11,419 research outputs found
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
Dynamic Influence Networks for Rule-based Models
We introduce the Dynamic Influence Network (DIN), a novel visual analytics
technique for representing and analyzing rule-based models of protein-protein
interaction networks. Rule-based modeling has proved instrumental in developing
biological models that are concise, comprehensible, easily extensible, and that
mitigate the combinatorial complexity of multi-state and multi-component
biological molecules. Our technique visualizes the dynamics of these rules as
they evolve over time. Using the data produced by KaSim, an open source
stochastic simulator of rule-based models written in the Kappa language, DINs
provide a node-link diagram that represents the influence that each rule has on
the other rules. That is, rather than representing individual biological
components or types, we instead represent the rules about them (as nodes) and
the current influence of these rules (as links). Using our interactive DIN-Viz
software tool, researchers are able to query this dynamic network to find
meaningful patterns about biological processes, and to identify salient aspects
of complex rule-based models. To evaluate the effectiveness of our approach, we
investigate a simulation of a circadian clock model that illustrates the
oscillatory behavior of the KaiC protein phosphorylation cycle.Comment: Accepted to TVCG, in pres
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Scalable Systems for Large Scale Dynamic Connected Data Processing
As the proliferation of sensors rapidly make the Internet-of-Things (IoT) a reality, the devices and sensors in this ecosystem—such as smartphones, video cameras, home automation systems, and autonomous vehicles—constantly map out the real-world producing unprecedented amounts of dynamic, connected data that captures complex and diverse relations. Unfortunately, existing big data processing and machine learning frameworks are ill-suited for analyzing such dynamic connected data and face several challenges when employed for this purpose.This dissertation focuses on the design and implementation of scalable systems for dynamic connected data processing. We discuss simple abstractions that make it easy to operate on such data, efficient data structures for state management, and computation models that reduce redundant work. We also describe how bridging theory and practice with algorithms and techniques that leverage approximation and streaming theory can significantly speed up connected data computations. The systems described in this dissertation achieve more than an order of magnitude improvement over the state-of-the-art
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