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

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

An important application of semantic technologies in industry has been the formalisation of information models usingOWL 2 ontologies and the use of RDF for storing and exchanging application data. Moreover, legacy data can be virtualised asRDF using ontologies following the ontology-based data access (OBDA) approach. In all these applications, it is important toprovide domain experts with query formulation tools for expressing their information needs in terms of queries over ontologies. Inthis work, we present such a tool, OptiqueVQS, which is designed based on our experience with OBDA applications in Statoil andSiemens and on best HCI practices for interdisciplinary engineering environments. OptiqueVQS implements a number of uniquetechniques distinguishing it from analogous query formulation systems. In particular, it exploits ontology projection techniquesto enable graph-based navigation over an ontology during query construction. Secondly, while OptiqueVQS is primarily ontologydriven, it exploits sampled data to enhance selection of data values for some data attributes. Finally, OptiqueVQS is built onwell-grounded requirements, design rationale, and quality attributes. We evaluated OptiqueVQS with both domain experts andcasual users and qualitatively compared our system against prominent visual systems for ontology-driven query formulation andexploration of semantic data. OptiqueVQS is available online and can be downloaded together with an example OBDA scenario

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

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

Repraesentatio Der Furstlichen Aufzug Und Ritterspil

REPRAESENTATIO DER FURSTLICHEN AUFZUG UND RITTERSPIL Repraesentatio Der Furstlichen Aufzug Und Ritterspil ([1]) Titelseite ([1]) Widmung ([2]) Kupferstiche ([3]) Kupfertitel. Aufzug des Friderichen Pfaltzgrafen bey Rhein. ([20]) Kupferstiche ([21]) Kupfertitel. Aufzug des Georg Friderichen Marggrafen zu Baden ([42]) Kupferstiche ([43]) Kupfertitel. Aufzug des Ludwig Friderichen Hertzogen zu Würtemberg ([61]) Kupferstiche ([62]

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

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.

An ontology-mediated analytics-aware approach to support monitoring and diagnostics of static and streaming data

Streaming analytics that requires integration and aggregation of heterogeneous and distributed streaming and static data is a typical task in many industrial scenarios including the case of industrial IoT where several pieces of industrial equipment such as turbines in Siemens are integrated into an IoT. The 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. We argue that a way to overcome those limitations is to extend OBDA to 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

Towards analytics aware ontology based access to static and streaming data

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. © Springer International Publishing AG 2016