The TrieJax Architecture

Kalinsky, Oren; Kimelfeld, Benny; Etsion, Yoav

doi:10.1145/3373376.3378524

Cited by 12 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There exists a fractional edge cover λ of B such that λ(t 1 ) = λ(t 2 ) = λ(t 3 ) = 1 2 ; taking ?a, we have that (B, G)) time writing the results in the worst case, such algorithms can be considered worst-case optimal (wco) [143]. Though such algorithms were initially proposed in a relational setting [143,195], they were later applied for processing joins over RDF graphs [95,81,137]. Note that traditional pairwise join algorithms are not wco.…”

Section: Worst Case Optimal Joinsmentioning

confidence: 99%

A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs

Ali¹,

Saleem²,

Yao³

et al. 2021

Preprint

View full text Add to dashboard Cite

Recent years have seen the growing adoption of non-relational data models for representing diverse, incomplete data. Among these, the RDF graph-based data model has seen ever-broadening adoption, particularly on the Web. This adoption has prompted the standardization of the SPARQL query language for RDF, as well as the development of a variety of local and distributed engines for processing queries over RDF graphs. These engines implement a diverse range of specialized techniques for storage, indexing, and query processing. A number of benchmarks, based on both synthetic and real-world data, have also emerged to allow for contrasting the performance of different query engines, often at large scale. This survey paper draws together these developments, providing a comprehensive review of the techniques, engines and benchmarks for querying RDF knowledge graphs.

show abstract

Section: Worst Case Optimal Joinsmentioning

confidence: 99%

A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs

Ali¹,

Saleem²,

Yao³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…For G the graph in Figure 17, (B, G)) time writing the results in the worst case, such algorithms can be considered worst-case optimal (wco) [143]. Though such algorithms were initially proposed in a relational setting [143,195], they were later applied for processing joins over RDF graphs [95,81,137]. Note that traditional pairwise join algorithms are not wco.…”

Section: Worst Case Optimal Joinsmentioning

confidence: 99%

A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs

Ali¹,

Yao²,

Hogan³

et al. 2021

Preprint

View full text Add to dashboard Cite

Recent years have seen the growing adoption of non-relational data models for representing diverse, incomplete data. Among these, the RDF graphbased data model has seen ever-broadening adoption, particularly on the Web. This adoption has prompted the standardization of the SPARQL query language for RDF, as well as the development of a variety of local and distributed engines for processing queries over RDF graphs. These engines implement a diverse range of specialized techniques for storage, indexing, and query processing. A number of benchmarks, based on both synthetic and real-world data, have also emerged to allow for contrasting the performance of different query engines, often at large scale. This survey paper draws together these developments, providing a comprehensive review of the techniques, engines and benchmarks for querying RDF knowledge graphs.

show abstract

“…Due to time constraints, we will only provide pointers to other exciting extensions, including those to machine learning [3,57,62,79,85,86], degree information [6,53], inequalities [2], negation [58], result compression [20], dynamic settings [46,47,56], and approaches aiming for stronger notions of optimality [59,75]. It is also worth noting that some of these novel join algorithms have been implemented in prototype systems for graph processing [1,45,55]. A historical perspective on WCO join algorithms together with open problems in the area have recently been summarized by Ngo [74].…”

Section: Part 2: Optimal Join Algorithmsmentioning

confidence: 99%

Optimal Join Algorithms Meet Top-k

Tziavelis

Gatterbauer

Riedewald

2020

Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

View full text Add to dashboard Cite

Top-k queries have been studied intensively in the database community and they are an important means to reduce query cost when only the "best" or "most interesting" results are needed instead of the full output. While some optimality results exist, e.g., the famous Threshold Algorithm, they hold only in a fairly limited model of computation that does not account for the cost incurred by large intermediate results and hence is not aligned with typical database-optimizer cost models. On the other hand, the idea of avoiding large intermediate results is arguably the main goal of recent work on optimal join algorithms, which uses the standard RAM model of computation to determine algorithm complexity. This research has created a lot of excitement due to its promise of reducing the time complexity of join queries with cycles, but it has mostly focused on full-output computation. We argue that the two areas can and should be studied from a unified point of view in order to achieve optimality in the common model of computation for a very general class of top-k-style join queries. This tutorial has two main objectives. First, we will explore and contrast the main assumptions, concepts, and algorithmic achievements of the two research areas. Second, we will cover recent, as well as some older, approaches that emerged at the intersection to support efficient ranked enumeration of join-query results. These are related to classic work on k-shortest path algorithms and more general optimization problems, some of which dates back to the 1950s. We demonstrate that this line of research warrants renewed attention in the challenging context of ranked enumeration for general join queries.

show abstract

The TrieJax Architecture

Cited by 12 publications

References 13 publications

A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs

A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs

A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs

Optimal Join Algorithms Meet Top-k

Contact Info

Product

Resources

About