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RDF knowledge graphs is already in use by major organizations across sectors:
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• [cyan]Big Tech:[/cyan] Google uses RDF principles in its Knowledge Graph; Microsoft uses RDF and linked data in its Azure Data Catalog and compliance tools.
• [cyan]Government:[/cyan] Agencies like NASA, NIH, and FDA use RDF for metadata management, research linkage, and traceability.
• [cyan]Publishing & Media:[/cyan] Thomson Reuters and the BBC use RDF to interlink editorial and legal content.
• [cyan]Pharma & Life Sciences:[/cyan] Companies like AstraZeneca use RDF to link biomedical data for drug discovery.
• [cyan]Industrial Engineering:[/cyan] Siemens, Bosch and Boeing use RDF for product knowledge management and digital twins.
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RDF is especially attractive to organizations that need long-term data resilience, cross-system knowledge sharing, and intelligent automation.

Tentris significantly reduces RAM consumption by executing analytical graph queries in a fundamentally different way than traditional graph databases. Instead of relying on chains of binary joins that materialize large intermediate results either in memory or on disk, Tentris uses [pink2]worst-case optimal multi-way joins[/pink2] that process all query relations simultaneously. This avoids memory-intensive intermediate states, keeps memory usage predictable, and enables efficient evaluation of complex, highly connected graph patterns [cyan](from more than 10 GBs to MBs).[/cyan]

At the same time, this algorithmic approach delivers substantially [cyan]higher query[/cyan] performance. Worst-case optimal multi-way join algorithms achieve asymptotically optimal runtimes for complex queries—such as triangle or cyclic patterns, which frequently occur in agent-based reasoning—by eliminating redundant computation and efficiently constraining the search space. Combined with a monolithic, highly compressed [pink2]Hypertrie index[/pink2] and a streaming execution model enabled by multi-way joins, Tentris executes analytical workloads that traditionally take minutes or hours in seconds or a few minutes, while using significantly less memory and compute resources.

[pink2]Worst-Case Optimal Joins (WCOJs)[/pink2] are advanced database algorithms that compute complex joins faster and more efficiently by processing multiple datasets simultaneously and not just pairwise. This is especially valuable in RDF graph databases, where queries often involve many interconnected triples.


RDF graph dbs store data as triples (subject-predicate-object), and answering even a simple question (like "Who authored a book published by Publisher X?") often requires joining many triples. Traditional pairwise joins can generate huge intermediate results that slow down performance. [cyan]WCOJs avoid this by computing the join as a whole,[/cyan] avoiding unnecessary work.



Let’s say your RDF graph contains the following triple patterns:



A SPARQL query might want to find all ?author who wrote a [pink2]?book[/pink2] published by [teal]PublisherX[/teal] and in the [blue]ScienceFiction[/blue] genre.


• A traditional engine would join ?author–[pink2]?book[/pink2], then the result with [teal]?publisher[/teal], and then with [blue]?genre[/blue]. Each step might create large intermediate sets, even if few results match all criteria.

• A WCOJ-based engine evaluates all three conditions together — finding only the matches that satisfy all conditions at once, without bloated intermediate steps.

An [pink2]RDF (Resource Description Framework)[/pink2] graph database is a type of database that stores data as triples: (subject, predicate, object). These triples represent facts, much like simple sentences, and when many are connected together, they form a knowledge graph. For example:


[teal]<Apple>[/teal][pink2]<hasFounder>[/pink2][blue]<Steve Jobs>[/blue] is a triple expressing a factual relationship.
Unlike traditional databases, which use rigid tables and schemas, RDF graph databases model data in a flexible, schema-light graph format that reflects the interconnected nature of real-world information.

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Key differences include:

• [cyan]Schema flexibility:[/cyan] RDF allows schema evolution without disruption.
• [cyan]Data integration:[/cyan] RDF excels at integrating disparate and heterogeneously structured data.
• [cyan]Relationship modeling:[/cyan] Relationships are first-class citizens, unlike in SQL, where JOINs are costly and complex.
• [cyan]Inference and reasoning:[/cyan] RDF supports automated logical inference based on ontologies and rules. By doing that, it enables machines to deduce new knowledge from existing facts.
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RDF graph databases are especially well-suited to domains where data relationships are as important as the data itself such as AI, compliance, biomedical research, and enterprise systems.

The Hypertrie stores only existing triples using a hash-identifier-based representation for subjects, predicates, and objects. This means that on [cyan]INSERT[/cyan] or [teal]DELETE[/teal], only the affected paths and nodes of the index need to be updated.


[pink2]More specifically:[/pink2]

• New RDF terms are mapped to compact identifiers.
• The corresponding triple is inserted or removed by updating only the relevant branches in the Hypertrie.
• Shared prefixes remain unchanged, so unaffected parts of the structure are not touched.

This avoids the need for full re-indexing and keeps updates localized.

[cyan]Both.[/cyan] At core, Tentris is build to excel in complex, analytic SPARQL queries but at the same time support high write throughput. In analytics, we focused for now on accelerating standard SPARQL queries that fail on traditional query engine.

Our worst-case optimal join engine mostly eliminates intermediate results and filters down possible solutions rapidly. [cyan]This allows you to run queries that would otherwise exceed your RAM[/cyan] or take ages processing circles or large snowflake triple patterns.

[cyan]For updates, we implement MVCC.[/cyan] So, you update throughput is not disturbed by you colleagues  heavy query workload.

[pink2]RDF (Resource Description Framework)[/pink2] represents data as triples (subject-predicate-object) using a W3C standard designed for semantic meaning, interoperability, and reasoning. It supports rich ontologies [cyan](RDFS, OWL)[/cyan], data validation [cyan](SHACL)[/cyan], and global identifiers [cyan](URIs)[/cyan], making it ideal for knowledge graphs, linked data, and AI.


[pink2]Labeled Property Graphs (LPGs)[/pink2] model data as nodes and edges, both with flexible key-value properties. LPGs are widely used in graph databases like Neo4j and are great for real-time analytics, social networks, and operational applications but lack formal semantic standards.