看到一个不错的写文献总结的提纲，列出来分享一下。
原本是一门课的作业，老师布置了几篇论文，让写个一页的总结，paper
summary。这个如果想糊弄，其实是很容易的，随便copy几段文字下来，就一页了。但我想这样做没什么意义，不如认认真真的理一些思路出来。于是在网上搜到了这个样板提纲。
我认为这个提纲好在，他比较重视introduction部分的阅读，前三个问题都是intro里面的。弄懂这三个问题，才能更好的把握文章背景信息。话说回来，要写一篇好的文章，前8点内容是必然要交代清楚的。如果从文章里提取不到这些信息，那文章不能说是成功的文章，关键的问题都没交代清楚。
模板精简并翻译如下：
1. What is the problem the authors are trying to solve? 作者想解决什么问题？(研究目的)
2. What other approaches or solutions existed at the time that this work was
done? 这个问题现在有什么解决方法？
3. What was wrong with the other approaches or solutions? 这些方法存在什么问题？
4. What is the authors' approach or solution? 作者提出了什么方法？
5. Why is it better than the other approaches or solutions?
为什么说要作者的这个方法好于其他方法？
6. How did they test their solution? 作者如何检验自己的方法？
7. How does it perform? 方法检验效果如何？
8. Why is this work important? 为什么该文的研究比较重要？（研究意义）
more comments/questions 附加评注，或者问题。随读者背景和认识程度的不同，可以灵活的提出一些问题。
本人拙作，也就是作业。欢迎拍砖。
summary on “A. Boucher (2009): Considering complex training images with
search tree partitioning, Computers & Geosciences, 35, 1151-1158.”
1. What is the problem the authors are trying to solve?
To solve the
difficulty of large complex TI to be used in SNESIM.
2. What other approaches or solutions existed at the time that this work was
done?
To decrease the size of search tree, general approaches are taking a
smaller TI or using a smaller template. Besides, there are two alternative
approaches in current practice: 1) region approach 2) probability ?eld
approach.
3. What was wrong with the other approaches or solutions?
A major issue
with the region approach is that there is no guarantee that the different TIs
used are compatible with one another. The problem with this probability ?eld
approach is that integration changes the conditional probability derived from
the search tree and affects the pattern reproduction in an unknown manner.
4. What is the authors' approach or solution?
The authors proposed a
search tree partitioning approach which includes:
Applying the
filters on the TI to gain a series of filter scores that are indicative of the
underlying patterns.
Defining partition classes through the filter
scores with a clustering algorithm
Building search tree for each
partition class
At each pixel along the simulation path, the
partition class is retrieved first and used to select the appropriate search
tree.
5. Why is it better than the other approaches or solutions?
The proposed
algorithm adds local information without using a probability field which
distorts the conditional distribution obtained from the TI. It improves the
region approach by requiring a single complete TI and implicitly models the
transitions between regions. It facilitates the use of hierarchical framework by
identifying the hierarchy structures with partition classes.
6. How did they test their solution?
They gave two examples of
implementation of their approach. The first example is to simulate a series of
fracture. The second is to simulate Shallow-water tidal system. They also made
the comparison to the result pattern by global search tree approach.
7. How does it perform?
Performance is very good. In the fracture case,
the trend in orientation is well reproduced. In the second one the geological
consistency of the TI is reproduced. These features are not exhibited in the
realization by global search tree approach. Besides, the speed improved.
8. Comments & other questions
*The simulation grid can be obtained in
many ways. Do the different simulation grids influence realization?
*FILTERSIM as a similar approach is interesting to compare with the authors
approach.
*A. Boucher’s effort to integrate new algorithm to SGeMS is
interesting and respectable. Open Geostatistic software will allow more
researchers to test, apply and extend the algorithm in an easier manner.
Sample Paper Summary
Name: Scott Brandt
Paper: Sage A. Weil, Scott A. Brandt, Ethan L. Miller, Darrell D. E.
Long,
and Carlos Maltzahn, ``Ceph: A Scalable, High-Performance,
Distributed
Object-based Storage System,'' Symposium on Operating
Systems Design
and Implementation (OSDI '06), Seattle, Washington,
November 6-8, 2006,
to appear.
1. What is the problem the authors are trying to solve?
Existing storage systems do not scale well to petabytes of data and
terabytes/second throughput.
2. What other approaches or solutions existed at the time that this work was
done?
Lots of other file systems existed. NFS is a standard for distributed
file systems. Lustre is a distributed object-based file system, as
is the
Panasas file system.
3. What was wrong with the other approaches or solutions?
All have limitations that prevent them from scaling to the desired
level.
Block-based file systems have problems dealing with the large number
of
blocks in such a system. Other object-based file systems fail to take
full
advantage of the object-based paradigm and still maintain object
lists.
4. What is the authors' approach or solution?
The authors solution includes:
- Object-based storage devices
-
A globally known mapping function for locating file data
(instead of
object lists)
- A scalable metadata manager that dynamically redelegates
authority
for directory subtrees based on load
- A distributed
autonomous system for managing the object stores
5. Why is it better than the other approaches or solutions?
It scales to petabytes, provides nearly linear performance
improvements
as storage devices are added, degrades gracefully as storage
devices are
removed, and provides very high performance.
6. How did they test their solution?
They ran parts of the storage system and observed their performance
under various workloads. Data performance was tested on a single object
store and on several object stores. Metadata performance was tested on
a
large cluster.
7. How does it perform?
Performance is very good. The system appears to achieve its goals,
although scalability could be improved in certain scenarios where a lot
of
sharing occurs.
8. Why is this work important?
This work is important because storage systems continue to grow in size
and data is becoming increasingly important.
3+ comments/questions
* Why didn't they directly compare the performance of their system against
that of any other storage systems?
* What happens if you scale to exabytes? Will the system still work?
What
factors will limit its ability to scale further?
* How much of the improvement is due to CRUSH, and how much to the design
of the other parts of the system? Why didn't they do any tests to
isolate
the benefits of the individual design decisions?
