The problem with Short Papers

Today, I will talk briefly about short papers at academic conferences, and the problems that they create (in my opinion). For those who don’t know, several conferences can reject or accept papers as short or full papers. A short paper means that the paper has fewer pages in the proceedings than the full papers. For instance, someone may submit a 12-page paper to a conference, which may then be accepted as a short paper that must fit in 6 pages.

Why there are short papers? The reason is quite simple. It allows conference organizers to accept more papers for their conference and thus to have more attendees, which means to earn more money through the registration fees (because it is the same price anyway for a short or a full paper). Short papers also allows conferences to accept some papers that are sometimes of low quality (almost rejected in some cases).

Why I dislike short papers? I think they are not a good idea. Let me explain why:

  • They force authors to remove many important details to try to squeeze their content in a small number of pages. For example, I recently got a paper accepted as short paper, and to make it fit within the page limit, I would have to either remove much of the technical details or remove some experiments. Either way, this will decrease the quality of the paper and the reader will miss some important details.
  • Not able to address the reviewers’ comments. In many cases, reviewers will give feedback on the paper saying that content must be added such as additional experiments. But if the paper is accepted as a short paper and the paper must be reduced by several pages, authors will be unable to address these comments, and authors may even have to do the opposite and remove experiments and details.
  • Quality control. Many conferences that accept short papers will not send the revised papers to reviewers again to ensure the quality of the revision. But after cutting let’s say 50% of the content, the quality has a good chance to decrease.
  • Less chances of having an impact. Short papers are less likely to have an impact or to be cited.
  • Probably less reproducible. Short papers are generally harder to reproduce because authors may have to left out much details.
  • The authors have to spend extra time to shorten their paper. To make a paper shorter, it can requires a few days of extra work.
  • It will likely not be possible to publish the results elsewhere as a full paper. Generally, if someone publish some results or ideas in a short paper, it will likely not be possible to make a full paper on the same idea at another conference (due to the overlap). Someone people may still do this by splitting their ideas into multiple papers but this is not a good practice.

For all these reasons, I dislike short papers, and often withdraw my papers if they are accepted as short papers, and then improve them and submit them to other conferences.

There are also some publishers such as Springer that acknowledge in some way the problem of short papers by mentioning that conferences are not expected to have too many short papers and that it is encouraged to not put a strict limit on the number of pages to avoid reducing the quality of papers.

That is all for today. If you have any comments, you may post them in the comment section below.

Philippe Fournier-Viger is a distinguished professor working in China and founder of the SPMF open source data mining software.

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(video) Rare Itemset Mining

I have uploaded a new 38 minutes video to explain rare itemset mining. This video is like a small course, where you can learn about infrequent itemsets, minimal rare itemsets and perfectly rare itemsets and some algorithms : AprioriInverse and AprioriRare.


To watch the video:

Direct link on my website: … _video.mp4
Link from my Youtube channel:

Hope that it is interesting!

Philippe Fournier-Viger is a distinguished professor working in China and founder of the SPMF open source data mining software.

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How to draw a sequence in latex (using TIKZ)

Today, I will show how to draw a sequence in Latex using the TIKZ package. A sequence is an ordered list of symbols. I often draw sequences for my research paper about sequential pattern mining or episode mining. To draw a sequence, I first import the TIKZ package by adding this line in the section for packages:


Then, to draw a sequence in a figure, I use this code:

\draw (-0.4,0) -- (7,0);
% The labels
\node[] at (-1,0.6) {\textbf{event sets}};
\node[] at (-1,-0.6) {\textbf{timestamps}};
% first element
\draw (0.4,-0.2) -- (0.4,0.3); \node[] at (0.4,0.6) {${a,c}$}; \node[] at (0.4,-0.6) {$1$};
% second element
\draw (1.2,-0.2) -- (1.2,0.3); \node[] at (1.2,0.6) {${a}$}; \node[] at (1.2,-0.6) {$2$};
% third element
\draw (2,-0.2) -- (2,0.3); \node[] at (2,0.6) {${a,b}$}; \node[] at (2,-0.6) {$3$};
% next element
\draw (2.8,-0.2) -- (2.8,0.3); \node[] at (2.8,0.6) {${a}$}; \node[] at (2.8,-0.6) {$6$};
% next element
\draw (3.6,-0.2) -- (3.6,0.3); \node[] at (3.6,0.6) {${a,b}$}; \node[] at (3.6,-0.6) {$7$};
% next element
\draw (4.4,-0.2) -- (4.4,0.3); \node[] at (4.4,0.6) {${c}$}; \node[] at (4.4,-0.6) {$8$};
% next element
\draw (5.2,-0.2) -- (5.2,0.3); \node[] at (5.2,0.6) {${b}$}; \node[] at (5.2,-0.6) {$9$};
% next element
\draw (6,-0.2) -- (6,0.3); \node[] at (6,0.6) {${d}$}; \node[] at (6,-0.6) {$11$};
% The arrow
\draw (6.8,-0.13) -- (7,0);
\draw (6.8,0.13) -- (7,0);
\caption{A complex event sequence with eight event sets}

The result looks like this:

draw a sequence in latex

Another example:

\draw[-][draw=black, very thick] (-0.1,0) -- (.5,0);
\draw[->][draw=black, very thick] (.5,0) -- (1.1,0);
%\draw [thick] (0,-.1) node[below]{0} -- (0,0.1);
\draw [thick] (0.25,-.1) node[below]{$t-\frac{\gamma-1}{2}$} -- (0.25,0.1);
\draw [thick] (0,-.1) node[below]{$t-\frac{\lambda\times\gamma-1}{2}$} -- (0,0.1);
%%%%  WINDOW A
\draw [thick, dashed] (0.25,0) -- (0.25,.5) -- (.5,.5) node[above]{
\scriptsize a) short term window (of length $\gamma$)
} -- (0.75,.5) -- (0.75,0) ;
\draw [thick, dashed] (0,0) -- (0,1) -- (.5,1) node[above]{
\scriptsize b) long term window (of length $\lambda \times \gamma $)
} -- (1,1) -- (1,0) ;
\draw [thick] (0.5,-.1) node[below]{$t$} -- (0.5,0.1);
\draw [thick] (0.75,-.1) node[below]{$t+\frac{\gamma-1}{2}$} -- (0.75,0.1);
\draw [thick] (1,-.1) node[below]{$t+\frac{\lambda\times\gamma-1}{2}$} -- (1,0.1);
%\draw [thick] (1,-.1) node[below]{1} -- (1,0.1);
%\caption{The windows for calculating the a) short term and b) long term moving average utility  for a timestamp $t$.}


The results is:

You could improve upon this using other options in Tikz to add colors, etc. Besides, as a bonus, if I want to write a sequence using Latex math notation in a paragraph, I write like this:

$S=\langle ({a,c}, $ $ 1),$ $
({a},2),$ $({a,b},3),$ $({a},6),$ $({a,b},7),$ $({c},8),$ $({b},9),$ $({d},{11}) \rangle$

The result will be:

latex sequence math

Hope this will be useful for your Latex documents

Philippe Fournier-Viger is a distinguished professor working in China and founder of the SPMF open source data mining software.

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A simple BAT script to unzip ZIP files in all sub-directories and then delete the ZIP files

In this blog post, I will share a simple script to recursively unzip all ZIP files in sub-directories of a directory, and then delete the ZIP files. Such script is useful for example when managing the submissions of papers at academic conferences. The script is based some code on StackOverflow where I have added the function to delete the ZIP files:

FOR /D /r %%F in ("*") DO (
    pushd %CD%
    cd %%F
        FOR %%X in (*.rar *.zip) DO (
            "C:\Program Files\7-zip\7z.exe" x "%%X"
		   del *.zip

This is for Windows. It assumes that you have 7ZIP installed on your computer and that the path to 7ZIP is correct. Save this script into a text file. Rename it to have the .bat extension. Then, double click on the file to run the script. That is all!

Philippe Fournier-Viger is a distinguished professor working in China and founder of the SPMF open source data mining software.

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Brief report about the DASFAA 2022 conference

This week, I am attending the DASFAA 2022 conference, which is held online from the 11th to the 14th April 2022. In this blog post, I will talk about this event, and will update the blog through the conference.

DASFAA 2022 is the  27th International Conference on Database Systems for Advanced Applications. It is a well-established conference on database and related research areas and applications such as data mining and machine learning. This year, it was supposed to be held in India, but due to the pandemic, it was held in online mode. The conference is organized by IIT Hyderabad, India.

The DASFAA Proceedings

The DASFAA conference proceedings are published by Springer in book(s) of the Lecture Notes in Computer Sciences series. This ensures that papers in DASFAA are indexed and have a good visibility.

This year the DASFAA program includes 5 keynote talks, 143 research papers, 12 industry presentations, a panel, 11 demos, 5 tutorials and 6 workshops (including the PMDB 2022 workshop on pattern mining and machine learning). It is thus a rather large conference. There was 420 registered attendees.

Acceptance rates

For the main track, there was 400 submissions, and 72 were accepted as full papers ( acceptance rate of 18%) and 76 as short papers (19%).

For the industry track, there was 36 submissions, from which there are 7 accepted full papers (19%) and 6 accepted short papers (17%).

For the demo track, 9 of the 18 submissions were accepted (50%).

For the PhD track, 2 out of 3 submissions were accepted (66%).

The online platform

The conference is held using an online platform called Airmeet. It allows to check the schedule, listen to talks and there are some video chat rooms to socialize with other participants. Here are a few screenshots of that platform. The DASFAA schedule page:

The DASFAA social lounge offering chat rooms:

Day 1 DASFAA Workshop 1 : PMDB 2022

This year, I have co-organized the PMDB 2022 workshop at DASFAA (1st Workshop on Pattern mining and Machine learning in Big complex Databases). The workshop has 6 papers and a keynote talk by Prof. Joshua Zhexue Huang.

The keynote talk by Prof. Huang, founding director of the big data institute at Shenzhen University was about big data approximate computing. He presented a model called RSP (Random Sample Partition) as an alternative to the popular Hadoop and Spark models. The key idea in RSP is to create distributed data blocks that are random samples of the original data. Then, using these random data blocks, big datasets can be used to train approximate models such as SVM. Using RSP, confidence intervals can be calculated on the errors of approximation. RSP allows to process very large datasets efficiently and provide excellent scalability. In some applications, RSP was shown to have better performance than traditional models such as Hadoop.

big data approximate computing by Joshua Zhexue Huang

Then, there was 6 accepted papers presentations:

9:45Paper #2 An Algorithm for Mining Fixed-Length High Utility Itemsets (PDF)
Le Wang
10:05Paper #3 A Novel Method to Create Synthetic Samples with Autoencoder Multi-layer Extreme Learning Machine (PDF)
Qihang Huang, Yulin He, Shengsheng Xu and Joshua Zhexue Huang
10:25Paper #4 Pattern Mining: Current Challenges and Opportunities (PDF, video)
Philippe Fournier-Viger, Wensheng Gan, Youxi Wu, Mourad Nouioua, Wei Song, Tin Truong and Hai Duong Van
10:45Paper #7 Why not to Trust Big Data: Identifying Existence of Simpson’s Paradox (PDF)
Rahul Sharma, Minakshi Kaushik, Sijo Arakkal Peious, Mahtab Shahin, Ankit Vidhyarthi and Dirk Draheim
11:05Paper #8 Localized Metric Learning for Large Multi-Class Extremely Imbalanced Face Database (PDF) (best paper award)
Seba Susan and Ashu Kaushik
11:25Paper #9  Top-k dominating queries on incremental datasets (PDF)
Jimmy Ming-Tai Wu, Ke Wang and Jerry Chun-Wei Lin

In particular, something special at this workshop is that we organized a collaborative paper (paper #4) with 7 well-established authors in pattern mining to talk about the current challenges and opportunities in pattern mining. You can watch the video of that talk on Youtube at: .

The PMDB workshop has been a success. Thus, we aim to organize it again next year and make it larger.

Day 2 – Conference opening

In the conference opening, the conference was presented. There was a lot of interesting information. As I missed one part of the opening, I would to thank Prof. P.K. Reddy for sharing the slides with me. I will provide a few screenshots of interesting content from the opening below.

dasfaa 2022 conferenc opening
dasfaa history
dasfaa acceptance rate
dasfaa tag cloud
dasfaa conference arangement
dasfaa program at a glance
dasfaa keynote talks
dasfaa best student paper
dasfaa best paper
dasfaa contribution award
dasfaa industry track
dasfaa demo track
dasfaa phd consortium

Keynote 1 : Fairness in Database Querying by Gautam Das

The first keynote of the conference was about fairness for database queries. This is an topic that has made the news in recent years with machine learning models that are for example deemed to be racist or unfair to some groups. Here are a few slides:

fairness in database querying by gautam das

Research paper presentations

There was numerous paper presentations. I was busy. Thus I will not specifically report on them.

Next year, DASFAA 2023

DASFAA2023 will be in Tianjin, China during April 2023


I have given a brief overview of the DASFAA 2022 conference. Hope that it has been interesting.

Philippe Fournier-Viger is a distinguished professor working in China and founder of the SPMF open source data mining software.

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Brief report about the CACML 2022 conference

Today, I have attended the CACML 2022 conference (2022 Asia Conference on Algorithms, Computing and Machine Learning), which was held virtually from Hangzhou, China from March 25 to 27.

This is a new conference on a very timely topic of machine learning and computing. The website is : The conference was well organized. The proceedings of the conference are published by IEEE in the IEEE Xplore digital library (indexed by IE, INSTP etc), which provides a good visibility to the paper.

I have been involved in this conference as conference chair, and as keynote speaker.

Keynote talk by Prof. Witold Pedrycz

The first talk was by Witold Pedrycz from University of Alberta, Canada, a well-known researcher and editor of the Information Science journal. He talked about machine learning, granular computing and federated learning. Here are a few slides:

As illustrated in the above slide, in federated learning, each client is the owner of his data. Then, if a server wants to build a machine learning model, the server will interact with the clients to build a model. But the client will not disclose their data to the server. Instead the clients will provide knowledge to the server to help build the model. This is interesting idea to protect the privacy of users. Below, there is another slide that shows how a model is updated in the case of gradient descent with federated learning. Each client returns a gradient to the server rather than sharing the data.

Then, there was more details about how federated learning and granular computing can be used jointly to build rule-based models, and the issue of how to evaluate the quality of the models built using federated learning (since the server does not have access to all the data). I will not report all the details. But it was an interesting talk.

Keynote talk by Prof. Chin Chen Chang

The second keynote by Prof. Chin Chen Chang from National Tsing Hua University, Taiwan was about information hiding that is how to hide information, for example, to send a secret message by hiding it inside images.

Keynote talk by Prof. Philippe Fournier-Viger

The third keynote was given by me about “Advances and challenges for the automatic discovery of interesting patterns in data“.

In that talk I briefly reviewed early studies on designing algorithms for identifying frequent patterns that can be used for instance to identify frequent alarms or faults in telecommunication networks. Then, I gave an overview of recent challenges and advances to identify other types of interesting patterns in more complex data. I introduced the concepts of high utility patterns, locally interesting patterns, and periodic patterns. Lastly, the SPMF open-source software will be mentioned and opportunities such as how to combine pattern mining with machine learning.

Keynote talk by Prof. Jie Yang

The last keynote talk was by Prof. Jie Yang from Shanghai Jiaotong University about attacks on deep learning models. Here are a few slides:

As shown above, some attacks can be on unnecessary features while other attacks can be about missing features.

There was also some discussion of defense techniques against attacks on neural networks, and attacks on the attention of deep learning models.


This is a short overview of the CACML 2022 conference. It is a medium-size conference (which is understandable since it is a new conference), but there was some good speakers and invited guests. I have enjoyed it, and I would attend again.

Philippe Fournier-Viger is a distinguished professor working in China and founder of the SPMF open source data mining software.

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A Tool to Generate Reviews of Academic Papers

Writing reviews is important but sometimes repetitive and time-consuming. Hence, today I built a tool to help automatize the process of review writing. You can try it at the website below:

The Review Generator ( )

This tool let you select some items and this will add predefined sentences to your review. Of course, this tool is not supposed to replace a human and generated reviews should be viewed as draft and edited by hand to add more details.

If you like the tool, you may boomark it and share it. And if you would like more features, please let me know. For example, if you would like that I add more content to the tool, please leave a comment below or send me an email.

** Credit ** That project is a modification of AutoReject ( Andreas Zeller, which was designed as a joke to automatically reject papers. I have reused the template but modified the text content to turn it into a serious tool. #review#academia#reviewgenerator#reviewprocess#journal#conference

Philippe Fournier-Viger is a distinguished professor working in China and founder of the SPMF open source data mining software.

Posted in Academia, General, Research | Tagged , , , , , | 2 Comments

What I learned from being a journal editor for two years?

For over two years, I have worked as associate editor-in-chief for a major journal published by Springer. For this job, I have handled around 500 papers from all the steps of the review process. Recently, I have decided to stop this work as it was taking too much of my time and as time is limited in life, I wanted to give priority to other aspects of my life. I was very happy to be associate editor-in-chief for that big journal as this has allowed me to learn many things. I will discuss what I have learned in this blog post.

Turned Off Laptop Computer

The key things that I have learned are:

  • Being an editor can be a lot of work. For a big journal, the workload of an editor is quite high. I would spend about one hour every day to try to find reviewers for papers, reading the papers or do other related tasks. One hour may not seem a lot but when you have a research team, a family and other things in your life, it is a lot of time and you have to think about your priorities. With this 1 hour, I could instead do some sport, spend more time with my kid or spend more time on my own research.
  • Finding reviewers for a paper is not so easy. Authors of research papers often think that reviewers are easy to find. But the truth is that often it is a hard task to find reviewers. The editor has to invite numerous reviewers. Each reviewer has one week or more to accept the invitation. And often the potential reviewers would not answer or decline. Thus, it can happen that it takes over one month to find suitable reviewers for a paper. Generally, if a topic is unpopular, it can be quite hard to find reviewers.
  • Reviewers are often late. Many authors do not know that it is quite common that reviewers submit their reviews late. Often, it is by a few days, but some reviewers can be late by up to a month in some cases. When a reviewer is late, the journal will automatically send reminders to the reviewer but still some reviewers can receive dozens of reminders and ignore them. In this case, the editor has to step in and find more reviewer(s).
  • The work of an editor can be repetitive. Although being an editor is interesting and it is also very important for academia, I feel that some work is repetitive such as clicking to find reviewers. Other people will have different opinions but rather than doing such tasks, I would rather spend time working on my own research.
  • When submitting a paper, if you can enter keywords or select topics from a taxonomy to describe your paper, choose them wisely. It is important to choose keywords that really describe your paper well as this will be used to find reviewers for your paper and can accelerate the review process.
  • Some reviewers are unethical. As editor, it is important to read carefully what the reviewers write as there are several reviewers that would act unethically such as by asking authors to cite many of their papers to increase their citation count. When i would see this, I would intervene to stop this, of course, but there are always a few reviewers that are trying to do this.
  • If authors do not do enough effort to revise their paper well (after a round of review), it is not uncommon that a reviewer will change his mind and suggest to reject the paper in the next round. The reviewers are doing their work for free. Some reviewers are very patient and will accept to review the same paper multiple times. But others will not be patient if the author does not make sufficient effort to address issues in the paper. Thus authors should always do their best to solve the problems in their paper.

That was just a short blog post to talk a little bit about the work of an editor and the review process of a journal. I hope it does not sound negative because actually, I have learned many things from this work and I do not regret accepting to do this work.

Hope that this has been interesting. If you have any comments or questions, please leave them in the comment section below!

Philippe Fournier-Viger is a distinguished professor working in China and founder of the SPMF open source data mining software.

Posted in Academia, Other, Research | Tagged , , , , , , , | 1 Comment


Special Session on Data Science for Social and Behavioral Analytics

This is to let you know that I co-organize a special session called DSSBA 2022 at the IEEE DSAA 2022 conference, and we need your papers ;-).

This special session is about social and behavior analytics. This includes topics such as:

•    Efficient and scalable algorithms for behavioral and social analytics
•    Evaluation of behavioral analytic models
•    Social computing and behavior analytics
•    Interpretation of data science models under cognitive theories
•    Business process analysis
•    Customer behavior analysis
•    Social network analysis
•    Abnormal behavior detection
•    Group behavior analysis
•    Behavior prediction
•    Models for targeted and personalized behavior services
•    Privacy-preserving behavior and social analytics
•    Security models and algorithms for behavioral analytic
•    Behavioral economics with data analysis
•    Case studies and real-world applications of the above

All the accepted papers will be published as regular papers in the proceedings of IEEE DSAA 2022. The papers are published by IEEE and EI-indexed.

The deadline is: 1st June 2022
The format is up to 10 pages in IEEE double-column format.

Website for more information about DSSBA :

IEEE logo

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The HUIM-Miner and FHM algorithms (video)

Today, I post one more new video to explain concepts about pattern mining. In the new video, I talk about high utility itemset mining, and explain the HUI-Miner and FHM algorithms. Those are two popular high utility itemset mining algorithms that have been used in hundreds of research papers.

high utility itemset mining video frame

Click here to watch the video: The HUI-Miner and FHM algorithm – 45 minutes

If you want to see more videos about pattern mining, you can check the video page of the SPMF website or my Youtube channel (which contains many of the same videos)

Philippe Fournier-Viger is a distinguished professor working in China and founder of the SPMF open source data mining software.

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