Identifying semantic types for attributes in relations, known as attribute semantic type (AST) identification, plays an important role in many data analysis tasks, such as data cleaning, schema matching, and keyword search in databases. However, due to a lack of unified naming standards across prevalent information systems (a.k.a. information islands), AST identification still remains as an open problem. To tackle this problem, we propose a context-aware method to figure out the ASTs for relations in this paper. We transform the AST identification into a multi-class classification problem and propose a schema context aware (SCA) model to learn the representation from a collection of relations associated with attribute values and schema context. Based on the learned representation, we predict the AST for a given attribute from an underlying relation, wherein the predicted AST is mapped to one of the labeled ASTs. To improve the performance for AST identification, especially for the case that the predicted semantic types of attributes are not included in the labeled ASTs, we then introduce knowledge base embeddings (a.k.a. KBVec) to enhance the above representation and construct a schema context aware model with knowledge base enhanced (SCA-KB) to get a stable and robust model. Extensive experiments based on real datasets demonstrate that our context-aware method outperforms the state-of-the-art approaches by a large margin, up to 6.14% and 25.17% in terms of macro average $F_1$ score, and up to 0.28% and 9.56% in terms of weighted $F_1$ score over high-quality and low-quality datasets respectively.

MongoDB is one of the first commercial distributed databases that support causal consistency. Its implementation of causal consistency combines several research ideas for achieving scalability, fault tolerance, and security. Given its inherent complexity, a natural question arises: "Has MongoDB correctly implemented causal consistency as it claimed?" To address this concern, the Jepsen team has conducted black-box testing of MongoDB. However, this Jepsen testing has several drawbacks in terms of specification, test case generation, implementation of causal consistency checking algorithms, and testing scenarios, which undermine the credibility of its reports. In this work, we propose a more thorough design of Jepsen testing of causal consistency of MongoDB. Specifically, we fully implement the causal consistency checking algorithms proposed by Bouajjani et al. and test MongoDB against three well-known variants of causal consistency, namely CC, CCv, and CM, under various scenarios including node failures, data movement, and network partitions. In addition, we develop formal specifications of causal consistency and their checking algorithms in TLA⁺, and verify them using the TLC model checker. We also explain how TLA⁺ specification can be related to Jepsen testing.