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Public opinion surveys constitute a widespread, powerful tool to study peoples’ attitudes and behaviors from comparative perspectives. However, even global surveys can have limited geographic and temporal coverage, which can hinder the production of comprehensive knowledge. To expand the scope of comparison, social scientists turn to ex-post harmonization of variables from datasets that cover similar topics but in different populations and/or at different times. These harmonized datasets can be analyzed as a single source and accessed through various data portals. However, the Survey Data Recycling (SDR) research project has identified three challenges faced by social scientists when using data portals: the lack of capability to explore data in-depth or query data based on customized needs, the difficulty in efficiently identifying related data for studies, and the incapability to evaluate theoretical models using sliced data. To address these issues, the SDR research project has developed the SDR Querier, which is applied to the harmonized SDR database. The SDR Querier includes a BERT-based model that allows for customized data queries through research questions or keywords (Query-by-Question), a visual design that helps users determine the availability of harmonized data for a given research question (Query-by-Condition), and the ability to reveal the underlying relational patterns among substantive and methodological variables in the database (Query-by-Relation), aiding in the rigorous evaluation or improvement of regression models. Case studies with multiple social scientists have demonstrated the usefulness and effectiveness of the SDR Querier in addressing daily challenges. 

Many database applications execute transactions under a weaker isolation level, such as READ COMMITTED. This often leads to concurrency bugs that look like race conditions in multi-threaded programs. While this problem is well known, philosophies of how to address this problem vary a lot, ranging from making a SERIALIZABLE database faster to living with weaker isolation and the consequence of concurrency bugs. This paper studies the consequences, root causes, and how developers fix 93 real-world concurrency bugs in database applications. We observe that, on the one hand, developers still prefer preventing these bugs from happening. On the other hand, database systems are not providing sufficient support for this task, so developers often fix these bugs using ad-hoc solutions, which are often complicated and not fully correct. We further discuss research opportunities to improve concurrency control in database implementations. 

Database applications frequently use weaker isolation levels, such as Read Committed, for better performance, which may lead to bugs that do not happen under Serializable. Although a number of works have proposed methods to identify such isolation-related bugs, the difficulty of analyzing reported bugs is often underestimated, since these bugs often involve multiple complicated transactions interleaved in a specific order and they often require users' feedback to improve the accuracy of bug analysis. This paper presents IsoBugView, a tool to visualize isolation bugs and incorporate users' feedback: to address the challenge that a complicated bug may include much information and thus is hard to present, IsoBugView displays a high-level overview of the bug first and displays further information of individual pieces if the developer needs further investigation. To incorporate users' feedback, IsoBugView embeds hook functions into the backend analysis tool to preprocess a dependency graph and postprocess a found cycle and further allows a user to apply predefined hook functions in its graphic user interface. Our experience shows that IsoBugView has greatly improved our productivity of analyzing isolation bugs. 

The physical data layout significantly impacts performance when database systems access cold data. In addition to the traditional row store and column store designs, recent research proposes to partition tables hierarchically, starting from either horizontal or vertical partitions and then determining the best partitioning strategy on the other dimension independently for each partition. All these partitioning strategies naturally produce rectangular partitions. Coarse-grained rectangular partitioning reads unnecessary data when a table cannot be partitioned along one dimension for all queries. Fine-grained rectangular partitioning produces many small partitions which negatively impacts I/O performance and possibly introduces a high tuple reconstruction overhead. This paper introduces Jigsaw, a system that employs a novel partitioning strategy that creates partitions with arbitrary shapes, which we refer to as irregular partitions. The traditional tuple-at-a-time or operator-at-a-time query processing models cannot fully leverage the advantages of irregular partitioning, because they may repeatedly read a partition due to its irregular shape. Jigsaw introduces a partition-at-a-time evaluation strategy to avoid repeated accesses to an irregular partition. We implement and evaluate Jigsaw on the HAP and TPC-H benchmarks and find that irregular partitioning is up to 4.2× faster than a columnar layout for moderately selective queries. Compared with the columnar layout, irregular partitioning only transfers 21% of the data to complete the same query. 

Networkswith Remote DirectMemoryAccess (RDMA) support are becoming increasingly common. RDMA, however, offers a limited programming interface to remote memory that consists of read, write and atomic operations. With RDMA alone, completing the most basic operations on remote data structures often requires multiple round-trips over the network. Data-intensive systems strongly desire higher-level communication abstractions that supportmore complex interaction patterns. A natural candidate to consider is MPI, the de facto standard for developing high-performance applications in the HPC community. This paper critically evaluates the communication primitives of MPI and shows that using MPI in the context of a data processing system comes with its own set of insurmountable challenges. Based on this analysis, we propose a new communication abstraction named RDMO, or Remote DirectMemory Operation, that dispatches a short sequence of reads, writes and atomic operations to remote memory and executes them in a single round-trip.