We invite submissions for the journal track of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD) 2017. The journal track of the conference is implemented in partnership with the Machine learning journal and the Data mining and Knowledge Discovery journal. The conference provides an international forum for the discussion of the latest high-quality research results in all areas related to machine learning, data mining, and knowledge discovery.
Papers on all topics related to machine learning, knowledge discovery, and data mining are invited. However, given the special nature of the journal track, only papers that satisfy the quality criteria of journal papers and at the same time lend themselves to conference talks will be considered. This implies that journal versions of previously published conference papers, or survey papers will not be considered for the special issue. Papers that do not fall into the eligible category may be rejected without formal reviews but can of course be resubmitted as regular papers.
Authors are encouraged to adhere to the best practices of Reproducible Research (RR), by making available data and software tools for reproducing the results reported in their papers. For the sake of persistence and proper authorship attribution, we require the use of standard repository hosting services such as dataverse, mldata, openml, etc. for data sets, and mloss, bitbucket, github, etc. for source code.
Authors who submit their work to the special ECMLPKDD issues of the journals commit themselves to present their results at the ECMLPKDD 2017 conference in case of acceptance.
The journal track allows continuous submissions from the end of July 2016 to the end of March 2017. We start with a single cutoff per month, increasing to two cutoffs per month starting October 2016 on which we distribute papers to reviewers. More precisely, we will have the following cut-off dates:
We strive for a high quality and efficient review process. Each submission will be evaluated by three experienced reviewers including members of the Guest Editorial Board. We aim to send the first decision letters 6-8 weeks from submission. This suggests that we should be able to consider all of the submissions for the special issue. However, the experience from the past editions of the journal track shows that often there is a need for revisions of the submissions and this extends the review process. Considering this, for submissions for the 5.2.2017 cut-off date and later, the chance of inclusion in the ECML PKDD 2017 special issue exponentially decreases and, consequently, will not make it on time for presentation at the 2017 conference. Inclusion of the delayed papers in forthcoming special issues and conference editions is subject to approval of the respective Program and Journal track chairs.
To submit to this track, authors have to make a journal submission to either the Springer Data Mining and Knowledge Discovery journal or the Springer Machine Learning journal, and select the type of submission to be for the ECMLPKDD 2017 special issue. It is recommended that submitted papers do not exceed 20 pages including references and appendices, formatted in the Springer journal style (svjour3, smallcondensed). This is a soft limit, but if a submission exceeds the limit, please provide a brief justification regarding the length in the cover letter. For submissions to both journals, authors are required to include an information sheet (for Machine learning submissions) or a cover letter (up to 2 pages) as a supplementary material (for Data Mining and Knowledge Discovery submissions) that contains a short summary of their contribution and specifically address the following questions:
You can contact the Journal Track Chairs at jt_chairs@ecmlpkdd2017.org
Important instructions for Springer LNCS publication of Reproducible Research papers
Submissions are solicited for the 2017 edition of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2017). The conference provides an international forum for the discussion of the latest high-quality research results in all areas related to machine learning and knowledge discovery in databases and other innovative application domains. The 2017 conference will take place in Skopje, Macedonia, 18‐22 September 2017.
Submissions are invited on all aspects of machine learning, knowledge discovery and data mining, including real-world applications. Following the tradition of ECML-PKDD, we expect high-quality papers in terms of their scientific contribution, rigour, correctness, quality of presentation and reproducibility of experiments.
Electronic submissions will be handled via Microsoft CMT. Please note that user accounts in each CMT conference are independent of other conferences, so you will need to create a new account.
Abstracts need to be registered by Thursday April 13, 2017 and full submissions will be accepted until Thursday April 20, 2017.
Papers must be written in English and formatted according to the Springer LNCS guidelines. Author instructions, style files and copyright form can be downloaded here.
The maximum length of papers is 16 pages in this format. Overlength papers will be rejected without review (papers with smaller page margins and font sizes than specified in the author instructions and set in the style files will also be treated as overlength).
Up to 10 MB of additional materials (e.g. proofs, audio, images, video, data or source code) can be attached to the submission. Note that the reviewers and the program committee reserve the right to judge the paper solely on the basis of the 16 pages of the paper; looking at any additional material is up to the discretion of the reviewers and is not required.
Authors who submit their work to ECML-PKDD commit themselves to present their paper at the ECML-PKDD 2017 conference in case of acceptance. Additionally, ECML-PKDD considers the author list submitted with the paper as final. No additions or deletions to this list may be made after paper submission, either during the review period, or in case of acceptance, at the final camera ready stage.
The review process is single-blind (authors identities known to reviewers). Submissions will be evaluated on the basis of technical quality, novelty, potential impact, and clarity. Authors will have the opportunity to point out factual errors, obvious mistakes, or misconceptions by reviewers during a rebuttal phase following the release of initial reviews.
Papers submitted should report original work. ECML-PKDD 2017 will not accept any paper that, at the date of April 26th 2017 (we have a 5-days withdrawal period), is under review or has already been accepted for publication in a journal or another conference. Authors are also expected not to submit their papers elsewhere during the review period. The dual submissions policy applies during the following period: from April 26 to June 22, 2017.
Authors are encouraged to adhere to the best practices of Reproducible Research (RR), by making available data and software tools for reproducing the results reported in their papers. Authors may flag their submissions as RR and make software and data accessible to reviewers who will verify the accessibility of software and data. Links to data and code must be inserted in the final version of RR papers. For the sake of persistence and proper authorship attribution, we require the use of standard repository hosting services such as Dataverse, mldata.org, OpenML, figshare, Zenodo, etc. for data sets, and mloss.org, Bitbucket, GitHub, figshare (where it is possible to assign a DOI) etc. for source code. If data or code gets updated after the paper is published, it is important to enable researchers to access the versions that were used to produce the results reported in the paper. Authors who do not have a preferred repository should consult Springer Nature’s list of repositories and research data policy (we adopt type 2).
The conference proceedings will be published by Springer in the Lecture Notes in Computer Science series (LNCS). This year, the proceedings will be published after the conference and will include only the papers that authors have presented at the conference. During the conference, a camera-ready version of the papers will be available for conference participants.
In addition to normal conference submissions, papers can be submitted to other tracks: Industrial, Governmental and NGO; Demo; Ph.D.; Nectar; journal track. Accepted papers in all the tracks, including journal track, will be presented at the conference.
For information about other tracks, please see the separate call for papers.
All deadlines expire on 23:59 Pacific Time Zone (UTC-8)
For any additional questions you can contact the Program Chairs (Michelangelo Ceci, Jaakko Hollmén, Ljupčo Todorovski, Celine Vens) at pc_chairs@ecmlpkdd2017.org
We solicit submissions for demos for ECML PKDD 2017 in Skopje, Macedonia. Submissions must describe working systems and be based on state-of-the-art machine learning and data mining technology. These systems may be innovative prototype implementations or mature systems that use machine learning techniques and knowledge discovery processes in a real setting. Systems that use basic statistics are not acceptable. Commercial software systems are not acceptable.
The accepted papers for demos will be included in the conference proceedings, to be published by Springer Verlag in the "Lecture Notes in Computer Science" (LNCS) Series. The demos will be presented in a special demonstration session. At least one of the demo submitters must register for the conference, and perform the demo on site. This year, the proceedings will be published after the conference and will include only the papers that authors have presented at the conference. During the conference, a camera-ready version of the papers will be available for conference participants.
All aspects of the submission and notification process will be handled online via the conference Microsoft CMT submission site. Please choose the right track during the submission. Instructions concerning the submission (except for the "reproducible research" part which is not relevant for a demo paper), camera-ready formatting and copyright transfer for conference papers also hold for demo papers, unless otherwise specified.
A demonstration submission must be up to 4 pages long. It must provide adequate information on the system's components and the way the system is operated, including e.g. screenshots. Submitters should keep in mind that the description of a demo has inherently different content than a research paper submitted to the main conference. A successful demonstration paper provides satisfactory answers to the following questions:
All deadlines expire on 23:59 Pacific Time Zone (UTC-8)
For further information please contact the Demo Track Chairs (Marinka Zitnik, Jesse Read) at demo_chairs@ecmlpkdd2017.org
The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD) provides an international forum for the discussion of the latest high-quality research results in all areas related to machine learning and knowledge discovery in databases and related application domains.
The goal of the Nectar Track, started in 2012, is to offer conference attendees a compact overview of recent scientific advances at the frontier of machine learning and data mining with other disciplines, as published in related conferences and journals. For researchers from the other disciplines, the Nectar Track offers a place to present their work to the ECMLPKDD community and to raise the community's awareness of data analysis results and open problems in their field. We invite senior and junior researchers to submit summaries of their own work published in neighboring fields, such as (but not limited to) artificial intelligence, big data analytics, bioinformatics, cyber security, games, computational linguistics, natural language processing, information retrieval, computer vision and image analysis, geoinformatics, health informatics, database theory, human computer interaction, information and knowledge management, robotics, pattern recognition, statistics, social network analysis, theoretical computer science, uncertainty in AI, network science, complex systems science, and computationally oriented sociology, economy and biology, as well as critical data science/studies.
Particularly welcome is work that summarizes a line of work that comprises older and more recent papers. The described work should be relevant to a broad audience within ECMLPKDD, and (a) illustrate the pervasiveness of data-driven exploration and modelling in science, technology, and the public, as well as innovative applications, and/or (b) focus on theoretical results.
Note that papers focusing only on software implementations rather than on the interdisciplinary use of ML/DM should rather be submitted to the demo track. Work at the core of ML/DM should target the main tracks of ECMLPKDD rather than the Nectar Track.
Papers must be 4 pages and should be formatted according to the Author instructions, style files and copyright form that can be found at "Lecture Notes in Computer Science" (LNCS) Series.
Submissions must clearly indicate which corresponding original publication(s) are presented, and must clearly motivate the relevance of the work in the context of machine learning and data mining. Papers should be submitted through the conference Microsoft CMT submission site (select from the menu the Nectar track). Accepted Nectar contributions will be presented as oral presentations and included in the conference proceedings. This year, the proceedings will be published after the conference and will include only the papers that authors have presented at the conference. During the conference, a camera-ready version of the papers will be available for conference participants.
All deadlines expire on 23:59 Pacific Time Zone (UTC-8)
In case you have any question, please do not hesitate to contact the Nectar Track Chairs (Donato Malerba, Jerzy Stefanowski) at nectar_chairs@ecmlpkdd2017.org
The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD, provides an international forum for the discussion of the latest high-quality research results and applications in all areas related to machine learning, data mining and knowledge discovery in databases, as well as other innovative application domains. The 2017 edition of ECML-PKDD will take place in Skopje, Macedonia, September 18-22.
The Applied Data Science Track of ECML-PKDD 2017 follows the success of the previous years with a separate Program Committee and a separate Proceedings volume. The track aims to bring together participants from academia, industry, governments and NGOs (non-governmental organizations) in a venue that highlights practical and real-world studies of machine learning, knowledge discovery and data mining. This track wants to encourage mutually beneficial interactions between those engaged in scientific research and practitioners working to improve big data mining and large-scale machine learning analytics. Novel and practical ideas, open problems in Applied Data Science, description of application–specific challenges and unique solutions adopted in bridging the gap between research and practice are some of the relevant topics for this track.
Submissions are invited on innovative real-world data systems and applications, state-of-the-art practices, identification of unsolved challenges in deploying research ideas in practical data science applications, surveys from real-world projects and industrial experiences that advance the understanding of the contributions and limitations of machine learning and data mining technologies in real-world applications.
The Applied Data Science Track is distinct from the Research Track in that submissions solve real-world problems and focus on applications and challenges. Submissions must clearly identify one of the following three areas they fall into: "Engineering Systems", "Data Analytics", or "Challenges".
The criteria for submissions are the following:
Papers submitted should report original work. ECML-PKDD 2017 will not accept any paper that, at the date of April 26th 2017 (we have a 5-days withdrawal period), is under review or has already been accepted for publication in a journal or another conference. Authors are also expected not to submit their papers elsewhere during the review period. The dual submissions policy applies during the following period: from April 26 to June 22, 2017.
The Applied Data Science Track proceedings of ECML-PKDD 2017 will be published by Springer in a separate volume of the Proceedings of ECML-PKDD 2017, in the "Lecture Notes in Computer Science" (LNCS) Series. At least one of the authors of each accepted paper must register for the conference to present the paper on site.
This year, the proceedings will be published after the conference and will include only the papers that authors have presented at the conference. During the conference, a camera-ready version of the papers will be available for conference participants.
The papers must be written in English and formatted according to the Springer LNCS guidelines. Author's instructions and style files can be downloaded at LNCS.
The maximum length of papers is 12 pages in this format. Longer papers will be rejected without review (papers with smaller page margins and font sizes than specified in the author instructions and set in the style files will also be treated as over length). Papers submitted should report original work; ECML-PKDD 2017 will not accept any paper, which, at the time of submission, is under review or has already been accepted for publication in a journal or another conference. Authors are also expected not to submit their papers elsewhere during the review period.
To submit a paper:
Submissions will be evaluated on the basis of relevance, novelty, originality, technical soundness, clarity, empirical and/or practical validation, external significance and validity, quality and consistency of presentation, and appropriate comparison to related work. Special emphasis will be placed on the relevance of the proposed contribution to practitioners. Authors are strongly encouraged to make data and code publicly available whenever possible.
Authors of papers submitted to the Applied Data Science Track of ECML-PKDD 2017 must identify the application domain that is the subject of their paper. Application domains include, but are not limited to the following: finance, government, e-commerce, retail, mobile, medicine, healthcare, security, public policy, science, engineering, law, manufacturing, and communications.
All deadlines expire on 23:59 Pacific Time Zone (UTC-8)
In case you have any question, please do not hesitate to contact the Applied Data Science Track Chairs (Yasemin Altun, Kamalika Das and Taneli Mielikäinen) at industrial_chairs@ecmlpkdd2017.org
The ECML-PKDD 2017 Organizing Committee invites proposals for half-day tutorials to be held on the first and last days of the conference (September 18 and 22, 2017), which will take place in Skopje, Macedonia.
Tutorials are intended to provide a comprehensive introduction to established or emerging research topics of interest for the machine learning and the data mining community. These topics include related research fields or applications but also well-developed tools and suites that support ML/DM research. The ideal tutorial should attract a wide audience. It should be broad enough to provide a basic introduction to the chosen area, but it should also cover the most important topics in depth. Each tutorial should be well-focused so that its content can be covered in a half-day slot. Proposals that exclusively focus on the presenter’s own work or commercial demonstrations are strongly discouraged.
Tutorial slides will be made available online at the ECML-PKDD website, although authors can provide them on additional websites as well. Depending on the budget, we expect to offer a waived registration fee for attendance on the workshop day to one speaker of each accepted tutorial (i.e., no guarantee).
Tutorial proposals should contain at least the following:
Especially for a relatively novel but rapidly maturing topic, a half day tutorial (4h incl. one 30 minute break) followed by a half day workshop (4h incl. one 30 minute break) could be a good format. We kindly ask you to write a single proposal for the tutorial and workshop that covers the different guidelines and requirements for tutorials and workshops. In addition, the links should be clearly described and the proposal should be submitted to the workshop and tutorial chairs (select both 'Workshop' and 'Tutorial' as topics). For workshop guidelines, please see the separate Call For Workshop Proposals bellow.
The proposal will be reviewed by the workshop and tutorial co-chairs, who may use the help of external reviewers, and experts on the submission topics.
The features that will be evaluated are:
The ECML-PKDD 2017 Organizing Committee invites proposals for workshops to be held on the first and last days of the conference (September 18 and 22, 2017), which will take place in Skopje, Macedonia. We invite proposals for both full- and half-day workshops in current and emerging topics in machine learning and data mining.
Workshops provide an opportunity to discuss novel topics in a small and interactive atmosphere. They can concentrate in-depth on research topics, but can also be devoted to application issues, or to questions concerning the economic and social aspects of machine learning and data mining. Multidisciplinary workshops that bring together researchers and practitioners from different communities are particularly welcome.
If the budget permits, one organizer or invited speaker of an accepted workshop will be offered the possibility of waived registration fee for attendance on the workshop day (only).
We welcome both full- and half-day workshop proposals. Full-day workshops will have a program of typically 8 hours including two 30-minute coffee breaks and a 90-minute lunch break. Half-day workshops will have a 4 hours program with a 30-minute coffee break. We would like to encourage proposers to aim for a program that is both varied and interesting. Especially where the format of the workshop is concerned, we would like you to think about ways of going beyond the usual list of presentations of accepted papers. Keep in mind that the main conference is necessarily more time-constrained and workshops therefore allow for group explorations of interesting topics, for example by means of discussions, demo sessions, invited talks, and panels.
Another way of extending the usual format is to include a specific challenge problem that can be addressed by the workshop participants, with a dedicated challenge session in the workshop program. Note, however, that the challenge should be only one of the components of the workshop, targeting a problem which is specific to the workshop topic(s).
For some workshops, it may be useful to first present an introduction to the state-of-the-art in the field given by experienced invited presenters, and afterwards discuss more technical or novel work in a standard (or non-standard!) workshop setting.
Especially for a relatively novel but rapidly maturing topic, giving the aforementioned introduction of the state-of-the-art may go beyond the scope of an invited presentation. In this case, a half day tutorial (4h incl. one 30 minute break) followed by a half day workshop (4h incl. one 30 minute break) could be a good format. We kindly ask you to write a single proposal for the tutorial and workshop that covers the different guidelines and requirements for tutorials and workshops. In addition, the links should be clearly described and the proposal should be submitted to the workshop and tutorial chairs (select both 'Workshop' and 'Tutorial' as topics). For tutorial guidelines, please see the separate Call For Tutorial Proposals above.
Workshop proposals should contain the necessary information for the workshop chairs and reviewers to judge the importance, quality, and community interest in the proposed topic (a minimum of 15-20 expected participants is required). Each workshop should have one or more designated organisers and a program. When proposing a workshop, please provide (at least) the following information:
Please submit your workshop proposals in PDF format using EasyChair. The submitted proposals will be reviewed in a close collaboration with the conference chairs and the program committee.
The following deadlines are important for the tutorial organizers:
The following deadlines are important for the workshop organizers:
For paper submission, reviewing and final revisions, please consider the following deadlines:
These deadlines are somewhat flexible, but consider as constraints that the paper submission deadline should be after conference author notification (June 19) and acceptance notification should be before the conference early registration deadline (July 31).
In case you have further questions, please do not hesitate to contact the Workshop and Tutorial Chairs (Panče Panov and Nathalie Japkowicz) at wt_chairs@ecmlpkdd2017.org. We are looking forward to your proposals.
The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) includes a PhD Forum. The purpose of this forum is to provide a friendly environment for junior PhD students to exchange ideas and experiences with peers in an interactive atmosphere and to get constructive feedback from senior researchers in areas covered by the conference.
The PhD forum aims at attracting junior PhD students, with roughly 1-3 years of research experience towards their dissertation, to present and discuss their work in progress, and to receive useful comments, advice and ideas for their further work. The forum will also be attended by senior researchers with experience in supervising PhD students who will provide constructive feedback to the participants. This will be an excellent opportunity for developing person-to-person networks of PhD students, which can be extremely valuable for their future careers.
The ECMLPKDD PhD Forum spans various topics of data mining, machine learning, and work in related fields such as databases, artificial intelligence, statistics, information retrieval, multimedia and the Web. Topics in specific domains such as bioinformatics and the more general science informatics are also encouraged. Participants with interdisciplinary work across the areas are particularly welcome.
The PhD forum is open for two types of submissions:
Work-in-progress papers could have been published earlier at other conferences or workshops. The purpose is to obtain technical feedback on the topic and writing style.
The purpose is to obtain feedback regarding future development of the work.
Note that:
The papers must be written in English and formatted according to the Springer LNAI guidelines.
Author instructions and style files can be downloaded here
To submit, please go to Microsoft CMT, and select from the menu the PhD track.
All deadlines expire on 23:59 Pacific Time Zone (UTC-8)
In case you have any question, please do not hesitate to contact the PhD Forum Chairs (Tomislav Šmuc and Bernard Ženko) at phdt_chairs@ecmlpkdd2017.org.
Nowadays, modern earth observation programs produce huge volumes of satellite images time series (SITS) that can be useful to monitor geographical areas through time. How to efficiently analyze such kind of information is still an open question in the remote sensing field. In the context of land cover classification, exploiting time series of satellite images, instead that one single image, can be fruitful to distinguish among classes based on the fact they have different temporal profiles.
The objective of this challenge is to bring closer the Machine Learning and Remote Sensing communities to work on such kind of data. The Machine Learning community has the opportunity to validate and test their approaches on real world data in an application context that is getting more and more attention due to the increasing availability of SITS data while, this challenge offers to the Remote Sensing experts a way to discover and evaluate new data mining and machine learning methods to deal with SITS data.
The challenge involves a multi-class single label classification problem where the examples to classify are pixels described by the time series of satellite images and the prediction is related to the land cover of associated to each pixel. A more detailed description follows.
Organizers:
Dino Ienco, UMR TETIS - IRSTEA, Montpellier, France
Raffaele Gaetano, UMR TETIS - CIRAD, Montpellier, France
The urgent need to reduce pollution emission has made renewable energy a strategic European Union (EU) and international sector. This has resulted in an increasing presence of renewable energy sources and thus, significant distributed power generation. The main challenges faced by this new energy market are grid integration, load balancing and energy trading.
In order to face these challenges, it is of paramount importance to monitor the production and consumption of energy, both at the local and global level, to store historical data and to design new, reliable prediction tools.
In this challenge, we focus our attention on photovoltaic (PV) power plants, due to their wide distribution in Europe. During the last years, the forecast of PV energy production has received significant attention since photovoltaics are becoming a major source of renewable energy for the world. Forecast may apply to a single renewable power generation system, or refer to an aggregation of large numbers of systems spread over an extended geographic area.
Organization:
Roberto Corizzo, University of Bari, Italy
It has now been more than 12 years that the Mars Express Orbiter (MEX) provides science data from Mars about its ionosphere and ground subsurface composition. The 3D imagery of Mars has provided the community with unprecedented information about the planet. Today, thanks to the work of careful and expert operators, Mars Express Orbiter still provides information that supports ground exploration missions on Mars (such as Curiosity, Opportunity, etc.) and a lot of other research.
The Mars Express Orbiter is operated by the European Space Agency from its operations centre (Darmstadt, Germany) where all the telemetry is analysed. The health status of the spacecraft is carefully monitored to plan future science observations and to avoid power shortages.
Operators of Mars Express keep track of the thermal power consumption thanks to the telemetry data. The spacecraft uses electric power coming from the solar arrays (or batteries, during eclipses) not only to supply power to the platform units, but also to the thermal subsystem, which keeps the entire spacecraft within its operating temperature range.
The Mars Express Power Challenge focuses on the difficult problem of predicting the thermal power consumption. More than six Earth years of Mars Express telemetry are made available and you are challenged to predict the thermal subsystem power consumption on the following 26 Earth months. If successful, the winning method will be integrated in the new tool helping operators of the Mars Express Orbiter to deliver science data for a longer period of time.
Organization:
ESA Data Analytics Team for Operations
Mars Express Flight Control Team
The EU Projects Forum at ECML PKDD 2017 is a novel initiative that encourages the dissemination of EU projects and their results to the targeted scientific audience of the conferences’ participants. It is an opportunity for the EU funded projects to present their vision and work to the conference audience, and an opportunity for the ECML PKDD 2017 audience to learn about the European scientific success stories in their research field. As a satellite event of ECML PKDD 2017, it is envisioned as a machine learning/data mining/big data/data science venue for gathering of ERC grantees, EU project consortia, EU project officers, with the ECML PKDD audience including the researchers and the interested industrial participants.
We invite ERC grantees in the machine learning/data mining field to present their projects and results at the EU Projects Forum at ECML PKDD 2017. The European Research Council (ERC) was established ten years ago by the EU to fund excellent scientists and their most creative ideas. Nearly 7,000 top researchers have been supported, including six who later received Nobel Prizes. ERC grants also created career opportunities for some 50,000 researchers, resulted in numerous scientific breakthroughs and led to over 800 patent applications that lay the foundations for growth and jobs.
We invite also Horizon 2020 EU projects related to machine learning/data mining to present their vision, disseminate research results, and potentially recruit researchersHorizon 2020 is the biggest EU Research and Innovation program ever with nearly €80 billion of funding available over 7 years (2014 to 2020) – in addition to the private investment that this money will attract. It promises more breakthroughs, discoveries and world-firsts by taking great ideas from the lab to the market.
Submissions should be send to the EU Projects Forum Chairs at eu_projects@ecmlpkdd2017.org.
In case you have any question, please do not hesitate to contact the EU Projects Forum Chairs (Petra Kralj Novak and Nada Lavrač) at eu_projects@ecmlpkdd2017.org.