From Glossaries to Ontologies: Disaster Management Domain

Our society’s reliance on a variety of critical infrastructures (CI) presents significant challenges for disaster preparedness, response and recovery. Experts from different domains including police, paramedics, firefighters and various other CI teams are involved in the fast paced response to a disaster, increasing the risk of miscommunication. To ensure clear communication, as well as to facilitate CI software interoperability, a common disaster ontology is needed. We propose using the knowledge stored in domain glossaries, vocabularies and dictionaries for the creation of a lightweight disaster management domain ontology. Glossaries, vocabularies and dictionaries are semi structured representations of domain knowledge, where significant human effort has been invested in choosing relevant terms, determining their definitions, acronyms, synonyms and sometimes even relations. We use that knowledge built into semi formatted documents for ontology learning. In particular, we look at five glossaries/vocabularies from the disaster management domain and analyze their content similarity and structure. A lightweight disaster ontology is created exploiting the structure of the semi-structured source documents

EEF-CAS: An Effort Estimation Framework with Customizable Attribute Selection

Existing estimation frameworks generally provide one-size-fits-all solutions that fail to produce accurate estimates in most environments. Research has shown that the accomplishment of accurate effort estimates is a long-term process that, above all, requires the extensive collection of effort estimation data by each organization. Collected data is generally characterized by a set of attributes that are believed to affect the development effort. The attributes that most affect development effort vary widely depending on the type of product being developed and the environment in which it is being developed. Thus, any new estimation framework must offer the flexibility of customizable attribute selection. Moreover, such attributes could provide the ability to incorporate empirical evidence and expert judgment into the effort estimation framework. Finally, because software is virtual and therefore intangible, the most important software metrics are notorious for being subjective according to the experience of the estimator. Consequently, a measurement and inference system that is robust to subjectivity and uncertainty must be in place. The Effort Estimation Framework with Customizable Attribute Selection (EEF-CAS) presented in this paper has been designed with the above requirements in mind. It is accompanied with four preparation process steps that allow for any organization implementing it to establish an estimation process. This estimation process facilitates data collection, framework customization to the organization’s needs, its calibration with the organization’s data, and the capability of continual improvement. The proposed framework described in this paper was validated in a real software development organization

Ontology–based Representation of Simulation Models

Ontologies have been used in a variety of domains for multiple purposes such as establishing common terminology, organizing domain knowledge and describing domain in a machine-readable form. Moreover, ontologies are the foundation of the Semantic Web and often semantic integration is achieved using ontology. Even though simulation demonstrates a number of similar characteristics to Semantic Web or semantic integration, including heterogeneity in the simulation domain, representation and semantics, the application of ontology in the simulation domain is still in its infancy. This paper proposes an ontology-based representation of simulation models. The goal of this research is to facilitate comparison among simulation models, querying, making inferences and reuse of existing simulation models. Specifically, such models represented in the domain simulation engine environment serve as an information source for their representation as instances of an ontology. Therefore, the ontology-based representation is created from existing simulation models in their proprietary file formats, consequently eliminating the need to perform the simulation modeling directly in the ontology. The proposed approach is evaluated on a case study involving the I2Sim interdependency simulator

Challenges for MapReduce in Big Data

In the Big Data community, MapReduce has been seen as one of the key enabling approaches for meeting continuously increasing demands on computing resources imposed by massive data sets. The reason for this is the high scalability of the MapReduce paradigm which allows for massively parallel and distributed execution over a large number of computing nodes. This paper identifies MapReduce issues and challenges in handling Big Data with the objective of providing an overview of the field, facilitating better planning and management of Big Data projects, and identifying opportunities for future research in this field. The identified challenges are grouped into four main categories corresponding to Big Data tasks types: data storage (relational databases and NoSQL stores), Big Data analytics (machine learning and interactive analytics), online processing, and security and privacy. Moreover, current efforts aimed at improving and extending MapReduce to address identified challenges are presented. Consequently, by identifying issues and challenges MapReduce faces when handling Big Data, this study encourages future Big Data research

Trust-based Service-Oriented Architecture

Service-Oriented Architecture (SOA) is an architectural style in building Web applications based on services. In SOA, the lack of trust between different parties affects the adoption of such architecture. Because trust is an important factor in successful online interactions, it is a major criterion for service selection. In the context of online services and SOA, the literature shows that the field of trust is not mature. The definitions of trust and its essential aspects do not reflect the true nature of trust online. This paper proposes a comprehensive trust-based SOA solution based on an identified trust definition and its principles for selecting services based on their trustworthiness. In particular, SOA is extended and a new component, the trust framework, which is responsible for the trust process, is added to the architecture. Consequently, its components are identified and built. The trust-based SOA is implemented through experiments and scenarios

From Glossaries to Ontologies: Disaster Management Domain

Our society’s reliance on a variety of critical infrastructures (CI) presents significant challenges for disaster preparedness, response and recovery. Experts from different domains including police, paramedics, firefighters and various other CI teams are involved in the fast paced response to a disaster, increasing the risk of miscommunication. To ensure clear communication, as well as to facilitate CI software interoperability, a common disaster ontology is needed. We propose using the knowledge stored in domain glossaries, vocabularies and dictionaries for the creation of a lightweight disaster management domain ontology. Glossaries, vocabularies and dictionaries are semi structured representations of domain knowledge, where significant human effort has been invested in choosing relevant terms, determining their definitions, acronyms, synonyms and sometimes even relations. We use that knowledge built into semi formatted documents for ontology learning. In particular, we look at five glossaries/vocabularies from the disaster management domain and analyze their content similarity and structure. A lightweight disaster ontology is created exploiting the structure of the semi-structured source documents

CEPSim: A Simulator for Cloud-Based Complex Event Processing

As one of the Vs defining Big Data, data velocity brings many new challenges to traditional data processing approaches. The adoption of cloud environments in complex event processing (CEP) systems is a recent architectural style that aims to overcome these challenges. Validating cloud-based CEP systems at the required Big Data scale, however, is often a laborious, error-prone, and expensive task. This article presents CEPSim, a new simulator that has been developed to facilitate this validation process. CEPSim extends CloudSim, an existing cloud simulator, with an application model based on directed acyclic graphs that is used to represent continuous CEP queries. Once defined, the queries can be simulated in different cloud environments under diverse load conditions. Moreover, CEPSim is also customizable with different operator placement and scheduling strategies. These features enable researchers and system architects to experiment with different configurations and strategies and to promote research in this field. Experimental results show that CEPSim can successfully simulate existing cloud-based CEP systems

Ontology–based Representation of Simulation Models

Ontologies have been used in a variety of domains for multiple purposes such as establishing common terminology, organizing domain knowledge and describing domain in a machine-readable form. Moreover, ontologies are the foundation of the Semantic Web and often semantic integration is achieved using ontology. Even though simulation demonstrates a number of similar characteristics to Semantic Web or semantic integration, including heterogeneity in the simulation domain, representation and semantics, the application of ontology in the simulation domain is still in its infancy. This paper proposes an ontology-based representation of simulation models. The goal of this research is to facilitate comparison among simulation models, querying, making inferences and reuse of existing simulation models. Specifically, such models represented in the domain simulation engine environment serve as an information source for their representation as instances of an ontology. Therefore, the ontology-based representation is created from existing simulation models in their proprietary file formats, consequently eliminating the need to perform the simulation modeling directly in the ontology. The proposed approach is evaluated on a case study involving the I2Sim interdependency simulator