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A common way to achieve scalability for processing SPARQL queries is to choose MapReduce frameworks like Hadoop or Spark. Processing basic graph pattern (BGP) expressions generating large join plans over distributed data partitions is a major challenge ...

Emerging applications face the need to store and analyze interconnected data that are naturally depicted as graphs. Recent proposals take the idea of data cubes that have been successfully applied to multidimensional data and extend them to work for ...

Graph pattern matching is an important and challenging operation on graph data. Typical use cases are related to graph analytics. Since analysts are often non-programmers, a graph system will only gain acceptance, if there is a comprehensible way to ...

The property graph model has recently gained significant popularity, combining great expressiveness with powerful declarative graph query languages. However, in order to take advantage of these features, data must be loaded into a specialised graph ...

Analytic graph processing has witnessed an ever-growing interest both in industry and academia with the focus on providing the most effective algorithm implementations to maximize single-query performance. In a complex application scenario, where ...

The use of synthetic graph generators is a common practice among graph-oriented benchmark designers, as it allows obtaining graphs with the required scale and characteristics. However, finding a graph generator that accurately fits the needs of a given ...

Graph querying and pattern matching is becoming an important feature of graph processing as it allows data analysts to easily collect and understand information about their graphs in a way similar to SQL for databases. One of the key challenges in graph ...

Big Data processing has become an integral part of many applications that are vital to our industry, academic endeavors, and society at large. To cope with the data deluge, existing Big Data platforms require significant conceptual and engineering ...

Graph querying and analytics are becoming an increasingly important component of the arsenal of tools for extracting different kinds of insights from data. Despite an immense amount of work on those topics, graphs are largely still handled in an ad hoc ...

Reachability and shortest paths are among two of the most common queries realized on graphs. While graph frameworks and property graph databases provide an extensive and convenient built-in support for these operations, it is still both clunky and ...

Regular path queries (RPQs) are a fundamental part of recent graph query languages like SPARQL and PGQL. They allow the definition of recursive path structures through regular expressions in a declarative pattern matching environment. We study the use ...

With the advent of online social networks, there is an increasing demand for storage and processing of graph-structured data. Social networking applications pose new challenges to data management systems due to demand for real-time querying and ...

Finding top-K shortest paths is fundamental and crucial to many graph applications, but known to be nontrivial over large graph data and large value of K. This problem becomes much more challenging when the shortest paths require to be simple (paths ...