice@domain3

ISE3@domain3

ISE3@domain3

ISE1@domain1

Capability

location

presence

Keyword

Location

Status, Mood,Location

Capability

address-book

consumption-profile

Keyword

Contact

Seafoodlover,Meatlover,Sportfan

Capability

location

presence

Looking forSeafoodlover in

Mont-royal

Directions for future

work include seman-

tic interface provi-

sioning for other

types of repositories

(i.e., other than SQL),

investigation into

charging, security

and privacy capabili-

ties for the searching

service, and the

exploration of vari-

ous methods for

automatic keyword-

base building.



of the ISEs’ capabilities and the ISE discoverybased on a given capability. The “Publish,”“UnPublish,” and “Discover” messagesdefined in Table 1 are implemented using thesame protocol.

The ISE federation “Query” message (Table1) is implemented using a SIP SUBSCRIBEmessage. We choose SIP because it is simple,supports multiple notifications, and does notplace any limitations on the message body. Wedefined a new event, “query,” that we put in theEvent header field of SUBSCRIBE. The SPAR-QL query is embedded in the message body. Theresponses to the “query” are conveyed by theSIP NOTIFY messages.

Protocols between ISEs and IRs — The pro-tocol between an ISE and an IR depends on theIR. In Fig. 5, the protocols between ISE1 andIR1 and between ISE2 and IR2 are bound toSIP and TCP, respectively.

To register to ISE1, IR1 uses SIP REGIS-TER. The IR1 capability list (i.e., “presence”) issent as an XML document in the body of theSIP message. To register to ISE2, IR2 sends asimilar XML document over a TCP socket, withthe capability indicated as “consumption pro-file.”

ISE1 sends SPARQL queries to IR1 usingSIP SUBSCRIBE messages, with the “event”parameter set to “query.” The SPARQL isembedded in the message body. The queryresponses are embedded in SIP NOTIFY mes-sage bodies. The SPARQL query/response mes-

sages exchanged between ISE2 and IR2 aredirectly conveyed over a TCP socket.

BEHAVIOR-BUILDING ONTOLOGY VALIDATIONThe behavior-building ontology of Fig. 2 was val-idated using Protégé 4.1, a free open sourceontology editor that supports OWL 2.0. The vali-dation reasoner is HermiT1.1, installed as a pro-tégé plug-in. A few individuals with differentpurchase/order records were input to the ontolo-gy, which was then queried to get the list of the“Seafood lovers.”

CONCLUSIONSThis article proposes a novel architecture thatenables the provisioning of new value-added ser-vices based on the subscriber data stored in next-generation telecommunication networks. Thearchitecture uses semantic web technology tobuild a knowledge base (KB) from the subscriberdata available in individual telecommunicationnetworks. It also federates the KBs across net-works to provide service providers with a unifiedinterface with which to access the different KBs.The federation is based on a P2P model, and theunified interface is REST-based.

Directions for future work include semanticinterface provisioning for other types of reposi-tories (i.e., other than SQL), investigation intocharging, security, and privacy capabilities forthe searching service, and the exploration of var-ious methods for automatic keyword-base build-ing.

Figure 4. Example of a search sequence diagram.

<PurchaseRecord> <Merchandise> <Food> <Seafood>Salmon</Seafood> <Vegetable>Broccoli</Vegetable> </Food> </Merchandise> <Date>2010-02-24</Date></PurchaseRecord>

1 : UploadPurchaseRecord

Alice@domain3ISE3@domain3ISE2@domain2ISE1@domain1

2: POST(XML query)

4 : getDestinationISEs()

5 : ISE@domain2(location)ISE@domain3(seafoodLover)

6 : Query(SPARQL:2km from the address:expire12h)7 : SPARQL

8 : Alice, Bob, Charles9 : Alice, Bob, Charles10 : Query)(SPARQL:Seafoodlover:expire0: Alice, Bob, Charles)

11 : SPARQL(Seafoodlover: Alice, Bob, Charles)

13 : Alice@domain312 : Alice@domain3

14 : 200(chunk2: Alice@domain3)

3 : 200(chunk1: http://www.intelse.com/33)

IS2@domain2(for location)

DeliItally

IS3@domain3(for consumption

profile)

SPARQL example query:SELECT ?uriFROM <http://www.semanticweb.org/ontologies/2010/1/OntologyISE_1.owl>WHERE { ?person a Seafoodlover; hasUri ?uri; hasName “Alice”, “Bob”, “Charles”. }

POST query body<query> <constraints> <interest>SeafoodLover</Interest> <location> <geopriv> <location-info> <civicAddress> <country>Canada</country> <A1>Quebec</A1> <A3>Mont-Royal</A3> <A6>Decarie</A6> <HNO>1111</HNO> <PC>H4P1K8</PC> <NAM>DeliItally</NAM> </civicAddress> </location-info> </geopriv> <range>2km</range> <location> </constraints></query>



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[4] T. C. Jepsen, “Just What is an Ontology, Anyway?,” IEEEIT Professional, Sep/Oct 2009, pp. 22–27.

[5] D. Ayers, “Delivered Deliverables The State of theSemantic Web, Part I,” IEEE Internet Computing,Jan./Feb. 2009, vol. 13, issue 1, pp. 86–89.

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BIOGRAPHIESFATNA BELQASMI ([email protected]) holdsPh.D. and M.Sc. degrees in electrical and computer engi-neering from Concordia University, Canada. She is aresearch associate at Concordia University. In the past, sheworked as a researcher at Ericsson Canada. She was partof the IST Ambient Network project (a research projectsponsored by the European Commission within the SixthFramework Programme, FP6). She worked as an R&D engi-neer for Maroc Telecom in Morocco. Her research interestsinclude next-generation networks, service engineering, dis-tributed systems, and networking technologies for emerg-ing economies.

CHUNYAN FU ([email protected]) currently works atTekelec. She worked for Ericsson Canada as a researcherfrom 2008 to 2010. She received her M.Sc. and Ph.D.degrees in electrical and computer engineering from Con-cordia University in 2004 and 2008, respectively. Shereceived her Bachelor’s degree in computer engineeringfrom Nanjing University of Posts and Telecommunications.From 1997 to 2001 she worked at China Mobile as a sys-tem integration engineer. Her research interests includesignaling, ad hoc networks, IMS, and services in next-gen-eration networks.

ZHONGWEN ZHU ([email protected]) holds aPh.D. degree in applied science from the Free Univeristy ofBrussels, Belgium, and an M.Sc. degree in mechnical engi-neering from Shanghai Jiao Tong University. After gradua-tion, he worked in the fields of genetic algorithms, neuralnetworks, and artifical intelligence in the National ResearchCouncil of Canada. In 1998 he joined BombardierAerospace as an engineering specialist, and led a team todevelop a three-dimensional numerical simulation model(computaional fluid dynamics) for the aerodynamic designof commerical aircraft. Then in 2001, he was attracted towork in Ericsson Canada to develop the application plat-form and service for a wireless communication network. Heis currently working as a senior system manager in theDepartment of Social Media. He holds and has filed 31patent applications around the world. He is also an Associ-ate Editor for the Journal of Security and CommunicationNetworks (Wiley). His current research interests are cloudcomputing, multimedia applications in IMS and non-IMSnetworks, M2M (Internet of Things), security, license con-trol mechanisms, and interworking between different socialnetwork communities.

ROCH GLITHO [SM] ([email protected]) holds a Ph.D.(Tekn. Dr.) in tele-informatics (Royal Institute of Technolo-gy, Stockholm, Sweden) and three M.Sc. degrees: businesseconomics (University of Grenoble, France), pure mathe-matics (University of Geneva, Switzerland), and computerscience (University of Geneva). He is an associate professorof networking and telecommunications at Concordia Uni-versity, where he holds the Canada Research Chair in End-User Service Engineering for Communications Networks,and leads the Telecommunications Service Engineering Lab-oratory (TSE Lab). He is also an adjunct professor at theDepartment of Computer Technology, University of Milan,Italy, and at the Institut de Mathématiques et SciencesPhysiques (IMSP), University of Abomey-Calavi, Republic ofBenin, West Africa. In the past he worked in industry foralmost a quarter of a century, and has held several seniortechnical positions at LM Ericsson in Sweden and Canada(e.g., expert, principal engineer, senior specialist). He is amember of several editorial boards including IEEE Networkand IEEE Communications Surveys & Tutorials. In the pasthe has served as an IEEE Communications Society Distin-guished Lecturer, Editor-In-Chief of IEEE CommunicationsMagazine, and Editor-In-Chief of IEEE Communications Sur-veys & Tutorials. His research areas include architecturesfor end-users services, distributed systems, non convention-al networking, and networking technologies for emergingeconomies. In these areas, he has authored more than 100peer-reviewed papers, more than 30 of which have beenpublished in refereed journals. He also holds 24 patents inthe aforementioned areas and has several pending applica-tions.

Figure 5. Implementation architecture.

ISs

ISEs

ISE2

ISE1

Information repository

Queryprocessor Converter

Data repositoryinterface

ISE interface

IR1IR2

SIPODBC

Presence server datarepository

Consumption profile datarepository

SPARQL/TCPSPARQL/SIP

ClientIntelligent search engine

RESTagent

SIPagent

ODBCagent

Message router

Federationhandler

Query handler

Query agent

RDF/OWLrepository


Documents

Subscriber data and semantic web for provisioning novel end-user services in telecommunication networks