Related papers: Join Processing for Graph Patterns: An Old Dog wit…
Evaluating the relational join is one of the central algorithmic and most well-studied problems in database systems. A staggering number of variants have been considered including Block-Nested loop join, Hash-Join, Grace, Sort-merge for…
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…
Join operations (especially n-way, many-to-many joins) are known to be time- and resource-consuming. At large scales, with respect to table and join-result sizes, current state of the art approaches (including both binary-join plans which…
Graph pattern matching (e.g., finding all cycles and cliques) has become an important component in many critical domains such as social networks, biology, and cyber-security. This development motivated research to develop faster algorithms…
Joins in native graph database management systems (GDBMSs) are predefined to the system as edges, which are indexed in adjacency list indices and serve as pointers. This contrasts with and can be more performant than value-based joins in…
In the graph database literature the term "join" does not refer to an operator used to merge two graphs. In particular, a counterpart of the relational join is not present in existing graph query languages, and consequently no efficient…
Graph databases have grown in popularity in recent years as they are able to efficiently store and query complex relationships between data. Incidentally, navigation data and road networks can be processed, sampled or modified efficiently…
In the last few years, much effort has been devoted to developing join algorithms in order to achieve worst-case optimality for join queries over relational databases. Towards this end, the database community has had considerable success in…
Efficient join processing is one of the most fundamental and well-studied tasks in database research. In this work, we examine algorithms for natural join queries over many relations and describe a novel algorithm to process these queries…
Graph data management (also called NoSQL) has revealed beneficial characteristics in terms of flexibility and scalability by differently balancing between query expressivity and schema flexibility. This peculiar advantage has resulted into…
We present a new approach to e-matching based on relational join; in particular, we apply recent database query execution techniques to guarantee worst-case optimal run time. Compared to the conventional backtracking approach that always…
Semantic query processing engines often support semantic joins, enabling users to match rows that satisfy conditions specified in natural language. Such join conditions can be evaluated using large language models (LLMs) that solve novel…
Worst-case optimal join algorithms are the class of join algorithms whose runtime match the worst-case output size of a given join query. While the first provably worst-case optimal join algorithm was discovered relatively recently, the…
Choosing and developing performant database solutions helps organizations optimize their operational practices and decision-making. Since graph data is becoming more common, it is crucial to develop and use them in big data with complex…
Recently there has been significant interest around designing specialized RDF engines, as traditional query processing mechanisms incur orders of magnitude performance gaps on many RDF workloads. At the same time researchers have released…
Graph pattern matching algorithms to handle million-scale dynamic graphs are widely used in many applications such as social network analytics and suspicious transaction detections from financial networks. On the other hand, the computation…
Design/methodology/approach This research evaluated the databases of SQL, No-SQL and graph databases to compare and contrast efficiency and performance. To perform this experiment the data were collected from multiple sources including…
Graph databases (GDB) have recently been arisen to overcome the limits of traditional databases for storing and managing data with graph-like structure. Today, they represent a requirement for many applications that manage graph-like data,…
Aggregate computation in relational databases has long been done using the standard unary aggregation and binary join operators. These implement the classical model of computing joins between relations two at a time, materializing the…
Graph similarity learning, crucial for tasks such as graph classification and similarity search, focuses on measuring the similarity between two graph-structured entities. The core challenge in this field is effectively managing the…