In the last few years, the use of Information and Communication Technologies has made available a huge amount of heterogeneous data in various real application domains. For example, in the urban scenario, Internet of Things (IoT) systems capture massive data collections describing the overall urban environment and citizen exploitation and perception of available services. In health care systems, electronic health records allow storing various information about patients as adopted treatments and monitored physiological conditions. At the same time, the Internet of Medical Things (IoMT) ensures the availability and processing of healthcare data through smart medical devices and the web. Moreover, in most domains, individuals play a crucial role in generating data on the one side, driving a user and context-aware analysis process, and finally demanding easily accessible and understandable knowledge at the end of the process.

Digging deep in these data collections can unearth a rich spectrum of knowledge in the targeted domain valuable to characterise user behaviours, identify weaknesses and strengths, improve the quality of provided services or even devise new ones. However, data analytics on these data collections is still a daunting task because they are generally too big and heterogeneous to be processed through available data analysis techniques. Consequently, various challenges about data science arise dealing with the creation, storage, search, sharing, modelling, analysis, and visualisation of data, information, and knowledge.

Suitable data fusion techniques and data representation paradigms should be devised to integrate the heterogeneous collected data into a unified representation describing all facets of the targeted domain. Moreover, a massive volume of data demands the definition of novel data analytics strategies that exploit recent analysis paradigms and cloud-based platforms such as Hadoop and Spark. Proper strategies can also be devised for data and knowledge visualisation, possibly also involving interactive user interfaces.

The workshop aims to allow academics and practitioners from various research areas to share their experiences designing cutting-edge analytics solutions for real-life applications. Researchers are encouraged to submit their work-in-progress research activity describing innovative methodologies, algorithms, and platforms that address all facets of a data analytics process that provides interesting and useful services.

Industrial implementations of data analytics applications, design and deployment experience reports on various issues raising data analytics projects are particularly welcome. We call for research and experience papers and demonstration proposals covering any aspect of data analytics solutions for real-life applications. 


Topics of interest

We invite the submission of work-in-progress research addressing various aspects of data management and analytics for real-life applications. The workshop welcomes submissions of technical, experimental, methodological papers, application papers, and papers on experience reports in real-life application settings addressing – though not limited to – the following topics:

  • Data management and analytics
  • Methodologies, models, algorithms, and architectures for applied data science
  • Big Data frameworks and architectures
  • Data warehouses and large-scale databases
  • NoSQL and NewSQL databases
  • Energy-efficient computing
  • Metadata management
  • Scalable and/or descriptive analytics algorithms
  • Concepts, transparency methodologies, innovative and transparency solutions for sensing, modeling, managing, mining, understanding citizens behavior, perceptions, activities, desiderata, and needs
  • Real-time data analytics
  • Machine learning and deep learning techniques
  • Reinforcement learning models
  • Next-Generation Sequencing data analysis
  • Cloud computing techniques for data science
  • Parallel and distributed computing for data science
  • Performance optimization and benchmarks
  • Crowdsourcing and collaborative analyses
  • Personalization and recommendation techniques for Big and Small Data
  • Question answering techniques and systems
  • Visualization methods for data-intensive applications
  • Privacy-aware access and usage control
  • Privacy and security policies enforcement mechanisms
  • Privacy-preserving data allocation and storage

 In one of – though not limited to – the following application scenarios:

  • Bio-sciences and healthcare
  • Internet of Things
  • Network traffic analytics
  • Urban economy and urban environments
  • Government transparency and IT against corruption
  • Public safety and disaster relief
  • Transportation
  • Energy
  • Financial applications
  • Customer relationship management
  • Agriculture
  • Mobile applications
  • e-commerce
  • Business analytics and finance
  • User-generated content (like tweets, micro-blog)
  • Industry 4.0
  • Data journalism
  • Education
  • Ethical issues, fairness and accountability
  • Topics aligned with the UN development goals: https://www.un.org/sustainabledevelopment/sustainable-development-goals/



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