Entity matching (EM) is a fundamental task in data integration and analytics, essential for identifying records that refer to the same real-world entity across diverse sources. In practice, datasets often differ widely in structure, format, schema, and semantics, creating substantial challenges for EM. We refer to this setting as Heterogeneous EM (HEM) . This survey offers a unified perspective on HEM by introducing a taxonomy, grounded in prior work, that distinguishes two primary categories– representation and semantic heterogeneity –and their subtypes. The taxonomy provides a systematic lens for understanding how variations in data form and meaning shape the complexity of matching tasks. We then connect this framework to the FAIR principles – Findability , Accessibility , Interoperability , and Reusability –demonstrating how they both reveal the challenges of HEM and suggest strategies for mitigating them. Building on this foundation, we critically review recent EM methods, examining their ability to address different heterogeneity types, and conduct targeted experiments on state-of-the-art models to evaluate their robustness and adaptability under semantic heterogeneity. Our analysis uncovers persistent limitations in current approaches and points to promising directions for future research, including multimodal matching, human-in-the-loop workflows, deeper integration with large language models and knowledge graphs, and fairness-aware evaluation in heterogeneous settings.

Recently, the software development industry has witnessed a growing trend toward migrating from monolithic systems to microservices. However, identifying microservice candidates from an existing monolith is a primary challenge in this migration process, often proving to be a complex and labor-intensive task. Current methods for identifying microservice candidates have major drawbacks. They fail to adequately cover the various dependencies between different system entities and their relative importance. Additionally, these methods neglect to simultaneously consider important microservice architectural characteristics, such as functional independence, data independence, and granularity. Typically, these identification methods involve graph modeling of system classes, followed by a clustering process to optimize coupling and cohesion between classes. Identifying microservices from such a graph in a large monolith requires significant time and computational power. To address these limitations, this paper proposes a method that utilizes structural, conceptual, behavioral, and database dependencies to identify microservice candidates from monolithic systems. This method simultaneously addresses key characteristics of the microservices architecture and attempts to manage the time cost of identifying microservices. The proposed method has been evaluated using four widely-used open-source projects as case studies, analyzing five metrics in total. The results show that our method outperforms existing approaches across various evaluation metrics.

Object models are gradually progressing from predicting just category labels to providing detailed descriptions of object instances. This motivates the need for large datasets which go beyond traditional object masks and provide richer annotations such as part masks and attributes. Hence, we introduce PACO: Parts and Attributes of Common Objects. It spans 75 object categories, 456 object-part categories and 55 attributes across image (LVIS) and video (Eg04D) datasets. We provide 641K part masks an-notated across 260K object boxes, with roughly half of them exhaustively annotated with attributes as well. We design evaluation metrics and provide benchmark results for three tasks on the dataset: part mask segmentation, object and part attribute prediction and zero-shot instance detection. Dataset, models, and code are open-sourced at https://github.com/jacebookresearch/paco.