Proceedings of the ACM on Management of Data

The Proceedings of the ACM on Management of Data (PACMMOD) is concerned with the principles, algorithms, techniques, systems, and applications of database management systems, data management technology, and science and engineering of data. It includes ...

In the field of data stream processing, there are two prevalent models, i.e., insertion-only, and turnstile models. Most previous works were proposed for the insertion-only model, which assumes new elements arrive continuously as a stream, and neglects ...

Detecting locally, non-overlapping, near-clique densest subgraphs is a crucial problem for community search in social networks. As a vertex may be involved in multiple overlapped local cliques, detecting locally densest sub-structures considering h-...

Non-volatile Memory (NVM) offers the opportunity to build large, durable B+ trees with markedly higher performance and faster post-crash recovery than is possible with traditional disk- or flash-based persistence. Unfortunately, cache flush and fence ...

Time series compression encodes the information in a time-ordered sequence of data points into fewer bits, thereby reducing storage costs and possibly other costs. Compression methods are either general or XOR-based. General compression methods are time-...

Bipartite graphs, formed by two vertex layers, arise as a natural fit for modeling the relationships between two groups of entities. In bipartite graphs, common neighborhood computation between two vertices on the same vertex layer is a basic operator, ...

Graph pattern query is a powerful tool for extracting crucial information from property graphs. With the exponential growth of sizes, property graphs are typically divided into multiple subgraphs (referred to as partitions) and stored across various ...

Connectivity query processing is a fundamental problem in graph processing. Given an undirected graph and two query vertices, the problem aims to identify whether they are connected via a path. Given frequent edge updates in real graph applications, in ...

Machine learning models are only as good as their training data. Simple models trained on well-chosen features extracted from the raw data often outperform complex models trained directly on the raw data. Data preparation pipelines, which clean and ...

Top-k queries, in particular those based on a linear scoring function, are a common way to extract relevant results from large datasets. Their major advantage over alternative approaches, such as skyline queries (which return all the undominated objects ...

In today's data-driven world, organizations face increasing pressure to comply with data disclosure policies, which require data masking measures and robust access control mechanisms. This paper presents Mascara, a middleware for specifying and enforcing ...

We revisit the join ordering problem in query optimization. The standard exact algorithm, DPccp, has a worst-case running time of O(3ⁿ). This is prohibitively expensive for large queries, which are not that uncommon anymore. We develop a new algorithmic ...

Spatial Database Management Systems (SDBMSs) aim to store, manipulate, and retrieve spatial data. SDBMSs are employed in various modern applications, such as geographic information systems, computer-aided design tools, and location-based services. ...

Data quality is critical across many applications. The utility of data is undermined by various errors, making rigorous data cleaning a necessity. Traditional data cleaning systems depend heavily on predefined rules and constraints, which necessitate ...

In this paper, we suggest a novel GPU-in-data-path architecture that leverages a GPU to accelerate the I/O path and thus can achieve almost in-memory bandwidth using SSDs. In this architecture, the main idea is to stream data in heavy-weight compressed ...

This paper aims to bridge the gap between fast in-memory query engines and slow but robust engines that can utilize external storage. We find that current systems have to choose between fast in-memory operators and slower out-of-memory operators. We ...

Range-filtering approximate nearest neighbor (RFANN) search is attracting increasing attention in academia and industry. Given a set of data objects, each being a pair of a high-dimensional vector and a numeric value, an RFANN query with a vector and a ...

Providers of high-availability data stores need to roll out software updates without causing noticeable downtimes. For distributed data stores like Redis Cluster, the state-of-the-art is a rolling update, where the nodes are restarted in sequence. This ...

Knowledge Graphs can be encoded using different data models. They are especially abundant using RDF and recently also as property graphs. While knowledge graphs in RDF adhere to the subject-predicate-object structure, property graphs utilize multi-...

The growing volume of graph data may exhaust the main memory. It is crucial to design a disk-based graph storage system to ingest updates and analyze graphs efficiently. However, existing dynamic graph storage systems suffer from read or write ...

Range filters are probabilistic data structures that answer approximate range emptiness queries. They aid in avoiding processing empty range queries and have use cases in many application domains such as key-value stores and social web analytics. However,...

Errors are common in time series due to unreliable sensor measurements. Existing methods focus on univariate data but do not utilize the correlation between dimensions. Cleaning each dimension separately may lead to a less accurate result, as some errors ...

Given a query vector, approximate nearest neighbor search (ANNS) aims to retrieve similar vectors from a set of high-dimensional base vectors. However, many real-world applications jointly query both vector data and structured data, imposing label ...

Temporal knowledge graphs (TKGs) are valuable resources for capturing evolving relationships among entities, yet they are often plagued by noise, necessitating robust anomaly detection mechanisms. Existing dynamic graph anomaly detection approaches ...

Data analytics tasks are often formulated as data workflows represented as directed acyclic graphs (DAGs) of operators. The recent trend of adopting machine learning (ML) techniques in workflows results in increasingly complicated DAGs with many ...

In the standard model of differential privacy (DP), every user's privacy is treated equally, which is captured by a single privacy parameter \varepsilon. However, in many real-world situations, users may have diverse privacy concerns and requirements, ...

Machine learning (ML) algorithms have advanced significantly in recent years, progressively evolving into artificial intelligence (AI) agents capable of solving complex, human-like intellectual challenges. Despite the advancements, the interpretability ...

Recent advances in Dual In-line Memory Modules (DIMMs) allow DIMMs to support Processing-In-DIMM (PID) by placing In-DIMM Processors (IDPs) near their memory banks. Prior studies have shown that in-memory joins can benefit from PID by offloading their ...

The recent ISO SQL:2023 standard adopts SQL/PGQ (Property Graph Queries), facilitating graph-like querying within relational databases. This advancement, however, underscores a significant gap in how to effectively optimize SQL/PGQ queries within ...

Database Management System (DBMS) developers have implemented extensive test suites to test their DBMSs. For example, the SQLite test suites contain over 92 million lines of code. Despite these extensive efforts, test suites are not systematically reused ...