Towards Analytics Aware Ontology Based Access to Static and Streaming Data (Extended Version)

Real-time analytics that requires integration and aggregation of heterogeneous and distributed streaming and static data is a typical task in many industrial scenarios such as diagnostics of turbines in Siemens. OBDA approach has a great potential to facilitate such tasks; however, it has a number of limitations in dealing with analytics that restrict its use in important industrial applications. Based on our experience with Siemens, we argue that in order to overcome those limitations OBDA should be extended and become analytics, source, and cost aware. In this work we propose such an extension. In particular, we propose an ontology, mapping, and query language for OBDA, where aggregate and other analytical functions are first class citizens. Moreover, we develop query optimisation techniques that allow to efficiently process analytical tasks over static and streaming data. We implement our approach in a system and evaluate our system with Siemens turbine data

OBDA Stream Access Combined with Safe First-Order Temporal Reasoning

Stream processing is a general information processing paradigm with different applications in AI. Most stream languages rely on the concept of a sliding window with a bag semantics, which is in order for relational streams but may lead to inconsistencies when applied on streams of assertions evaluated against a concep- tual model. Our approach uses a different semantics based on ABox sequencing. The query language provides an expressive first order temporal logic for inter-ABox reasoning. Safety conditions tame the expressiveness so that a meaning preserving transformation of the query to backend queries on the sources as foreseen in the OBDA paradigm is guaranteed. OBDA Stream Access Combined with Safe First-Orde

Addressing Streaming and Historical Data in OBDA Systems: Optique’s Approach (Statement of Interest)

Abstract. In large companies such as Siemens and Statoil monitoring tasks are of great importance, e.g., Siemens does monitoring of turbines and Statoil of oil behaviour in wells. This tasks bring up importance of both streaming and historical (temporal) data in the Big Data challenge for industries. We present the Optique project that addresses this problem by developing an Ontology Based Data Access (OBDA) system that incorporates novel tools and methodologies for processing and analyses of temporal and streaming data. In particular, we advocate for modelling time time aware data by temporal RDF and reduce monitoring tasks to knowledge discovery and data mining.

Ontology-Based Integration of Streaming and Static Relational Data with Optique

Real-time processing of data coming from multiple heterogeneous data streams and static databases is a typical task in many industrial scenarios such as diagnostics of large machines. A complex diagnostic task may require a collection of up to hundreds of queries over such data. Although many of these queries retrieve data of the same kind, such as temperature measurements, they access structurally different data sources. In this work we show how Semantic Technologies implemented in our system OPTIQUE can simplify such complex diagnostics by providing an abstraction layer ontology that integrates heterogeneous data. In a nutshell, OPTIQUE allows complex diagnostic tasks to be expressed with just a few high-level semantic queries. The system can then automatically enrich these queries, translate them into a collection with a large number of low-level data queries, and finally optimise and efficiently execute the collection in a heavily distributed environment. We will demo the benefits of OPTIQUE on a real world scenario from Siemens