The Self-Service Semantic Suite (S4) by Ontotext provides a platform for on-demand Smart Data management. With S4 developers get instant access to services for text analytics, knowledge graphs and semantic graph database-as-a-service in the Cloud. The goal of S4 is to increase the speed and reduce the cost of building Smart Data analytics prototypes.
The capabilities that the S4 platform provides are:
The capabilities of the Self-Service Semantic Suite are based on enterprise-grade technology by Ontotext successfully applied within use cases in verticals such as media & publishing, healthcare & life sciences, compliance, cultural heritage and digital libraries.
As of January 1st, all S4 services are available with a transparent, pay-per-use pricing based on the volume of text content processed and the volume of RDF data stored in the semantic graph database. Startups and SMBs can now experiment more easily with smart data prototypes, since there’s no need for complex resource budgeting and software license commitments
S4 makes it easy for companies to start with small prototypes, utilising the free tier of S4 services, or small volume text processing and semantic graph databases, and only increase the service usage if their prototypes successfully get traction – pay-as-you-grow is easy with the Self-Service Semantic Suite.
The text analytics services available on S4 are available with a pay-per-use pricing, per gigabyte of input data processed (plain text, HTML, or PDF / MS Office formats)
|First 250 MB of data processed||FREE|
|News analytics||$99 / GB|
|News classifier||$99 / GB|
|Biomedical analytics||$99 / GB|
|Twitter analytics||$29 / GB|
For every developer account S4 provides a large free tier of 250 MB of data processed. Depending on the type of data being processed, the free tier should be sufficient to process the following number of documents:
The self-managed RDF database is based on the Standard or Free editions of Ontotext’s GraphDB database. The self-managed database is available via the AWS Marketplace and can be deployed with just a few clicks.
The pricing is based on the database instance size (in terms of EC2 instances) as follows:
|EC2 instance type||Virtual cores||RAM (GB)||EC2 cost
|Data volume estimate*
|GraphDB Free||GraphDB Standard|
|T2.large / M4.large||2||8||0.126||FREE||0.35||500 million|
* Note that the optimum server configuration for your data volume will depend on several factors, such as: the nature of the data (how connected is the graph, what is the ratio of explicit to implicit statements, etc.), the query volume and the complexity of the queries. The provided estimates should be used only as a starting point and we recommend that additional benchmarking is performed with data & queries specific to the use case, so that the optimum server configuration can be selected.
The costs for the network attached SSD storage (EBS volumes, charged at $0.10 per GB per month) also depends on the database size: as an average metric, 1 million triples will require at least 100 MB of storage space. Also note that the users are charged for the software purchased via the AWS Marketplace only when the AMI is running – if the instance is stopped or terminated, there are no ongoing charges, except for the EBS storage used by the database at rest.
The fully managed database-as-a-service provides a version of the GraphDB semantic graph database where developers do not need to configure, operate and maintain the database instances. All DBA related tasks are taken care of by the S4 platform on behalf of the developers. Several DBaaS options are available, with monthly subscription pricing based on database size:
|DBaaS type||Data volume
The fully managed databases are instantly available to developers, and accessible via simple RESTful API based on the OpenRDF API standard for RDF data management.
S4 provides capabilities for Smart Data prototyping, based on enterprise Semantic Technology by Ontotext. Register for a developer account, take a look at the documentation and sample code in various programming languages, and start experimenting with text analytics, semantic graph databases and open knowledge graphs!