In this blog post, I will share the video of our new paper about analyzing alarms in telecomunication networks presented at the AIOPS 2020 workshop. This work is part of an industrial collaboration project. The motivation for this project is that there are typically thousands of alarms in a telecomunication network, and not all of them are important. To allows network operators to focus on fixing issues that are the most important, we propose a method to discover correlations between alarms.
For this purpose, we view a telecommunication network as an attributed graph where nodes represent devices, edges indicates connections between devices, and attributes of vertices represent alarms. Then, we apply a novel algorithm to find rules of the form A–>B indicating that if alarm A appears, Alarm B is likely to occur. Then, using these rules, we can reduce the number of alarms presented to network maintenance workers. Though, the approach is designed for analyzing alarms it could be applied to other data modelled as graphs.
Fournier-Viger, P., Ganghuan, H., Zhou, M., Nouioua1, M., Liu, J. (2020). Discovering Alarm Correlation Rules for Network Fault Management. Proc. of the International Workshop on Artificial Intelligence for IT Operations (AIOPS), in conjunctions with the 18th International Conference on Service-Oriented Computing (ICSOC2020) conference,
Today, I would like to wish all readers of this blog and users of my SPMF data mining software a merry Christmas and a happy new year!
This year has been a special year due to the worldwide pandemic with several challenges and changes in our habits. But this year will soon be behind us. And I wish you all health, hapiness and success for 2021.
I would like at the same time to thank all the users of SPMF and readers of this blog for supporting those projects. For the SPMF software, a new version will be released very soon with several new algorithms! I am working on it these days! Keep you updated soon…
Today, I talk about my collection ofconference badges that I have collected since I was a PhD student till today. I have attended over 50 events and have kept all of the conference badges except maybe one or two. Here is a picture of all these conference badges:
Totally, I have visited 28 countries and/or special territories but not all of them for attending conferences. Sometimes, it was only for a research visit or vacation. Below I will talk about what is a great conference badge and take a look at some of them to compare the different designs.
Generally, a good badge should have the following characteristics: (1) it is big enough, (2) the name is written in big letters, (3) it does not contain irrelevant information (e.g. it is unecessary to write the conference dates and hotel), (4) it is also beautiful, and (5) it cannot flip or otherwise it is printed on both sides.
The simple black and white badges
The badge below for DEXA 2018 is the most simple one. Printed on a piece of standard paper with a black and white printer, it only indicates the conference name, attendee name and country. Simple and effective. But could be more beautiful.
This is another simple black and white badge, for KDD 2018:
The simple badges with color
The badge below is still quite simple but has a bit more color which makes it more enjoyable than the black and white badges.
The one below is simple from IEA AIE 2018, colorful and effective as the key information is easy to read and big enough:
The one below from PAKDD 2014 is also quite good as the name is really big and the design is nice and colorful. However, there is a lot of empty space at the bottom. The bottom third of the badge could be cut entirely.
The one below from PAKDD 2017 is a bit better in my opinion as it is more beautiful. But the font for the name is a bit hard to read. Generally, it is better to put the first name bigger and to put the first name and the last name on different lines to avoid squeezing all letters on a single line like below.
I like badges like the one below from IDA 2014 that are simple, colorful and just contain the key information (name, affiliation and conference acronym), and are also beautiful. That one uses a color picture which is nice.
Badges with text that is too small
Some badge like the one below from ADMA 2018 are very big but do not use the space very well. The name of the attendee is actually very small. More than 50% of the space is basically empty.
Badge with too many information
The badge below from PAKDD 2018 is beautiful but really contains too much information. It is not necessary for attendees of the conference to know the full conference name, dates, name of the hotel (!), and country. If we are attending the conference, we already know at which hotel we are and what is the date.
Badges where you write your name by yourself
For some conferences, I had to write my name by myself. This is not a very good idea… Look at the messy result below when the ink does not dry well at ADMA 2013!
Badges with a fancy design
The badge below is one of my favorite as it is made of plastic and has a very beautiful design representing the architecture of a famous tower in the city (Liaocheng). It could have been improved by adding the names of attendees.
Badges with a special material
Another badge that is quite special is the one below for the BDA 2019 conference as it has been etched in a piece a wood. That is the most unique material for a conference badge that I have seen, and for this it is really nice. However, I think that some information could be removed like the full conference name and dates. Just writing BDA 2019 would be enough and would make it easier to read.
Badges with photo
Badges for some events also havea photo. Below is an example. Having a photo is nice and probably also a security measure to ensure that the badge is not stolen and used by someone else.
Another badge with photo is below. This one is really nice but a problem is that the name is really small.
The badges with no names
A few conferences have given badge with no names like below. Although I have enjoyed these conferences, I have to say that having a name on the badge would have been much better. It is important to help starting conversations with other attendees!
Badge with text that is too small and too many colors
And the following badge is one of the worst (in my opinion). The problem with this badge is that it is really small (smaller than a credit card) and that the text is really hard to read because of the colors. At that time I was a graduate student and I had printed these badges and helped to do the design so I am partly responsible for that! What happened is that we first bought paper for badges that were too small and did not know how it would look like when printed in color. Also, I had no experience in designing badges and we were in a rush, so we did not had time to print them again. Today, I would not do like that 😉
But I also did the design of that badge at the same time and it looked a bit better:
In this blog post, I have talked about how a good conference badge should be designed and have shown some of the best and worst badges from my collection. 😉
Do you also keep all your conference badges? Which badge do you like the most or think is the worst? You may tell me in the comment section below.
Year 2020 is soon ending, and it has been a quite special year due to the coronavirus pandemic around the world. This has forced many researchers to work from home, and to cancel or change their research travel plans. Moreover, may academic conferences in 2020 have been held online as virtual conferences as a safety measure and due to travel restrictions in several countries. In this blog post, I will talk about this new trend of holding virtual conferences and the advantages and benefits compared to “real” conferences (held in a physical location).
Since the begining of the year, I have attended several virtual conferences such as PAKDD 2020, ICDM 2020, IEA AIE 2020, and the AIOPS 2020 and UDML 2020 workshops, as well as the DAWAK 2020 conferences. Generally, these events have been well-organized. While some conferences took great care of scheduling talks of researchers based on their time zones, some other events had some small time management problems. For example, a session chair thought that a session was starting earlier due to a wrong time conversion, and the wrong time zone was indicated in the program of another conference, which led to some confusion. But on overall, it worked as planned.
Benefits of virtual conferences
Listening to a conference online has some benefits. One of them is that it is not necessary to travel very far to give a talk. Rather than flying to a location, one can just connect to a server, which is not time-consuming. Online conferences also provides flexibility as one can listen to talks while doing some other things at home, or from various locations. Moreover, a few conferences have provided a playback option to watch the videos of previous presentations in case we missed them. Another benefit of online conferences is that the registration fees have been often reduced, and that in some cases, attending the conferences became free. This may have helped some students or researchers to attend some conferences that they would otherwise have not attended.
Drawbacks of virtual conferences
There are also some drawbacks to online conferences. The first one is that the schedule is not suitable for everyone. For example, one may have to present a paper in the middle of the night due to the time difference. This was generally not a problem in my case, but I know some other researchers that had problems with this.
A second drawback is that the ability to socialize with other researchers is greatly reduced in online conferences. In a real conference, we can shake hands and talk with many people that we know or don’t know, especially during the coffee breaks and other social activities. This is important to establish contact with other researchers. However, in virtual conferences, there is not much opportunities for that… Some conferences like ICDM have adopted some online systems such as Gather.Town where we could walk using an avatar in a virtual room to talk with other people using a webcam and microphone but I found that the room was essentially empty every time I checked or with only a few inactive people. Thus, although that concept was nice, in practice, I was not able to talk with anyone using it.
Another issue with virtual conferences is that it is easy to not feel motivatedto listen to the talks since they are all online and the schedule is often conflicting with real-life activities. Some talks may be in the middle of the night, or during work hours or lunch. Thus, I personally did not listen to many talks, while at a real conferences, I would attend most of the sessions.
Another thing that I don’t like so much about virtual conferences is that we often do not see the audience when we give a talk (unless they open their webcams). In this case, we are in front of the computer talking with our microphone but we have little feedback during the presentation. And in many cases, the talks are required to be pre-recorded, which do not make them interactive at all.
Attending real conferences again
Recently, I attended some real conferences again. This is because the pandemic is under control in the country where I live (China). The second week of December 2020 was the first time that I attended a real conference this year. And it was really enjoyable feeling to be able to meet again researchers and talk with them face to face. I met some very nice people and those were some great events. In general, the life where I am has gone back to normal already since several months, which I am very happy about. However, I am looking forward to the day where I can also attend international conferences abroad as I used to do many times per year, in the past. I think next year, real conferences will start to happen again… or perhaps some hybrid conferences that will be partly online and partly offline (e.g. IEA AIE 2020).
In this blog post, I talked about the experience of attending real and virtual conferences, and especially the benefits and drawbacks of virtual conferences. I hope that it has been interesting. If you want to share your thoughts and experience about that, please leave a comment below! I will be happy to read you.
As some of you know, I am editor-in-chief of the Data Science and Pattern Recognition (DSPR) journal. This journal has started in 2017 and four volumes have been published already with 28 papers, which I consider as a success. One of the reason is that the DSPR journal has a strong editorial committee to select quality papers. Currently, we have big plans for the journal as this year, we should reach 40 papers and apply for EI indexing. This will help to make the journal grow much more quickly.
Recently, we have analyzed the citations for papers published in the DSPR journal, and here are the 10 most cited papers from DSPR:
Philippe Fournier-Viger, Jerry Chun-Wei Lin, Rage Uday Kiran, Yun Sing Koh, and Rincy Thomas, “A Survey of Sequential Pattern Mining,” Data Science and Pattern Recognition, vol. 1(1), pp. 54-77, 2017. [citation: 277]
Today, I will talk about an important topic for graduate students, which is how to prepare for your thesis defense. I will explain what should be done to prepare yourself well., and also talk about my experience as student and currently as professor and judge for thesis defenses.
Before the thesis defense
If you have a chance, attend some thesis defenses by other graduate students to get familiar with the process.
Ask about how the thesis defense are done at your institution and who will be the judges. Especially, you need to know about the amount of time that you will have to give your presentation.
Start to prepare early and talk to your thesis supervisor about your preparation. Your supervisor may give you some good advices, especially with respect to how defenses are conducted at your school.
Spend a good amount of time to prepare your presentation. Preferably, prepare your slides a week earlier and show them to your supervisor and friends for comments. You may read my advices about how to give a good talk. In particular, avoid putting too many slides and too many details., and make sure there are no errors or typos.
Rehearse your presentation several times to make sure you are comfortable giving it, and that you can present whitin the time limit. You may ask some friends to listen to your presentation.
Eat well and have a good sleep before the talk. This can make a big difference. For example, in the past, I was judge for a thesis defense where a student felt down and almost loose consciousness due to the high stress, fatigue and not eating breakfast. To be able to sleep well and be at your best, you need to finish your preparation at least one day before the defense.
Prepare a list of questions that you think judges may ask you and a list of corresponding answers. This will help you to better answer questions.
If you prepare yourself well, you will not be stressful and you will perform better.
During the defense
Wear some suitable clothing. Be polite.
Don’t talk too fast. A common mistake is that some students will try to talk very fast to say more things. But this is not necessary. Instead, summarize and talk about what is important at normal speed.
Look at your audience. Another common mistake is to look at your screen instead of looking at your audience. A presentation is much more interesting when the presenter look at attendees.
Keep track of the time. This is one of the most important thing. You need to make sure that you will not exceed the time limit. Thus, keep an eye on the clock, cellphone or your watch to know how much time is left.
Listen carefully to the questionsfrom judges before answering. If you did not understand, ask to clarify the questions or repeat the question in your own words, before answering. This is important because If you did not understand a question, you may give an unrelated answer.
When answering a question, remember that the judge may not be an expert on your topic. Thus, try to give an answer that is easy to understand if you think the judge may not be familiar with your research area.
In this blog post, I gave some advices about how to prepare for your thesis defense. Hope it will be useful. If you think I missed something or would like to talk about your experience, please leave a comment below!
In this blog post, I will talk about the IEEE ICDM 2020 conference that I have attended virtually. The conference was supposed to be held in Italy but due to the coronavirus pandemic, it was held online.
About the ICDM conference
This year was the 20th edition of the IEEE ICDM conference. It is a well-known conference that is quite competitive. It is one of the top data mining conferences. The proceedings are published by IEEE. The conference has a research paper track, as well as a dozen workshops and tutorials.
The first day was mainly for workshops. On the second day, there was the conference opening. In the opening, the organizers were introduced, and an overview of the conference was given. Here are some of the slides, below.
The main research topics this year were:
Some statistics about the review process and accepted papers:
Most accepted papers are from China and the US, followed by Australia, Germany, India, France and Japan.
The online conference system
The conference was held on the website Underline.io where the prerecorded videos of papers could be viewed at anytime. Then, during sessions of the conferences, authors would join a Zoom session and give a 3 minutes summary of their papers and answer questions live, assuming that people had watched the videos already. A few sessions like the conference opening ceremony were held live.
Besides, there is an interesting function on Underline called the Lounge, implemented in with Gather.town, which allows to perform a video\audio chat with other conference attendees in a game-like virtual world (see picture below). In the lounge, the chat function is proximity-based. You can move your avatar close to the avatar of other persons to initiate a discussion with that person or listen to a discussion.
This is an interesting concept that aims to recreate how people would talk with each other during the coffee breaks of an on-site conference. However, in practice, there was not so many people in the lounge. I checked a few times during the first days of the conference and there was about 3 to 5 persons there。 But no one was discussing with each other. So it seems that this function is an interesting concept but in practice I did not see it being used.
My opinion about Underline is that it is relatively simple and it did the job but it relies on external services such as Zoom and Gather.town. Thus, Underline is more like a hub for different services for the conference. Having all these services under a single website or software would have been better in my opinion.
The registration was quite low this year at 500 $ USD due to the conference being held online (because of the coronavirus pandemic). This is appreciated as ICDM is typically quite expensive, just like some other top conferences.
UDML 2020 worksop on Utility Pattern Mining and Learning
This year was the third edition of the UDML workshop on Utility Driven Mining and Learning (UDML 2020). This year, eleven papers were submitted and five were selected for publication for an acceptance rate of 45%. Three of the selected papers are about algorithm for high utility pattern mining, while another is related to spatiotemporal data mining, and another about multi-objective recommendation.
Here is a picture of the five accepted papers:
There was a good discussion during the workshop and it was nice to see some researchers that I knew already.
In this blog post, I will talk about changing to another research areafor researchers and what it implies. Moreover, I will talk about what is a good research area, and the importance of continuity for researchers. I will also discuss about my own experience related to changing research areas.
Reasons for changing research areas
There are several reasons for considering a change of research area at different points in the career of a researcher, and also for graduate students. Some reasons are:
Changing for a more popular research area. One may wants to work on a more popular research area to follow some new trends. For example, in computer science, one may want to change from a more traditional research area like compiler design to a more popular topic like big data, data science, the internet of things, sensor networks, or machine learning. By following some trends, it may be easier to find a job, get some research funding, get some industrial collaboration projects, publish papers in special issues or workshops, get more citations and have a greater research impact, etc.
Personal interests. A researcher may want to try something new or he may feel more interested into a different research area to explore new problems and learn other things.
Joining a research team that works on a specific research area. For example, a professor joining a university may want to slightly change research area to integrate with a research team that is specialized on a research topic.
Changing for a research area where it is easier to publish articles. For many universities, publishing papers is a performance evaluation criterion. In this context, some researcher will want to work on topics where it is easier to do new contributions, carry experiments and publish articles.
Those are some of the key reasons that a researcher may consider. Whether those are good reasons or not depends on each case. For example, a researcher may not care about working on a popular topic but may rather work on something that he really likes.
I will talk about my own experience as example. In my early research career, I have been working on intelligent tutoring systems and cognitive modelling but found that it was a difficult topic for carrying research as it required to do experiments with people to evaluate my proposals, which was very time-consuming. Moreover, the research community around intelligent tutoring systems is quite small (maybe a few hundred people), so the possibility of having a great research impact was in my opinion limited. Also, I have a personal interest in algorithm design and optimization. Hence, at the end of my Ph.D., I started to switch from this research area towards doing research on data mining. Nowadays, my research area is data mining, and more specifically pattern mining. I think it was a good decision in my case because data mining is a more popular research area, I like this field, and it is easier to do research and write papers, and there is more job opportunities. Besides, by working at a more fundamental level (algorithm design) rather than at the level of applications, I can have a greater research impact. For instance, my algorithms are not limited to only be applied in intelligent tutoring systems but can be used in other fields. If I would keep working on a narrow topic with a small research community, it would be harder to get citations (not so important, but it is still a performance evaluation criterion at some universities).
What is a good research area?
There is no absolute answer to this question. But a researcher can try to answer these questions to assess a research area:
Is this research area that is interesting for you?
Is this a research area where you can make some good contributions?
Is this related to your current expertise? This is important to avoid starting again from zero… If you change to a research area that is somewhat related to your current research area, it may be better.
Is this a popular research area?
Can you get some special opportunities in that research area (join a team, get a job, funding, etc.)?
Those are some important criteria but it is not necessary to meet all there criteria.
The importance of continuity Changing research area can be good. However, continuity is also important in the career of a researcher. Changing too often from one research area to another is not good. It will show a lack of focus and it may seem that the researcher is a specialist of nothing. It is better for the career of a researcher to focus on a specific research area and make several good contributions in that area over the years to become more and more famous in that area and benefit from this. As a researcher continue to work in the same area, it becomes easier (and faster) to make better research contributions and write papers. The researcher can also build many collaborations with other researchers over the years, and it becomes also easier to obtain research funding in a research area where you have published many papers.
In my opinion, the best time to change research area is at the begining of the career of a researcher. For example, I gradually changed towards data mining towards the end of my Ph.D. and now mostly only do data mining research. Ten years later, I would not change research area again, because now, I am well-established researcher in that area, and I am also happy to work on this. If I would change again to another research area, then it would become harder to publish papers, obtain grants, and I would have to learn many things again. So my focus is on data mining, but I am still sometimes work on other topics as side-projects. 😉
Changing a research area also requires some planning and to think ahead of time. It is also better to gradually change toward the new research area, if possible.
In this blog post, I talked about changing research areas as it is a concern for several researchers especially early in their career. Hope that it has been interesting. If you would like to share your own experience or have comments related to this, please post in the comment section below!
In this blog post, I will talk about how to write a cover letter for a journal paper. This is an important topic for researchers swho submit research papers to journals, as many journals require to write and submit a cover letter with the paper.
What is the purpose of the cover letter?
A researcher write a cover letter to inform the editor of the journal that he is submitting a paper. The cover letter should be adressed to the editor of the journal.
The content of cover letters can vary and some journals have specific requirements about the content of a cover letter. Thus, before writing a cover letter, it is important to check if there are some requirements. Some journals also do not require to write a cover letter.
A cover letter typically contains the following content:
The title of the paper
One or two sentences to explain what is the topic of the paper and why it is suitable for the journal (if not obvious)
A statement to say that the work is original and has not been submitted to other journals
A statement to say that all authors have agreed to the submission
The list of all author names
Personally, I think that a cover letter does not need to be very complicated. But at least a cover letter should not contain errors and should be written in a polite way. If a cover letter contains several English errors, then it may give a bad impression to the editor.
Two examples of cover letters
Here is a first example of cover letter that I have used recently:
We wish to submit an original research article entitled “NAME_OF_THE_PAPER” for consideration by NAME_OF_THE_JOURNAL.
I would like to declare on behalf of my co-authors that this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere. Moreover, we have no conflicts of interest to disclose.
We believe that this manuscript is appropriate for publication by NAME_OF_THE_JOURNAL because it fits with the principal objectives of the journal by proposing a new solution to one important research problem in pattern mining. More precisely, a new efficient solution is proposed for the problem of XXXXXXX.
We deeply appreciate your consideration of our manuscript, and we look forward to receiving comments from the reviewers. If you have any queries, please don’t hesitate to contact us at: EMAIL_ADDRESS
Thank you for your consideration of this manuscript.
CORRESPONDING AUTHOR NAME CORRESPONDING AUTHOR AFFILIATION CORRESPONDING AUTHOR EMAIL
And here is another example that is slightly different:
We are submitting this paper proposing a new algorithm to …….. , named ……. . This is an important problem in the context of …….. Unlike previous work who focused on ……. we propose an algorithm that consider …. . To develop an algorithm for that measure, novel theoretical results are presented, …… We show in experiments that the proposed outperforms the compared algorithms in terms of execution time and memory usage
This manuscript is the authors’ original work and has not been published. Moreover, it has not been submitted simultaneously elsewhere.
All authors have checked the manuscript and have agreed to the submission.
Thanks for handling this manuscript. We are looking forward to receive feedback from the evaluation process.
AUTHOR NAME AUTHOR AFFILIATION AUTHOR E-MAIL
In this blog post, I have discussed how to write a cover letter for a journal paper, and given some examples. You may use this as basic templates for writing your own cover letters or make your own while keeping the key information that a cover letter should have. Hope that this will be helpful.
In this blog post, I will talk about how to find a good thesis topic on machine learning. This is an important question for many students that are required to select a topic for their research and want to work on machine learning. Choosing a good researchtopic is a critical step in the research process to ensure the success of the research and for publishing good papers.
What is a good research topic?
A good research topic is a research problem that is: (1) novel, (2) challenging to solve (cannot just be solved by applying existing techniques) but not too challenging or long, (3) useful (otherwise, there is no reason to do the research), and (4) a problem that is interesting for other researchers or has applications.
It should also be clear that a research topic is not equal to doing a programming or software development project. Just solving a software development problem is generally not research. Research is about solving a novel and difficult problem that requires to develop some inovative solution.
How to find a good research topic on machine learning?
To find a good research topic, it is important to know what other researchers have done in recent years. Thus, to select a topic, one should first read papers in good journals and conferences to see what other researchers have been doing. By reading recent papers, one can try to think about the limitations of these studies and what could be improved, or what other researchers have not done yet (because there is no point to do the same thing again). Reading the literature requires some time and is not so easy to do but is very important to choose a good topic. For the young students, it is recommended to ask advices from their supervisor during this step. The supervisor should be able to suggest some good papers to read as starting point and to validate the research topic ideas. When reading paper, one can pay attention to the related work section and conclusion, which sometimes highlight some limitations of previous studies and can give some ideas for research.
Generally, it is a good to work on some research area that other researchers are interested in rather than working on some obscure problems that have few applications.
After finding some idea that is novel, it is also important to keep searching for papersto make sure that the idea is really novel and no one has done it before. If someone did it already, it is better to find this issue as early as possible to avoid wasting time on pursuing a research idea that has been done before. When searching for other papers, it is also important to find the right keywords for searching. Some research topic may have already been studied but with a different name. Thus, when searching for papers, it is important to try various keywords and to keep searching to make sure that the literature has been checked carefully.
How to describe your research topic?
A common misconception about choosing a research topic is to think that just choosing a title is good enough for choosing a research topic. But it is not. A research topic should be defined clearly and with more details.
For example, a topic title like ‘machine learning for image processing’ is too broad and does not mean much about what one would like to do. What kind of machine learning technique? What kind of image processing task? And what is the originality? All of this could be explained more clearly with a more detailed description.
To clearly define your research topic, I recommend to write some text explaining: – the title, – why the problem is important? – what are the limitations of previous studies that this research will address? – why that research problem is challenging? – and give a sketch of some possibilities for solving the problem.
If you can answer the above questions, then it means that you have carefully thought about your research topic.
You may also ask some senior researchers to look at your research topic to confirm that it is a good topic.
In this blog post, I have discussed the problem of searching for a research topic in machine learning. Hope this has been helpful. If you have some comments, please leave them in the comment section below.