LEDS Linked Enterprise Data Services

Collaboration is one of the most important topics regarding the evolution of the World Wide Web and thus also for the Web of Data. In scenarios of distributed collaboration on datasets it is necessary to provide support for multiple different versions of datasets to exist simultaneously, while also providing support for merging diverged datasets.

With the increasing adoption of NoSQL data base systems like MongoDB or CouchDB more and more applications store structured data according to a non-relational, document oriented model. Exposing this structured data as Linked Data is currently inhibited by a lack of standards as well as tools and requires the implementation of custom solutions.

A large part of the free knowledge existing on the Web is available as heterogeneous, semi-structured data, which is only weakly interlinked and in general does not include any semantic classi fication. Due to the enormous amount of information the necessary preparation of this data for integrating it in the Web of Data requires automated processes.

Coherent and consistent tracking of provenance data and in particular update history information is a crucial building block for any serious information system architecture. Version Control Systems can be a part of such an architecture enabling users to query and manipulate versioning information as well as content revisions.

Large corporations have to master vast amounts of heterogeneous data in order to stay competitive. While existing approaches have attempted to consolidate and manage the data by forcing it into a single shared data model, data lakes recently emerged that instead provide a central storage point for holding all data sets in their original form.

Semantic technologies offer a wide range of benefits in an increasing number of application fields such as data management, business intelligence, machine learning etc. However - likely due to their complexity and the ambiguous business value - semantics yet play a little role in SME applications.

Since the most of the world’s data is unstructured, the mining of required information from text was, is and will be essential. However, the requirements for text mining are becoming more complex, as rare languages or new domains need to be supported, social media content has often a worse grammar or mixed languages, and the amount and velocity of the data growths constantly.

The innovative growth core project Linked Enterprise Data Services (LEDS) is aiming at developing, establishing and merchandising the next generation of semantic, interlinked data driven applications and services based on the Linked Data paradigm.

In the last years, Big Data as well as Linked Data technologies gained wide attention. The primary goal of Big Data technologies is the high-performance analysis of large-scale data (volume and velocity), while Linked Data aims at integrating distributed, heterogeneous data (Variety).