References of "Fouquet, François 50003376"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailA Temporal Model for Interactive Diagnosis of Adaptive Systems
Mouline, Ludovic UL; Benelallam, Amine; Fouquet, François UL et al

in Mouline, Ludovic; Benelallam, Amine; Fouquet, François (Eds.) et al 2018 IEEE International Conference on Autonomic Computing (ICAC) (2018, September)

The evolving complexity of adaptive systems impairs our ability to deliver anomaly-free solutions. Fixing these systems require a deep understanding on the reasons behind decisions which led to faulty or ... [more ▼]

The evolving complexity of adaptive systems impairs our ability to deliver anomaly-free solutions. Fixing these systems require a deep understanding on the reasons behind decisions which led to faulty or suboptimal system states. Developers thus need diagnosis support that trace system states to the previous circumstances –targeted requirements, input context– that had resulted in these decisions. However, the lack of efficient temporal representation limits the tracing ability of current approaches. To tackle this problem, we first propose a knowledge formalism to define the concept of a decision. Second, we describe a novel temporal data model to represent, store and query decisions as well as their relationship with the knowledge (context, requirements, and actions). We validate our approach through a use case based on the smart grid at Luxembourg. We also demonstrate its scalability both in terms of execution time and consumed memory. [less ▲]

Detailed reference viewed: 63 (7 UL)
Full Text
Peer Reviewed
See detailEnabling Temporal-Aware Contexts for Adaptative Distributed Systems
Mouline, Ludovic UL; Benelallam, Amine; Hartmann, Thomas UL et al

in SAC 2018: SAC 2018: Symposium on Applied Computing , April 9--13, 2018, Pau, France (2018)

Distributed adaptive systems are composed of federated entities offering remote inspection and reconfiguration abilities. This is often realized using a MAPE-K loop, which constantly evaluates system and ... [more ▼]

Distributed adaptive systems are composed of federated entities offering remote inspection and reconfiguration abilities. This is often realized using a MAPE-K loop, which constantly evaluates system and environmental parameters and derives corrective actions if necessary. The OpenStack Watcher project uses such a loop to implement resource optimization services for multi-tenant clouds. To ensure a timely reaction in the event of failures, the MAPE-K loop is executed with a high frequency. A major drawback of such reactivity is that many actions, e.g., the migration of containers in the cloud, take more time to be effective and their effects to be measurable than the MAPE-k loop execution frequency. Unfinished actions as well as their expected effects over time are not taken into consideration in MAPE-K loop processes, leading upcoming analysis phases potentially take sub-optimal actions. In this paper, we propose an extended context representation for MAPE-K loop that integrates the history of planned actions as well as their expected effects over time into the context representations. This information can then be used during the upcoming analysis and planning phases to compare measured and expected context metrics. We demonstrate on a cloud elasticity manager case study that such temporal action-aware context leads to improved reasoners while still be highly scalable. [less ▲]

Detailed reference viewed: 142 (9 UL)
Full Text
Peer Reviewed
See detailA New Modelling Framework over Temporal Graphs for Collaborative Mobility Recommendation Systems
Toader, Bogdan UL; Moawad, Assaad UL; Fouquet, François UL et al

in A New Modelling Framework over Temporal Graphs for Collaborative Mobility Recommendation Systems (2018, March 15)

Over the years, collaborative mobility proved to be an important but challenging component of the smart cities paradigm. One of the biggest challenges in the smart mobility domain is the use of data ... [more ▼]

Over the years, collaborative mobility proved to be an important but challenging component of the smart cities paradigm. One of the biggest challenges in the smart mobility domain is the use of data science as an enabler for the implementation of large scale transportation sharing solutions. In particular, the next generation of Intelligent Transportation Systems (ITS) requires the combination of artificial intelligence and discrete simulations when exploring the effects of whatif decisions in complex scenarios with millions of users. In this paper, we address this challenge by presenting an innovative data modelling framework that can be used for ITS related problems. We demonstrate that the use of graphs and time series in multi-dimensional data models can satisfy the requirements of descriptive and predictive analytics in real-world case studies with massive amounts of continuously changing data. The features of the framework are explained in a case study of a complex collaborative mobility system that combines carpooling, carsharing and shared parking. The performance of the framework is tested with a large-scale dataset, performing machine learning tasks and interactive realtime data visualization. The outcome is a fast, efficient and complete architecture that can be easily deployed, tested and used for research as well in an industrial environment. [less ▲]

Detailed reference viewed: 154 (35 UL)
Full Text
Peer Reviewed
See detailRaising Time Awareness in Model-Driven Engineering
Benelallam, Amine; Hartmann, Thomas UL; Mouline, Ludovic UL et al

in 2017 ACM/IEEE 20th International Conference on Model Driven Engineering Languages and Systems (2017, September)

The conviction that big data analytics is a key for the success of modern businesses is growing deeper, and the mobilisation of companies into adopting it becomes increasingly important. Big data ... [more ▼]

The conviction that big data analytics is a key for the success of modern businesses is growing deeper, and the mobilisation of companies into adopting it becomes increasingly important. Big data integration projects enable companies to capture their relevant data, to efficiently store it, turn it into domain knowledge, and finally monetize it. In this context, historical data, also called temporal data, is becoming increasingly available and delivers means to analyse the history of applications, discover temporal patterns, and predict future trends. Despite the fact that most data that today’s applications are dealing with is inherently temporal current approaches, methodologies, and environments for developing these applications don’t provide sufficient support for handling time. We envision that Model-Driven Engineering (MDE) would be an appropriate ecosystem for a seamless and orthogonal integration of time into domain modelling and processing. In this paper, we investigate the state-of-the-art in MDE techniques and tools in order to identify the missing bricks for raising time-awareness in MDE and outline research directions in this emerging domain. [less ▲]

Detailed reference viewed: 141 (9 UL)
Full Text
Peer Reviewed
See detailAnalyzing Complex Data in Motion at Scale with Temporal Graphs
Hartmann, Thomas UL; Fouquet, François UL; Jimenez, Matthieu UL et al

in Proceedings of the 29th International Conference on Software Engineering and Knowledge Engineering (2017, July)

Modern analytics solutions succeed to understand and predict phenomenons in a large diversity of software systems, from social networks to Internet-of-Things platforms. This success challenges analytics ... [more ▼]

Modern analytics solutions succeed to understand and predict phenomenons in a large diversity of software systems, from social networks to Internet-of-Things platforms. This success challenges analytics algorithms to deal with more and more complex data, which can be structured as graphs and evolve over time. However, the underlying data storage systems that support large-scale data analytics, such as time-series or graph databases, fail to accommodate both dimensions, which limits the integration of more advanced analysis taking into account the history of complex graphs, for example. This paper therefore introduces a formal and practical definition of temporal graphs. Temporal graphs pro- vide a compact representation of time-evolving graphs that can be used to analyze complex data in motion. In particular, we demonstrate with our open-source implementation, named GREYCAT, that the performance of temporal graphs allows analytics solutions to deal with rapidly evolving large-scale graphs. [less ▲]

Detailed reference viewed: 182 (13 UL)
Full Text
Peer Reviewed
See detailThe Next Evolution of MDE: A Seamless Integration of Machine Learning into Domain Modeling
Hartmann, Thomas UL; Moawad, Assaad; Fouquet, François UL et al

in Software & Systems Modeling (2017)

Machine learning algorithms are designed to resolve unknown behaviors by extracting commonalities over massive datasets. Unfortunately, learning such global behaviors can be inaccurate and slow for ... [more ▼]

Machine learning algorithms are designed to resolve unknown behaviors by extracting commonalities over massive datasets. Unfortunately, learning such global behaviors can be inaccurate and slow for systems composed of heterogeneous elements, which behave very differently, for instance as it is the case for cyber-physical systems andInternet of Things applications. Instead, to make smart deci-sions, such systems have to continuously refine the behavior on a per-element basis and compose these small learning units together. However, combining and composing learned behaviors from different elements is challenging and requires domain knowledge. Therefore, there is a need to structure and combine the learned behaviors and domain knowledge together in a flexible way. In this paper we propose to weave machine learning into domain modeling. More specifically, we suggest to decompose machine learning into reusable, chainable, and independently computable small learning units, which we refer to as microlearning units.These micro learning units are modeled together with and at the same level as the domain data. We show, based on asmart grid case study, that our approach can be significantly more accurate than learning a global behavior, while the performance is fast enough to be used for live learning. [less ▲]

Detailed reference viewed: 244 (12 UL)
Full Text
Peer Reviewed
See detailWeaving Rules into Models@run.time for Embedded Smart Systems
Mouline, Ludovic UL; Hartmann, Thomas UL; Fouquet, François UL et al

in Mouline, Ludovic; Hartmann, Thomas; Fouquet, François (Eds.) et al Programming '17 Companion to the first International Conference on the Art, Science and Engineering of Programming (2017, April)

Smart systems are characterised by their ability to analyse measured data in live and to react to changes according to expert rules. Therefore, such systems exploit appropriate data models together with ... [more ▼]

Smart systems are characterised by their ability to analyse measured data in live and to react to changes according to expert rules. Therefore, such systems exploit appropriate data models together with actions, triggered by domain-related conditions. The challenge at hand is that smart systems usually need to process thousands of updates to detect which rules need to be triggered, often even on restricted hardware like a Raspberry Pi. Despite various approaches have been investigated to efficiently check conditions on data models, they either assume to fit into main memory or rely on high latency persistence storage systems that severely damage the reactivity of smart systems. To tackle this challenge, we propose a novel composition process, which weaves executable rules into a data model with lazy loading abilities. We quantitatively show, on a smart building case study, that our approach can handle, at low latency, big sets of rules on top of large-scale data models on restricted hardware. [less ▲]

Detailed reference viewed: 216 (21 UL)
Full Text
Peer Reviewed
See detailNear Real-Time Electric Load Approximation in Low Voltage Cables of Smart Grids with Models@run.time
Hartmann, Thomas UL; Moawad, Assaad UL; Fouquet, François UL et al

in 31st Annual ACM Symposium on Applied Computing (SAC'16) (2016, April)

Micro-generations and future grid usages, such as charging of electric cars, raises major challenges to monitor the electric load in low-voltage cables. Due to the highly interconnected nature, real-time ... [more ▼]

Micro-generations and future grid usages, such as charging of electric cars, raises major challenges to monitor the electric load in low-voltage cables. Due to the highly interconnected nature, real-time measurements are problematic, both economically and technically. This entails an overload risk in electricity networks when cables must be disconnected for maintenance reasons or are accidentally damaged. Therefore, it is of great interest for electricity grid providers to anticipate the load in networks and quicker detect failures. However, computing the electric load in cables requires computational intensive power flow calculations and live consumption measurements. Today’s view of the grid is usually based on on-field documentation of cables, fuses, and measurements by technicians and therefore often outdated. Thus, the electric load is usually only simulated in case of major topology variations. However, live measurements of smart meters provide new opportunities. In this paper we present a novel approach for a near real-time electric load approximation by deriving in live the current electric topology and cable loads from smart meter data. We leverage the models@run.time paradigm to combine live measurements with topology characteristics of the grid. Our approach enables to approximate the load in cables, not only for the current grid topology, but also to simulate topology changes for maintenance purposes. We showed that this allows a near real-time approximation while remaining very accurate (average deviation of 1.89% compared to offline power-flow calculation tools). Developed with a grid operator, this approach will be integrated in a monitoring and warning system and as an embeddable solution for on-field simulation. [less ▲]

Detailed reference viewed: 159 (13 UL)
Full Text
Peer Reviewed
See detailScapeGoat: Spotting abnormal resource usage in component-based reconfigurable software systems
Gonzalez-Herrera, Inti; Bourcier, Johann; Daubert, Erwan et al

in Journal of Systems and Software (2016)

Detailed reference viewed: 73 (4 UL)
Full Text
Peer Reviewed
See detailKevoreeJS: Enabling dynamic software reconfigurations in the Browser
Tricoire, Maxime; Barais, Olivier; Leduc, Manuel et al

in WICSA/CompArch 2016 Proceedings (2016, March)

Detailed reference viewed: 112 (15 UL)
Full Text
Peer Reviewed
See detailSquirrel: Architecture Driven Resource Management
Gonzalez-Herrera, Inti; Bourcier, Johan; Rudametkin, Walter et al

in 31st Annual ACM Symposium on Applied Computing (SAC'16) (2016)

Resource management is critical to guarantee Quality of Service when various stakeholders share the execution environment, such as cloud or mobile environments. In this context, providing management ... [more ▼]

Resource management is critical to guarantee Quality of Service when various stakeholders share the execution environment, such as cloud or mobile environments. In this context, providing management techniques compatible with standard practices, such as component models, is essential. Resource management is often realized through monitoring or pro- cess isolation (using virtual machines or system containers). These techniques (i) impose varying levels of overhead de- pending on the managed resource, and (ii) are applied at different abstraction levels, such as processes, threads or ob- jects. Thus, mapping components to system-level abstractions in the presence of resource management requirements can lead to sub-optimal systems. We propose Squirrel, an approach to tune component deployment and resource management in order to reduce management overhead. At run- time, Squirrel uses an architectural model annotated with resource requirements to guide the mapping of components to system abstractions, providing different resource management capabilities and overhead. We present an implementation of Squirrel, using a Java component framework, and a set of experiments to validate its feasibility and over- head. We show that choosing the right component-to-system mappings at deployment-time reduces performance penalty and/or volatile main memory use. [less ▲]

Detailed reference viewed: 63 (2 UL)
Full Text
Peer Reviewed
See detailSuspicious Electric Consumption Detection Based on Multi-Profiling Using Live Machine Learning
Hartmann, Thomas UL; Moawad, Assaad UL; Fouquet, François UL et al

in 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm) (2015, November)

The transition from today’s electricity grid to the so-called smart grid relies heavily on the usage of modern information and communication technology to enable advanced features like two-way ... [more ▼]

The transition from today’s electricity grid to the so-called smart grid relies heavily on the usage of modern information and communication technology to enable advanced features like two-way communication, an automated control of devices, and automated meter reading. The digital backbone of the smart grid opens the door for advanced collecting, monitoring, and processing of customers’ energy consumption data. One promising approach is the automatic detection of suspicious consumption values, e.g., due to physically or digitally manipulated data or damaged devices. However, detecting suspicious values in the amount of meter data is challenging, especially because electric consumption heavily depends on the context. For instance, a customers energy consumption profile may change during vacation or weekends compared to normal working days. In this paper we present an advanced software monitoring and alerting system for suspicious consumption value detection based on live machine learning techniques. Our proposed system continuously learns context-dependent consumption profiles of customers, e.g., daily, weekly, and monthly profiles, classifies them and selects the most appropriate one according to the context, like date and weather. By learning not just one but several profiles per customer and in addition taking context parameters into account, our approach can minimize false alerts (low false positive rate). We evaluate our approach in terms of performance (live detection) and accuracy based on a data set from our partner, Creos Luxembourg S.A., the electricity grid operator in Luxembourg. [less ▲]

Detailed reference viewed: 254 (26 UL)
Full Text
Peer Reviewed
See detailStream my Models: Reactive Peer-to-Peer Distributed Models@run.time
Hartmann, Thomas UL; Moawad, Assaad UL; Fouquet, François UL et al

in Lethbridge, Timothy; Cabot, Jordi; Egyed, Alexander (Eds.) 2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS) (2015, September)

The models@run.time paradigm promotes the use of models during the execution of cyber-physical systems to represent their context and to reason about their runtime behaviour. However, current modeling ... [more ▼]

The models@run.time paradigm promotes the use of models during the execution of cyber-physical systems to represent their context and to reason about their runtime behaviour. However, current modeling techniques do not allow to cope at the same time with the large-scale, distributed, and constantly changing nature of these systems. In this paper, we introduce a distributed models@run.time approach, combining ideas from reactive programming, peer-to-peer distribution, and large-scale models@run.time. We define distributed models as observable streams of chunks that are exchanged between nodes in a peer-to-peer manner. lazy loading strategy allows to transparently access the complete virtual model from every node, although chunks are actually distributed across nodes. Observers and automatic reloading of chunks enable a reactive programming style. We integrated our approach into the Kevoree Modeling Framework and demonstrate that it enables frequently changing, reactive distributed models that can scale to millions of elements and several thousand nodes. [less ▲]

Detailed reference viewed: 223 (22 UL)
Full Text
Peer Reviewed
See detailBeyond Discrete Modeling: A Continuous and Efficient Model for IoT
Moawad, Assaad UL; Hartmann, Thomas UL; Fouquet, François UL et al

in Lethbridge, Timothy; Cabot, Jordi; Egyed, Alexander (Eds.) 2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS) (2015, September)

Internet of Things applications analyze our past habits through sensor measures to anticipate future trends. To yield accurate predictions, intelligent systems not only rely on single numerical values ... [more ▼]

Internet of Things applications analyze our past habits through sensor measures to anticipate future trends. To yield accurate predictions, intelligent systems not only rely on single numerical values, but also on structured models aggregated from different sensors. Computation theory, based on the discretization of observable data into timed events, can easily lead to millions of values. Time series and similar database structures can efficiently index the mere data, but quickly reach computation and storage limits when it comes to structuring and processing IoT data. We propose a concept of continuous models that can handle high-volatile IoT data by defining a new type of meta attribute, which represents the continuous nature of IoT data. On top of traditional discrete object-oriented modeling APIs, we enable models to represent very large sequences of sensor values by using mathematical polynomials. We show on various IoT datasets that this significantly improves storage and reasoning efficiency. [less ▲]

Detailed reference viewed: 242 (18 UL)
Full Text
Peer Reviewed
See detailAdaptive Blurring of Sensor Data to balance Privacy and Utility for Ubiquitous Services
Moawad, Assaad UL; Hartmann, Thomas UL; Fouquet, François UL et al

in The 30th Annual ACM Symposium on Applied Computing (2015, April)

Given the trend towards mobile computing, the next generation of ubiquitous “smart” services will have to continuously analyze surrounding sensor data. More than ever, such services will rely on data ... [more ▼]

Given the trend towards mobile computing, the next generation of ubiquitous “smart” services will have to continuously analyze surrounding sensor data. More than ever, such services will rely on data potentially related to personal activities to perform their tasks, e.g. to predict urban traffic or local weather conditions. However, revealing personal data inevitably entails privacy risks, especially when data is shared with high precision and frequency. For example, by analyzing the precise electric consumption data, it can be inferred if a person is currently at home, however this can empower new services such as a smart heating system. Access control (forbid or grant access) or anonymization techniques are not able to deal with such trade-off because whether they completely prohibit access to data or lose source traceability. Blurring techniques, by tuning data quality, offer a wide range of trade-offs between privacy and utility for services. However, the amount of ubiquitous services and their data quality requirements lead to an explosion of possible configurations of blurring algorithms. To manage this complexity, in this paper we propose a platform that automatically adapts (at runtime) blurring components between data owners and data consumers (services). The platform searches the optimal trade-off between service utility and privacy risks using multi-objective evolutionary algorithms to adapt the underlying communication platform. We evaluate our approach on a sensor network gateway and show its suitability in terms of i) effectiveness to find an appropriate solution, ii) efficiency and scalability. [less ▲]

Detailed reference viewed: 128 (14 UL)
Full Text
Peer Reviewed
See detailPolymer: A Model-Driven Approach for Simpler, Safer, and Evolutive Multi-Objective Optimization Development
Moawad, Assaad UL; Hartmann, Thomas UL; Fouquet, François UL et al

in Hammoudi, Slimane; Pires, Luis Ferreira; Desfray, Philippe (Eds.) et al MODELSWARD 2015 - Proceedings of the 3rd International Conference on Model-Driven Engineering and Software Development (2015, February)

Multi-Objective Evolutionary Algorithms (MOEAs) have been successfully used to optimize various domains such as finance, science, engineering, logistics and software engineering. Nevertheless, MOEAs are ... [more ▼]

Multi-Objective Evolutionary Algorithms (MOEAs) have been successfully used to optimize various domains such as finance, science, engineering, logistics and software engineering. Nevertheless, MOEAs are still very complex to apply and require detailed knowledge about problem encoding and mutation operators to obtain an effective implementation. Software engineering paradigms such as domain-driven design aim to tackle this complexity by allowing domain experts to focus on domain logic over technical details. Similarly, in order to handle MOEA complexity, we propose an approach, using model-driven software engineering (MDE) techniques, to define fitness functions and mutation operators without MOEA encoding knowledge. Integrated into an open source modelling framework, our approach can significantly simplify development and maintenance of multi-objective optimizations. By leveraging modeling methods, our approach allows reusable optimizations and seamlessly connects MOEA and MDE paradigms. We evaluate our approach on a cloud case study and show its suitability in terms of i) complexity to implement an MOO problem, ii) complexity to adapt (maintain) this implementation caused by changes in the domain model and/or optimization goals, and iii) show that the efficiency and effectiveness of our approach remains comparable to ad-hoc implementations. [less ▲]

Detailed reference viewed: 220 (39 UL)
Full Text
Peer Reviewed
See detailGenerating Realistic Smart Grid Communication Topologies Based on Real-Data
Hartmann, Thomas UL; Fouquet, François UL; Klein, Jacques UL et al

in 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm) (2014, November)

Today’s electricity grid must undergo substantial changes in order to keep pace with the rising demand for energy. The vision of the smart grid aims to increase the efficiency and reliability of today’s ... [more ▼]

Today’s electricity grid must undergo substantial changes in order to keep pace with the rising demand for energy. The vision of the smart grid aims to increase the efficiency and reliability of today’s electricity grid, e.g. by integrating renewable energies and distributed micro-generations. The backbone of this effort is the facilitation of information and communication technologies to allow two-way communication and an automated control of devices. The underlying communication topology is essential for the smart grid and is what enables the smart grid to be smart. Analyzing, simulating, designing, and comparing smart grid infrastructures but also optimizing routing algorithms, and predicating impacts of failures, all of this relies on deep knowledge of a smart grids communication topology. However, since smart grids are still in a research and test phase, it is very difficult to get access to real-world topology data. In this paper we provide a comprehensive analysis of the power-line communication topology of a real-world smart grid, the one currently deployed and tested in Luxembourg. Building on the results of this analysis we implement a generator to automatically create random but realistic smart grid communication topologies. These can be used by researchers and industrial professionals to analyze, simulate, design, compare, and improve smart grid infrastructures. [less ▲]

Detailed reference viewed: 385 (33 UL)
Full Text
See detailOptimizing Multi-Objective Evolutionary Algorithms to Enable Quality-Aware Software Provisioning
El Kateb, Donia UL; Fouquet, François UL; bourcier, Johann et al

Scientific Conference (2014, October)

Detailed reference viewed: 113 (5 UL)
Full Text
Peer Reviewed
See detailModel-based time-distorted Contexts for efficient temporal Reasoning
Hartmann, Thomas UL; Fouquet, François UL; Nain, Grégory UL et al

Poster (2014, July 02)

Intelligent systems continuously analyze their context to autonomously take corrective actions. Building a proper knowledge representation of the context is the key to take adequate actions. This requires ... [more ▼]

Intelligent systems continuously analyze their context to autonomously take corrective actions. Building a proper knowledge representation of the context is the key to take adequate actions. This requires numerous and complex data models, for example formalized as ontologies or meta-models. As these systems evolve in a dynamic context, reasoning processes typically need to analyze and compare the current context with its history. A common approach consists in a temporal discretization, which regularly samples the context (snapshots) at specific timestamps to keep track of the history. Reasoning processes would then need to mine a huge amount of data, extract a relevant view, and finally analyze it. This would require lots of computational power and be time-consuming, conflicting with the near real-time response time requirements of intelligent systems. This paper introduces a novel temporal modeling approach together with a time-relative navigation between context concepts to overcome this limitation. Similarly to time distortion theory, our approach enables building time-distorted views of a context, composed by elements coming from different times, which speeds up the reasoning. We demonstrate the efficiency of our approach with a smart grid load prediction reasoning engine. [less ▲]

Detailed reference viewed: 114 (21 UL)
Full Text
Peer Reviewed
See detailReasoning at Runtime using time-distorted Contexts: A Models@run.time based Approach
Hartmann, Thomas UL; Fouquet, François UL; Nain, Grégory UL et al

in Proceedings of the 26th International Conference on Software Engineering and Knowledge Engineering (2014, July)

Intelligent systems continuously analyze their context to autonomously take corrective actions. Building a proper knowledge representation of the context is the key to take adequate actions. This requires ... [more ▼]

Intelligent systems continuously analyze their context to autonomously take corrective actions. Building a proper knowledge representation of the context is the key to take adequate actions. This requires numerous and complex data models, for example formalized as ontologies or meta-models. As these systems evolve in a dynamic context, reasoning processes typically need to analyze and compare the current context with its history. A common approach consists in a temporal discretization, which regularly samples the context (snapshots) at specific timestamps to keep track of the history. Reasoning processes would then need to mine a huge amount of data, extract a relevant view, and finally analyze it. This would require lots of computational power and be time-consuming, conflicting with the near real-time response time requirements of intelligent systems. This paper introduces a novel temporal modeling approach together with a time-relative navigation between context concepts to overcome this limitation. Similarly to time distortion theory, our approach enables building time-distorted views of a context, composed by elements coming from different times, which speeds up the reasoning. We demonstrate the efficiency of our approach with a smart grid load prediction reasoning engine. [less ▲]

Detailed reference viewed: 249 (51 UL)