SIMPLIFY 2021 : 1st International Workshop on Data Analytics and Machine Learning Made Simple
Call For Papers
There exists a plethora of current applications, with widely different characteristics though, that are generating and need to process massive amounts of static or streaming data. For example, Data Lakes gather large amounts of diverse data from a multitude of data sources with the aim to enable data analysts to perform ad hoc, self-service analytics, and to train machine learning models, reducing the time from data to insights. These operations are also particularly challenging in the case of applications that are processing streaming Big Data. Achieving this goal requires addressing various challenges relating to data volume, velocity, dynamicity, heterogeneity, and potentially (geo-)distributed data processing.
Although there exists a plethora of techniques, algorithms and tools to manage, query and analyze various types of data, they typically require a high degree of data management skills and expertise, as well as significant time and effort for data preparation, parameter tuning and design and implementation of data analytics and machine learning pipelines.
The aim of the SIMPLIFY workshop is to bring together computer scientists with interests in this field to present recent innovations, find topics of common interest and to stimulate further development of new approaches that greatly simplify the work of a data analyst when performing data analytics, or when employing machine learning algorithms, over Big Data.
We invite submissions of novel research, completed or in-progress work, vision, and system papers. The page limit for regular research papers is 6 pages. Additionally, we welcome submission of short papers, up to 4 pages, of the following types: (a) papers that describe ongoing work that has not yet reached the maturity required for a full research paper; (b) vision papers that describe a vision for the future of the field; (c) system/application papers and demos.
Papers must present original work and not have been submitted or accepted for publication in any other workshop, conference or journal.
Papers must follow the ACM Proceedings Format and should be submitted electronically as PDF documents using the online EasyChair submission system:
All workshop papers will be indexed by DBLP and will be published online at CEUR.
List of Topics
- Novel architectures for data analytics and ML over data lakes
- Novel architectures for data analytics and online ML over streaming data
- Query processing over heterogeneous data
- Query processing over geo-distributed data
- Query optimization of data processing workflows
- Algorithms for mining and analytics over heterogeneous data
- Algorithms for online machine learning and data mining
- Similarity search and entity resolution
- Interactive data exploration
- Visual analytics over heterogeneous data
- Deep learning platforms
- Application papers demonstrating the impact of techniques relevant to SIMPLIFY
* Organizing committee
- Antonios Deligiannakis, Technical University of Crete
- Manolis Koubarakis, National and Kapodistrian University of Athens
- Dimitris Skoutas, Athena Research Center
* Program Committee
- Alexander Artikis, NCSR "Demokritos"
- Konstantina Bereta, National and Kapodistrian University of Athens
- Daniele Bonetta, Oracle Labs
- Bikash Chandra, Ecole Polytechnique Fédérale de Lausanne
- Nikos Giatrakos, Athena Research Center
- Damien Graux, ADAPT Centre and Trinity College Dublin
- Asterios Katsifodimos, Delft University of Technology
- Georgia Koutrika, Athena Research Center
- Matteo Lissandrini, Aalborg University
- Davide Mottin, Aarhus University
- Ioannis Mytilinis, Ecole Polytechnique Fédérale de Lausanne
- Eirini Ntoutsi, L3S Research Center
- Odysseas Papapetrou, Eindhoven University of Technology
- Matthias Renz, Christian-Albrechts-Universität zu Kiel
- Alkis Simitsis, Athena Research Center
- Giovanni Simonini, Universita di Modena e Reggio Emilia
- Thanasis Vergoulis, Athena Research Center
- Nikolay Yakovets, Eindhoven University of Technology