Frequently Asked Questions
timbr is a platform that enables the creation of virtual SQL Knowledge Graphs over any data engine, used as intelligent enterprise data fabric and data catalog.
Timbr is named after Tim Berners Lee who, together with timbr.io’s advisor Jim Hendler, pioneered the Semantic Web.
A SQL Knowledge Graph is the implementation of ontologies and graph theory in standard SQL. It has two components: (i) a virtual SQL ontology of connected, context-enriched concepts with inference capabilities and graph analytics features and, (ii) a mapping of the virtual SQL ontologies to existing databases accessible in SQL. The SQL Knowledge Graph closes the gap between knowledge representation and enterprise databases/legacy systems/data warehouses/data lakes, to conveniently enable smart, semantic data fabrics and digital twins without need to change DBMS infrastructure.
timbr offers a fast, easy and no-risk implementation of the semantic graph. The main reasons are that there’s no need to move data or learn any new proprietary query languages to work with the Knowledge Graph.
Modeling a SQL ontology can be done either manually or automatically from an ERD, OWL ontologies, or from data catalogs.
The mapping of the data to the Knowledge Graph is also done either manually or semi-automatically.
Conceptual modeling is a representation of the real world. It is the first step of data modeling, a method developed to help with the design of databases and defining a formal vocabulary for the organization.
The process leading to the actual modeling and creation of databases leaves out information that is key to understanding and using data effectively. To make up for this information left behind, enterprises require coding complex queries in complex applications.
Ontologies are an effective means to re-create the information left behind, giving back business meaning to the data, simplifying data access and delivering unique analytical capabilities.
An ontology defines a common vocabulary for an organization that needs to share information in a domain.
This includes machine-interpretable definitions of basic concepts in the domain and relations among them.
An ontology is structured as a graph, where every node on the graph represents a “concept.”
A concept could be anything: Person, Place, Customer, Car, Country, Product, Event etc.
SQL ontologies are ontologies that implement the Semantic Web in SQL (what is an ontology?) and are designed to provide common business meaning to data distributed in varied sources and enable them as concepts with inference and graph traversal capabilities to facilitate discovery, use and access to data.
With timbr you can model and explore your ontology visually or in standard SQL. The SQL Ontology is exposed to the SQL user as a virtual schema with virtual tables (concepts) using any SQL client with JDBC/ODBC.
Semantic SQL is SQL used for querying SQL ontologies instead of directly querying the underlying data. By querying the ontology’s concepts, users benefit from graph traversals and semantic reasoning features, so SQL queries become significantly less complex and query size is reduced significantly.
A semantic data catalog is an intelligent catalog/inventory of data assets that automatizes sharing common meanings of data across data silos and provides a means to define hierarchies and relationships featuring semantic reasoning. It serves as a queryable, AI-enabled knowledge encyclopedia of the organization. timbr enables the fastest and most convenient implementation of semantic data catalogs connected to your databases and business intelligence tools. Contact us to schedule a demo.
The semantic data fabric is a flexible, reusable layer and set of data services used as the single source providing universal meaning and context to data for the entire organization. The data fabric integrates on-premise and cloud data sources in use by the organization, handing them semantic capabilities to provide answers to complex queries and to facilitate understanding and use of data. It provides consistent capabilities across on-premises and multiple cloud environments to accelerate digital transformation. timbr enables the fastest and most convenient implementation of semantic data fabric connected to your cloud and on-premise databases and business intelligence tools. Contact us to schedule a demo.
A digital twin refers to a digital replica of potential and actual physical assets, processes, people, places, systems and devices that can be used for various purposes. The digital representation provides both the elements and the dynamics of how an Internet of things (IoT) device operates and lives throughout its life cycle.
Digital twins have two important characteristics.
1. each definition emphasizes the connection between the physical model and the corresponding virtual model or virtual counterpart.
2. this connection is established by generating real-time data using sensors.
timbr helps enterprises create digital twins by enabling the definition of the virtual model using SQL ontologies and by connecting the virtual model to data lakes that contain the sensor’s data. Contact us to schedule a demo to see why timbr facilitates the fastest and most convenient implementation of digital twins.
The Semantic Web is a project devised by Tim Berners Lee and James Hendler (et al), and adopted by the W3C (the manager of the Internet). The Semantic Web implements ontologies so that machines connected to the Web “understand” each other by sharing common meaning of data using a set of standards. The standards developed by the W3C define among others, an ontology modeling language (OWL) and a query language (SPARQL).
timbr implements the principles of the Semantic Web in standard SQL, meaning that both the ontology modeling and the queries are done in SQL.
Creating a SQL Knowledge Graph is a simple process:
1. Connect your databases to the virtual layer using JDBC connectors.
2. Model the SQL ontology visually or using timbr SQL DDL statements, or import from other sources (data catalogs, OWL ontologies, ERD tools).
3. Map the ontology concepts to the data.
That’s it, your SQL Knowledge Graph is ready for use and can start delivering unique insights via SQL queries, graph data exploration, your BI tools, or using timbr’s embedded charts and dashboard module.
No, you just need a database, timbr will guide you through the rest of the simple process.
The SQL Knowledge Graph serves as a virtual graph for all the enterprise data engines. Organizations use it to integrate, analyze and explore their data sources and silos of information without the need to move or transform data. Data consumers benefit from a 360° access to data to get fast answers to key business questions. By querying concepts instead of the tables, SQL queries are reduced in length and complexity significantly. The SQL Knowledge Graph seamlessly integrates with popular business intelligence tools so business analysts can focus on the business questions and derive deeper insights.
timbr is not a database. timbr is a platform used for creating virtual SQL Knowledge Graphs that enable semantic (ontology-based) graph capabilities on existing data engines (data warehouses and data lakes). The SQL Knowledge Graphs integrate data sources into a semantic data fabric queryable in SQL. timbr does not require to copy or transform data (no ETL operations), no new DBMS infrastructure and no new skills as required by graph databases.
No, it is not possible. Property graphs use proprietary query languages lacking in semantics (unlike Knowledge Graphs which are based on RDF, SPARQL and OWL). Currently, timbr works with data engines that support SQL (mainly data warehouses and data lakes).
There’s no community version of timbr, but any organization with a use-case in mind can contact us asking for credentials to test drive timbr free of cost.
If your database is HIPAA complaint then timbr is as well.
If your database is GDPR complaint then timbr is as well.
A knowledge graph is a knowledge base that uses a graph-structured data model or topology to integrate data. Knowledge graphs are often used to store interlinked descriptions of objects, events or concepts with free-form semantics. Knowledge graphs use ontologies to put data in context via linking and semantic metadata and this way provide a framework for data integration, unification, analytics and sharing.
They are also prominently associated with and used by Google, Bing, and Yahoo, and with question-answering services such as Google Assistant, Siri and Alexa. All these examples were developed with proprietary tools.
For organizations that look to benefit from knowledge graphs, the available solutions in the market require significant changes in their IT departments. This is due to the fact that most data in the world is stored in formats that are not compatible with the format in which data is stored in knowledge graphs, so data needs to be extracted from its current DBMS, transformed to a new format and loaded into a separate, suitable DBMS. Another reason being is that to use knowledge graphs, data engineers and consumers need to acquire news skills to model in OWL and query in SPARQL.
Different from most other solutions, the timbr SQL Knowledge Graph platform creates a virtual layer that works in standard SQL to seamlessly connect to existing databases and is implemented without requiring new skills.
Contact us to learn how timbr can help your organization join the knowledge revolution.
How do external tools connect to timbr? Are providing any additional drivers (like JDBC) required in order to connect using different IDEs or custom applications?
timbr supports both JDBC and ODBC. We reuse the thrift-server protocol of Apache Hive and Spark. This means you can connect to timbr’s Knowledge Graph using Hive/Spark JDBC/ODBC drivers (in most BI tools they already come embedded, so no installation needed).
Creating an ontology: You can either use our Visual Ontology Modeler (no SQL needed) or use timbr extended SQL DDL statements.
Mapping data to the ontology: You can either use our Visual Ontology Data Mapper (no SQL needed) or use timbr’s extended SQL DDL statements.
Querying the Knowledge Graph: SQL, Python/R, dataframes, and natively in Apache Spark (SQL, Python, R, Java, Scala). GraphQL can be supported by integrating external open source projects that support the translation of GraphQL to SQL.
Yes, GraphQL is supported by integrating external open source projects that support the translation of GraphQL to SQL.
Yes, this could be generated easily (creating the SQL DDL statements of timbr directly from the XML hierarchy/relationships).
Yes, moving from SPARQL to timbr’s simplified SQL is quite trivial and easy to do.
Yes, timbr works extensively with SQLAlchemy. Another valid option for python users is DataFrames.
Yes, timbr is compatible with OWL-DL and some OWL-2 inferences.
If there is a clear business value to add more OWL-2 inferences, we can support them as well. timbr’s inference engine is based on query-rewriting techniques. If timbr encounters slow queries/performance, timbr can specifically materialize the part of knowledge that is required.
Timbr offers a visual data mapper to manually or semi-automatically select tables and columns from the database, as well as the option for coders to conveniently use SQL DDL statements. timbr can filter, clean and transfer the data that is been mapped to the ontology. No need for ETL operations.
timbr supports applying rules to concepts to classify the data or embed business logic in the ontology.
Adult: Person where age > 21
ExpensiveProduct: Product where price > 1000.
Yes, timbr is transparent to the SQL user and uses the same dialect and syntax as the underlying database. You can add hints and use all the underlying database functionality.
timbr allows creating virtual PKs for concepts (used as unique identifiers), and FK to PKs in the ontology (used as relationships between concepts). As long as the ontology author maps the physical tables PKs to the ontology PKs, client join will follow these declarations. In the ontology, you can create relationships between concepts using FK statements. In each relationship, you specify the properties in the ontology that represent the relationship (used for the JOIN).
Yes, timbr is accessible in JDBC/ODBC and the ontology can be created programmatically using timbr SQL DDL statements:
CREATE CONCEPT (extension of CREATE TABLE statement)
CREATE MAPPING (extension of CREATE VIEW statement)
In many cases, we build small scripts to generate parts of the ontology programmatically.
The ontology definition is in SQL DDL statements and it is stored in timbr’s internal metadata DB.
You can access the ontology definitions using timbr system tables: SYS_CONCEPTS, SYS_RELATIONSHIPS, SYS_PROPERTIES, SYS_ONTOLOGY, SYS_MAPPINGS, etc.
What is the approach for promoting content from test to prod environment? Does it require platform’s downtime?
No, any change to the ontology is immediately reflected to all the users. This means no downtime.
We plan to support GIT-like behavior, so ontologies can be deployed in a similar way to code.
By querying the ontology instead of the data itself, SQL queries are reduced in length and complexity.
In timbr there is no need to write JOINs as timbr exposes the ontology in a denormalized virtual schema. In the denormalized schema, timbr enables graph traversals leveraging virtual columns that eliminate the need for writing JOINs.