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IMC'09 Review #4A
Updated Wednesday 10 Jun 2009 2:39:30am CEST
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Paper #4: Measuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
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Overall merit: 3. Top 25% but not top 10% of
submitted papers
Reviewer qualification: 3. I know the material, but am not an
expert
Novelty: 4. New contribution
===== Paper summary =====
The paper analyzes a dataset of data traffic from more than 280k
mobile phone users. From that dataset the paper approximates mobility
pattern (to the precision of base station), classifies application
usage by mobile and non-mobile users, identifies "hotspots", and looks
for users accessing common locations, either at the same time or
different.
===== Exciting =====
The paper has a large, real dataset evaluating what people really do
with mobile phones, there's a lot of potential in this paper.
===== Run-of-the-mill =====
Nothing.
===== Limitations =====
Some of the clustering seems questionable, particularly the regional
analysis (sec. 5).
===== Comments to author =====
This seems like a very nice paper with a very interesting dataset.
Some questions:
- can you comment explicitly in the paper on dataset availability?
- the claim in section 2.1 that the data "completely preserves user
privacy" seems in accurate given recent work mapping home and work
locations to user identity. See "On the Anonymity of Home/Work
Location Pairs" by Golle and Partridge.
- It is not obvious if your three definitions in section 3.1.1 are
overlapping or not. Can you please clarify that? Also, the
definitions are very awkward and hard to follow. The English is
basically math written out with words. Can you clarify them?
Also, would disappearance be easier to recast as movement to the
location "nowhere", allowing you to eliminate one rule?
- Several of the conclusions seems obvious. For example, in
Sec. 3.1.4: "Interestingly more than 70% of mobile users visit at
least one common location on every single day...". Doesn't that mean:
everyone with a mobile device has a home?
- Some of the clustering seems questionable, particularly the regional
analysis (sec. 5): You group the data into k clusters there, for 1<=k<=5.
Is 5 a meaningful number of clusters for 280k users? I think you
could get arbitrary results in this section by varying k, and I'm not
convinced these arbitrary clusters are particularly meaningful.
For example, you draw the inference in section 5.1 that the number of
unique people sharing the same interests is larger in regions with
more people. This conclusion seems obvious. But also, it seems
nearly useless, because the definition of "interest" is very broad
(if I understand, anyone running music, or mail, or any trading app,
anywhere in downtown, has the same interest as I do by the paper's definition).
Minor comments:
Table 1: please define BSID. And Is "avg." here mean or median?
Table 2: your footnote on music is labeled "2", but appears as "3" at
the bottom of the page.
Figure 2: there seems no real difference between these values.
Figure 4: wouldn't this figure be clearer as a stacked bar chart?
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IMC'09 Review #4B
Updated Thursday 11 Jun 2009 11:09:12am CEST
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Paper #4: Measuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
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Overall merit: 3. Top 25% but not top 10% of
submitted papers
Reviewer qualification: 4. I know a lot about this area
Novelty: 4. New contribution
===== Paper summary =====
This work mines data comm data from 280,000 users of a 3G network over a week and reports on their top
intentions and mobility patterns.
===== Exciting =====
Correlation of mobility patterns to Internet access interests is interesting.
===== Run-of-the-mill =====
The paper does not raises why and provide supporting evaluation. Strictly data-driven to the point that
results are either predictable and boring or matter-of-factly without knowing why we see that behavior.
===== Comments to author =====
I would like to point out one distinction that is not clear in the paper but would have an impact in the
overall evaluation. Can you distinguish between cell phone and modem uses? I find it rather strange
that the most popular application at home is music. People have radios, TV music channels, audio systems
and MP3 players at home and I see no reason to listen to music over 3G network at home. But if the
person is streaming over the network, maybe. The device of choice should have a big impact on the
overall user data comm pattern and be made clear. Certain devices double as navigation tools.
Device-dependent features can have a bias on the outcome. If the data does not have this information,
the paper should be explicit about the limitation in the interpretation of the analysis.
Social network-based LBS should start with social network, while the data of this work contains no such
information. Lack of this information about human network make it very hard to evaluate the feasibliity
of LBS services. People of common interests at the same location could be introduced, but having the
same interest is not as important as the intention to participate in such a service. Once joining sucha
recomm service, people can choose to be introduced to those with opposite interests, or solely based on
the look. If I am at Kings Cross in London, I'm sure there'll be at least a hundred of people who will
share some common interests with me. Should I be interested? I find the analysis in Section 5 too
limited in the sense that they analyze and evaluate synthetic questions of little relevance in human
social behavior.
"SLAW: A Mobility Model for Human Walks" offers a mobility model and also explains the grouping of users
in mobility. Their work is relevant to the user affinity analysis in certain locations and should be
tried.
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IMC'09 Review #4C
Updated Thursday 16 Jul 2009 12:14:06pm CEST
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Paper #4: Measuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
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Overall merit: 4. Top 10% but not top 5% of submitted
papers
Reviewer qualification: 4. I know a lot about this area
Novelty: 4. New contribution
===== Paper summary =====
The paper describes an analysis of the 3G access patterns of 280K users in a large metropolitan area.
Thanks to the data, which includes handovers between cells, the authors can associated user's mobility to
web access patterns. They classify the web pages visited by the users into 15 different groups ranging
from dating to video in order to study the interplay between location, time of the day and web usage.
The authors present many findings that can be extremely useful for the fields of content distribution
networks and delay tolerant networks. Although some of the observations were to be expected they were
lacking
empirical evidence. Some of the observations, however, are not so trivial and they can have a strong
impact
on system design.
===== Exciting =====
+ Analysis of a novel data-set that combines location with web access on a metropolitan 3G network.
+ Relevance of the findings of the paper to to other fields such as DTN's and CDN's.
+ The paper contains many interesting observations. Some of them were to be expected although no
empirical evidence existed prior to this work. Other observations are not so trivial and can have a
strong influence on content distribution strategies for cellular networks.
===== Run-of-the-mill =====
The paper lacks a proper study on the temporal patterns of web access. Section 4.1 is not
convincing enough. I would have expected an analysis on the application access by time without
accounting for hotspots, which could be introducing confounders.
===== Comments to author =====
This is an excellent paper that could be on the top 5% if it wasn't for some issues enumerated
below (specially #5)
1) Is the common location that 70% of users return to their home? You should be able to infer that from
the base station location as well as with density. In any case, I was expecting the highest common
location
to be larger a higher probability, 30% of users do not return to their home during one day?
2) The results of the inter-session movement and stationary depicted in Figure 3 are kind of surprising.
Although the daily patterns (night and day) appear, the confidence probability of movement at its highest
is only about 3 times larger that in the middle of the night. I would not expect such a small difference
in the confidence probability. This point should be further discussed in the paper, otherwise it might
arise
some suspicions on the bias of the data-set.
3) There are other hypothesis besides bandwidth and battery consumption to why music is so high in the
comfort zone. As a matter of fact bandwidth should not be a factor since the data-set only accounts for
3G
networks and the residential coverage is not as extensive as downtown coverage. It would be interesting
to
know whether the users are accessing music from streaming (pandora) or not. Users could be building a
a playlist for the next day which requires more attention and therefore it has to be done in the comfort
zone. You do not take into consideration what is an evident distinction between the applications; whether
the applications is work or leisure related. If you factor this aspect most of the observations are clear
cut.
Naturally work related applications are more frequent outside comfort zone and during work hours, while
leisure
such as music or dating operate within the comfort zone.
4) The classification of URL into interest is completely ad-hoc, which is fine. However, more details
should have been included in the paper. What percentage of the URL are not classified in any group? What
is the overlapping between URL's? I see quite a few holes in the keyword selections e.g. loopt, youtube,
twitter.
5) The biggest concern of the paper is whether application access is mobility or time based. This
question arises in section 3.2.1. and it is somehow addressed in section 4.1. However, section 4.1
introduces the hotspots that can confound the results. You should have analyzed the temporal application
access to fully discard temporal correlations. Yet it is true that from Fig. 8 one can see that location
is driving the application access this could be done due to a different macro-behaviour, for instance
people
commuting.
6) In page 4 there are 2 footnotes labelled 3.
7) The last issue is on section 5.1. I concur that the empirical findings of this paper can guide the
design
of better content distribution systems that leverage random encounters. However, the authors are way too
optimistic on the drawn numbers. The high number of interactions -- or encounters -- is misleading. The
fact that
they are in the same cell do not imply a proximity contact by which data could be exchanged via wifi or
bluetooth.
Furthermore, doing the transfers at the cell level could be too cumbersome for the operator. Another
aspect by which
the results of this section are not conclusive is that the authors assume that application access
correlates with interest.
Not everybody who access email is interested in meeting other email users, not all people who listen
music are
actually interested in the same kind of music, etc. The point of this section is understood and it is one
of the many
applications of the author's measurements, however, it has to be contextualized better.
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IMC'09 Review #4D
Updated Friday 3 Jul 2009 12:53:23pm CEST
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Paper #4: Measuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
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Overall merit: 2. Top 50% but not top 25% of
submitted papers
Reviewer qualification: 5. I am an expert on this topic
Novelty: 3. Incremental improvement
===== Paper summary =====
The paper uses a data log of 280,000 users of a 3G mobile network in a large metropolitan area to
characterise the relationship between people's interests and mobility properties. Their analysis reveals
that (i)people's movement patterns are correlated with the applications they access, (ii)location affects
the applications accessed by users, (iii)and the number of serendipitous meetings between users of
similar cyber interest is larger in regions with higher density of hotpost. (i) and (ii) actually are
talking about the same thing, and (iii) is as expected.
===== Exciting =====
It is an interesting topic to study human mobility and the application they access during different
mobility mode.
===== Run-of-the-mill =====
The definition of the different rules are a bit confusing, and it is easier to express by a few world
instead of puting something confusing.
===== Limitations =====
There are several major problem that I would propose to put the paper into a weak reject category
1. It is not sure that the users are using UMTS or other 3G USB sticks with their laptops or they are
accessing the 3G network on their mobile phones. This definitely make large different for the results and
analysis of the paper. If the users are using UMTS USB stickt, the behaviour during the so called
hotspot/comfort zones would be very similar to normal internet usage. For example in Europe or some
developed city in Asia, people may only have a mobile UMTS USB stick for even home use instead of
subscribing to a broadband. And of course, when the people at home, they will use the network to listen
to music and doing social network. And when you are traveling, you will probably just check emails since
you will be too busy to do music listening stuffs. The authors should make it clear on this aspect, and
this is one major problem. I am sure there are some laptop or desktop users among them, we can see from
from page 9 "because users can have more than one application affilic
ation, the sumer of normalized affiliations does not equal to one". On mobile phone, usually you can run
one of such application at the same time.
2. The authors is kind of missleading by giving a large number of 280,000 users. How many of them are
active users? Looking at the support on Figure 3, the maximum support is less than 37,500. A futher
evidences of this unreliable of this number is that on page 9, they identify only 23 day hotspots, 28
noon hotspots, and 8 evening hotspot. Is that true that all thease 280,000 users only have 23 day
hotspots? This is not convencing, especially many areas are covered by multiple base station. I think if
the authors want to draw a more scientific conlusion, they should extract the activit users instead of
giving a big number.
3. Also there is a major problem for classifying mobility. Currently, the authors classify movememnt as
change of cellular tower ID. It is well know that in cellualr network, the cellular phone will keep
associate with different cellular towers nearby even the phones are in stationary. This is normal for a
place covered by multiple cellular base stations. This can explain why the author observed 84% of session
spend less than 10 seconds in motion (page 6). Many of this kind of motion can be because of the celluarl
phone or laptop keeping swinging among different base stations at the same area. If the authors cannot
filter out this effect, the analysis of mobility cannot produce a scientific conclusion.
===== Comments to author =====
Minor problem:
1. In the conclusion, the author said" in this paper we conducted, to the best of our knowledge, the
first large-scale experital study", this is missleading. They did not conduct the experiment, but got the
data from operator.
2.citation [10,16] are not using GPS information as the authors mentioned. and also Levy flight is not
reandom models.
3. I am not sure the bipartite graph G they constructed in page 11 is correct. There are 281,394 users,
and 1,196 locations, and as they said if a users never visted a location, the weight of the edge is 0 but
there should be still an edge. Then the total number of edge 936,280 should be wrong.
4. some conclusions are a bit obvious, for example "suggesting that users regularly revisit their useral
location", "the probability of meeting different people is larger in a more populated region"
5. It is better to plot Figure 5 as a CDF.
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Comment
Paper #4: Measuring Serendipity: Connecting People, Locations and
Interests in a Mobile 3G Network
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Among the issues and comments that arose while discussing you paper at the
PC meeting are the following:
* What kind are the user-terminals? What devices were used? (Handhelds, smart-phones, UMTS-sticks,
vendor, etc.)