96,592 research outputs found
Statistical analysis of chemical computational systems with MULTIVESTA and ALCHEMIST
The chemical-oriented approach is an emerging paradigm for programming the behaviour of densely distributed and context-aware devices (e.g. in ecosystems of displays tailored to crowd steering, or to obtain profile-based coordinated visualization). Typically, the evolution of such systems cannot be easily predicted, thus making of paramount importance the availability of techniques and tools supporting prior-to-deployment analysis. Exact analysis techniques do not scale well when the complexity of systems grows: as a consequence, approximated techniques based on simulation assumed a relevant role. This work presents a new simulation-based distributed tool addressing the statistical analysis of such a kind of systems, which has been obtained by chaining two existing tools: MultiVeStA and Alchemist. The former is a recently proposed lightweight tool which allows to enrich existing discrete event simulators with distributed statistical analysis capabilities, while the latter is an efficient simulator for chemical-oriented computational systems. The tool is validated against a crowd steering scenario, and insights on the performance are provided by discussing how these scale distributing the analysis tasks on a multi-core architecture
A Distributed Context-Aware Trust Management Architecture
The realization of a pervasive context-aware service platform imposes new challenges for the security and privacy aspects of the system in relation to traditional service platforms. One important aspect is related with the management of trust relationships, which is especially hard in a pervasive environment because users are supposed to interact with entities unknown before hand in an ad-hoc and dynamic manner. Current trust management solutions do not adapt nor scale well in this dynamic service provisioning scenario because they require previously defined trust relationships in order to operate. The objective of this thesis is to design, prototype and validate a context-aware distributed trust management architecture in order to address: (a) the lack of integration between available trust solutions and security and privacy management languages, and (b) the dynamic characteristics of a context-aware service platform
User-centered visual analysis using a hybrid reasoning architecture for intensive care units
One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care
Smartphone sensing platform for emergency management
The increasingly sophisticated sensors supported by modern smartphones open
up novel research opportunities, such as mobile phone sensing. One of the most
challenging of these research areas is context-aware and activity recognition.
The SmartRescue project takes advantage of smartphone sensing, processing and
communication capabilities to monitor hazards and track people in a disaster.
The goal is to help crisis managers and members of the public in early hazard
detection, prediction, and in devising risk-minimizing evacuation plans when
disaster strikes. In this paper we suggest a novel smartphone-based
communication framework. It uses specific machine learning techniques that
intelligently process sensor readings into useful information for the crisis
responders. Core to the framework is a content-based publish-subscribe
mechanism that allows flexible sharing of sensor data and computation results.
We also evaluate a preliminary implementation of the platform, involving a
smartphone app that reads and shares mobile phone sensor data for activity
recognition.Comment: 11th International Conference on Information Systems for Crisis
Response and Management ISCRAM2014 (2014
Human-Centric Process-Aware Information Systems (HC-PAIS)
Process-Aware Information Systems (PAIS) support organizations in managing
and automating their processes. A full automation of processes is in particular
industries, such as service-oriented markets, not practicable. The integration
of humans in PAIS is necessary to manage and perform processes that require
human capabilities, judgments and decisions. A challenge of interdisciplinary
PAIS research is to provide concepts and solutions that support human
integration in PAIS and human orientation of PAIS in a way that provably
increase the PAIS users' satisfaction and motivation with working with the
Human-Centric Process Aware Information System (HC-PAIS) and consequently
influence users' performance of tasks. This work is an initial step of research
that aims at providing a definition of Human-Centric Process Aware Information
Systems (HC-PAIS) and future research challenges of HC-PAIS. Results of focus
group research are presented.Comment: 8 page
Recursion Aware Modeling and Discovery For Hierarchical Software Event Log Analysis (Extended)
This extended paper presents 1) a novel hierarchy and recursion extension to
the process tree model; and 2) the first, recursion aware process model
discovery technique that leverages hierarchical information in event logs,
typically available for software systems. This technique allows us to analyze
the operational processes of software systems under real-life conditions at
multiple levels of granularity. The work can be positioned in-between reverse
engineering and process mining. An implementation of the proposed approach is
available as a ProM plugin. Experimental results based on real-life (software)
event logs demonstrate the feasibility and usefulness of the approach and show
the huge potential to speed up discovery by exploiting the available hierarchy.Comment: Extended version (14 pages total) of the paper Recursion Aware
Modeling and Discovery For Hierarchical Software Event Log Analysis. This
Technical Report version includes the guarantee proofs for the proposed
discovery algorithm
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