77 research outputs found
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Effect of Virtualization on Enterprise Network, Server/Desktop Systems on Small and Mid-Size Businesses (SMB)
Enterprise small and mid-size businesses (SMB) are embracing virtualization because of the need to reduce risks associated to IT outages and data loss. Most of these establishments have loss critical enterprise data due to systems failures, accidents or natural causes. Virtualization platforms increase application availability which can shorten disaster recovery time and improve SMBs business continuity preparedness. This study will explore these benefits to find critical issues that can enable SMBs to maintain competiveness by utilizing less to do more
On the representational bias in process mining
Process mining serves a bridge between data mining and business process modeling. The goal is to extract process related knowledge from event data stored in information systems. One of the most challenging process mining tasks is process discovery, i.e., the automatic construction of process models from raw event logs. Today there are dozens of process discovery techniques generating process models using different notations (Petri nets, EPCs, BPMN, heuristic nets, etc.). This paper focuses on the representational bias used by these techniques. We will show that the choice of target model is very important for the discovery process itself. The representational bias should not be driven by the desired graphical representation but by the characteristics of the underlying processes and process discovery techniques. Therefore, we analyze the role of the representational bias in process mining
Infrastructure network vulnerability
The work presented in this paper aims to propose a methodology of analyzing infrastructure network vulnerability in the field of prevention or reduction of the natural disaster consequences. After a state of the art on vulnerability models in the academic literature, the various vulnerability factors are classified and discussed. Eventually, a general model of vulnerability analysis including societal parameters is presented
Zone-based verification of timed automata: extrapolations, simulations and what next?
Timed automata have been introduced by Rajeev Alur and David Dill in the
early 90's. In the last decades, timed automata have become the de facto model
for the verification of real-time systems. Algorithms for timed automata are
based on the traversal of their state-space using zones as a symbolic
representation. Since the state-space is infinite, termination relies on finite
abstractions that yield a finite representation of the reachable states.
The first solution to get finite abstractions was based on extrapolations of
zones, and has been implemented in the industry-strength tool Uppaal. A
different approach based on simulations between zones has emerged in the last
ten years, and has been implemented in the fully open source tool TChecker. The
simulation-based approach has led to new efficient algorithms for reachability
and liveness in timed automata, and has also been extended to richer models
like weighted timed automata, and timed automata with diagonal constraints and
updates.
In this article, we survey the extrapolation and simulation techniques, and
discuss some open challenges for the future.Comment: Invited contribution at FORMATS'2
Unfolding-Based Process Discovery
This paper presents a novel technique for process discovery. In contrast to
the current trend, which only considers an event log for discovering a process
model, we assume two additional inputs: an independence relation on the set of
logged activities, and a collection of negative traces. After deriving an
intermediate net unfolding from them, we perform a controlled folding giving
rise to a Petri net which contains both the input log and all
independence-equivalent traces arising from it. Remarkably, the derived Petri
net cannot execute any trace from the negative collection. The entire chain of
transformations is fully automated. A tool has been developed and experimental
results are provided that witness the significance of the contribution of this
paper.Comment: This is the unabridged version of a paper with the same title
appearead at the proceedings of ATVA 201
Crisis Mapping during Natural Disasters via Text Analysis of Social Media Messages
Recent disasters demonstrated the central role of social media during emergencies thus motivating the exploitation of such data for crisis mapping. We propose a crisis mapping system that addresses limitations of current state-of-the-art approaches by analyzing the textual content of disaster reports from a twofold perspective. A damage detection component employs a SVM classifier to detect mentions of damage among emergency reports. A novel geoparsing technique is proposed and used to perform message geolocation. We report on a case study to show how the information extracted through damage detection and message geolocation can be combined to produce accurate crisis maps. Our crisis maps clearly detect both highly and lightly damaged areas, thus opening up the possibility to prioritize rescue efforts where they are most needed
Multi-Agent Systems for Social Network Modelling
Práce poskytuje ÄŤitateli Ăşvod do tĂ©mat sociálnĂch sĂtĂ a multi-agentnĂch systĂ©mĹŻ. JejĂm cĂlem je popsat návrh a implementaci funkÄŤnĂho modelu sociálnĂ sĂtÄ› jakoĹľto multi-agentnĂho systĂ©mu postavenĂ©ho na frameworku Jason a na závÄ›r zhodnotit tuto snahu.This thesis introduces the reader to topics of social networks and multi-agent systems. It's goal is to describe design and implementation of a functional model of social network as a multi-agent system built on Jason framework, and, in the end evaluate this effort.
Prototyping Component-Based Self-Adaptive Systems with Maude
Software adaptation is becoming increasingly important as more and
more applications need to dynamically adapt their structure and behavior to
cope with changing contexts, available resources and user requirements. Maude
is a high-performance reflective language and system, supporting both equational
and rewriting logic specification and programming for a wide range of
applications. In this paper we describe our experience in using Maude for prototyping
component-based self-adaptive systems so that they can be formally
simulated and analyzed. In order to illustrate the benefits of using Maude in this
context, a case study in the robotics domain is presented.Ministerio de Ciencia e InnovaciĂłn TIN2009-08572FundaciĂłn SĂ©neca-CARM 15374/PI/1
Applications of ontology in the Internet of Things: a systematic analysis
Ontology has been increasingly implemented to facilitate the Internet of Things (IoT) activities, such as tracking and information discovery, storage, information exchange, and object addressing. However, a complete understanding of using ontology in the IoT mechanism remains lacking. The main goal of this research is to recognize the use of ontology in the IoT process and investigate the services of ontology in IoT activities. A systematic literature review (SLR) is conducted using predefined protocols to analyze the literature about the usage of ontologies in IoT. The following conclusions are obtained from the SLR. (1) Primary studies (i.e., selected 115 articles) have addressed the need to use ontologies in IoT for industries and the academe, especially to minimize interoperability and integration of IoT devices. (2) About 31.30% of extant literature discussed ontology development concerning the IoT interoperability issue, while IoT privacy and integration issues are partially discussed in the literature. (3) IoT styles of modeling ontologies are diverse, whereas 35.65% of total studies adopted the OWL style. (4) The 32 articles (i.e., 27.83% of the total studies) reused IoT ontologies to handle diverse IoT methodologies. (5) A total of 45 IoT ontologies are well acknowledged, but the IoT community has widely utilized none. An in-depth analysis of different IoT ontologies suggests that the existing ontologies are beneficial in designing new IoT ontology or achieving three main requirements of the IoT field: interoperability, integration, and privacy. This SLR is finalized by identifying numerous validity threats and future directions
Microservices and Machine Learning Algorithms for Adaptive Green Buildings
In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings
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