Angela Bonifati

Categorical time series (CTS) are time series that take their values from categorical types. Despite the fact that they have a broad range of applications, they have been less studied than other types of time series. In this article, we study the problem of finding matches between disjoint sets of unordered categorical types thanks to CTS taking their values in them. To tackle this problem, we propose a novel iterative algorithm called Cluster and Map (CluMap) that finds matches using feature-based similarities of the elements of the above sets in their respective CTS. Our algorithm discovers these similarities across pairwise sets to be mapped and then finds clusters of elements in one set to be associated with clusters in the opposite set. We also propose specific evaluation metrics for this problem, since classical metrics for quality assessment of clustering are not suitable. We experimentally validate our approach on both synthetic and real-world CTS showing its superiority with respect to baselines and its underlying accuracy.

Property graphs are powerful data models, yet they often suffer from inconsistencies caused by violations of integrity constraints. Existing repair strategies are either fully automated—struggling with ambiguous cases that require domain expertise—or human-driven, which lack scalability and overlook budget and user heterogeneity. In this work, we propose a novel approach to property graph repair that integrates automatic inference with user expertise. We introduce a Graph Neural Network (GNN)-based difficulty estimator that jointly captures topological and semantic aspects of violations, enabling the system to distinguish between cases suitable for automatic repair and those requiring human input. Building on this, we design a budgeted, difficulty- and user-aware assignment algorithm that allocates violations to users based on their expertise and capacity while maximizing expected repair quality under resource constraints. We prove the NP-hardness of the problem and provide a Lagrangian relaxation. Extensive experiments demonstrate that our approach significantly improves repair accuracy compared to baselines and substantially reduces the number of involved users compared to both other interactive approaches.

The Proceedings of the ACM on Management of Data (PACMMOD) is concerned with the principles, algorithms, techniques, systems, and applications of database management systems, data management technology, and science and engineering of data. It includes articles reporting cutting-edge data management, data engineering, and data science research. We are pleased to present the 3rd issue of Volume 4 of PACMMOD. This issue contains papers that were submitted to the SIGMOD research track in October 2025 and is the last issue of the research track of SIGMOD 2026.

The Proceedings of the ACM on Management of Data (PACMMOD) is concerned with the principles, algorithms, techniques, systems, and applications of database management systems, data management technology, and science and engineering of data. It includes articles reporting cutting-edge data management, data engineering, and data science research. We are pleased to present the 1st issue of Volume 4 of PACMMOD. This issue contains papers that were submitted to the SIGMOD research track in July 2025.

We survey graph reachability indexing techniques for efficiently processing reachability queries in two popular graph models: plain graphs and edge-labeled graphs . Reachability queries determine whether a directed path exists between a source and a target vertex, forming a core class of navigational queries in graph analytics. Reachability indexes are specialized data structures that accelerate such query processing. Work on this topic goes back four decades—we include 33 of the proposed techniques. Plain graphs consist of only vertices and edges, with reachability queries checking for the existence of a path. Edge-labeled graphs extend plain graphs by adding labels to edges, and their queries further impose constraints on the labels along the path. We categorize indexing techniques for both plain and edge-labeled graphs and discuss them based on this classification, using representative methods to illustrate key ideas. We discuss the main challenges within each category and how these might be addressed in other approaches. We conclude with a discussion of the open challenges and future research directions, along the lines of integrating reachability indexes into modern graph database management systems. This survey serves as a comprehensive resource for researchers and practitioners interested in the advancements, techniques, and challenges of reachability indexing in graph analytics.

The Proceedings of the ACM on Management of Data (PACMMOD) is concerned with the principles, algorithms, techniques, systems, and applications of database management systems, data management technology, and science and engineering of data. It includes articles reporting cutting-edge data management, data engineering, and data science research. We are pleased to present the 6th issue of Volume 3 of PACMMOD. This issue contains papers that were submitted to the SIGMOD research track in April 2025.