Full day workshops

Half day workshops

23rd International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data (DOLAP 2021)

Full day workshop

Research in data warehousing and OLAP has produced important technologies for the design, management, and use of information systems for decision support. Nowadays, due to the advent of Big Data, Decision Support Systems (DSS) embrace a wider range of systems, in which novel solutions combining advanced data management and data analytics, (semi-)automating the data lifecycle (from ingestion to visualization). Yet, the DSS principles remain the same: these systems acknowledge the relevance to manage data in an efficient way (by means of data modelling and optimized data processing) to serve innovative data analysis bringing added value to organizations.

DSS of the future will consequently be significantly different than what the current state-of-the-practice supports. The trend is to move to more dynamic systems that allow the semi-automation of the decision making process. This means that systems partially guide their users towards data discovery, management and system-aided decision making via intelligent techniques and visualization. In the back stage, the advent of the big data era, requires that new methods, models, techniques and architectures are developed to cope with the increasing demand in capacity, data type diversity, schema and data variability and responsiveness.

DOLAP 2021 features a special theme on Data Exploration! Specifically, to promote novel solutions to tackle data management for novel DSS, DOLAP 2021 will devote a session to Data Exploration and their impact on novel Big Data Management and Analytics approaches.

Important Dates

  • Abstract submission: November 23, 2020
  • Paper submission: November 30, 2020
  • Authors notification: January 10, 2021
  • Revised paper submission: January 20, 2021
  • Revised authors notification: February 10, 2021
  • Camera ready: February 20, 2021
  • Early registration deadline: February 22, 2021

4th International Workshop on Big Data Visual Exploration and Analytics (BigVis 2021)

Full day workshop

Information Visualization is nowadays one of the cornerstones of Data Science, turning the abundance of Big Data being produced through modern systems into actionable knowledge. Indeed, the Big Data era has realized the availability of voluminous datasets that are dynamic, noisy and heterogeneous in nature. Transforming a data-curious user into someone who can access and analyze that data is even more burdensome now for a great number of users with little or no support and expertise on the data processing part. Thus, the area of data visualization, visual exploration and analysis has gained great attention recently, calling for joint action from different research areas from the HCI, Computer graphics and Data management and mining communities.

In this respect, several traditional problems from these communities such as efficient data storage, querying & indexing for enabling visual analytics, new ways for visual presentation of massive data, efficient interaction and personalization techniques that can fit to different user needs are revisited. The modern exploration and visualization systems should nowadays offer scalable techniques to efficiently handle billion objects datasets, limiting the visual response in a few milliseconds along with mechanisms for information abstraction, sampling and summarization for addressing problems related to visual information overplotting. Further, they must encourage user comprehension offering customization capabilities to different user-defined exploration scenarios and preferences according to the analysis needs. Overall, the challenge is to offer self-service visual analytics, i.e. enable data scientists and business analysts to visually gain value and insights out of the data as rapidly as possible, minimizing the role of IT-expert in the loop.

The BigVis workshop aims at addressing the above challenges and issues by providing a forum for researchers and practitioners to discuss exchange and disseminate their work. BigVis attempts to attract attention from the research areas of Data Management & Mining, Information Visualization and Human-Computer Interaction and highlight novel works that bridge together these communities.

    4th International Workshop on Big Mobility Data Analytics (BMDA 2021)

    Full day workshop

    From spatial to spatio-temporal and, then, to mobility data. So, what’s next? it is the rise of mobility-aware integrated Big Data analytics. The Big Mobility Data Analytics (BMDA) workshop, initiated in 2018 with EDBT Conference, aims at bringing together experts in the field from academia, industry and research labs to discuss the lessons they have learned over the years, to demonstrate what they have achieved so far, and to plan for the future of “mobility”.

    In its 4th edition, BMDA workshop will foster the exchange of new ideas on multidisciplinary real-world problems, discuss proposals about innovative solutions, and identify emerging opportunities for further research in the area of big mobility data analytics, such as deep learning on mobility data, edge computing, visual analytics, etc.. The workshop intends to bridge the gap between researchers and big mobility data stakeholders, including experts from critical domains, such as urban / maritime / aviation transportation, human complex networks, etc.

    5th International workshop on Data Analytics solutions for Real-LIfe APplications (DARLI-AP 2021)

    Full day workshop

    DARLI-AP is a workshop aimed at promoting and sharing research and innovation on data analytics solutions/strategies for real-life and cutting-edge applications. The use of Information and Communication Technologies has made available a huge amount of heterogeneous data in various real application domains (e.g., smart cities, health care systems, financial applications, banking, and insurance, Industry 4.0). A data scientist is required to tackle the no-trivial task of selecting the best techniques to effectively and efficiently deal with issues related to storage, search, sharing, modeling, analysis, and visualization of data, information, and knowledge. The complexity of the task increases with variable data distribution, data heterogeneity, and data volume. Furthermore, a rich spectrum of knowledge can be extracted from real-data to characterize user behaviors, identify weaknesses and strengths, improve the quality of provided services, or even devise new ones, thus increasing the benefits of real-life applications.

    The aim of the workshop is 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, platforms addressing all facets of a data analytics process providing 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 as well as demonstration proposals covering any aspect of data analytics solutions for real-life applications.

    1st International Workshop on Data Analytics and Machine Learning Made Simple (SIMPLIFY)

    Half day workshop

    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.

    International Workshop on Processing Information Ethically: a plus for data Quality (PIE+Q)

    Half day workshop

    Digital transformation comes with ethical concerns about how flexible information systems can be used and misused, posing new challenges for researchers and practitioners across the whole spectrum of Information Systems Engineering.

    Similarly, ethics-related aspects are becoming prominent in the data management community, where traditional processes for searching, querying, or analyzing data hardly pay any specific attention to the social problems their outcomes could bring about. These demands are broadly reflected into codes of ethics and in legally binding regulations.

    The 3rd International Workshop on “Processing Information Ethically: a plus for data Quality” (PIE+Q) will acknowledge the need for the design of responsible Information Systems with a Data Quality perspective. PIE+Q 2021 will encourage papers on the conceptual and technological approaches for dealing with ethical issues in data quality and of all data management activities, including source selection, knowledge extraction, data integration and analysis.