[Paper Review] Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151)

Serge Abiteboul, Marcelo Arenas+19 more|arXiv (Cornell University)|Jan 31, 2017

Advanced Database Systems and Queries36 references13 citations

TL;DR

This paper identifies seven core research themes—Managing Data at Scale, Multi-model Data, Uncertain Information, Knowledge-enriched Data, Data Management and Machine Learning, Process and Data, and Ethics and Data Management—proposing principled, mathematically grounded approaches to address evolving data management challenges in big data, web-scale systems, and ethical data use. It emphasizes cross-disciplinary integration and practical applicability to advance foundational data management research.

ABSTRACT

In April 2016, a community of researchers working in the area of Principles of Data Management (PDM) joined in a workshop at the Dagstuhl Castle in Germany. The workshop was organized jointly by the Executive Committee of the ACM Symposium on Principles of Database Systems (PODS) and the Council of the International Conference on Database Theory (ICDT). The mission of this workshop was to identify and explore some of the most important research directions that have high relevance to society and to Computer Science today, and where the PDM community has the potential to make significant contributions. This report describes the family of research directions that the workshop focused on from three perspectives: potential practical relevance, results already obtained, and research questions that appear surmountable in the short and medium term.

Motivation & Objective

• To identify and articulate the most impactful research directions in Principles of Data Management (PDM) with high relevance to society and computer science.
• To address the growing complexity of data management in the era of big data, web-scale data, and ethical concerns through principled, formal approaches.
• To strengthen cross-disciplinary collaboration between PDM and fields such as machine learning, statistics, verification, and social sciences.
• To guide funding agencies, universities, and researchers by highlighting timely, foundational research challenges with practical and societal impact.
• To promote ethical data management by integrating insights from cognitive science, sociology, and philosophy into data management systems and practices.

Proposed method

• Organizing research challenges around seven core themes derived from a Dagstuhl Perspectives Workshop attended by leading PDM researchers.
• Drawing on existing theoretical foundations in logic, complexity theory, and knowledge representation to address modern data challenges.
• Proposing formal frameworks for managing data variety, uncertainty, and provenance using query languages, ontologies, and integrity constraints.
• Integrating machine learning and data-aware processes through principled query optimization, data exchange, and transaction models.
• Advocating for cryptographic and decentralized access control mechanisms, including blockchain, to support privacy and trust on the web.
• Designing tools for responsible data analysis 'by design' that embed fairness, bias detection, and data quality evaluation from the start.

Experimental results

Research questions

• RQ1 How can principled data management approaches improve scalability and efficiency in processing massive, distributed data workloads?
• RQ2 What formal models and query languages are needed to manage multi-model and heterogeneous data sources effectively?
• RQ3 How can uncertainty in data—such as probabilistic or incomplete information—be formally represented and reasoned about in a scalable way?
• RQ4 In what ways can knowledge graphs and semantic enrichment enhance data understanding and interoperability across diverse systems?
• RQ5 How can data management principles be integrated with machine learning to ensure robustness, fairness, and interpretability in analytics pipelines?

Key findings

• The PDM community has made significant contributions to foundational data management through formal frameworks, including algebraic and calculus-based query languages, integrity constraints, and transaction models.
• Recent advances in $n$-way join processing in parallel systems have outperformed traditional binary join approaches, demonstrating progress in managing data volume and velocity.
• Research on uncertain information and data quality enables better reasoning about trust, bias, and provenance in human-related data, especially on the web.
• Personal Information Management Systems (PIMS) offer a promising path to rebalancing data control toward individuals, improving privacy and ethical alignment.
• Ethical data management requires interdisciplinary research integrating computer science with sociology, psychology, and political science to address fairness, bias, and societal impact.
• The integration of formal methods with machine learning and systems research is essential for building trustworthy, scalable, and verifiable data management solutions.