Big Data Management

From Big Data to Smart Data Using Semantics

Dealing with Big Data involves a number of different challenges, including volume (amount of data), variety (number of different data types and data sources) and velocity (the rate at which data streams in and how fast it needs to be processed).

The Information Workbench in the Context of Big Data

Most Big Data solutions available on the market today focus on volume only, in particular supporting vertical scalability. But a major problem is that of horizontal scale. Looking at the wealth of data that is published in open data initiatives or stored in enterprise-internal systems and databases, one is faced with a massive number of data sources, with a high degree of variety and heterogeneity in coverage, data models, and structure.

Applying semantic technologies and the Linked Data paradigm, the Information Workbench enables both business and IT users to automatically integrate and interlink heterogeneous, distributed Big Data in a central place. It provides a semantic end-to-end connection between users and Big Data regardless of the data origins, types, schemas or structures. Using familiar vocabularies and conceptualizations, the Information Workbench allows for scalable, on-demand user access to Big Data and for quick creation of intuitive queries in federated environments, thus reducing the turnaround time for information requests from days to minutes. Powerful interactive exploration and analysis of data as well as widget-based visualizations on top optimize and accelerate decision making processes.

Use Cases

fluid Operations substantially expands its solution portfolio in the area of semantic cloud and data management solutions in Optique, a large-scale European project focusing on the comprehensive and timely end-user access to very large data sets. The Information Workbench was chosen as the integration platform for Optique. Within the project, innovative results from leading European universities will be integrated in order to solve real-world Big Data challenges at Statoil and Siemens:

• In the Siemens scenario, diagnosis engineers for power plants try to detect events from time-stamped sensor data. To operate their tools for visualization and trend detection they need to query several TBs of sensor data and several GBs of data about events, such as “alarm triggered at time T”, distributed across several databases. With a 30-GB daily growth, the total amount of raw data exceeds what they can currently record.
• In the Statoil scenario, experts in geology and geophysics develop stratigraphic models of unexplored areas on the basis of data acquired from previous operations at nearby geographical locations. To feed data into their advanced visual analytics tool they need to query a pool of more than 1,000 TBs of relational data, structured according to several schemes with a total of more than 2,000 tables distributed across several databases.

The Optique platform provides a semantic end-to-end connection between users and data sources enabling users to rapidly formulate intuitive queries. It will seamlessly integrate data spread across multiple distributed data sources, including streaming sources.

Interested?

Please contact us for more information or visit the Information Workbench product page.