1520-9202/11/$26.00 © 2011 IEEE P u b l i s h e d b y t h e I E E E C o m p u t e r S o c i e t y computer.org/ITPro 29

SOCIAL NETWORKING & COLLABORATION

Konstantinos Christidis and Gregoris Mentzas, National Technical University of Athens

Dimitris Apostolou, University of Piraeus

Semantic and linked-data technologies are key to leveraging Enterprise 2.0. Integrating such technologies into a mainstream content management system can bring relevant information to employees, encourage innovation, and increase business performance.

Andrew McAfee coined the term “Enter-prise 2.0” in 2006, using it to describe how organizations could apply Web 2.0 technologies in their intranet and ex-

tranets.1 Enterprise 2.0 digital platforms focus on knowledge workers’ practices and output, offering components, such as search queries, that make it easier for them to discover information and create links between information items. Such platforms also help knowledge workers publish content, or-ganize information with tags and annotations, and apply recommendation and notification services.

In the five years since McAfee coined the term, Enterprise 2.0 has evolved to include the use of social software, such as wikis, blogs, and predic-tion markets, to better engage employees, custom-ers, and trading partners and make companies more creative, agile, and productive. For example, in some companies, human resources depart-ments have coupled their management systems with external networking sites such as LinkedIn

Recruiter and with social-media-based sites, such as Twitjobsearch, to find qualified candidates and better advertise job postings. However, social software greatly increases the volume of action-able information within organizations, which in turn increases the challenge of tracking and searching through such information.

Fortunately, new technological approaches have emerged that could further boost the business impact of Enterprise 2.0. In particular, seman-tic and linked-data technologies can leverage the wealth of internal information and couple it with external information to make it actionable.2 Here, we introduce these technologies and describe how we’ve integrated them into a content management system to enhance Enterprise 2.0 platforms.

Semantic and Linked-Data TechnologiesWe use the term “semantic” to denote technolo-gies that encode meaning. The main thrust of

Supercharging Enterprise 2.0

itpro-13-04-apo.indd 29 6/28/11 1:05 PM

30 IT Pro July/August 2011

SOCIAL NETWORKING & COLLABORATION

such technologies in the Semantic Web has been on ontologies and related standards and languages such as the Resource Description Framework (RDF) and OWL (Web Ontology Language).3,4

Statistical and Machine Learning ApproachesIn addition to expert and informal ontologies as a tool for giving semantic structure to data, sta-tistical and machine learning offer approaches that can recognize document topics and extract and classify concepts and their associations.5 For example, topic models are a family of techniques that presuppose the existence of thematic corre-lation between words in documents and employ statistical models to infer these topics (see the sidebar). Such a model could identify that an ar-ticle relates to sports if it contains words such as “game,” “play,” and “win.”

Statistical and machine learning techniques are also widely used in public websites to ana-lyze user behavior and infer user profiles to

support recommendations based on user pref-erences about books or music. User prefer-ences, often in comparison with those of other users, can generate important clues about future preferences.

Linked DataToday’s Web is an ecosystem of public, open, and interlinked data. Public knowledge repositories are resources that provide semantic informa-tion, complying with linked open data standards. Linked open datasets based on publicly avail-able information include DBpedia (a dataset containing data extracted from Wikipedia) and Geonames (a freely downloadable geographical database). Furthermore, access to people-related information from social networking sites such as LinkedIn and the emergence of vocabularies that let groups of people describe their social net-works (such as FOAF—Friend of a Friend) pres-ent new opportunities for companies to move beyond current Enterprise 2.0 platforms.

Topic Models and Latent Dirichlet Allocation

In machine learning and natural language process-ing, a topic model is a type of statistical model for

describing the abstract “topics” that occur in a collec-tion of documents. An early topic model was Thomas Hoffmann’s probabilistic latent semantic indexing (PLSI).1 In 2002, David Blei, Andrew Ng, and Michael Jordan developed latent dirichlet allocation, perhaps the most common topic model currently in use. LDA is a generalization of PLSI that allows documents to have a mixture of topics.2

In topic models, the word order doesn’t matter. Words that convey no meaning, such as “the” or “is,” are ignored, and multiple forms of the same lemma (“sports” and “sport”) are simplified. The model is based on the hypothesis that a probabilistic process generates documents in a two-step random sampling for each word of every document in a collection. The process samples a topic from a topic distribution and samples a word from the selected topic. It then generates documents as mixtures of topics (see Figure A) and topics as a probability distribution over words.

If we invert this generative process and use approxi-mate statistical inference to infer the set of latent topics that generated a collection of known documents, we can use the resulting topic model to semantically represent both analyzed and unseen documents. This model becomes the basis for applications that offer

recommendations, enhance search functionality, and identify related communities.

In recommender systems, latent topics help de-scribe the content of objects. For example, we don’t need a document’s full text to describe the content. Instead, we can use its topics—for example, sports and entertainment might be the topics for an article on the World Cup. Then, regardless of the specific words used, when another document about the World Cup appears, the system can identify it as relevant. Ad-ditionally, this probabilistic topic model can identify the important words in each topic—such as “game” or “team.” Such applications have been implemented in

Figure A. A sampling from two topics. A probabilistic model is used to generate documents on education and environment.

Environment

Education

flower, tree, paper,book, teacher, paper

paper, books,library, book

Topics

Documentstree, leaves,field, nature, tree, flower

itpro-13-04-apo.indd 30 6/28/11 1:05 PM

computer.org/ITPro 3 1

Moreover, some semantic services use state-of-the-art algorithms and knowledge structures to analyze the unstructured text of arbitrary web-sites. For example, Zemanta (www.zemanta.com) and OpenCalais (www.opencalais.com) analyze the content of online articles as they’re written and offer suggestions to the user for adding tags and pictures.

World Cup Website ExampleBusinesses are increasingly using semantic and linked-data technologies; the BBC, for example, has been an early adopter of both technolo-gies.6 The BBC’s 2010 World Cup website used ontology-based modeling of both domain con-cepts (players, teams, groups, and games) and authorship assets (stories, blogs, profiles, images, videos, and statistics) to produce a dynamic pub-lishing platform.

The site integrated a variety of data from both internal and external sources. For example, the BBC platform mapped Press Association statistics

to webscale identifiers to assimilate the data seamlessly into its knowledge repository. In addi-tion, journalists annotated resources with web-scale identifiers so they could map the resources to the BBC’s sports ontology. An autocategoriza-tion feature helped journalists annotate stories by providing unambiguous entities based on the ontology.

The ontology also allowed inference. For ex-ample, the reasoner could infer the relationships between groups and teams and include articles with “Italian team” annotations on the “Group G” page. The sports ontology could help answer visitors’ questions, too. For example, a passionate fan could find a collection of resources related to the national team of England and to player Wayne Rooney. The platform could mash up in one place all statistics, games, related blog posts, and stories. Additionally, the platform could col-lect the latest Web stories about Rooney from external Web resources and present them to the visitor.

Topic Models and Latent Dirichlet AllocationWikipedia articles,3 tag recommendations,4 enterprise resources,5 and even nontextual objects.6

Topic models can also enhance search functionality. Because certain words appear together in topics, the search algorithm can deduce a semantic relationship. This relationship can help expand the user search que-ries. For example, if you’re looking for “team games,” you’re likely interested in “sports.” These similarities can be stored as an index file and used to support enterprise search functionality.5,7

Finally, topics can summarize individuals’ interests and thus can help find latent communities. For ex-ample, topic models can help identify groups of people who are reading, writing, or commenting on sports articles or literature reviews. Topic models help support social networking sites8 and can generate social maps in enterprise social software.9

References1. T. Hofmann, “Probabilistic Latent Semantic Indexing,”

Proc. 22nd Ann. Int’l ACM SIGIR Conf. Research and

Development in Information Retrieval, ACM Press, 1999,

pp. 50–57.

2. D.M. Blei, A.Y. Ng, and M.I. Jordan, “Latent Dirichlet

Allocation,” J. Machine Learning Research, vol. 3, Mar.

2003, pp. 993–1022.

3. C. Haruechaiyasak and C. Damrongrat, “Article

Recommendation Based on a Topic Model for Wikipedia

Selection for Schools,” Proc. 11th Int’l Conf. Asian Digital

Libraries: Universal and Ubiquitous Access to Information

(ICADL 08), Springer-Verlag, 2008, pp. 339–342.

4. R. Krestel, P. Fankhauser, and W. Nejdl, “Latent Dirichlet

Allocation for Tag Recommendation,” Proc. Third ACM

Conf. Recommender Systems (RecSys 09), ACM Press, 2009,

pp. 61–68.

5. K. Christidis and G. Mentzas, “Using Probabilistic Topic

Models in Enterprise Social Software,” Proc. Business

Information Systems (BIS 2010), LNBIS 47, Springer Berlin

Heidelberg, 2010, pp. 23–34.

6. K. Christidis, D. Apostolou, and G. Mentzas, “Exploring

Customer Preferences with Probabilistic Topics Models,”

presented at the European Conference of Machine

Learning, Workshop on Preference Learning, 2010; www.

ke.tu-darmstadt.de/events/PL-10/papers/3-Christidis.pdf.

7. L. Park and K. Ramamohanarao, “Efficient Storage and

Retrieval of Probabilistic Latent Semantic Information for

Information Retrieval,” J. Very Large Data Bases, vol. 18,

no. 1, 2009, pp. 141–155.

8. W.Y. Chen et al., “Collaborative Filtering for Orkut

Communities: Discovery of User Latent Behavior,”

Proc. 18th Int’l Conf. World Wide Web, ACM Press, 2009,

pp. 681–690.

9. S. Zhao et al., “Who Is Talking about What: Social

Map-Based Recommendation for Content-Centric Social

Websites,” Proc. 4th ACM Conf. Recommender Systems,

ACM Press, 2010, pp. 143–150.

itpro-13-04-apo.indd 31 6/28/11 1:05 PM

32 IT Pro July/August 2011

SOCIAL NETWORKING & COLLABORATION

The platform mapped all data to DBpedia and other linked-data identifiers. The content avail-able on the website also became available to ex-ternal websites. If a sports blog needed to present dynamic information on World Cup matches, it could request this information from the BBC. A user might ask, “Which games has Wayne Rooney played in?” The BBC could then respond in a machine readable format that he had played against Germany, Slovenia, Algeria, and the US, and the information could be styled and presented in the sports blog.

Enhancing Enterprise 2.0In the Enterprise 2.0 context, semantic tech-nologies can help analyze community-generated content and an employee’s social environment to support everyday tasks with recommendations. In particular, such technologies can analyze so-cial networks to recommend relevant content and appropriate tags and identify like-minded people (see Figure 1).

Using semantic technologies, employees have access to sophisticated search and filtering func-tionality that’s based on the content’s underlying semantics. This functionality leverages networks of trusted individuals and their opinions about particular items. The search experience becomes social by considering varying metadata sources through the collaborative discovery of webpages,

tags, social rankings, and com-ments on bookmarks, news, and other webpages.

Using linked-data technologies, employees aren’t confined to content generated and residing within their organization. Content from external repositories can be seamlessly inte-grated with the enterprise Web, and some enterprise data can be pub-lished to the Web of linked data. The enterprise Web thus reaches beyond the organization limits to content generated within partner networks and public social networks and to the social media at large.

Yet Enterprise 2.0 isn’t just about content—it’s also about people. Suc-cessful community networks in the enterprise space (both internally and externally) reveal the emergence of

a vital new channel for innovation, customer relationships, and productive output. Social media linked to enterprise software can help users discover experts and communicate with customers. Employees can receive feedback from people in the networks who can help them design, build, test, and sell new products and services.

Integration with a Content Management SystemCompanies can ease the challenge of enhancing Enterprise 2.0 platforms by using one of many content management systems that offer basic community support. Drupal (www.drupal.org) is one of the top-three open source content man-agement systems in terms of market share,7 with a strong community of developers produc-ing modules that extend its basic functionality. Drupal’s latest version integrates state-of-the-art semantic technologies and includes taxono-mies and related tools for their management and portability. Furthermore, it implements RDFa (a W3C specification for attributes of Web content) by default, so it maps all data structures to com-patible ontologies and can export content.8 Also, an API is available for building custom modules and preparing queries, and various modules are available for linking with external networks and semantic services.

Figure 1. Enhancing Enterprise 2.0 with semantic and linked-data technologies. These technologies help build connections between customers, social networks, and social media.

Enterprise

Recommendations

Trading partners

Customernetwork

World Wide Web

Social platform

Search

Web of linked andsocial-network data

Employeenetwork

Public socialnetworks andsocial media

Text and activityanalysis

Content

Semanticservices

Linked dataContent integration

Expert finding, community building

Social search, analytics, and filtering

itpro-13-04-apo.indd 32 6/28/11 1:05 PM

computer.org/ITPro 3 3

As part of the Organik research project, we’ve integrated additional state-of-the-art seman-tic and linked-data technologies into Drupal (see www.organik-project.eu and http://organik.opendfki.de/wiki/Drupal). In particular, we em-ploy latent dirichlet allocation (LDA), a prominent statistical text-mining approach for providing recommendations, enhancing search functional-ity, and identifying communities (see the sidebar).

Here, we demonstrate how our Drupal imple-mentation could enhance a company’s Enter-prise 2.0 platform.

Sample ImplementationBob works for a company that trains marine of-ficers on how to handle situations on ships in ac-cordance with the latest laws and regulations. As a trainer, Bob typically spends part of his morning

skimming through the news headlines on various maritime websites. He scans through items that could help him create new training offerings and communicate with prospective and current clients.

One article of interest discusses a new regula-tion that’s expected to become effective next year. Bob adds a social bookmark to the article so the rest of the company can view it. The Drupal sys-tem uses probabilistic topic models to analyze the text and Bob’s previous behavior so it can recom-mend possible keywords to describe the book-mark. Bob chooses “2011,” “decontamination,” and “regulation.” (See Table 1 for a description of this functionality and the related Drupal implementation).

The system sends notifications about the new bookmark to people in the company with related interests. The system can determine their

Table 1. Enhanced Enterprise 2.0 implementation for a training company for the maritime industry.

Scenario Functionality Enhanced Drupal platform

Bob scans news items and posts relevant material and keywords.

The system recommends keywords for annotating items, accelerating annotation and reusing enterprise-taxonomy terms.

The Organik tag recommendation module enables collaborative- and content-based tag recommendations from probabilistic topic models.

The system notifies colleagues and recommends similar resources.

The system notifies users of relevant new content and related resources, helping employees discover how similar business needs were previously tackled and helping customers discover similar offerings.

Drupal can be extended with community developed modules and APIs for resource recommendations such as MoreLikeThis and the Recommender API.

People discuss the article and work on a new training session as an internal project.

Internal communities emerge in enterprise social software so employees can efficiently complete internal projects.

Groups can become a part of the social enterprise software by employing Drupal Groups.

The system connects to a professional social networking site.

The system integrates with social networking sites and interconnects with public social networking tools to help build a visible company profile and identify experts.

Drupal can be interconnected with various external social sites such as LinkedIn, Facebook, and Twitter.

Bob posts an entry to the public company website on new training.

Content published from the enterprise social platform is automatically available in a Semantic Web compatible way.

Available modules provide ontology integration, statement abstraction layers, import/export functionality, and additional visualization options.

A seminar attendee suggests adding case studies. Laura’s search for “case studies” returns material for new slides.

Simple keyword searches are enhanced by expanding queries using taxonomies and ontologies and by having the system infer semantic relationships based on probabilistic topic models, which it can use for query expansion and search ranking.

Search extensions are available from open source vendors like Apache, while Organik also provides advanced search modules.

Laura blogs about the updated training. An external semantic analysis services tags her post and adds pictures.

Public repositories can be used in order to automatically generate a tree of concepts. Tagging and inserting media objects can be assisted by external semantic services.

OpenCalais (www.opencalais.com) and Zemanta (www.zemanta.com) can tag and recommend pictures. Also, Organik lets users bootstrap corporate taxonomies using DBpedia.

Laura tweets a link to this new post to the seminar attendee, who can offer feedback.

Colleagues or clients can comment on or rate company products, services, or ideas, encouraging the exchange of ideas between employees and between employees and external users (customers).

Drupal includes a commenting system, and several community modules support explicit feedback in the form of ratings.

itpro-13-04-apo.indd 33 6/28/11 1:05 PM

34 IT Pro July/August 2011

SOCIAL NETWORKING & COLLABORATION

interests by viewing the employee profiles or using specialized collaborative-filtering tech-niques. Furthermore, employees can specify the frequency of such notifications and the delivery method (messages, emails, or RSS).

Additionally, the system can exploit community- developed modules and APIs to identify and recommend resources related to the news item (based on the content and users’ preferences). Bob receives several suggestions, one of which describes an existing training offering about decontamination regulations.

Bob and his colleagues discuss the possibility of a new training offering. They exchange com-ments and ideas through a social website, which saves the discussion for future reference. They decide to start a new offering as a small internal project. After getting approval, they create a new community for this project using the Organic Groups module (http://drupal.org/project/og) and set up a simple set of roles and permissions.

The system recognizes the interests and expe-riences of the group members and connects to a professional social networking site to suggest additional members. It retrieves user profiles of people from outside and within the company with related expertise. Bob reviews the profiles and sees an old friend from his previous com-pany, so he asks him to participate.

As the project reaches an end, Bob posts a new entry to the public company website titled, “New Training Offerings for Upcoming Regulations.” Available modules provide ontology integration, statement abstraction layers, import and export functionality, and additional visualization op-tions. These modules express Bob’s entry in open vocabularies, complying with Semantic Web standards, so the entry automatically be-comes a part of the Web of linked data.

A few months later, a client who has attended the new seminar has a suggestion, which she posts in a microblogging service: “Why not in-clude specific case studies in the seminar?” She tags the post with the company name, “#MaritimeTrainingCo,” so the project commu-nity automatically receives a notification. The community discusses the possible addition, and most think it’s a good idea. One person, Laura, then searches for relevant resources using the term “case studies.” The system automati-cally links the term with related terms, such as

“teaching examples” and “teaching illustrations.” Laura uses material from the result set to create new slides and update the presentation.

Laura creates a blog post about the updated training offering and publishes it in the public enterprise blog. Interconnection with external semantic analysis services helps her correctly tag her post and add relevant pictures.

Laura tweets a link to this new post to the origi-nal client. Drupal’s commenting system then in-vites the client to provide ratings and feedback about the services provided. This client will also re-ceive recommendations for new training offerings.

W e’ve deployed and evaluated the en-hanced Drupal platform in five small and medium enterprises. The platform

helped employees find, identify, record, and cor-relate internal and external information. It also made business information more readily avail-able through the use of wikis, blogs, social book-marking, and the integration of public data. Fur-thermore, its system-tagging capabilities helped organize and cross-reference this information.

Linking enterprise data with publicly available data requires due diligence with regard to the company’s core business. Nevertheless, com-bining easy access to employee-generated and socially enriched business information with rel-evant public information and contributions has potential for increased innovation and business performance.

AcknowledgmentsThe European Commission partially funded research presented in this article under contract FP7: Research for the Benefit of SMEs 222225. We thank all project partners for their critical discussions and insightful contributions.

References 1. A.P. McAfee, Enterprise 2.0: New Collaborative Tools for

Your Organization’s Toughest Challenges, Harvard Busi-ness Publishing, 2009.

2. A. Passant et al., “Enhancing Enterprise 2.0 Eco-systems Using Semantic Web and Linked Data Tech-nologies: The SemSLATES Approach,” Linking Enter-prise Data, D. Wood, ed., Springer Science+Business Media, LLC, 2010, pp. 79–102.

3. V. Janev and S. Vranes, “Semantic Web Technologies: Ready for Adoption?” IT Professional, vol. 11, no. 5, 2009, pp. 8–16.

itpro-13-04-apo.indd 34 6/28/11 1:05 PM

computer.org/ITPro 3 5

4. T. Jepsen, “Just What Is an Ontology, Anyway?” IT Professional, vol. 11, no. 5, 2009, pp. 22–27.

5. A. Halevy, P. Norvig, and F. Pereira, “The Unreason-able Effectiveness of Data,” IEEE Intelligent Systems, vol. 24, no. 2, 2009, pp. 8–12.

6. S. Oliver, “Enhancing the BBC’s World Cup Coverage with an Ontology Driven Information Architecture,” presented at the Int’l Semantic Web Conf. (ISWC 10), 2010; http://iswc2010.semanticweb.org/pdf/550. pdf.

7. R. Shreves, “Open Source CMS Market Share,” white paper, Water&Stone, Summer 2008; www. waterandstone.com/downloads/2008OpenSource CMSMarketSurvey.pdf.

8. S. Corlosquet et al., “Produce and Consume Linked Data with Drupal!” The Semantic Web—ISWC 2009, LNSC 5823, Springer, pp. 763–778, 2009.

Konstantinos Christidis is a PhD candidate in the School of Electrical and Computer Engineering at the Na-tional Technical University of Athens (NTUA), Greece. His research interests include social computing, semantic tech-nologies, machine learning, and latent semantic analysis.

Christidis has a Diploma in electrical and computer engi-neering from NTUA. Contact him at kchrist@mail.ntua.gr.

Gregoris Mentzas is a full professor in the School of Elec-trical and Computer Engineering at the National Technical University of Athens (NTUA), Greece, and director of the Information Management Unit at the Institute of Commu-nication and Computer Systems, Athens. His expertise is information technology management, and his research con-cerns e-government, knowledge management, and e-service technologies. Mentzas received his PhD in operations re-search and information systems from NTUA. Contact him at gmentzas@mail.ntua.gr.

Dimitris Apostolou is an assistant professor in the De-partment of Informatics at the University of Piraeus, Greece. His research focuses on group support systems, knowledge-based decision support systems, and knowledge management systems. Apostolou received his PhD in elec-trical and computer engineering from NTUA. He’s a mem-ber of the IEEE Computer Society. Contact him at dapost@unipi.gr.

PURPOSE: The IEEE Computer Society is the world’s largest association of computing professionals and is the leading provider of technical information in the field.MEMBERSHIP: Members receive the monthly magazine Computer, discounts, and opportunities to serve (all activities are led by volunteer members). Membership is open to all IEEE members, affiliate society members, and others interested in the computer field.COMPUTER SOCIETY WEBSITE: www.computer.org

Next Board Meeting: 23–27 May 2011, Albuquerque, NM, USA

EXECUTIVE COMMITTEEPresident: Sorel Reisman*President-Elect: John W. Walz;* Past President: James D. Isaak;* VP, Standards Activities: Roger U. Fujii;† Secretary: Jon Rokne (2nd VP);* VP, Educational Activities: Elizabeth L. Burd;* VP, Member & Geographic Activities: Rangachar Kasturi;† VP, Publications: David Alan Grier (1st VP);* VP, Professional Activities: Paul K. Joannou;* VP, Technical & Conference Activities: Paul R. Croll;† Treasurer: James W. Moore, CSDP;* 2011–2012 IEEE Division VIII Director: Susan K. (Kathy) Land, CSDP;† 2010–2011 IEEE Division V Director: Michael R. Williams;† 2011 IEEE Division Director V Director-Elect: James W. Moore, CSDP* *voting member of the Board of Governors †nonvoting member of the Board of Governors

BOARD OF GOVERNORSTerm Expiring 2011: Elisa Bertino, Jose Castillo-Velázquez, George V. Cybenko, Ann DeMarle, David S. Ebert, Hironori Kasahara, Steven L. TanimotoTerm Expiring 2012: Elizabeth L. Burd, Thomas M. Conte, Frank E. Ferrante, Jean-Luc Gaudiot, Paul K. Joannou, Luis Kun, James W. MooreTerm Expiring 2013: Pierre Bourque, Dennis J. Frailey, Atsuhiro Goto, André Ivanov, Dejan S. Milojicic, Jane Chu Prey, Charlene (Chuck) Walrad

EXECUTIVE STAFFExecutive Director: Angela R. Burgess; Associate Executive Director, Director, Governance: Anne Marie Kelly; Director, Finance & Accounting: John Miller; Director, Information Technology & Services: Ray Kahn; Director, Membership Development: Violet S. Doan; Director, Products & Services: Evan Butterfield; Director, Sales & Marketing: Dick Price

COMPUTER SOCIETY OFFICESWashington, D.C.: 2001 L St., Ste. 700, Washington, D.C. 20036-4928Phone: +1 202 371 0101 • Fax: +1 202 728 9614Email: hq.ofc@computer.orgLos Alamitos: 10662 Los Vaqueros Circle, Los Alamitos, CA 90720-1314Phone: +1 714 821 8380 • Email: help@computer.org

MEMBERSHIP & PUBLICATION ORDERSPhone: +1 800 272 6657 • Fax: +1 714 821 4641 • Email: help@computer.orgAsia/Pacific: Watanabe Building, 1-4-2 Minami-Aoyama, Minato-ku, Tokyo 107-0062, JapanPhone: +81 3 3408 3118 • Fax: +81 3 3408 3553Email: tokyo.ofc@computer.org

IEEE OFFICERSPresident: Moshe Kam; President-Elect: Gordon W. Day; Past President: Pedro A. Ray; Secretary: Roger D. Pollard; Treasurer: Harold L. Flescher; President, Standards Association Board of Governors: Steven M. Mills; VP, Educational Activities: Tariq S. Durrani; VP, Membership & Geographic Activities: Howard E. Michel; VP, Publication Services & Products: David A. Hodges; VP, Technical Activities: Donna L. Hudson; IEEE Division V Director: Michael R. Williams; IEEE Division VIII Director: Susan K. (Kathy) Land, CSDP; President, IEEE-USA: Ronald G. Jensen

revised 5 May 2011

itpro-13-04-apo.indd 35 6/28/11 1:05 PM