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

Distinct roles of presynaptic dopamine receptors in the differential modulation of the intrinsic synapses of medium-spiny neurons in the nucleus accumbens

Background: In both schizophrenia and addiction, pathological changes in dopamine release appear to induce alterations in the circuitry of the nucleus accumbens that affect coordinated thought and motivation. Dopamine acts principally on medium-spiny GABA neurons, which comprise 95% of accumbens neurons and give rise to the majority of inhibitory synapses in the nucleus. To examine dopamine action at single medium-spiny neuron synapses, we imaged Ca2+ levels in their presynaptic varicosities in the acute brain slice using two-photon microscopy. Results: Presynaptic Ca2+ rises were differentially modulated by dopamine. The D1/D5 selective agonist SKF81297 was exclusively facilitatory. The D2/D3 selective agonist quinpirole was predominantly inhibitory, but in some instances it was facilitatory. Studies using D2 and D3 receptor knockout mice revealed that quinpirole inhibition was either D2 or D3 receptor-mediated, while facilitation was mainly D3 receptor-mediated. Subsets of varicosities responded to both D1 and D2 agonists, showing that there was significant co-expression of these receptor families in single medium-spiny neurons. Neighboring presynaptic varicosities showed strikingly heterogeneous responses to DA agonists, suggesting that DA receptors may be differentially trafficked to individual varicosities on the same medium-spiny neuron axon. Conclusion: Dopamine receptors are present on the presynaptic varicosities of medium-spiny neurons, where they potently control GABAergic synaptic transmission. While there is significant coexpression of D1 and D2 family dopamine receptors in individual neurons, at the subcellular level, these receptors appear to be heterogeneously distributed, potentially explaining the considerable controversy regarding dopamine action in the striatum, and in particular the degree of dopamine receptor segregation on these neurons. Assuming that post-receptor signaling is restricted to the microdomains of medium-spiny neuron varicosities, the heterogeneous distribution of dopamine receptors on individual varicosities is likely to encode patterns in striatal information processing

The Role of Executive Functioning and Academic Achievement in the Academic Self-Concept of Children and Adolescents Referred for Neuropsychological Assessment

The current study evaluated a model of youth academic self-concept which incorporates practical executive functioning behaviors and academic achievement. Though greater academic achievement has been linked to both positive self-concept and better executive functioning, these constructs have not been examined simultaneously. It was hypothesized that academic achievement would mediate the association between problems with executive functioning and academic self-concept such that youth with more problems with executive functioning would have lower academic achievement and, in turn, lower academic self-concept. Clinical data was analyzed from a diagnostically heterogeneous sample of youth (n = 122) who underwent neuropsychological evaluation. Problems with executive functioning were assessed using the Behavior Rating Inventory of Executive Function. Academic achievement was assessed using the Woodcock–Johnson Tests of Achievement or Wechsler Individual Achievement Test. Academic self-concept was assessed using the youth-report version of the Behavioral Assessment System for Children. Surprisingly, findings indicate that academic achievement is not significantly associated with problems with executive functioning or academic self-concept. However, greater problems with executive functioning are associated with decreased academic self-concept. The overall model included several covariates and accounted for 10% of the variance in academic self-concept. Findings suggest that executive skills may be essential for aligning academic achievement with classroom performance. Though various child characteristic covariates were included, the model accounted for a small amount of variance suggesting that future studies should examine contributing contextual factors

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 (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

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

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. © 2019 Elsevier B.V

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