Proceedings of the ACM on Management of Data

Welcome to this issue of the Proceedings of the ACM on Management of Data (Volume 1, Issue 4 (SIGMOD)). While this issue has papers from the SIGMOD track, PACMMOD will soon also have issues with papers from the newly created PODS track. Out of 189 ...

Large scale analytics engines have become a core dependency for modern data-driven enterprises to derive business insights and drive actions. These engines support a large number of analytic jobs processing huge volumes of data on a daily basis, and ...

Entity matching, a core data integration problem, is the task of deciding whether two data tuples refer to the same real-world entity. Recent advances in deep learning methods, using pre-trained language models, were proposed for resolving entity ...

Many big data systems are written in languages such as C, C++, Java, and Scala to process large amounts of data efficiently, while data analysts often use Python to conduct data wrangling, statistical analysis, and machine learning. User-defined ...

The Log-Structure Merged tree (LSM-tree) based key-value (KV) store has been widely adopted as the storage engine for blockchain systems, such as Ethereum, in which blockchain data are uniformly transformed into randomly distributed KV items for ...

Proving the equivalence between SQL queries is a fundamental problem in database research. Existing solvers model queries using algebraic representations and convert such representations into first-order logic formulas so that query equivalence can be ...

What is a minimal set of tuples to delete from a database in order to eliminate all query answers? This problem is called "the resilience of a query" and is one of the key algorithmic problems underlying various forms of reverse data management, such as ...

Distributed computing is promising to enable large-scale graph neural network (GNN) model training. However, care is needed to avoid excessive computational and communication overheads. Sampling is promising in terms of enabling scalability, and sampling ...

IEEE 754 doubles do not exactly represent most real values, introducing rounding errors in computations and [de]serialization to text. These rounding errors inhibit the use of existing lightweight compression schemes such as Delta and Frame Of Reference (...

Cloud infrastructure is experiencing a shift towards disaggregated setups, especially with the introduction of the Compute Express Link (CXL) technology, where byte-addressable ersistent memory (PM) is becoming prominent. To fully utilize the potential ...

System logs have long been recognized as valuable data for analyzing and diagnosing system failures. One fundamental task of log processing is to convert unstructured logs into structured logs through log parsing. All previous log parsing approaches ...

Analytical query workloads are prone to rapid fluctuations in resource demands. These rapid, hard to predict resource demand changes make provisioning a challenge. Users must either over provision at excessive cost or suffer poor query latency when ...

Membership (membership query/membership testing) is a fundamental problem across databases, networks and security. However, previous research has primarily focused on either approximate solutions, such as Bloom Filters, or exact methods, like perfect ...

A common analysis task over a stream of time series is to find all pairs of windows whose correlation is above a given threshold. For a large number of streams, doing so naively, i.e., checking the Cartesian product, is too expensive. In essence, finding ...

Video streaming applications (VSAs) are increasingly being deployed on large-scale edge platforms, which have the potential to significantly improve the quality of service (QoS) and end-user experience (QoE), ultimately maximizing business outcomes. ...

Dynamic Graph Neural Network (DGNN) has shown a strong capability of learning dynamic graphs by exploiting both spatial and temporal features. Although DGNN has recently received considerable attention by AI community and various DGNN models have been ...

Star-join query is the fundamental task in data warehouse and has wide applications in On-line Analytical Processing (olap) scenarios. Due to the large number of foreign key constraints and the asymmetric effect in the neighboring instance between the ...

Trend analysis is a fundamental type of analytical query in online analytical processing (OLAP) systems. In trend analysis, a key step is to identify k valuable attributes whose distributions in two subsets under different predicates significantly differ ...

For datasets exhibiting long tail phenomenon, we identify a fairness concern in existing top-k algorithms, that return a "fixed" set of k results for a given query. This causes a handful of popular records (products, items, etc) getting overexposed and ...

This paper proposes a federated, fair, and fast k-means algorithm (F3KM) to solve the fair clustering problem efficiently in scenarios where data cannot be shared among different parties. The proposed algorithm decomposes the fair k-means problem into ...

Machine learning systems are deployed in domains such as hiring and healthcare, where undesired classifications can have serious ramifications for the user. Thus, there is a rising demand for explainable AI systems which provide actionable steps for lay ...

Tabular data is becoming increasingly important in Natural Language Processing (NLP) tasks, such as Tabular Natural Language Inference (TNLI). Given a table and a hypothesis expressed in NL text, the goal is to assess if the former structured data ...

Answering the shortest-path distance between two arbitrary locations is a fundamental problem in road networks. Labelling-based solutions are the current state-of-the-arts to render fast response time, which can generally be categorised into hub-based ...

Machine-generated data is rapidly growing and poses challenges for data-intensive systems, especially as the growth of data outpaces the growth of storage space. To cope with the storage issue, compression plays a critical role in storage engines, ...

Full-graph training on graph neural networks (GNN) has emerged as a promising training method for its effectiveness. Full-graph training requires extensive memory and computation resources. To accelerate this training process, researchers have proposed ...

With the expansion of modern database services, multi-user access has become a crucial feature in various practical application scenarios, including enterprise applications and e-commerce platforms. However, if multiple users submit queries within a ...

Dashboards are vital in modern business intelligence tools, providing non-technical users with an interface to access comprehensive business data. With the rise of cloud technology, there is an increased number of data sources to provide enriched ...

LSM-based key-value stores have been leveraged in many state-of-the-art data-intensive applications as storage engines. As data volume scales up, a cost-efficient approach is to deploy these applications on hybrid cloud storage with hot/cold separation, ...

Time series data are used in a wide variety of applications. The explosive growth of the amount of time series data poses a significant challenge in efficient data storage and query processing. Unfortunately, existing compression techniques either show ...

Community search has been extensively studied in the past decades. In recent years, there is a growing interest in attributed community search that aims to identify a community based on both the query nodes and query attributes. A set of techniques have ...