The following workshops are all co-located with the EDBT/ICDT 2020 conference in Copenhagen, Denmark.
Refer to the websites of each workshop for the pertinent call for papers.
Machine learning (ML) is a driving force for many successful applications in Artificial Intelligence. ML pipelines ensure guarantees on the entirety of the system (i.e., horizontal certification) as well as on each single component (i.e., vertical certification). The horizontal certification covers the full pipeline from data acquisition to data visualization. Moreover, it spans over user-centered, technical, financial, and regulatory aspects of the system. The vertical certification exploits the theory of ML to guarantee error bounds, sampling complexity, energy consumption, execution time, time-to-think, and memory and communication demands. The understandability of an ML pipeline in its entirety requires the collaboration of researchers from the database and the ML communities.
ETMLP workshop will examine the aforementioned opportunities and their associated challenges. The main objective of this workshop is to create a forum where researchers from machine learning, data management, and practitioners engage with ideas around explainability and certified trustworthiness of ML pipelines, at the pipeline level, as well as the component level.
The ultimate goal of the workshop is to discuss recommendations for further work in science and industry and society regarding explainable ML pipelines.
Further information, can be found in the ETMLP 2020's homepage.
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 modeling 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 from current systems that are "data presenting" to more dynamic systems that allow the semi-automation of the decision making process (including both data management and data analysis tasks). This means that systems partially guide their users towards data discovery, management and system-aided decision making via intelligent techniques (beyond OLAP) 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. And of course, this does not necessarily mean to re-invent the wheel, but rather, complement the wealth of research in DSS with other approaches. We envision DOLAP 2020 as a forum to discuss, foster and nurture novel ideas around these new landscapes of decision support systems in the era of big data in order to produce new exciting results, within a strong, vibrant community around these areas.
DOLAP 2020 strengths its commitment towards and open community attracting new ideas around decision support systems by introducing a special theme "Semantic Web Technologies meet Big Data Management and Analytics". Semantic Web Technologies allow the enrichment of tasks / processes by means of rich annotations. Such annotations have already been successfully exploited by previous works in the field of decision support system to (semi-)automate data management tasks such as data integration, provenance, evolution or even query optimization via past evidences traced, among others. Similarly, semantic web technologies had a big impact on data analytics. For example, using annotations to enable reinforced learning, or exploiting their underlying graph-based formalisms to unleash graph traversals, pattern matching, graph algorithms or graph mining that naturally fit graph-alike data.
DOLAP 2020 welcomes novel ideas on advanced data management and analytics for next generation decision support systems and will devote one session to the special theme. Since the main objective is to promote discussion, short papers presenting interesting ideas not fully developed are welcome and might also be considered for the DOLAP 2020 best papers journal special issue.
Twenty one DOLAP workshops have been held in the past with great success. During these years, DOLAP has been established as one of the reference places for researchers to publish their work in the area of data decision support systems and maintains a high quality of accepted papers. Like the previous DOLAP workshops, DOLAP 2020 aims at synergistically connecting the research community and industry practitioners and provides an international forum where both researchers and practitioners can share their findings in theoretical foundations, current methodologies, and practical experiences, and where industry technology developers can describe technical details about their products and companies exploiting BI and Big Data technology can discuss case studies and experiences.
Further information, can be found in the DOLAP 2020's homepage.
The SEA Data workshop will provide a forum for researchers and practitioners to exchange ideas, results, and visions on challenges in data management, information extraction, exploration, and analysis of heterogeneous data and multiple data models at once, such as data sheets, polystores, knowledge bases and knowledge graphs.
SEA Data aims to gather researchers and practitioners from various communities related to databases. We gladly accept submissions that present initial ideas and visions, just as much as reports on early results, or reflections on completed projects. The workshop will focus on discussion and interaction, rather than static presentations of what is in the paper. A list of relevant topics is presented below.
Further information, can be found in the SEAData 2020's homepage.
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.
The BigVis 2020 held in conjunction with the 23rd Intl. Conference on Extending Database Technology (EDBT 2020) & 23rd Intl. Conference on Database Theory (ICDT 2020), Copenhagen, Denmark.
Further information, can be found in the BigVis 2020's homepage.
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 2nd International Workshop on “Processing Information Ethically” (PIE) will acknowledge the need for the design of responsible Information System with a Data Management perspective. PIE 2020 will encourage papers on the conceptual and technological approaches for dealing with ethical issues of data management and of all the activities of the whole information lifecycle, including source selection, knowledge extraction, data integration and analysis.
Further information, can be found in the PIE's homepage.
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 (eg, 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.
The aim of the workshop is to allow academics and practitioners from various research areas to share their experiences on 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.
Further information, can be found in the DARLI-AP 2020's homepage.
Nowadays, we have the means to collect, store and process mobility data of an unprecedented quantity, quality and timeliness. This is mainly due to the wide spread of GPS-equipped devices, including new generation smartphones. As ubiquitous computing pervades our society, mobility represents a very useful source of information. Movement traces left behind, especially when combined with societal data, can aid transportation engineers, urban planners, and eco-scientists towards decision making in a wide spectrum of applications, such as traffic engineering and risk management. The objective of the BMDA workshop is to bring together researchers and practitioners interested in scalable data-intensive applications that manage and analyze big mobility data. The workshop will foster the exchange of new ideas on multidisciplinary real-world problems, discussion on proposals on innovative solutions, and identify emerging opportunities for further research in the area of big mobility data analytics, covering all layers of the Big Data Value Analytics (BDVA) reference model, namely data management, data processing, data analytics, and data visualization and user interaction. BMDA intends to bridge the gap between researchers and big data stakeholders, including experts from critical domains, such as urban / maritime / aviation transportation, human complex networks, etc. Most importantly, it aims to solve real-world problems and show off novel solutions in such domains that require innovative data analytics solutions. covering all layers of the Big Data Value Analytics (BDVA) reference model, namely data management, data processing, data analytics, and data visualization and user interaction. BMDA intends to bridge the gap between researchers and big data stakeholders, including experts from critical domains, such as urban / maritime / aviation transportation, human complex networks, etc. Most importantly, it aims to solve real-world problems and show off novel solutions in such domains that require innovative data analytics solutions. covering all layers of the Big Data Value Analytics (BDVA) reference model, namely data management, data processing, data analytics, and data visualization and user interaction. BMDA intends to bridge the gap between researchers and big data stakeholders, including experts from critical domains, such as urban / maritime / aviation transportation, human complex networks, etc. Most importantly, it aims to solve real-world problems and show off novel solutions in such domains that require innovative data analytics solutions.
Further information, can be found in the BMDA 2020's homepage.