This is the third eccenca Corporate Memory release in 2017. The highlights of this release are the new hierarchical mapping feature as well as the addition of more dataset types. With hierarchical mappings our users are now able to create mappings from flat or nested structures into a hierarchy of classes connected with object properties and annotated with values.

The new mapping editor in the Dataset module provides a much better usability for creating and editing mapping rules. With a convenient tree navigation the user has a clear overview of the mapping hierarchy and can easily add, edit and remove object and value mapping rules. This allows for creation of semantically rich RDF graphs out of legacy datasets in just a few minutes.

Furthermore, Corporate Memory supports more dataset types than before. Besides CSV files and virtual datasets, the following types can now be used as a dataset resource: SPARQL endpoint (remote), RDF graph, ORC file, JDBC endpoint and Hive endpoint.

eccenca Corporate Memory connects existing datasets with new technologies. We bridge the gap between yesterday’s legacy systems, and today’s aggregated data lake. eccenca Corporate Memory manages huge quantities of data with dozens of data sources. It is based on an advanced data discovery model, combining the capabilities of semantic technology and big data scalability with structured data connectivity. This approach enables eccenca to substantially improve access to information. Our solution is most powerful by combining visual knowledge graphs with large data lakes. Corporate Memory extends existing document-based corporate intranets, often containing disparate or silo databases, to include all relevant sources of data. It creates a single and comprehensive repository of the company’s key operational data and metrics.

eccenca Corporate Memory SYNC is a data synchronization software to support decentralized data management. It is used to create peer-to-peer data networks for B2B communication scenarios by publishing and/or subscribing graph data. The communication infrastructure is data payload agnostic. The typical payload of the graphs is based on RDF(S) and OWL ontologies, thus following established W3C standards.