On Implementing Temporal Query Answering in DL-Lite

Ontology-based data access augments classical query answering over fact bases by adopting the open-world assumption and by including domain knowledge provided by an ontology. We implemented temporal query answering w.r.t. ontologies formulated in the Description Logic DL-Lite. Focusing on temporal conjunctive queries (TCQs), which combine conjunctive queries via the operators of propositional linear temporal logic, we regard three approaches for answering them: an iterative algorithm that considers all data available; a window-based algorithm; and a rewriting approach, which translates the TCQs to be answered into SQL queries. Since the relevant ontological knowledge is already encoded into the latter queries, they can be answered by a standard database system. Our evaluation especially shows that implementations of both the iterative and the window-based algorithm answer TCQs within a few milliseconds, and that the former achieves a constant performance, even if data is growing over time

Optique: Zooming in on Big Data

Despite the dramatic growth of data accumulated by enterprises, obtaining value out of it is extremely challenging. In particular, the data access bottleneck prevents domain experts from getting the right piece of data within a constrained time frame. The Optique Platform unlocks the access to Big Data by providing end users support for directly formulating their information needs through an intuitive visual query interface. The submitted query is then transformed into highly optimized queries over the data sources, which may include streaming data, and exploiting massive parallelism in the backend whenever possible. The Optique Platform thus responds to one major challenge posed by Big Data in data-intensive industrial settings

Scalable Semantic Access to Siemens Static and Streaming Distributed Data

Abstract. Numerous analytical tasks in industry rely on data integration solutions since they require data from multiple static and streaming data sources. In the context of the Optique project we have investigated how Semantic Technologies can enhance data integration and thus facilitate further data analysis. We introduced the notion Ontology-Based Stream-Static Data Integration and developed the system Optique to put our ideas in practice. In this demo we will show how Optique can help in diagnostics of power generating turbines in Siemens Energy. For this purpose we prepared anonymised streaming and static data from 950 Siemens power generating turbines with more than 100,000 sensors and deployed Optique on distributed environments with 128 nodes. The demo attendees will be able to see do diagnostics of turbines by registering and monitoring continuous queries that combine streaming and static data; to test scalability of our devoted stream management system that is able to process up to 1024 concurrent complex diagnostic queries with a 10 TB/day throughput; and to deploy Optique over Siemens demo data using our devoted interactive system to create abstraction semantic layers over data sources