Use r - G e n e r ate d Co nte nt

IEEE Pervasive Computing, 7(4),pp. 20-27. (Draft version)

Object-Blog System
for EnvironmentGenerated Content
The Object-Blog system automatically converts raw sensor data
into environment-generated content, facilitating data searching
and browsing.

V

annevar Bush’s Memex,1 which
stores everyday artifacts and links
them for future access, has inspired
many research projects. MyLifeBits,2 for example, stores all typesof captured digital media, including photos, videos, and sounds. The applications described by
Khai Truong, Gregory Abowd, and Jason Brotherton store and support events such as lecturesand meetings by using capture devices (including
cameras, microphones, and electronic whiteboards) installed
Takuya Maekawa,in indoor environments. 3 By
Yutaka Yanagisawa,
attaching tiny ubiquitous senYasue Kishino, Koji Kamei,
sors to various indoor objects
Yasushi Sakurai,
and furniture, we can observe
and Takeshi Okadome
their use and phenomena occurNTT Communication Science
ring around them. These senLaboratories
sors include radio frequency
identification (RFID) tags,
accelerometers, thermometers,
cameras, microphones, and object-specific sensors for doors, drawers, and cabinets that detect
their opening and shutting.4 Recent reductions indata storage costs make it possible to store realworld data related to normal daily living observedwith ubiquitous sensors. However, Ramesh Jain
suggests that many raw logs will be write-only
logs; they will be written but never accessed.5 Jain
also recommends processing raw logs into meaningful data events and aggregating the events intoa kind of chronicle. We agree with these suggestions. To move from raw logs to chronicles in the

78 P ER VA SI V E computingubiquitous-sensor environment, our approach
involves automatic content generation from realworld phenomena.
We automatically generate understandable,user-friendly formatted content such as text reports of real-world events and of statistics calculated from sensor data. This environment-generated content (EGC) includes reports and diaries
created by users with reference to other EGC
or to events detected in the environment. To establish design guidelines for an EGC system indaily-living environments, we first use a questionnaire survey to clarify the kinds of data thatusers want to record in their daily lives and thecharacteristics of that data. The survey results
indicate that large quantities of EGC would be
generated every day. Also, the recorded data canserve many purposes, including aiding memory,
improving daily life, and sharing information.
To achieve these purposes while dealing withlarge quantities of EGC, we established six requirements for storing, managing, and providing EGC. We designed the Object-Blog system(www.kecl.ntt.co.jp/csl/sirg/people/maekawa/egc) as an EGC service application that meetsthese six requirements and incorporates oursurvey’s findings. We assume an indoor environment in which wireless sensor nodes equippedwith sensors such as accelerometers and thermometers are attached to objects. In the system,
personified objects automatically post entries toa weblog about events those objects experienceand their use. To our knowledge, ours is the first

Published by the IEEE CS n   1536-1268/08/$25.00 © 2008 IEEEIEEE Pervasive Computing, 7(4),pp. 20-27. (Draft version)

(a)

(b)

Figure 1. Weblog, with entries describing (a) a door and (b) temperature changes during one day.

comprehensive system that ranges fromgenerating Web content about normal
daily lives to providing users with methods for accessing this content.

Toward EGC services
Researchers have developed many sensor-data-processing methods to detect
activities of daily living (ADLs) suchas making tea, vacuuming, and sleeping,6 and object-specific events such as
opening and shutting doors and drawers.4 We employ these methods of detecting meaningful events to generatecontent. Our goal is to summarize therequirements for generating, storing,and providing EGC, and to design andimplement an EGC service that satisfies these requirements. We try to store
everything automatically through ubiquitous sensors and provide the stored
sensor data by automatically converting it into content. Also, because ourapproach generates large quantities of
EGC, we provide users with effective
EGC access methods.
Many studies have implemented
recording and access applications for

October–November 2008

particular circumstances such as meetings, lectures, academic conferences,
battlefields, travel, Web browsing, andonline shopping. 2,3 There are also several studies that detect daily activities
by employing wearable sensors, including cameras and accelerometers,
to make media clips when users perform specific activities.7 Few studies,
however, have dealt with a comprehensive system that ranges from recording various normal daily events by using environment-embedded sensors toproviding users with methods for accessing those events. Gamhewage De
Silva, Toshihiko Yamasaki, and Kiyoharu Aizawa have implemented a lifelogging system with a GUI that permits easy retrieval of multimedia datasuch as video, audio, and floor sensor
data, all captured from a home-like environment.8 Users can retrieve video
recorded during the occurrence oftypical events, such as a change in thehouse-lighting level. The system isn’t acontent generation system but a multimedia data provision system designed
to be a memory aid. Mik Lamming andMike Flynn originally implemented
systems that generated episodes abouthuman activities.9 P epys generates an
episode based on user location dataand sends the episode to a user by email
at the end of each day.10 The episodeconsists of text-formatted summaries
of a day’s activities. Forget-Me-Not
provides daily summaries for mobile
devices such as PDAs.9 Because mobile devices have small screens, ForgetMe-Not provides these summaries as
icons. These systems are designed as
memory aids. Our lifelog system, on
the other hand, provides daily-life
management and information sharing,as well as memory aids.
Figure 1a shows a weblog entry that
an office entrance door posted when it
was opened during the night. Figure 1b
shows a weblog entry of temperaturechanges. This entry includes a movie
that shows the temperature changes ina room during one day created by using thermometers attached to objects.
(A virtual object posted this entry.) Our
EGC system automatically generates
such entries in the weblog.

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IEEE Pervasive Computing, 7(4),pp. 20-27. (Draft version)

User-Generated content

Questionnaire survey
Khai Truong, Elaine Huang, and Gregory Abowd investigated desired capture
applications through questionnaires.11
In their investigation, however, the participants proposed only about a dozen
applications. In addition, although thesurvey included some active recording
applications such as voice memo pads,

Anind Dey and his colleagues described desired context-aware applications.12 Because object-related applications accounted for the majority in thesurvey, they stated that users focus on
objects in their environment.12 This
statement accords with our result.
Classifying the data according to
the participants’ intended purpose lets

The main idea is to make the game persistently
available to the players anytime. ALIGN THE TOP
BAR WITH BASELINE. NEED PULL QUOTE.
it didn’t address the requirement of realizing automatic (passive) recording byusing environment-embedded sensors.
We used a 90-minute free-form questionnaire to ask five participants (notresearchers) what kinds of data theywanted to store and how they wanted
to reuse the stored data. Before giving
the participants the questionnaire, we
explained sensing and activity recognition technologies and asked them toassume an indoor environment such asa future home or office in which eventsand activities could be detected.
In our surveys, we obtained 66 answers that included the data and associated services the participants wanted
to store and reuse. We categorized thisdata into three types: object related(75.8 percent), person related (21.2 percent), and location related (3.0 percent).
Object-related data concerns an object’s
states, uses, and related activities—for
example, “Record which shoes to wear
to my office. I want to monitor this because wearing the same shoes for several consecutive days is not good for the
shoes.” Person-related data concerns aperson’s states—for example, “If mysleep time is recorded, I can manage
it and check my biorhythms.” Location-related data concerns a location’s
states—for example, “If the temperature in a room is recorded, I can know
which part of the room is too cool.”
Most data is object related.

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P ER VA SI V E computing 

us categorize this data as memory aids
(51.2 percent), daily-life management(83.3 percent), and information sharing (22.7 percent). Users can employ the
daily-life management data to improvetheir lives by exploiting stored data andmonitoring daily activities and environments. Most data is multipurpose—for
example, “Record what we eat. Suchinformation is useful for maintaining awell-balanced diet.” This example has
two purposes: as a memory aid and fordaily-life management. Another example
is “Monitor CD players. We can know
who likes what kind of music.” This canserve as a memory aid and for information sharing. “Record flower wateringto control the amount of water provided
because in an office with multiple persons there may be too much or too little
watering” has daily-life management
and information-sharing roles.
We also found that most data in theanswers should be translated to content
periodically (87.9 percent) or when auser requests it (93.4 percent)—for example, “Record the number of visits to
the restroom per day. Then I can takecare of my health and monitor my digestive system.” By computing sensordata statistics, EGC systems can issue
restroom reports every week or for agiven number of days to meet the user’s
request. From this result, we see that thesystems continually generate a considerable amount of content. Thus, users

need effective content-access methods.
The systems translate some data to content when particular events occur (21.2
percent)—for example, “Monitor an
entrance because recordings of comings
and goings at unusual times are usefulfor security.” This entrance example isenvironment dependent, because what
constitutes an unusual time is different
for homes than for offices. Of the data
we obtained, 60.6 percent of services required domain-specific information.
Dey and his colleagues obtained somecontext-aware applications via questionnaire surveys12 —for example, “Whenclose friends are over, they know a lotof my music, so I’d like to expose themto some new quirky stuff” that reflects
the participants’ preferences. Unlike thecase in context-aware applications, allservices obtained in our survey—suchas temperature recording, sleep time recording, and meal recording—would be
useful to any user. Also, 72.8 percent
of all data (including duplication) overlapped with other data, and the services
desired by users were very similar. Thisis because our survey focused on logging systems for environments—that is,for everyone rather than for specific individuals, which would reflect the participants’ preferences.
Six requirements for storing,
managing, and providing EGC
Our survey indicates that a lot of EGC
is generated automatically. Here, we
focus particularly on how to achieve
the three purposes of memory aid,
daily-life management, and information sharing, while dealing with large
quantities of EGC. We established thefollowing six requirements for storing,
managing, and providing EGC by referring to studies that also deal with massive amounts of content such as lifetime
data and experience:• Generating content. We generate EGC
with, for example, texts, graphs, andfigures. Figure 2 conceptualizes theprocess from EGC generation to EGC
use. The upper portion shows content

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IEEE Pervasive Computing, 7(4),pp. 20-27. (Draft version)

Figure 2. Concept of environmentgenerated content service. The EGC
system calculates events and statistics
by processing sensor data in real time.
The system generates episodes from theevents and statistics. Users can browse
and search for the generated episodes.
They can also annotate the episodes,
link them, or make an episode suchas a diary.

automatically generated from eventsand statistics obtained through sensordata processing. We call such content
an episode. Examples of events are “adoor is opened” detected by a door
sensor, and an ADL of making tea inferred by using sensors attached to akettle and cups. Examples of statistics
are the temperature changes exhibitedby an object during a week, and thenumber of times plants are wateredeach day. Generated content such as atext-formatted report of an observed
event or a graph depicted according
to calculated statistics is useful fordaily-life management because users
can easily grasp characteristics ofthe event and the statistics of theirlives at a glance. We can also leverage generated content such as text for
text searches.
• Search and collection. The management of massive amounts of data is
shifting from a hierarchical structure
to search and collection—for example, Gmail, Macintosh Spotlight, andWindows Desktop Search. We deal
with a large number of episodes andevents to aid memory and achieve
daily-life management. Thus, oursearch interface must enable searching for episodes and events using simple keywords. Incorporating multiple
ways of looking at things helps foster
user understanding and insight in relation to those things.2 We must also
provide various visualization methods, such as sorting search results
for an episode in terms of time andcategorizing the search results foran event. We can improve episode

October–November 2008

Link

Episode• Annotating• Linking• Episode making• Browsing• Searching

Event

Statistics

Sensor data

Sensor data

Sensors

accessibility by categorizing a massive number of episodes similar to theway Gmail uses labels.
• Sharing. Many studies aim to share
recorded experiences, memories, andknowledge. 3 Our survey obtainedsome information-sharing services.
Information sharing among users in
an environment could encourage communication and mutual understanding. To achieve information sharing,
we need to provide generated episodes
through easily accessible media.
• Summarization. In environments
in which events and changes occur
frequently, users can miss some ofthem. Ramesh Jain suggested thatchronicling systems could provide
summaries of important events. 5 We
must provide summaries of events(episodes) such as the index pagesof news sites. We can improve thesesummaries by highlighting important episodes. This function is usefulfor daily-life management.
• Annotation. Nontext media such as
those representing an episode containing graphs and videos requireannotations for a keyword search. 2
Even for a text episode, if users canannotate the episode with what they
think about it, the annotations will

help them to reuse it.
• Linking. MyLifeBits links media andtheir annotations, as well as the original media and other media made fromthem, so that users can find context
and commentary related to the media.2 In general, links enable easy retrieval of related information. In ourcase, if we connect one episode withanother that’s written with reference
to the former via a two-way link, we
can easily trace the ancestry and posterity of episodes. An event should
also be connected with an episodethat refers to the event. Ali Mazalek, Glorianna Davenport, and Hiroshi Ishii have implemented a systemthat provides multiviewpoint storiesof characters (storytellers) by using atangible interface.13 Providing multiple viewpoints of an event lets users
grasp a holistic image of the event. Assume many users write episodes aboutan event. If the event connects the episodes, we can easily provide the users
with various views of the event.

Object-Blog system
We implemented the Object-Blog system in our experimental environment,
which Figure 3a shows. Ten workers (not researchers) undertook their

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IEEE Pervasive Computing, 7(4),pp. 20-27. (Draft version)

User-Generated content

Break-time goods

Radio
ZigBeecommunication
Sensors
Thermometer
Illuminometer
3-axis accelerometer
Direction sensor

Break
area
Cookingequipment
Refrigerator

Officearea(a)ordinary work from 9 a.m. to 5 p.m.
every weekday. We installed eight video
cameras and two microphones in theroom and taped our implemented sensor nodes to 50 daily objects such as
doors, drawers, cups, a coffee maker,
a kettle, a watering can, slippers, anda toothbrush. Figure 3b shows a sensor node and its specifications. We used
simple generic sensors to detect object
usage. Emmanuel Tapia, Stephen Intille,
and Kent Larson employ a low-power
consumption sensor node especially designed to detect object use.14 The node
wakes up and senses its acceleration
only when a piezoelectric-film sensor
detects motion.
Design guidelines
We established design guidelines for
the EGC provision service to meet our
six requirements and incorporate theknowledge obtained from the questionnaire survey. These guidelines address
four essential elements: Web publishing, object centricity, rule-based system, and easy setting. Although we describe these essentials in relation to the
Object-Blog system, the basic idea behind them can apply to any other EGC
provision service.
Web publishing. Many logging applica-

tions use the Web to share experiencesand events. 3 Because the responses to82

P ER VA SI V E computing 

Figure 3. Our experimental
settings: (a) experimental
environment and (b) our
implemented sensor nodeand its specifications (radio
communication protocol,
installed sensors, andsampling rate of sensors).

Audio-time equipment

Sampling

30 Hz

(b)

the survey also mentioned informationsharing services, we published episodes
as Web content with high accessibility.
On the other hand, the logging applications on the Web involve privacy issues.
However, according to David Brin,the major issue is not whether activities should be recorded but rather who
should have access to the data.15 We cancontrol user access by employing various types of Web security technology,
including basic access authenticationand electronic signatures. We selecteda weblog, one of the most popular tools
for experience archiving, as the medium
for publishing episodes. Thus, we publish episodes as weblog entries.
Weblogs already have some of the elements necessary for providing EGC.
Most weblog systems have collection
functions, such as monthly and daily
entry archives and entry categorization. However, the entry search functions installed in weblog systems aren’t
powerful. Thus, we must prepare another search function with various
visualizations. Also, weblog systems
provide a summary: the index page of
the weblog and the resource description
framework (RDF) site summary. Moreover, because users can leave comments
on weblog entries, they can communicate with one another and share theirexperiences via a weblog. In addition,
users can easily attach annotations toentries (episodes) by leaving
comments on the entries. Finally, all weblog systems havea track-back function thatlinks one episode to anotherthat refers to the former.

Object centricity. The participants in our survey were interested in
objects and those objects’ states andchanges. Also, an earlier survey on context-aware applications showed thatparticipants would normally interact
with devices (objects) in their home.12
Thus, in our application, personified
objects tell stories in the first person
about their experiences by posting episodes about their events to a weblog.
Personified objects can provide familiar,
more natural logs for family members
than impersonal logs. Furthermore,because we assume that each object
has its own personality, we let objects
communicate with one another via theweblog—that is, an object can post acomment to an entry that another object posted. Assume, for example, thata door posts an entry about a detected
anomaly. Then, other objects can postcomments to the entry about their use
around the time of the anomaly. To accomplish this, we can naturally present
information aggregated from multiple
objects, and present additional information if needed.
Rule-based system. Object-Blog is a rule-

based system, meaning it has a set of
condition-action rules. Each rule is associated with a physical object. Whena rule’s condition is satisfied, the system
posts an episode about the object associated with the rule. The rules’ conditions

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IEEE Pervasive Computing, 7(4),pp. 20-27. (Draft version)

Figure 4. Weblog, with entries
indicating (a) activity levels during one
day and (b) a comment-able movie.

fall into three categories: periodic (forexample, every Wednesday at 12 midnight), when an event occurs, and whena user makes a request that triggers episode generation in our survey. Users canrequest episode generation by posting anentry to Object-Blog. (For example, they
can request an episode that includes adigest of the clothes they’ve worn overthe past 15 days.) Thus, if a user posts anentry that requests episode generation,
Object-Blog posts the requested episodeas an entry comment. (The present system requires the user to give a commandfor episode generation.)

Kikuchi’s activity levels (2008/03/17)

I’m T. Kikuchi’s slipper. I’dlike to show Kikuchi’s activity
levels today (2008/03/17).
50

How was today’s tea time? (2008/03/19)
I’m Ceylon tea. How wastoday’s tea time? It looks
exciting! Please comment on
the following movie oftoday’s tea time.

KikuchiSlipper move

40
30
20
10
0
0:00
6:00
12:00
18:00
0:00
Above graph is generated by using my movement
today. Kikuchi started her activity at about
10:45:00.

(a)

(b)

Easy setting. Many rule-based context-

aware systems use rules that users create because their applications are environment specific, and these rules reflectuser preferences.12 However, we don’t
have to use rules created by users, because the services obtained in our survey should be useful to any user, andthe desired services are similar. We
combine prepared rules in advance ina domain-independent format with domain-specific information that users
input, because many services obtainedin our survey use domain-specific information. For example, consider a service
consisting of the following: “Monitor
an entrance. Recordings of comings
and goings at unusual times are usefulfor security.” In this example, users input the definition of “unusual times”
into a database in advance. Preparedrules make it possible to reduce user
effort because users don’t have to construct rules.
Also, we introduce the concept ofa character package into the system.
A character package is a collection ofrules that define an object’s personality. It has its own name—for example,
a voluble cabinet, a concerned entrance
door, or a proud kettle. Users canbreathe life into an object by simply as-

October–November 2008

sociating a character package with anobject that has a sensor node, on theirPCs. If required, a user can input domain-specific information. Then, theobject automatically generates episodesby using its sensor data according torules in the character package.
An important issue concerning rulebased content generation systems isuser tiredness. The simplest solution tothis issue is to provide a wide variety ofrules. The way to realize the solution isby disclosing the rule description specifications to encourage rule development
according to the history of softwareplug-in development.
Implementation
We implemented an Object-Blog system that follows the design guidelines.
We used Movable Type as the weblog
platform and the C# language to implement sensor-data processing, event
detection, and automatic episode making. This implementation has three key
components: event detection, episodemaking, and episode and event search.
Event detection. We implemented four

event detection plug-ins in the present
system: The conversation detector de-tects conversations from microphonesignals. The physical-phenomena detector detects roughly 10 different
kinds of physical phenomena (such as
the movement, dropping, and rotationof objects) by using acceleration signals.
The object event detector detects events
that are specific to approximately 10
kinds of objects, such as doors, cabinets, and drawers. For example, it detects the opening and shutting of theobjects by using signals from accelerometers and direction sensors. The ADL
detector uses machine learning to detect roughly 20 kinds of ADLs, such as
make tea, cook pasta, cook rice, makejuice, brush teeth, listen to music, andpractice aromatherapy. We based our
implementation of the ADL detector on
the ADL inference algorithm presented
by Tapia, Intille, and Larson.6
Episode making. To provide content ef-

fectively, we implemented plug-ins for
episode making such as the movie plugin, graph plug-in, and superimposedpicture plug-in. We used the movie andsuperimposed-picture plug-ins to create the movie in Figure 1b representing
temperature changes. Figure 4a showsan entry that includes a graph of a user’s

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IEEE Pervasive Computing, 7(4),pp. 20-27. (Draft version)

User-Generated content

Figure 5. Screenshots of the eventepisode explorer, which provides a
keyword search function for entries andevents and provides various views of
search results: (a) timeline, (b) graph ofsimilar episodes, and (c) event-centricmodes. (Each rectangle with a whitecolored frame represents a weblog
entry. The purple-colored circle in (c)represents a physical event.)(a)

could input casual comments because
the objects generate enjoyable, conversation-like logs. By providing EGC on
the Web, we could implement some
services that can’t be achieved solely
using sensor data.

(b)

(c)

References
activity levels during one day. It’s necessary to provide graphs and charts sothat users can more easily understand aholistic image and find new characteristics about their lives. Annotating is atroublesome chore. So, we implemented
the comment-able movie plug-in so thatusers could annotate easily and enjoyably. Figure 4b shows an entry that encourages us to annotate a conversation
during tea time. The entry includes amovie with a voice recorded during tea
time. If a user leaves a comment consisting of time and text on the entry,
the movie shows the text at that time in
the Times Square format, which scrolls
the text on one horizontal line acrossthe screen. This function was inspiredby the system used by the video-sharingsite Nico Nico Video (www.nicovideo.
jp). Using this function, we can annotate episodes by sharing and exchanging opinions and experiences.
Episode and event search. The event-epi-

sode explorer is a search interface for
episodes and events that provides various visualizations. Users can launch itby selecting a hyperlink on the ObjectBlog page. Figure 5a shows keyword

84

P ER VA SI V E computing 

search results of episodes sorted byday. The keywords extracted from an
episode and images of objects related
to the episode surround one of the images representing the episode. The keywords and images help explain what
the episode is about. Also, by selecting
an episode, users can browse the corresponding entry page. Figure 5b shows
an episode, along with other similar episodes, represented in a graph structure.
To remind users of past experiences by
association, we provide a similarity
search. Users can also browse multiple
viewpoints of an event by selecting theevent (“Make GreenTea”), causing episodes that refer to the event to appear
around it (Figure 5c).

P

articipants working in our
experimental environment
have provided feedback about
Object-Blog. Some said thatthey could more easily record the daily
events of their lives than they could ina paper-based diary because objects
automatically record logs on theirbehalf, and participants simply commented on the logs. Also, participants

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the Authors
Takuya Maekawa is a researcher at NTT Communication Science Laboratories
in Kyoto, Japan. His research interests include Web content engineering in mobile and ubiquitous environments. Maekawa received his PhD in information
science and technology from Osaka University. Contact him at maekawa@
cslab.kecl.ntt.co.jp.

Yutaka Yanagisawa is a researcher at NTT Communication Science Laboratories, in Kyoto, Japan. His research interests include localization systems for mobile computing environments, programming languages to develop middleware and operating systems for small mobile devices, and augmented-reality
systems to support mobile device users. Yanagisawa received his PhD in engineering from Osaka University. Contact him at yutaka@cslab.kecl.ntt.co.jp.

Yasue Kishino is a researcher at NTT Communication Science Laboratories in
Kyoto, Japan. Her research interests include ubiquitous computing and humaninterfaces. Kishino received her PhD in information science and technology
from Osaka University. Contact her at yasue@cslab.kecl.ntt.co.jp.

12. A.D. Dey et al., “iCAP: Interactive Prototyping of Context-Aware Applications,”
Proc. Pervasive Computing, LNCS 3968,
Springer, 2006, pp. 254–271.
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Ishii, “Tangible Viewpoints: A PhysicalApproach to Multimedia Stories,” Proc.
ACM Int’l Conf. Multimedia, ACM
Press, 2002, pp. 153–160.
14. E.M. Tapia, S.S. Intille, and K. Larson,“Portable Wireless Sensors for Object
Usage Sensing in the Home: Challenges
and Practicalities,” Proc. European Conf.
Ambient Intelligence (AmI 07), LNCS
4794, Springer, 2007, pp. 19–37.
15. D. Brin, The Transparent Society, Perseus
Books, 1998.

Koji Kamei is a researcher at NTT Communication Science Laboratories in
Kyoto, Japan. His research interests focus on sharing of real-world events,
including event extraction from sensors and ontology construction for realworld events. Kamei received his ME in electronics and communication engineering from Kyoto University. Contact him at kamei@cslab.kecl.ntt.co.jp.

Yasushi Sakurai is a senior research scientist at NTT Communication Science Laboratories in Kyoto, Japan. His research interests include indexing andsearch algorithms, data mining, and sensor data processing. Sakurai received
his PhD in engineering from the Nara Institute of Science and Technology. He
is a member of the ACM. Contact him at yasushi@cslab.kecl.ntt.co.jp.

Takeshi Okadome is a group leader at NTT Communication Science Laboratories in Kyoto, Japan. His research interests focus on creating content for
real-world events using sensors. Okadome received his PhD in computer science from the University of Tokyo. He is a member of the ACM. Contact him athoumi@idea.brl.ntt.co.jp.

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October–November 2008

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