Graph pattern mining (GPM) is an important problem in graph processing. There are many parallel frameworks for GPM, many of which suffer from low performance. GPU is a powerful option for accelerating graph processing, but parallel GPM algorithms ...

Given two sets of vertices $S_a$ and $S_b$ of a graph, computing their common vertices, namely set intersection, is one primitive operation in many graph algorithms such as triangle counting, maximal clique enumeration, and subgraph matching. Therefore, ...

Multi-core scalability presents a major implementation challenge for data system designers today. Traditional methods such as latching no longer scale in today’s highly parallel architectures. While the designer can make use of techniques such as ...

Butterfly (a cyclic graph motif) counting is a fundamental task with many applications in graph analysis, which aims at computing the number of butterflies in a large graph. With the rapid growth of graph data, it is more and more challenging to ...$\frac{{}^{\mathrm{}}}{\mathit{}}$$\frac{{}^{\mathrm{}}}{\mathit{}}\frac{}{\sqrt{}}$$\mathsf{}$$\mathsf{}$$\mathsf{}$$\mathsf{}$$\mathsf{}$$\mathsf{}$$\mathsf{}$$\mathsf{}$$\mathsf{}$$\mathsf{}$$\mathsf{}$$\mathsf{}$$\mathsf{}$${\mathrm{}}^{\mathrm{}}$

Distributed transaction processing over the TCP/IP network suffers from the weak transaction scalability problem, i.e., its performance drops significantly when the number of involved data nodes per transaction increases. Although quite a few of ...

Transactional stream processing (TSP) strives to create a cohesive model that merges the advantages of both transactional and stream-oriented guarantees. Over the past decade, numerous endeavors have contributed to the evolution of TSP solutions, ...

Machine learning (ML) is increasingly used to automate impactful decisions, and the risks arising from this widespread use are garnering attention from policy makers, scientists, and the media. ML applications are often brittle with respect to ...

Given a user-specified minimum degree threshold $\gamma$, a $\gamma$-quasi-clique is a subgraph where each vertex connects to at least $\gamma$ fraction of the other vertices. Quasi-clique is a natural definition for dense structures, so finding large and hence ...$$$$

Knowledge graphs represented as RDF datasets are integral to many machine learning applications. RDF is supported by a rich ecosystem of data management systems and tools, most notably RDF database systems that provide a SPARQL query interface. ...

A frequent pattern is a substructure that appears in a database with frequency (aka. support) no less than a user-specified threshold, while a closed pattern is one that has no super-pattern that has the same support. Here, a substructure can ...

Finding from a big graph those subgraphs that satisfy certain conditions is useful in many applications such as community detection and subgraph matching. These problems have a high time complexity, but existing systems that attempt to scale them ...

Although compiling queries to efficient machine code has become a common approach for query execution, a number of newly created database system projects still refrain from using compilation. It is sometimes claimed that the intricacies of code ...

With an escalating arms race to adopt machine learning (ML) in diverse application domains, there is an urgent need to support declarative machine learning over distributed data platforms. Toward this goal, a new framework is needed where users ...

Data science teams often collaboratively analyze datasets, generating dataset versions at each stage of iterative exploration and analysis. There is a pressing need for a system that can support dataset versioning, enabling such teams to ...${\mathrm{}}^{\mathrm{}}$$\mathrm{}$$\mathrm{}$

Index joins present a case of pointer-chasing code that causes data cache misses. In principle, we can hide these cache misses by overlapping them with computation: The lookups involved in an index join are parallel tasks whose execution can be ...

Processor manufacturers build increasingly specialized processors to mitigate the effects of the power wall in order to deliver improved performance. Currently, database engines have to be manually optimized for each processor which is a costly and ...

Companies providing cloud-scale data services have increasing needs to store and analyze massive data sets, such as search logs, click streams, and web graph data. For cost and performance reasons, processing is typically done on large clusters of tens ...

We consider MapReduce clusters designed to support multiple concurrent jobs, concentrating on environments in which the number of distinct datasets is modest relative to the number of jobs. In such scenarios, many individual datasets are likely to be ...

Testing a specific feature of a DBMS requires controlling the inputs and outputs of the operators in the query execution plan. However, that is practically difficult to achieve because the inputs/outputs of a query depend on the content of the test ...