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SIGMOD 2008
2008 ACM SIGMOD International Conference on Management of Data
June 9-12 2008
Vancouver
Canada
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Review: 1 |
Reviewer:
| Naoko Kosugi |
Email:
| kosugi.naoko@lab.ntt.co.jp |
Organization:
| NTT |
Review:
| Question | Response |
1 | Overall Rating |
Weak Accept
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2 | Reject due to technical incorrectness |
No
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3 | Novelty |
Medium
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4 | Technical Depth |
High
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5 | Is the relevant and reasonably well known state of the art fairly treated (note this may come from outside the database literature and even outside computer science)? |
yes, allowing for space limitations
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6 | Experimental results should meet the following expectations: deal with materially relevant cases (e.g., updates as well as queries, different scales) allowing for space limitations; statistically meaningful results; and use standard datasets or benchmarks (as opposed to a cherry-picked one) when possible. How did the experiments rate? |
adequate
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7 | If experiments were presented, would you want them checked for repeatability (as might be required in a future sigmod)? |
no or not applicable
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8 | Presentation |
Adequate
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9 | Reviewer Confidence |
Medium
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10 | Name of External Reviewer (if applicable) |
Yoshiharu Ishikawa
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11 | Summary of the paper's main contributions and impact (up to one paragraph) |
Query processing technique for objects on road networks is one of the interested research subjects in spatio-temporal databases. In this paper, a new type of queries is proposed and the corresponding algorithms are presented and evaluated. The feature is to consider a set of moving objects on a road network, then they propose three types of proximity relations defined over sets of moving objects and provide effective algorithms for query processing.
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12 | Three strong points of the paper (please number them S1,S2,S3) |
S1: This paper proposes an interesting problem for query processing on road network, and the provided solutions (effective computation of distances and network partitioning) are not based on well-investigated ideas.
S2: The proposed techniques seem solid. Some proofs are omitted due to page limit, but the results would be probably correct.
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13 | Three weak points of the paper (please number them W1,W2,W3) |
W1: The problem of the proposed techniques is that it is not clear there are any practical applications for them. Definition of three proximity relations are reasonable, but I'm not sure they can be utilized in real situations. The authors should provided example scenarios for each proximity relation.
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14 | Detailed comments (please number each point) |
The paper is well written. The problems and soltions are clear, and it was interesting for me.
My primary concern was, as described above, practical applications of the proposed methods. However, the quality of the paper would be on the acceptance level.
In addition, the assumption of linear movements would be a strong requirement. Of course, the definition is necessary to derive algorithms and would be effective as approximation, that would be a weak point of the work.
The following may be typos:
- Eq.(4)
- p. 3, right, "min - sum(P)" -> "min-sum(P)"
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15 | Comments for the Program Committee |
The quality of the paper is over the SIGMOD acceptance level, but the proposed problem may not be able to find practical applications in the real world. That point is a weak point of the paper.
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16 | Is this paper a candidate for the Best Paper Award? |
No
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17 | Would author feedback be useful for this Review? (if "Yes", please answer Q. 18) |
No
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18 | List specific clarifications you seek from the Authors (if you have answered "Yes" to Q. 17) |
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Review: 2 |
Reviewer:
| Nick Roussopoulos |
Email:
| nick@cs.umd.edu |
Organization:
| University of Maryland |
Review:
| Question | Response |
1 | Overall Rating |
Weak Accept
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2 | Reject due to technical incorrectness |
No
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3 | Novelty |
Medium
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4 | Technical Depth |
Medium
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5 | Is the relevant and reasonably well known state of the art fairly treated (note this may come from outside the database literature and even outside computer science)? |
to a limited extent
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6 | Experimental results should meet the following expectations: deal with materially relevant cases (e.g., updates as well as queries, different scales) allowing for space limitations; statistically meaningful results; and use standard datasets or benchmarks (as opposed to a cherry-picked one) when possible. How did the experiments rate? |
adequate
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7 | If experiments were presented, would you want them checked for repeatability (as might be required in a future sigmod)? |
no or not applicable
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8 | Presentation |
Adequate
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9 | Reviewer Confidence |
Low
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10 | Name of External Reviewer (if applicable) |
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11 | Summary of the paper's main contributions and impact (up to one paragraph) |
The paper presents algorithms for efficient generation of proximity relations in road networks. It uses Eucledian Geometry to reduce the n**2 computation. It is a useful tool.
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12 | Three strong points of the paper (please number them S1,S2,S3) |
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13 | Three weak points of the paper (please number them W1,W2,W3) |
Presentation: the paper is not easy to follow.
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14 | Detailed comments (please number each point) |
Add some overall explanation at the beginning of each section. You can elliminate or reduce the figures with the algorithms.
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15 | Comments for the Program Committee |
This approach is not totally new. I am aware of a group in my department that has a solution for the same problem. I am not aware of the details that work, but I am assuming that the reviewing system has worked and did not assign me a paper of a colleague.
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16 | Is this paper a candidate for the Best Paper Award? |
No
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17 | Would author feedback be useful for this Review? (if "Yes", please answer Q. 18) |
No
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18 | List specific clarifications you seek from the Authors (if you have answered "Yes" to Q. 17) |
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Review: 3 |
Reviewer:
| Xiaofang Zhou |
Email:
| zxf@itee.uq.edu.au |
Organization:
| University of Queensland |
Review:
| Question | Response |
1 | Overall Rating |
Weak Reject
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2 | Reject due to technical incorrectness |
No
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3 | Novelty |
Medium
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4 | Technical Depth |
Medium
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5 | Is the relevant and reasonably well known state of the art fairly treated (note this may come from outside the database literature and even outside computer science)? |
yes, allowing for space limitations
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6 | Experimental results should meet the following expectations: deal with materially relevant cases (e.g., updates as well as queries, different scales) allowing for space limitations; statistically meaningful results; and use standard datasets or benchmarks (as opposed to a cherry-picked one) when possible. How did the experiments rate? |
not good
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7 | If experiments were presented, would you want them checked for repeatability (as might be required in a future sigmod)? |
yes
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8 | Presentation |
Adequate
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9 | Reviewer Confidence |
Medium
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10 | Name of External Reviewer (if applicable) |
Ke Deng
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11 | Summary of the paper's main contributions and impact (up to one paragraph) |
This paper presents a work on computing network-based proximity relations for a moving object set. Three locations constraints are studied. First, max-pairwise-distance is the maximal pairwise-distance of any pair of objects in the set. Second, min-sum-distance is the minimum distance summations from all objects in the set to a location in the network. The work is to find such locations from the entire network. Third, min-max-distance is the minimum value of the maximum distances from all objects in the set to a location, which can be at any position in the network. If these distance constraints are no longer met (i.e., over a given value), an alert may be issued to remind the proximity change in the application. To reduce the problem space, the network is split into partitions such that only a part of the entire network is processed.
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12 | Three strong points of the paper (please number them S1,S2,S3) |
S1: The problems studied in this paper are interesting and the solutions are valid.
S2: The work is systematic, considering all major steps towards solving the problems.
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13 | Three weak points of the paper (please number them W1,W2,W3) |
W1: The solutions imply the existence of pre-computed network distances between all pairs of network nodes. This important aspect of query processing performance, however, is not explicitly discussed in the paper.
W2: The data used for empirical performance evaluation is too small.
W3: Writing can be improved.
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14 | Detailed comments (please number each point) |
S1. In the basic algorithm, all edges of the underlying network need to be examined to determine the min-sum-distance and the min-max-distance. To reduce the problem space, the network is split into partitions such that only a part of the entire network needs to be processed. The problems studied in this paper are interesting and the solutions seem valid.
W1: Computing shortest path distances in a (large) road networks is very costly. It is not practical to assume that the network distance between any pair of nodes can be pre-computed (due to not only the storage overhead but also the high update costs when the road network is updated – which is not uncommon). The discussions in section 3.1 seem overly simplistic. Solutions to this problem could have a huge impact on the overall processing performance thus should be discussed explicitly.
W2. The experiments are conducted using a network of 30x30 nodes, which is quite small for real world applications. I don't understand the meaning of measuring the dataset complexity using km distances (does that only depend on the units used?). The experiments based on google-ride-finder should provide more and sufficient details about the data set and the performance. (It is possible that the overhead of storing/computing all-pair distances is not that important in this paper, just due to the small road network data used?)
W3: Writing can be improved (there are many minor grammar errors in the paper), and the notations in Section 2 seem unnecessarily complex (e.g., why not call pd_G simply d_G?), and inconsistent (e.g., use G in pd but not in mpd; sd(x^*) is not defined). Description of the cost distribution function in section 3 is another example. Also, the definition for a cut should be clearly illustrated with an example. Only from the discussion of 'cut' I realized that G is a directional graph – this point has not been mentioned clearly enough in previous discussions and in the example figures. Another point is that some key proofs (in [7]?) should be contained this paper to improve its readability.
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15 | Comments for the Program Committee |
The tech report [7] is needed to fully evaluate the correctness of this paper, if anyone votes to accept this paper.
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16 | Is this paper a candidate for the Best Paper Award? |
No
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17 | Would author feedback be useful for this Review? (if "Yes", please answer Q. 18) |
No
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18 | List specific clarifications you seek from the Authors (if you have answered "Yes" to Q. 17) |
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