Supporting Colocated Interactions Using RFID and ... - Semantic Scholar

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Supporting Colocated Interactions Using RFID and Social Network Displays DeaiExplorer uses RFID technology to dynamically derive interconnected social clusters from a publication database. It reveals these social networks on a display, letting colocated conference participants discover interpersonal connections.

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oday, everything seems to be available on the Internet. Many conference proceedings are available online for downloading. It’s even possible for researchers to participate in conferences remotely over the Internet using multimedia communication technologies. You might think such technologies represent a major step toward replacing academic conferences with digital media. But despite information-sharing and Shin’ichi Konomi communication tools’ wide University of Colorado at Boulder availability, many people still seem to prefer spending the Sozo Inoue time and money to physically Kyushu University attend conferences. Indeed, Takashi Kobayashi academic conferences offer many benefits that their digiTokyo Institute of Technology tal counterparts can’t: rich, Masashi Tsuchida interactive presentations and Hitachi demonstrations of late-breaking results, opportunities to Masaru Kitsuregawa make new friends and discover University of Tokyo shared interests, thought-provoking conversations with system designers and developers, and the sharing of informal knowledge that can only be learned through personal interaction. We believe conferences and similar events will continue playing unique and important roles in academic research communities. 48 P ER VA SI V E computing

RFID technology enables a new approach to supporting academic events that’s in line with the vision of pervasive computing: using digital media to augment physical and social spaces1 rather than replacing them with disembodied virtual spaces. Existing projects have explored uses of passive and active RFID tags for a conference check-in service2 and monitoring attendance. 3 However, the rest of the design space for RFID-based conference support is relatively unexplored. Our ultimate goal is to use RFID technology to make academic conferences more meaningful and fun for participants and to help them turn conference experiences into valuable long-term relationships (for example, in the form of successful collaboration projects). So, we built DeaiExplorer (www.tkl.iis.u-tokyo. ac.jp/socialnet), which exploits participants’ RFID conference badges and a large publication database to integrate a research community’s history into the space where social interaction occurs.

Extracting social networks Our basic assumption is that presenting certain information to colocated conference participants can effectively support their communication, an idea that’s much in line with existing projects.3,4 The challenge is to present the right information in the right way in the right con-

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Related Applications

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e can learn from important related projects that explore large computer displays for supporting event participants or help users find relevant people using online information resources. Proactive Display uses public display devices and RFID readers to detect nearby users and show information based on corresponding user profiles.1 Its developers deployed it during formal paper sessions and breaks at an academic conference. Existing information resources including publication databases could potentially be integrated with the system to help conference attendees discover interesting information and social networks without relying on manual data entry or unobtrusive monitoring of attendees’ whereabouts. IntelliBadge uses active RFID tags to track conference attendees’ locations and shows aggregate information on public displays using the location data and user profiles.2 Its developers deployed it at a large academic conference, capturing and statistically analyzing extensive usage data. However, they didn’t report much on the system’s impacts on attendees’ experiences. Polyphonet uses Web mining to extract and display an academic society’s social networks.3 WebFountain is a scalable system for deriving social networks from the Web.4 We believe that wireless identification technologies such as RFID play a key role in integrating sophisticated technologies for uncovering social networks into conference experiences. Developers recently used Polyphonet to build a Web site that also shows traces of contact information that conference participants exchanged.

text to best support participants. Most projects have explored only location information or the kind of information you can easily describe in user profiles, such as names, affiliations, and countries (see the sidebar). In contrast, we explored the potential of information representing implicit human-human relationships in large databases. Social network services such as Orkut (www.orkut.com) and Friendster (www. friendster.com) have accumulated data representing vast webs of personal connections. However, their networks include numerous connections irrelevant to people’s professional research activities yet lack relevant connections. We extracted social networks from a JULY–SEPTEMBER 2006

Recommender systems,5 widely used on e-commerce Web sites including Amazon (www.amazon.com) and eBay (www. ebay.com), learn from customers and recommend products or sellers that could fulfill their needs and wants. Online matchmaking systems have a similar goal of helping users find the right people. Social practices that are developing around these popular systems can inform the design of useful, usable, and privacypreserving social network displays for supporting colocated users.

REFERENCES 1. J.F. McCarthy et al., “Augmenting the Social Space of an Academic Conference,” Proc. 2004 ACM Conf. Computer Supported Cooperative Work (CSCW 04), ACM Press, 2004, pp. 39–48. 2. D. Cox, V. Kindratenko, and D. Pointer, “IntelliBadge: Towards Providing Location-Aware Value-Added Services at Academic Conferences,” Proc. 5th Int’l Conf. Ubiquitous Computing (UbiComp 03), Springer, 2003, pp. 264–280. 3. S. Mori et al., “Web Mining Approach for a User-Centered Semantic Web,” Proc. 2nd Int’l European Semantic Web Conf. Workshop End User Aspects on the Semantic Web (ESWC 05), 2005, pp. 177–187; www. CEUR-WS.org/Vol-137/15_mori_final.pdf. 4. D. Gruhl et al., “How to Build a WebFountain: An Architecture for Very Large-Scale Text Analytics,” IBM Systems J., vol. 43, no. 1, 2004, pp. 64–77. 5. J.B. Schafer, J. Konstan, and J. Riedl, “Recommender Systems in ECommerce,” Proc. 1st ACM Conf. Electronic Commerce, ACM Press, 1999, pp. 158–166.

history-rich publication database called DBLP, 5 which includes over 600,000 publication records of hundreds of thousands of authors in data engineering and related research fields. The algorithms we implemented fi nd personal connections from historical records of research activities by taking into consideration coauthoring, publishing in the same proceedings, citation, cocitation, and bibliographic coupling. Citation relationships in particular can reveal numerous interesting nontrivial personal connections. Well-known approaches for analyzing citation data include bibliographic coupling, which is based on the idea that papers citing the same papers are likely similar, and

cocitation analysis, which is based on the idea that papers cited by the same papers are likely similar. Authors of similar papers could potentially be connected through their similar research interests. Bibliographic coupling can’t uncover papers’ similarity as well as cocitation analysis. Cocitation analysis can’t produce accurate results if the number of papers citing each paper is dramatically different. To extract meaningful connections from citation data, we use the research flow technique,6 which takes into account the conditional probability of citation relationships to eliminate cocitation analysis’ inaccuracy.

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Figure 1. Conference participants used DeaiExplorer (a) to view relevant social networks during breaks and (b) to communicate with other participants.

Bringing social networks into a physical space We built and deployed DeaiExplorer and assessed its impacts on conference participants’ experiences. We selected locations for deployment that we thought would support rather than interfere with our envisioned services. These locations included hallways, lounge areas, poster presentation and demonstration spaces, and Internet access rooms. Our understanding of physical and social constraints and people’s expectations regarding these locations influenced the design of information content (that is, what we present to users), processing and presentation, interaction processes, privacy policies, and strategies for handling breakdowns. Functionality DeaiExplorer communicates with RFID tags attached to participants’ conference badges and visualizes their mutual connections derived from the DBLP. The system scans RFID tags and uses participants’ names to retrieve records in the DBLP database. Participants can either passively view the displayed networks or actively explore the networks through interactive zooming and scrolling. Figure 1 shows people 50 P ER VA SI V E computing

using DeaiExplorer at a conference. The system visually represents participants’ relationships using different colors and node shapes in a graph structure (see figure 2). As figure 2a shows, circles represent nearby users detected by RFID; ellipses people in the users’ social networks; small circular dots, publication items; and rectangles, conferences or journals. Tom presented a paper he cowrote with John and Peter at the 20th International Conference on Data Engineering (ICDE 04) and a paper with Alice and Kim at the ACM/IEEE Joint Conference on Digital Libraries 2002 (DL 02). Two paths connect Tom and Taro because they both presented papers at ICDE 04, and Kim is their common coauthor. Each of these paths indicates their two-step connection through a coauthor or a conference. The system computes up to four-step connections for coauthor relationships. Shiro cited the paper Taro presented at the 29th International Conference on Very Large Data Bases (VLDB 03). System architecture and implementation The system comprises multiple stations and a database server connected via a local network. Each station includes

a PC, two RFID readers, and a display device. You can use different types of display devices to tailor the system considering physical and social context. We used two 42-inch plasma display panels as public displays and three computer monitors as semipublic displays. The RFID chips we used (Hitachi ␮Chips) are one of the smallest available types (0.4 mm × 0.4 mm). They have a read-only memory with a pre-assigned 128-bit unique ID, and they operate at an ultrahigh-frequency (2.45 GHz) band. The tags’ overall size, including both chips and antennas, is 2 mm × 70 mm. They’re passive RFID tags with a read range of approximately 30 cm or less. They don’t have a singulation mechanism, which means one ␮-Chip reader can, in principle, read only one tag at a time. The communication between a reader and a tag isn’t encrypted. We chose passive over active RFID technology primarily because of the cost advantage. Some passive RFID tags that operate at the 950-MHz band provide a singulation mechanism as well as a communication range of several meters. We couldn’t use them when we deployed our system, however; the Japanese Radio Law hadn’t yet been revised for the use of RFID in this band. www.computer.org/pervasive

Figure 2. A fictional social network (a) illustrates different types of nodes and edges, and two sample screenshots (b) and (c) show real networks DeaiExplorer generated for different pairs of researchers. The system generates a similar but isolated network when just one person uses it.

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We used Java and PostgreSQL (www. postgresql.org) to implement the main software components that manage RFID readers, databases, data processing, and visualization as well as agents that integrate and manage these software components. In addition, we used Graphviz (www.graphviz.org) and ZGRViewer (http://zvtm.sourceforge.net/zgrviewer. html) to enable interactive visualization of social structures.

Data analysis We deployed DeaiExplorer at the 21st International Conference on Data Engineering (ICDE 05), which took place in Tokyo from 5–8 April 2005. Prior to the conference, we attached ␮-Chip tags to 500 preregistered conference participants’ badges using detachable adhesive tape (479 of those participants came to the conference). The official conference guidebook included information about the system. We also distributed a sheet explaining our service, technology, and policy for handling privacy-sensitive data as well as how to opt out of the service (by physically removing the tag from the badge). We installed five DeaiExplorer stations in the large hallway outside the meeting rooms for plenary and parallel paper sessions (see figure 1). Also in the hallway were a space for poster presentations, a lounge area, and tables for serving food and drinks during breaks. A local network, which was separate from the conference venue’s Internet infrastructure, connected the five computers. Participants and their communities ICDE is one of the largest data engiJULY–SEPTEMBER 2006

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neering conferences. In 2005, 825 people from over 30 countries and several hundred organizations attended. Figure 3 shows aggregated data illustrating participants’ social structures in the DBLP database. Participants are connected by various relationships that can’t be inferred from conference registration information. The coauthor lines represent connections based on coauthor relationships. A pair of participants who have written a paper together have a connection of distance d = 1. For example, the number of participants who wrote papers with one to 10 other participants is more than 300. A pair having common 52 P ER VA SI V E computing

coauthors has a connection of distance d = 2. Similarly, a pair whose coauthors wrote a paper together has a connection of distance d = 3. A pair whose coauthors have common coauthors has a connection of distance d = 4. The conference/journal line represents connections among participants based on common conference proceedings, academic journals, and so on in which they published. The citation, cocitation, and bibliographic coupling lines represent connections among participants based on citation relationships. For example, 85 participants wrote papers that one to 10 other participants cited.

Seventy-seven of this conference’s participants cited 106 participants’ papers. Participants cited 3,104 nonparticipants’ papers. Participants’ papers were cited by 1,441 nonparticipants. These numbers will likely increase because the citation data in the DBLP database might eventually cover more journals and conferences. Usage data Overall, 293 people (61 percent of those having RFID conference badges) used DeaiExplorer 1,066 times during the four days of the conference. Forty percent used it more than once. They used it for 73 seconds on average, with www.computer.org/pervasive

Figure 4. Evolution of a network that emerged through the use of DeaiExplorer on days (a) one, (b) two, (c) three, and (d) four of the conference.

a standard deviation of 94 seconds. We ignored short periods of failed scans to avoid splitting a use period into less meaningful periods of time. Pairs used the system 40 percent of the time (604 times by 302 pairs) and individuals 60 percent (462 times). This might suggest that the participants also found value in using the system alone to access information about their publications, coauthors, citations, and so on. Single-user uses also took place when the user was with a peer or a group. For example, displaying your own information on a large public display can influence the social interactions of a group standing in front of it. The number of daily uses was the highest on the fi rst day (470 times by 193 people). It was about the same on the second and third days (261 times by 117 people on the second and 267 times by 87 people on the third). On the fourth day, the number was much smaller (68 times by 35 people). The conference ended at 12:30 p.m. on the fourth day without a plenary event and was much less crowded than on previous days. One hundred and eighty-one people used the service for one day, 90 people for two days, 17 people for three days, and five people for four days. During the first three days, the ratio of pair uses increased day by day: 35 percent (123 pairs) on the first day, 41 percent (76 pairs) on the second, 46 percent (85 pairs) on the third, and 36 percent (16 pairs) on the fourth. Figure 4 illustrates the evolution of the network that emerged as participants used DeaiExplorer. Each node represents a participant and each edge (line) a pair use of the system. The lightest gray indicates the nodes and edges that appeared on the first day. Darker colors represent the second and third days, and the darkest gray indicates JULY–SEPTEMBER 2006

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the nodes and edges that appeared on the last day. Multiple connected components grew in parallel, and the number of loops increased as time went by. The three largest hub nodes (the large clusters to the left) represent our staff members, who were near the system for much longer than normal participants.

Qualitative analysis of participants’ experiences Our staff members collected qualitative data through observation, short informal interviews, and survey forms distributed to all conference attendees. Twelve people responded to the survey. In addition, we interviewed seven nonusers after the conference via email. Because the respondents and

interviewees represent only a small portion of participants, we’re unable to draw any general conclusions from statistical analysis of the survey and interview data. So, we present our fi ndings through anecdotal statements and evidence, additionally referring to usage and publication data to support our theses. User experiences Most survey respondents were researchers in databases and data-mining fields. More than half of them had participated in past ICDE conferences, and nine knew what RFID was before the conference. All but one respondent actually used our service. DeaiExplorer had a positive or very positive impact on nine responP ER VA SI V E computing

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dents’ conference experiences. The rest answered that the impact was neutral or nonexistent. Two users’ comments acknowledged positive impacts from the perspectives of fun and utility: • “Fun, useful. Provoked interaction and thought.” • “Found interesting connections to my colleagues.” Despite such positive comments, our usage data show that 60 percent of those having RFID conference badges used the system only once or not at all. We identified several potential reasons for this based on the informal postconference email interviews. Privacy concerns. Two interviewees elab-

orated on common privacy concerns related to RFID technology in general, such as tracking and recording current and past behavior. Another comment was that people who aren’t willing to talk with others wouldn’t like to show their information on a public display. Publication records. A young researcher

was aware that he didn’t have many publication records in the DBLP database and was disappointed to see few papers and coauthors in response to his RFID tag. Such experiences could have discouraged continuous use. In fact, 27 percent of nonusers and 14 percent of users had no publication records in the DBLP database. This suggests the need for universal service design that considers users’ historical contexts. Other issues. One interviewee didn’t

know about the service. Another said he gave up using the system because of a technical error. A busy local professor only participated in a key conference event and his own session, having no time to spare for other conference activities. 54 P ER VA SI V E computing

Support for connecting with peers We hypothesized that most conference participants are interested in professional social networks and that presenting those networks in a contextualized manner could help participants communicate and develop relationships. Participants were interested in different types of information. The largest number of respondents said “names of people who cited their papers” were interesting, followed by “overall shapes of network structures,” “links connecting you and your friend,” “names of your friend’s coauthors,” and “names of people who cited your friend’s papers.” Only a small fraction of respondents were interested in “names of their own coauthors” or “names of the conferences they and/or their friends participated in.” Connections via research flow could also be interesting, personally meaningful, and nontrivial; however, they were much rarer than other types of connections. Sixty percent of respondents used the system with their acquaintances, and 50 percent used it with strangers. Few respondents said that the system impacted their relationships with acquaintances. In contrast, most respondents who used the system with people they had never talked to before said it helped them to communicate. Privacy Different respondents perceived privacy issues in pervasive computing differently. One survey question asked if respondents had privacy concerns related to RFID7 and other pervasive technologies in general. The numbers of respondents who answered “very much,” “somewhat,” and “no concerns” were evenly distributed. Some users raised the issues of unobtrusive and detailed capture as well as the difficulty of selective control over information once it’s captured:

• “Unknown/unobserved collection. Very detailed use.” • “Primarily, the difficulty of selective control—use of information when and only when, and only what for, I choose.” Few respondents had privacy concerns about our service in particular, although one was concerned about “RFID having personal information.” We must take such concerns seriously. For instance, we could have made it clearer that neither our system’s tags nor its readers stored any personal information and that unique IDs retrieved only publicly available information. RFID tags could track participants’ locations, but only half of the respondents were willing to use location-based information systems that track their whereabouts. Many people thought information about who they talked with at a conference was private. Other information many people considered private included records of the sessions, demos, and posters they attended, their hotel name, and their cell phone number. Using publicly available data, we could derive any information and present it anywhere at anytime. However, we might have to think more carefully about what people feel about this kind of derived information, because privacy implications can change when information is processed, aggregated, or presented in different ways. As one user said, Since the amount of papers one has published is clearly visible, some people could potentially be intimidated or unwilling to use the system, especially with a large public display.

Researchers have actively studied privacy issues in personalization and recommender systems,8 pervasive computing,9 www.computer.org/pervasive

and data management.10 Although combining RFID data with existing databases and personalization techniques requires us to consider a wide spectrum of issues, these studies suggest general guidelines for supporting existing social practices,10 modeling benefits and risks,8 and providing users with appropriate feedback and control.9

Discussion Our service’s unique strengths mainly come from the richness of its information content as well as its hardware and software features and certain deployment aspects, such as our collaborative relationship with the conference committees. However, designing RFID applications with a large amount of information introduces unique challenges. Our system demanded a large amount of computing resources to generate different information structures according to different combinations of nearby RFID tags. It might also have to process many data records and lay out complex graph structures in real time. The number of data records and the social networks’ complexity vary enormously depending on recognized RFID tags. The challenge is not only to devise powerful, efficient system mechanisms but also to design a usable environment that meets users’ expectations about flexibility, response time, quality, and information representation. Attention in a social space At the conference, nearby participants gave focused attention to the information the system displayed. Many actively used a mouse to explore different aspects of that information. For passersby or people at a distance, the displayed information would have gotten peripheral attention. To support a wider range of colocated participants beyond the pair of people in front of the screen, we could extend JULY–SEPTEMBER 2006

the system by incorporating different visual and auditory representations that consider the different types of attention people pay to them and how people shift across modes of attention. The read range of RFID tags affects the social space in front of the system in an interesting way. Our system’s relatively short read range required participants to explicitly show their RFID badges to the readers most of the time. So, participants had to negotiate with each other before using the system as a pair. RFID tags that operate at the 950-MHz band with a longer read range would eliminate the need for such negotiation. For future versions of our system, we’re particularly interested in a read range of a few or several meters. A very long read range (such as tens or hundreds of meters), which active RFID systems typically provide,3 could cause privacy problems and confusion. Of course, the read range depends on applications, users, and context. Moreover, maintaining the correct read range could be difficult in certain social RFID applications. For example, human bodies’ presence in a crowded hallway can affect wireless communication and shorten the read range. The process of shifting from unfocused interaction11 to face-to-face engagement or conversations is delicate and complex. Technologies supporting such a process shouldn’t force anyone to talk with anyone else at anytime. They should instead augment the process by also supporting people who don’t want to talk with others in certain contexts. Communities and content Although our analysis is largely based on anecdotal statements and evidence, it suggests that RFID-based applications for augmenting colocated interactions can positively impact certain users. It also suggests that a system design that’s more attentive to participants’ contexts

could increase the number of positively impacted users. The DBLP database wasn’t designed with our service in mind; however, it was useful for realizing the service, partly because it targets the same research community that the conference did. Data resources as rich as DBLP wouldn’t emerge without the identity of the research community they belong to. Strategies to effectively build RFID-based applications for supporting social interactions should make an effort to identify the relevant community and exploit its knowledge capital. Finally, accumulating and analyzing the network structures that emerged through our system’s use (see figure 4) could let us increase our service’s usefulness and efficiency in the future.

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o effectively support social interactions using RFID, we must address several important challenges. How do we build a system that enhances users’ social practices rather than interferes with them? How do we model users and their context? How do we design and represent information content? How do we find and integrate existing information resources? How do we process information efficiently and respond to changes in real time? How do we design usable and useful environments for all users? How do we protect users’ privacy? Our analysis uncovered a set of specific issues and suggested certain design principles for addressing these challenges. Our future work will focus on iterative redesign and assessment for improving and enhancing DeaiExplorer on the basis of these experiences.

ACKNOWLEDGMENTS We gratefully acknowledge Yasunobu Nohara and Kenichirou Oyama’s contributions to the system implementation, deployment, and evaluaP ER VA SI V E computing

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the AUTHORS Shin’ichi Konomi is a research associate at the University of Colorado at Boulder’s Center for LifeLong Learning and Design. His research interests include interaction design and social implications of RFID systems, pervasive computing, human-computer interaction, context awareness, and privacy-enhancing technologies. He received his PhD in computer science from Kyoto University. He’s a member of the ACM, the Database Society of Japan, and the Information Processing Society of Japan. Contact him at Campus Box 430, Center for LifeLong Learning and Design, Univ. of Colorado at Boulder, CO 80309-0430; [email protected]; [email protected]; l3d.cs.colorado.edu/~konomi. Sozo Inoue is an assistant professor in Kyushu University’s Graduate School of Information Science and Electrical Engineering and System Large Scale Integration (LSI) Research Center. His research interests include RFID information systems, security, privacy and reliability in RFID systems, system LSIs, and database systems. He received his PhD in engineering from Kyushu University. He’s a member of the IEEE Computer Society, the ACM, the Database Society of Japan, and the Information Processing Society of Japan. Contact him at the Division of Computer Science and Communication Eng., Graduate School of Information Science and Electrical Eng., Kyushu Univ., 6-1 Kasuga-koen, Kasuga, Fukuoka 816-8580, Japan; [email protected]. Takashi Kobayashi is an assistant professor in the Global Scientific Information and Computing Center, Tokyo Institute of Technology. His research interests include software patterns and architecture, multimedia information retrieval, and data mining. He received his PhD in computer science from the Tokyo Institute of Technology. He’s a member of the ACM, the Information Processing Society of Japan, Japan Society for Software Science and Technology, and the Database Society of Japan. Contact him at Global Scientific Information and Computing Center, Tokyo Inst. of Technology, E2-12, 2-12-1 Ohokayama, Meguro-Ku, Tokyo 152-8550, Japan; [email protected]; www.de.cs.titech.ac.jp/~tkobaya. Masashi Tsuchida is a senior manager in Hitachi’s Software Division, Advanced Middleware Development Department. His research interests include database language design, database architecture, Web form systems, and business intelligence systems. He received his master’s degree in computer science from the University of Tsukuba. He’s a member of the ACM, the Database Society of Japan (DBSJ), and the Information Processing Society of Japan (IPSJ). Contact him at the Advanced Middleware Development Dept., Software Division, Hitachi, Ltd., Hitachi Systemplaza Shinkawasaki, 890 Kashimada, Saiwai, Kawasaki, Kanagawa 212-8567, Japan; [email protected]; [email protected]. Masaru Kitsuregawa is a professor and director of the Center for Information Fusion at the University of Tokyo’s Institute of Industrial Science. His research interests include database engineering, Web archiving and mining, advanced storage system architecture, parallel database processing and data mining, digital earth, and transaction processing. He received his PhD in engineering from the University of Tokyo. He’s a fellow of the Information Processing Society of Japan and the Institute of Electronics, Information, and Communication Engineers (IEICE) and serves as a director of the Database Society of Japan. He’s a member of the IEEE and the IEEE Computer Society. Contact him at the Center for Information Fusion, Inst. of Industrial Science, Univ. of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan; [email protected].

2. T. Watanabe et al., “An RFID-Based Multi-Service System for Supporting Conference Events,” Proc. Int’l Conf. Active Media Technology (AMT 05), IEEE Press, 2005, pp. 435–439. 3. D. Cox, V. Kindratenko, and D. Pointer, “IntelliBadge: Towards Providing Location-Aware Value-Added Services at Academic Conferences,” Proc. 5th Int’l Conf. Ubiquitous Computing (UbiComp 03), Springer, 2003, pp. 264–280. 4. J.F. McCarthy et al., “Augmenting the Social Space of an Academic Conference,” Proc. 2004 ACM Conf. Computer Supported Cooperative Work (CSCW 04), ACM Press, 2004, pp. 39–48. 5. M. Ley, “DBLP Bibliography,” 2005, www. informatik.uni-trier.de/~ley/db. 6. M. Yoshida, T. Kobayashi, and H. Yokota, “Comparison of the Research Mining and the Other Methods for Retrieving MacroInformation from an Open ResearchPaper DB,” IPSJ Trans. Databases (TOD 22), vol. 45, no. SIG7, 2004, pp. 24–32 (in Japanese). 7. S. Garfinkel and B. Rosenberg, eds., RFID: Applications, Security, and Privacy, Addison-Wesley, 2005. 8. N. Ramakrishnan et al., “Privacy Risks in Recommender Systems,” IEEE Internet Computing, vol. 5, no. 6, 2001, pp. 54–63. 9. V. Bellotti and A. Sellen, “Design for Privacy in Ubiquitous Computing Environments,” Proc. 3rd European Conf. Computer-Supported Cooperative Work (ECSCW 93), Kluwer Academic Publishers, 1993, pp. 77–92. 10. R. Agrawal et al., “Hippocratic Databases,” Proc. 28th Int’l Conf. Very Large Data Bases, Morgan Kaufmann, 2002, pp. 143–154. 11. E. Goffman, Behavior in Public Places, Free Press, 1963.

tion. We also thank the ICDE 2005 committees. In particular, we’re grateful to Haruo Yokota, Takashi Tomii, Yoshifumi Masunaga, and Jun Adachi. Hiroyuki Kato and Norio Katayama helped us with the hardware equipment and deployment at the conference. We thank Yukari Shirota, Ichiro Satoh, and Naoharu Yamada for their valuable input for our application scenario development. We also thank Yoji Taniguchi and Takeshi Nakatani for their technical support.

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REFERENCES 1. J.F. McCarthy and D.M. Boyd, “Digital Backchannels in Shared Physical Spaces: Experiences at an Academic Conference,” Extended Abstracts of the 2005 Conf. Human Factors in Computing Systems (CHI 05), ACM Press, 2005, pp. 1641–1644.

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