In the last few years, Information and Communication Technologies have 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 as well as citizen exploitation and perception of available services. In healthcare systems, electronic health records allow storing a variety of information about patients as adopted treatments and monitored physiological conditions, while Internet of Medical Things (IoMT) ensures the availability and processing of healthcare data through smart medical devices and web. Moreover, in most domains, the individual plays a crucial role in generating data on the one side, but also driving user and context-aware analysis process, and finally demanding easily accessible and understandable knowledge at the end of the process.

Digging deep into these data collections can unearth a rich spectrum of knowledge in the targeted domain valuable to characterize user behaviors, 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 data analysis techniques currently available. Consequently, various challenges in data science arise in the creation, storage, search, sharing, modeling, analysis, and visualization 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 huge volume of data demands the definition of novel data analytics strategies also exploiting recent analysis paradigms and cloud-based platforms such as Hadoop and Spark. Proper strategies for data and knowledge visualization can also be devised, possibly involving user interactive interfaces.

The workshop aims to allow academics and practitioners from various research areas to share their experiences in 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 addressing all facets of a data analytics process, providing interesting and useful services.

Industrial implementations of data analytics applications, design, and deployment experience report 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.

Submission Guidelines

Submissions should present original results and substantial new work not currently under review or published elsewhere.

This workshop accepts:

• Research papers (up to 8 pages), including novel approaches for methodologies and algorithms in real-life applications
• Application papers (up to 6 pages) describing the exploitation of data management and analytics solutions in real-life applications
• Industrial papers (up to 6 pages) describing experiences in the industry sectors.

All papers will be published online at CEUR (http://ceur-ws.org/). Workshop papers must follow the CEUR *Double Column* Format (http://ceur-ws.org/Vol-XXX/CEURART.zip). The font size, margins, inter-column spacing, and line spacing in the templates must be kept unchanged.

Papers should be submitted in PDF format using the online easychair submission system (https://easychair.org/conferences/?conf=darliap2024).

Topics of interest

• 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

• 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 5.0
• Data journalism
• EducationEthical issues, fairness and accountability
• Topics aligned with the UN development goals: https://www.un.org/sustainabledevelopment/sustainable-development-goals/

Contact

All questions about submissions should be emailed to tania DOT cerquitelli AT polito DOT it