SIMPLIFY 2021

Scope

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.

Topics

Topics of interest include (but are not limited to):
      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

Submission guidelines

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.

Submitted papers must follow the ACM Proceedings Format (adapted template for EDBT 2021 can be found here) and should be submitted electronically as PDF documents using the online EasyChair submission system:
https://easychair.org/conferences/?conf=simplify2021
All workshop papers will be indexed by DBLP and will be published online at CEUR.