Özgür Lütfü Özçep

How Semantic Technologies Can Enhance Data Access at Siemens Energy

We present a description and analysis of the data access challenge in the Siemens Energy. We advocate for Ontology Based Data Access (OBDA) as a suitable Semantic Web driven technology to address the challenge. We derive requirements for applying OBDA in Siemens, review existing OBDA systems and discuss their limitations with respect to the Siemens requirements. We then introduce the Optique platform as a suitable OBDA solution for Siemens. Finally, we describe our preliminary installation and evaluation of the platform in Siemens.

Optique: Zooming in on Big Data

Optique overcomes problems in current ontology-based data access systems pertaining to installation overhead, usability, scalability, and scope by integrating a user-oriented query interface, semi-automated managing methods, new query rewriting techniques, and temporal and streaming data processing in one platform.

Optique: Towards OBDA Systems for Industry

The recently started EU FP7-funded project Optique will develop an end-to-end OBDA system providing scalable end-user access to industrial Big Data stores. This paper presents an initial architectural specification for the Optique system along with the individual system components.

A Stream-Temporal Query Language for Ontology Based Data Access

The paper contributes to the recent efforts on temporalizing and streamifiying ontology based data access (OBDA) by discussing aspects of rewritability, i.e., compilability of the TBox into ontology-level queries, and unfoldability, i.e., transformability of ontology-level queries to queries on datasource level, for the new query-language framework STARQL. The distinguishing feature of STARQL is its general stream windowing and ABox sequencing strategy which allows it to plugin well-known query languages such as unions of conjunctive queries (UCQs) in combination with TBox languages such as DL-Lite and do temporal reasoning with a sorted first-order logic on top of them. The paper discusses safety aspects under which STARQL queries that embed UCQs over DL-Lite ontologies can be rewritten and unfolded to back-end relational stream query languages such as CQL. With these results, the adoption of description logic technology in industrially relevant application areas such as industrial monitoring is crucially fostered.

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.

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.

Ontology Based Data Access on Temporal and Streaming Data

Though processing time-dependent data has been investigated for a long time, the research on temporal and especially stream reasoning over linked open data and ontologies is reaching its high point these days. In this tutorial, we give an overview of state-of-the art query languages and engines for temporal and stream reasoning. On a more detailed level, we discuss the new language STARQL (Reasoning-based Query Language for Streaming and Temporal ontology Access). STARQL is designed as an expressive and flexible stream query framework that offers the possibility to embed different (temporal) description logics as filter query languages over ontologies, and hence it can be used within the OBDA paradigm (Ontology Based Data Access in the classical sense) and within the ABDEO paradigm (Accessing Big Data over Expressive Ontologies).

Stream-Query Compilation with Ontologies

Rational agents perceiving data from a dynamic environment and acting in it have to be equipped with capabilities such as decision making, planning etc. We assume that these capabilities are based on query answering with respect to (high-level) streams of symbolic descriptions, which are grounded in (low-level) data streams. Queries need to be answered w.r.t. an ontology. The central idea is to compile ontology-based stream queries (continuous or historical) to relational data processing technology, for which efficient implementations are available. We motivate our query language STARQL (Streaming and Temporal ontology Access with a Reasoning-Based Query Language) with a sensor data processing scenario, and compare the approach realized in the STARQL framework with related approaches regarding expressivity.

Enabling semantic access to static and streaming distributed data with optique: demo

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, which can be easily formulated with our visual query formulation system. Optique can then automatically enrich these queries, translate them into a large collection of low-level data queries, and finally optimise and efficiently execute the collection in a heavily distributed environment.

Semantic access to streaming and static data at Siemens

We present a description and analysis of the data access challenge in Siemens Energy. We advocate Ontology Based Data Access (OBDA) as a suitable Semantic Web driven technology to address the challenge. We derive requirements for applying OBDA in Siemens, review existing OBDA systems and discuss their limitations with respect to the Siemens requirements. We then introduce the Optique platform as a suitable OBDA solution for Siemens. The platform is based on a number of novel techniques and components including a deployment module, BootOX for ontology and mapping bootstrapping, a query language STARQL that allows for a uniform querying of both streaming and static data, a highly optimised backend, ExaStream, for processing such data, and a query formulation interface, OptiqueVQS, that allows to formulate STARQL queries without prior knowledge of its formal syntax. Finally, we describe our installation and evaluation of the platform in Siemens.