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Personalized Application Enablement by Web Session Analysis and Multisource User Profiling Armen Aghasaryan, Murali Kodialam, Sarit Mukherjee, Yann Toms, Christophe Senot, Stéphane Betgé-Brezetz, T. V. Lakshman, and Limin Wang Mastering knowledge of the user profile is one of the technical cornerstones for service providers who handle a large amount of end user service consumption data and are well positioned to dynamically infer user interest domains. This paper presents a holistic approach to service personalization by offering a means to gather a user’s consumption data from different multimedia services, to create and track user profiles in real time, and to monetize these profiles through targeted content or other personalized service offers. We describe a multisource profiling engine that deals with both operator-controlled domains and over-the-top (OTT) applications. In particular, to cover the Web domain, we combine the multisource profiling engine with a deep packet inspection (DPI)-based keyword inference engine characterizing users’ Web browsing sessions in terms of the most relevant keywords searched. © 2010 Alcatel-Lucent.

Introduction End users of large telecommunications service providers today are offered a wide spectrum of content services delivered over different media and terminals: Internet Protocol television (IPTV), mobile portal/TV, and Web access via their mobile device or fixed personal computer (PC) or television (TV). Both the extensive amount of content and services available as well as its diversity creates a demand for personalized applications that will provide these end users with an engaging choice of content. Creating and leveraging personalized applications offer new opportunities for telcos to increase their average revenue per user (ARPU) as well as to prevent churn and create customer stickiness. In particular, telcos can tap into new revenue streams like targeted advertising

and the brokering of subscriber profile information to third parties [2, 12]. To provide personalized applications, telcos need to know their users not just as static sociodemographic segments, but as individuals with unique profiles. Another important aspect, emphasized by the analysts in [5] and [9], is the benefit of maintaining a common subscriber profile and management architecture as opposed to service-specific silos. Indeed, knowledge around user consumption in one domain (e.g., IPTV) can be leveraged for promoting relevant content to the same or similar users in the service provider’s other domains (e.g., mobile video). Telcos will thus obtain a significant competitive advantage versus the over-the-top (OTT) service

Bell Labs Technical Journal 15(1), 67–76 (2010) © 2010 Alcatel-Lucent. Published by Wiley Periodicals, Inc. Published online in Wiley InterScience (www.interscience.wiley.com) • DOI: 10.1002/bltj.20424

Panel 1. Abbreviations, Acronyms, and Terms ARPU—Average revenue per user CA—Consumption act DM—Domain model DPI—Deep packet inspection EPG—Electronic program guide HTML—Hypertext Markup Language HTTP—Hypertext Transfer Protocol IPTV—Internet Protocol television IT—Information technology OTT—Over-the-top OWL—Web Ontology Language PC—Personal computer

providers that do not have such a rich service delivery infrastructure. In this paper, we advocate a holistic multisource profiling and multi-application personalization approach that leverages diverse usage data collected from multiple service domains (e.g., IPTV, mobile portal, and Web). It is fundamentally different from current efforts based only on Web or e-commerce technologies. For example, solutions such as Google AdWords*, AdSense*, and Amazon* Recommender are dedicated to a particular Web-based personalized application. With such a global personalization solution, telecom providers can differentiate themselves from Web and information technology (IT) players by conducting a comprehensive profile analysis and by personalizing a variety of applications in different service domains, e.g., TV program/video on demand (VoD) recommenders, personalized search tools, and personalized push-content to mobile or social network enablers. In the vein of so-called generic user modeling systems [11], we suggest a personalization approach adapted to the requirements of multi-screen service providers. In particular, we address a critical issue for large telcos dealing with two different types of domain: • The controlled domain, where the telco maintains its own services/content and associated descriptions. This is typically the case with service portals (e.g., Web portals or wireless application portals [WAPs] and electronic program guides [EPGs] for fixed or mobile TV), where the service provider can define

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PDA—Personal digital assistant QoA—Quantity of affiliation QoC—Quantity of consumption QoI—Quantity of interest SCI—Session concept inference SKI—Session keyword inference TV—Television UM—User model VoD—Video on demand WAP—Wireless application portal URL—Uniform resource locator

the metadata characterizing the content of each page while ensuring its consistency with the internal representation of unified subscriber profile data. User interests can thus be directly expressed using the same dictionary/ontology—the same structure and terms—as the metadata characterizing the content. • The uncontrolled domain, where the telco only provides access to services/content offered by third parties (OTT providers) and therefore does not control the content metadata. This is typically the case in the Web domain, where each OTT provider may maintain its own content characterization and even its own user profiling approach. In this case, to enrich its subscriber profiles, the telco has to discover the content characteristics (e.g., the user-entered keywords) and interpret them by dynamically mapping them to its own ontology. To deal with the (uncontrolled) Web domain, we have combined a multisource profiling module with a keyword inference module which characterizes each user’s Web browsing session in terms of the most relevant keywords. This data is merged via multisource profiling with the information coming from other (controlled) domains such as IPTV/VoD or a mobile portal with news, shopping, and video streaming offers. The next section introduces the general architecture for multisource profiling. Next, we describe the profiling engine and explain the keyword inference

and mapping functions for the Web domain. We follow by describing a system implementation, and then offer our conclusions and perspectives.

•

Interpreting the relative importance of each usage trace depending on its source platform, user interaction type, consumed content semantics, and consumption intensity measure. • Aggregating these usage traces and maintaining a dynamically evolving user profile over time, without losing information which may potentially be needed by a large spectrum of personalized applications. • Allowing different applications to benefit from profile data through a centralized access point (profile query interface). For scalability reasons as well as for facilitating integration with third party platforms, the processing of platform-specific data can be delegated to profiling proxies hosted by different service domains and

Multisource Profiling: General Principles and Architecture The multisource profiling and multi-application personalization approach has a three layer architecture, as shown in Figure 1. The middle layer (enablement) is represented by a central profiling component, the profiling engine. It is charged with linking the heterogeneous user consumption data (usage traces) coming from different service domains with the diverse personalized applications running on the top of these domains. The role of such a central profiling component consists of the following:

Content personalization applications

News recommender

Shopping recommender

Video recommender

…

Targeted advertisement (cross-marketing, auction, …)

Profile query interface User profile DB

Enablement layer

Profiling engine

Explicit profiling and privacy management

Profile update

Service domains

VoD/IPTV platform

Profiling proxy

Profiling proxy

Profiling proxy

IPTV wireline network

Mobile portal/video platform

3G wireless network

IP network DPI

IPTV services 3G—Third generation DB—Database

DPI—Deep packet inspection IP—Internet Protocol

Web browsing

Mobile services

IPTV—Internet Protocol television VoD—Video on demand

Figure 1. Architecture for multi-source profiling and multi-application personalization.

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communicating with the central profiling engine through a unique interface (profile update interface). Finally, the architecture provides a privacy management function in order to comply with regulatory constraints, as well as to ensure end user trust via the ability to set and maintain his own privacy options and preferences. The solution allows a user both to specify the sources of information that may be used for profiling and also to define the parts of his profile visible to various personalized services (either within or outside the controlled domain). Multisource Profiling Engine Semantic concepts constitute a core element of the profiling engine’s data model. According to the overlay approach [3], the user profile is composed of a set of ⬍concept, value⬎ pairs, where the (semantic) concepts are abstract terms expressing user interests (e.g., “action movie” or “sports news”), while the associated values represent the user’s degree of interest. These concepts can form a flat set of keywords, be structured as a taxonomy [6], or be part of a more complete ontology [8]. Under the assumption of a shared model of semantic concepts (which is the case with controlled domains), one can represent the content metadata (categories) as well as the semantics of consumption acts (purchase events, viewing or browsing sessions) with the same terms in the user profile (interests) [1]. In this paper, we drop this assumption in order to deal with uncontrolled domains, and we accept a multi-model setting where each domain has a specific model of content categories, which we term the domain model (DM), while the user profile relies on a core user model, the UM. We then propose three quantities to structure and describe the complete profiling process. Quantity of affiliation. The quantity of affiliation, (i) QoADM
[0,1], characterizes the degree of affiliation of a given content item to the semantic concept i in a specific domain model. The set of positive QoA for a (i) given content item, {QoADM : i
DM}, is obtained by content indexing (assigning metadata either manually or automatically); e.g., for the video “Shrek”* affiliaanimation comedy tions can be {QoADM ⫽ 0.9, QoA DM ⫽ 0.8}. In a multi-model setting, the (sets of) terms of the domain model can be translated into the ones of the core user

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(i) (i) model: {QoADM : i
DM} S {QoADM : j
UM}, in which case the remaining part of the profiling process will evolve as in the case of a shared model [1]. However, when this cannot be done in advance for the whole set of content items, this translation can be reasonably postponed to a latter stage of the profiling process. This is typically the case for uncontrolled domains and, in particular, for the Web domain. Let the consumption act, CAn, be the nth element in a sequence of consumption acts referring to an interaction of the user with a content item Cn. CAn is then characterized both by the semantics of Cn (a set of QoAs as defined above) and by pure quantitative parameters like the duration of consumption, the frequency of keywords in a browsing session, the price paid, the user rating, or the consumption environment. For example, 30 minutes of Web browsing on a specific topic on a personal computer (PC) does not express the same interest as 30 minutes of browsing similar contents on a personal digital assistant (PDA); the latter gives a stronger evidence of user interest in this topic. Quantity of consumption. The quantity of con(i ) sumption, QoC DM,n
[0, 1], characterizes the intensity of a consumption act CAn with respect to a given semantic concept i in a specific domain model, DM. It is defined by a general expression: (i) (i) QoC DM,n = Mn(QoADM )

(1)

where Mn() is a monotonic function modulating the respective QoA of the content item Cn by taking into account all the relevant quantitative parameters characterizing the consumption act CAn. This provides a normalized measure of user interest as observed in his current interaction with the system. In a multi-model setting, we also need a translation (i) (i) mechanism, {QoC DM ,n : i
DM} S {QoC UM,n : j
UM}, which is explained in the section titled “Session Concept Inference.” Quantity of interest. The quantity of interest, Qo l (iUM)
[0, 1], characterizes the degree of interest of the user for a semantic concept i in the user model; the user profile is composed of a set of such QoIs. In the profiling engine, two complementary profile update functions coexist: 1) consumption event-based

QoI learning and 2) time-based QoI decay. The first function helps to evolve user interests by combining previously estimated levels of interest with new interest expressions. We have considered a particular family of functions where the new QoI is obtained cumulatively by a weighted addition of the newly observed QoC with the previous QoI: (i) (i) (i) (i) QoI UM,n⫹1 ⫽ QoI UM,n ⫹ W (QoI UM,n ) * QoC UM,n (2)

where (i) (i) (i) W (QoI UM,n ) ⬍ Min (Q UM,n , 1 ⫺ QoI UM,n )

(3)

The variable weight given by the function W() defines how significantly the new observation influences the profile evolution. Its upper bound, shown in equation 3, results in a so-called learning curve type of QoI evolution, described in equation 2, where given a sequence of positive QoC, secondary interests grow relatively slowly (because of a low weight W) and primary interests tend to reach the upper limit of 1 (because of a diminishing variable weight W). Note that the term “learning curve” makes reference to the relationship between the duration of a student’s learning period and the knowledge or experience gained. In addition, a time-based decay function is introduced to reflect the aging of user profile data. For example, the QoI values will periodically decrease depending on the frequency of consumption for respective semantic concepts [1]. At any moment, the available user profile data can be accessed by various personalized applications via the profile query interface. In addition, these applications can benefit from some reusable profile exploitation tools like distance computation [1]. In a multi-model environment, this interface will be used by OTT applications where content is characterized with domain-dependent models which differ from the core user model. Therefore, a model translation function is again required.

Web Domain Profiling Proxy This profiling proxy (Figure 1) performs two functions: 1) browsing session characterization through a set of weighted keywords that form the domain model

(as described in the section titled “Deep Packet Inspection and Session Keyword Inference”) and 2) translation of these characteristics into the concepts of the core user model (as described in “Session Concept Inference”). These functions are distributed over the user home gateway and a network-based server. Note that the tasks performed by the Web domain profiling proxy are not the same as a traditional Web proxy. Finally, an assumption behind the work presented below is that the Web sessions are statically defined by fixed periods of user browsing. Deep Packet Inspection and Session Keyword Inference The first step is to mine the Web traffic generated by a broadband home user. This is done by performing deep packet inspection (DPI) on all the Web sites visited by the user. Note that in our approach, we need to inspect only the Web traffic traveling upstream from the user (the user’s HTTP requests), which makes the system scalable and maintains a low processing load in the network. From the requests, the DPI module extracts the destination Web site’s address (uniform resource locator [URL]) and any user input keywords to that site. For example, if the user goes to Amazon.com and searches for “coffee maker,” the DPI module captures information for both “www.amazon.com” and “coffee maker.” Figure 2 (at top) shows the actions taken by the DPI on a packet. Following the browsing session DPI, the results (URLs and user inputs, if any) are sent to a session keyword inference (SKI) engine located deeper in the network. The latter is responsible for creating a set of weighted keywords that best describe the user’s current behavior. Its first task is therefore to garner keywords relevant to the Web pages (i.e., a set of QoA metrics). Usually a Web page contains a few keywords assigned to it by the designer, which can be found under the HTML “title” tag or meta tags “keyword” and “description.” Our algorithm uses these keywords as the basis for an iterative method of keyword assignment. Note that these keywords cannot just be accepted on their face value since different Web designers assign them differently, and they serve different purposes as well. In the absence of this data, the algorithm can start with an empty initial baseline.

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TCP traffic going to port 80 (HTTP) HTTP method is GET/POST/… Collect destination URL If destination URL is a search engine then collect search keywords Web domain profiling proxy

IP header (protocol = TCP) TCP destination port = 80 GET http://www.nytimes.com/ HTTP/1.1 Referer: http://www.nytimes.com/ User-Agent: Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1) Host: www.nytimes.com

Session concept inference

j) } {QoC(um

Session keyword inference

{QoC(i) } dm

(i) } {QoAdm

Packet information Deep packet inspections

Web page metadata Home gateway

Web sites

DM—Domain model HTTP—Hypertext Transfer Protocol IP—Internet Protocol QoA—Quantity of afiliation

QoC—Quantity of consumption TCP—Transmission Control Protocol UM—User model URL—Uniform resource locator

Figure 2. User’s Web usage information mining by deep packet inspection at residential gateway.

Next, we find all the Web pages pointing into (inlink nodes) and pointing out of (outlink nodes) the given page (self node). While outlinks can be obtained by analyzing the HTML page, inlinks are obtained from a Web graph generated using a crawler (e.g., inlink information provided by Yahoo! or Google). All the keywords from inlink, outlink, and self nodes are collected and rank-ordered into a set of keywords for the self node; this corresponds to the Web page QoA. This process is iterative since the keyword set for the self node can now influence its inlink and outlink neighbors’ keyword sets. Note, however, that at any time one can use the current keyword set for a page

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without waiting for algorithm termination. These keywords are then weighted based on the frequency of the user’s visits to the corresponding Web sites during the given browsing session (QoC computation). In addition, the algorithm assigns higher weights to keywords directly input by a user. In the description above, we have assumed that the Web page is not encrypted, which is usually the case for Web browsing activities. The DPI engine does not examine any encrypted page. While this is a shortcoming of DPI, we do not believe this hinders appropriate profile generation for a user since most Web sites today are not encrypted.

Session Concept Inference As the SKI component generates sets of weighted keywords which are, most likely, not part of the user model, i.e., not meaningful for the profiling engine, a kind of translation or mapping needs to be carried out. This is the role of the session concept inference (SCI) function, which translates the keywords provided by the SKI component into user model concepts, (i) (i) {QoC DM ,n : i
DM} S {QoC UM, n : i
UM}. The first step is to define a concept dictionary, where each semantic concept in the user model is described with a cloud of relevant keywords (Figure 3), i.e., keywords having a semantic proximity or ones frequently associated with that semantic concept. The relevance value is given by a function D(i, j)
[0,1], where i is a keyword, and j is a concept of the user model, e.g., synonymy being the closest existing relationship for a word, synonyms would be assigned the highest value of 1. The dictionary is constructed semi-automatically, first by using the Wordnet* ontology (synsets, hypernyms, hyponyms, and synonyms) and Wikipedia*

User model

UM concept j

Mapping coefficient

Keyword i D (i, j )

UM—User model

Figure 3. Keyword to concept mapping representation.

 dow + jons 0.3  banking 0.5   0.6  bond Market 0.8   (a) Keyword set of a browsing session

⇒

[4, 8], and then, by enriching it manually taking into account unmapped keywords obtained after several Web browsing sessions. In order to increase the probability of finding the keywords generated by the SKI component in the concept dictionary, we employ approximate keyword matching techniques as well as the stemming of dictionary keywords; the latter reduces also the size of the dictionary. Figure 4 provides an illustration of the dictionary construction concept. For instance, in the keyword set shown in Figure 4a, the word “bank” would be used instead of “banking.” Now, given such a dictionary, D(i,j), and keeping in mind that the same concept j may be pointed out by several keywords in the browsing session, an initial approach for translating the session keywords into core model concepts, illustrated in Figure 4b, would be the following: (j) ( i) QoC um,n ⫽ max [QoC dm,n * D(i, j)] i
DM

(4)

Note that other aggregation functions like mean() or min() could also be applied. However, the problem of disambiguation is yet to be solved for the cases where the same keyword i points out to semantically different concepts (e.g., homonyms). For instance, the keyword “bond” in Figure 4a introduces an ambiguity between two possible semantic concepts: “economy_ news” and “action_movie.” The idea is to identify and remove such conflicting concepts by relying on the semantic context of the browsing session created by the “majority” of candidate concepts. The disambiguation algorithm will then remove, among the set of concepts returned by equation 4, the concepts having a weak semantic similarity with the other candidate concepts, as illustrated by Figure 4c. Given a set

 action_movie  news   economy_news

0.3 0.5  0.6

(b) Corresponding core user model concepts

⇒

 news   economy_news

0.5  0.8

(c) Remaining concepts after disambiguation

Figure 4. Concept of dictionary construction.

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(j) {QoC um,n : j
UM}obtained by equation 4, we define a semantic measure (j) (j) ( k) ⫽ QoC Centralityum,n um,n * a Sim(j, k) * QoC um,n

(5)

k
UM

where Sim(j,k) is a semantic similarity between two concepts j and k computed from the structure of the user model (taxonomy or ontology), e.g., [7, 10]. We can now use equation 5 to filter out the conflicting concepts that have the semantic centrality measure below a predefined threshold. Following the example above, the concept “action_movie” is filtered out in Figure 4c because it has a low QoC value and similarity to the two other candidate concepts and, therefore, a low semantic centrality value.

Implementation A system prototype has been implemented which supports 1) a video on demand service for converging fixed (IPTV) and mobile (mobile video) content delivery platforms and 2) a classic Internet access service. Using a diverse set of terminals (TV set top box, mobile phone, and laptop), the profiling mechanism is illustrated using two key personalized applications, namely, a targeted advertisement and item recommender (video content, shopping articles, or news). The implementation combines Web Ontology Language (OWL) for the description of the core user/metadata model and Web services technology for the northbound and southbound interfaces. The southbound interface (profile update interface) is the entry point for the consumption traces collected by the profiling proxies on their respective platforms. In the case of a Web browsing domain (the Web session profiling proxy), the DPI module, located inside the user’s home gateway, sends the requested URLs to the SKI server. The latter analyzes the corresponding Web pages and sends the weighted keywords characterizing each browsing session to the SCI server at predefined time intervals. The SCI server, in turn, sends the data to the profiling engine after translation and disambiguation. The northbound interface (profile query interface) allows a wide range of personalized applications to have access to the profiles. The system was tested using an automatic browser simulating users surfing the Web by launching Web

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page requests periodically. To support the load, the SKI server implements multithreading and caching of Web page metadata in order to avoid the repetitive extraction of keywords for popular pages. Similarly, the SCI and the profiling engine multithread the processing of usage traces in order to speed up the profile updates. In a production environment, the SKI/SCI servers and the profiling engine can obviously be replicated to build a cluster of servers with appropriate load balancing. Note that the scalability of DPI processing is ensured by the fact that it is done on the user access gateways.

Conclusion and Perspectives We have described a multisource profiling engine which dynamically aggregates and learns the subscriber profiles in a multi-domain environment with both controlled and uncontrolled domains. We have shown how this solution can address multiple service-specific domain models. To deal with the Web domain, we have combined multisource profiling with a DPI-based keyword inference function allowing the characterization of the users’ Web browsing sessions in terms of the most relevant keywords. The translation of these data into the core user model has been described. It remains a subject of future research to improve the automation of such a translation process and relevancy determination. This work was conducted under the assumption that Web browsing sessions are statically defined by a fixed time period. Other methods for dynamic identification of thematic user sessions should be further studied. For example, a first step in this direction can be the definition of a browsing session by a sequence of consecutively requested URLs all belonging to the same Web domain name. In addition, the keyword inference approach could be extended for mobile Internet access as well. An important next step is the large scale testing of this solution by involving real users over various service domains. *Trademarks AdSense and AdWords are registered trademarks of Google, Inc. Amazon is a registered trademark of Amazon Technologies, Inc. Shrek is a registered trademark of DreamWorks Animation L.L.C. Wikipedia is a registered trademark of Wikimedia Foundation, Inc.

Wordnet is a registered trademark of Trustees of Princeton University.

References [1] A. Aghasaryan, S. Betgé-Brezetz, C. Senot, and Y. Toms, “A Profiling Engine for Converged Service Delivery Platforms,” Bell Labs Tech. J., 13:2 (2008), 93–103. [2] M. Beccue and D. Shey, Service Personalization: Subscriber Data Management, Subscriber Profiling, Policy Control, Real-Time Charging, Location, and Presence, ABI Research, 2Q 2009. [3] P. Brusilovsky and E. Millan, “User Models for Adaptive Hypermedia and Adaptive Educational Systems,” The Adaptive Web: Methods and Strategies of Web Personalization (P. Brusilovsky, A. Kobsa, and W. Nejdl, eds.), Springer, Berlin, Heidelberg, New York, 2007, pp. 136–154. [4] I. Cantador, M. Szomszor, H. Alani, M. Fernández, and P. Castells, “Enriching Ontological User Profiles With Tagging History for Multi-Domain Recommendations,” Proc. 1st Internat. Workshop on Collective Semantics: Collective Intelligence and the Semantic Web (CISWeb ‘08) (Tenerife, Sp., 2008), pp. 5–19. [5] C. Chappell, “Subscriber Data Management: It’s Time to Get Personal, Light Reading’s Services Software Insider,” Vol. 4, No. 1, Feb. 2008. [6] J.-P. Evain and H. Murret-Labarthe, “TVAnytime Phase 1—A Decisive Milestone in Open Standards for Personal Video Recorders,” EBU Tech. Rev., 295:3 (2003). [7] P. Ganesan, H. Garcia-Molina, and J. Widom, “Exploiting Hierarchical Domain Structure to Compute Similarity,” ACM Trans. Inform. Syst., 21:1 (2003), 64–93. [8] A. Gómez-Pérez, M. Fernández-López, and O. Corcho, Ontological Engineering: With Examples From the Areas of Knowledge Management, e-Commerce and the Semantic Web, Springer, London, New York, 2004. [9] J. Hodges, “Subscriber Data Management & the Era of Analytics,” Heavy Reading, Vol. 7, No. 4, May 2009. [10] R. Knappe, Measures of Semantic Similarity and Relatedness for Use in Ontology-Based Information Retrieval, Ph.D. Dissertation, Roskilde University, 2005. [11] A. Kobsa, “Generic User Modeling Systems,” The Adaptive Web: Methods and Strategies of Web Personalization (P. Brusilovsky, A. Kobsa,

and W. Nejdl, eds.), Springer, Berlin, Heidelberg, New York, 2007, pp. 136–154. [12] S. Pastuszka, S. Vergnault. S. Betgé-Brezetz, A. Aghasaryan, and P. Lopes, “Mitigating Risk in the New Economy: Addressing Changing User Needs and Market Trends,” Enriching Commun., 2:2 (2008). (Manuscript approved December 2009) ARMEN AGHASARYAN is a project leader in the Service Infrastructure Research Domain at AlcatelLucent Bell Labs in Villarceaux, France. He received an M.Sc. degree in control system engineering from the Yerevan Polytechnic Institute and an M.Sc. degree in industrial engineering from the American University of Armenia, both in Yerevan, Armenia. He received his Ph.D. degree in signal processing and telecommunications from the University of Rennes, France. Before joining AlcatelLucent, he spent two years with France Telecom in their research department in Lannion, France. Dr. Aghasaryan has worked on several European and French collaborative research projects on network management, fault diagnosis, and alarm correlation. His current interests include user modeling and profiling, usage analysis, ontologies, service personalization, and privacy protection. He is a member of the Alcatel-Lucent Technical Academy. MURALI KODIALAM is a member of technical staff in the Network Protocols and Systems Research Department at Alcatel-Lucent Bell Labs in Holmdel, New Jersey. He obtained a Ph.D. in operations research from the Massachusetts Institute of Technology, Cambridge, Massachusetts, and joined Bell Labs shortly thereafter. He has been at Bell Labs for 19 years. Dr. Kodialam’s current research focus is on resource allocation and performance of communication systems including routing in multiprotocol label switching (MPLS) systems, topology construction and routing in ad-hoc wireless networks, and reliable routing in optical networks. He is a member of IEEE and the Institute for Operations Research and the Management Sciences (INFORMS). SARIT MUKHERJEE is a technical manager in the Alcatel-Lucent Bell Labs Network Protocols and System Research Department in Murray Hill, New Jersey. He is responsible for the research and development of emerging network applications for next-generation data service technologies. Prior to this, he held a

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technical manager position with Lucent Technologies managing the research and development of Internet content distribution appliances, managed the design and development of streaming appliances in a New York–based start-up company, and led the videonetworking group at the Panasonic Information and Networking Technology Lab at Princeton. He received his M.S. and Ph.D. in computer science from the University of Maryland, College Park. Dr. Mukherjee has published more than 40 research papers in renowned technical journals and conferences, served on the technical committees of a number of international conferences, and holds dozen of U.S. patents. His research interests include high-speed network architectures and protocols and multimedia applications. YANN TOMS is research engineer in the Service Infrastructure Research Domain at AlcatelLucent Bell Labs in Villarceaux, France. He graduated from the Engineering School of Information Technologies and Management (EFREI) in Paris, France. After three years as a software engineer at Capgemini Telecom, a worldwide system integrator, he has worked for three years in the Service Infrastructure Research Domain on user profiling and personalized application topics. CHRISTOPHE SENOT is a researcher in the Services Infrastructure Research Domain at AlcatelLucent Bell Labs in Villarceaux, France. He graduated from the Engineering School of Information Technologies and Management (EFREI) and TELECOM ParisTech (ENST) in Paris, France. He worked for a short time as a software engineer at Capgemini, a worldwide systems integrator, prior to joining Alcatel-Lucent. His current research activities are on user and group profiling and personalized applications. STÉPHANE BETGÉ-BREZETZ is head of the Service Infrastructure Research Domain at AlcatelLucent Bell Labs in Villarceaux, France. He received a diploma in engineering from Ecole des Hautes Etudes Industrielles in Lille, France, and a Ph.D. in robotics from University Paul Sabatier at Laboratory of Analysis of Architectural Systems–National Center for Scientific Research (LAAS-CNRS), Toulouse, France. Dr. BetgéBrezetz has worked on several European and French research projects on software engineering, network and service management, and service delivery platforms. His current interests include user profiling,

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Bell Labs Technical Journal

DOI: 10.1002/bltj

service personalization, and privacy technologies. T. V. LAKSHMAN is the director of the Network Protocols and Systems Research Department at Alcatel-Lucent Bell Labs in Murray Hill, New Jersey. He received M.S. and Ph.D. degrees in computer science from the University of Maryland, College Park. He has worked on a spectrum of networking topics including switch architectures, network design, Transmission Control Protocol (TCP) performance, traffic management, and video transmission over packet networks. He has received several Best Paper Awards from the Association for Computing Machinery (ACM) and the IEEE, including most recently the 2008 IEEE Leonard Abraham Prize. Dr. Lakshman was an editor of IEEE/ACM Transactions on Networking from 1996 to 2002. He is currently an editor of EEE Transactions on Mobile Computing. He is a fellow of the IEEE and ACM. LIMIN WANG is a member of technical staff in the Network Protocols and Systems Research Department at Alcatel-Lucent Bell Labs, in Murray Hill, New Jersey. His main research interests are in computer network systems, such as content distribution networks and multimedia networks; networking protocols; and mobile communications. Recently, his research has been focused on the technologies that can improve users’ quality of experience on carrier networks, and he is actively involved in research activities around network information mining, dynamic user profiling, and network service personalization. He holds a Ph.D. and M.A. degree from Princeton University, and a B.S. degree from Peking University, all in computer science. Dr. Wang publishes in ACM, IEEE, and USENIX journals and conferences. Prior to joining Bell Labs, he built the CoDeeN content distribution network on PlanetLab at Princeton and served on the faculty of Case Western Reserve University. ◆