Christian Neuenstadt

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.

Optique: OBDA Solution for Big Data

Accessing the relevant data in Big Data scenarios is increasingly difficult both for end-user and IT-experts, due to the volume, variety, and velocity dimensions of Big Data.This brings a hight cost overhead in data access for large enterprises. For instance, in the oil and gas industry, IT-experts spend 30-70% of their time gathering and assessing the quality of data [1]. The Optique project ( http://www.optique-project.eu/ ) advocates a next generation of the well known Ontology-Based Data Access (OBDA) approach to address the Big Data dimensions and in particular the data access problem. The project aims at solutions that reduce the cost of data access dramatically.

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.

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.

Advances in Accessing Big Data with Expressive Ontologies

Ontology-based query answering has to be supported w.r.t. secondary memory and very expressive ontologies to meet practical requirements in some applications. Recently, advances for the expressive DL \(\mathcal{SHI}\) have been made in the dissertation of S. Wandelt for concept-based instance retrieval on Big Data descriptions stored in secondary memory. In this paper we extend this approach by investigating optimization algorithms for answering grounded conjunctive queries.

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.

Querying industrial stream-temporal data: An ontology-based visual approach

An increasing number of sensors are being deployed in business-critical environments, systems, and equipment; and stream a vast amount of data. The operational efficiency and effectiveness of business processes rely on domain experts’ agility in interpreting data into actionable business information. A domain expert has extensive domain knowledge but not necessarily skills and knowledge on databases and formal query languages. Therefore, centralised approaches are often preferred. These require IT experts to translate the information needs of domain experts into extract-transform-load (ETL) processes in order to extract and integrate data and then let domain experts apply predefined analytics. Since such a workflow is too time intensive, heavy-weight and inflexible given the high volume and velocity of data, domain experts need to extract and analyse the data of interest directly. Ontologies, i.e., semantically rich conceptual domain models, present an intelligible solution by describing the domain of interest on a higher level of abstraction closer to the reality. Moreover, recent ontology-based data access (OBDA) technologies enable end users to formulate their information needs into queries using a set of terms defined in an ontology. Ontological queries could then be translated into SQL or some other database query languages, and executed over the data in its original place and format automatically. To this end, this article reports an ontology-based visual query system (VQS), namely OptiqueVQS, how it is extended for a stream-temporal query language called STARQL, a user experiment with the domain experts at Siemens AG, and STARQL’s query answering performance over a proof of concept implementation for PostgreSQL.

A semantic approach to polystores

In the database community Polystores is an emerging and promising approach for data federation that aims at designing a unified querying layer over multiple data models. In the Semantic Web community a similar in spirit approach of Ontology-Based Data Access (OBDA) has been recently proposed, attracted a lot of attention, and proved its success in several industrial scenarios. In this paper we discuss a semantic approach to building polystores using the OBDA paradigm. We also present our system Optique that is utilized in an industrial application of performing turbine diagnostics in Siemens.