ACM SIGMOD Record

doi: 10.1145/3774303.3774305pmid: N/A

Entity resolution (ER) is a central task in data quality, which is concerned with identifying pairs of distinct constants or tuples that refer to the same real-world entity. Declarative approaches, based upon logical rules and constraints, are a natural choice for tackling complex, collective ER tasks involving the joint resolution of multiple entity types across multiple tables. This paper provides an overview of recent advances in logicbased entity resolution, with a particular focus on the Lace framework, first introduced at PODS'22 and subsequently extended with additional features (IJCAI'23, KR'23) and equipped with an answer set programmingbased implementation (KR'24, KR'25).

doi: 10.1145/3774303.3774307pmid: N/A

This issue's contributors cover the impact of paying attention to the low-level implementation details, a paradigm shift in the way we approach stream processing, and the value of combining theoretical analysis with experimental evaluation. Furthermore, one of our contributors, rather than picking one paper, highlights the importance of putting the time to practice reading, reviewing, and learning from papers, not only from one's own field of interest but also from other fields. VLDB, similar to some systems conferences, launched a Shadow Program Committee for this purpose following the VLDB 2026 (Vol 19) submission cycles1. We wish to continue this effort in the future VLDB cycles. Enjoy reading! While I will keep inviting members of the data management community, and neighboring communities, to contribute to this column, I also welcome unsolicited contributions. Please contact me if you are interested.

doi: 10.1145/3774303.3774309pmid: N/A

I start with a disclaimer: I do not think it is my business to tell others what to do. Rather, how we choose to think and act are our personal responsibility. This said, I am happy sharing my observations and views and, in that sense, giving advice. Others may consider this as input when deciding on how to think and act.

doi: 10.1145/3774303.3774311pmid: N/A

Securing analytics on shared data is important but expensive. Analyzing datasets from multiple data owners can yield valuable insights [1, 2, 3, 4, 5] but poses significant security risks. Even within enterprises - our primary focus - precautions are necessary when handling data across subsidiaries and geographic regions [6, 7]. Existing security solutions based on Trusted Execution Environments (TEEs) [8, 9], fully homomorphic encryption [10], and structured encryption [11] offer strong protections, albeit in a physically centralized manner. For more decentralization, there are exciting approaches based on Secure Multi-Party Computation (MPC) [12] that do not need a trusted third party nor merging datasets at a central location. Recent projects [6, 13, 14, 15] show that MPC can reduce the risk of leaks for analytics on shared data under stronger security guarantees. However, MPC queries are often impractically slow, requiring orders of magnitude more computation and communication than plain-text or TEEbased query execution.

doi: 10.1145/3774303.3774313pmid: N/A

Welcome to ACM SIGMOD Record's series of interviews with distinguished members of the database community. I'm H. V. Jagadish, Professor of Computer Science at the University of Michigan. Sihem Amer-Yahia is my guest today. She is a Silver Medal Research Director at the French National Center for Scientific Research (CNRS) and Deputy Director of the Laboratoire d'Informatique de Grenoble, one of the largest research labs in Computer Science in France, with CNRS and INRIA Researchers and University Professors. She has won many awards, including the 2024 IEEE TCDE Impact Award, the ACM SIGMOD Contributions Award, and the VLDB Women in Database Research Award. Welcome, Sihem!

doi: 10.1145/3774303.3774315pmid: N/A

The database community's Diversity, Equity, and Inclusion (DEI) initiative began in 2020 as the Diversity/ Inclusion initiative [1]. This report highlights our activities from 2024. Our goal as a community is to make all DB conference attendees feel included, regardless of their scientific views or personal backgrounds. As a leadership team, the DEI group supports DEI chairs across conferences, preserves institutional memory of DEI efforts, shapes a shared vision, and fosters collaboration to advance inclusion. These efforts are carried out by core members (Figure 1) and liaisons from each conference's executive committee (Figure 2). The initiative was relaunched in January 2024 with a new structure based on five key actions: COORDINATE, to support collaboration between core members, liaisons, and DEI chairs; SCOUT, to gather best DEI practices from other communities; ETHICS, to create and promote ethical guidelines for writing and reviewing; MEDIA, to collect and share digital content from DEI@DB events [4]; and DIVERSIFY, to analyze data on diversity, accessibility, and the adoption of DEI principles in research and academia. DBCARES1 is now officially part of the DEI initiative. The mission of DBCARES is to create an inclusive and diverse Database community with zero tolerance for abuse, discrimination, or harassment. As part of this integration, we unified the Code of Ethics and introduced clear guidelines for DB conference organizers. Several conferences-including SIGMOD, VLDB, ICDE, and EDBT-continued using CLOSET [2] to ensure fair and transparent reviewer assignments.