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Large organizations usually have difficulties in dealing with the exponential growth of information. Therefore, there is a high demand for innovative solutions to deal with such growth and to integrate such information. This paper proposes a new approach, called Emergent Ontologies (EOs), toward the generation of a single organizational ontology through which it becomes possible to browse all information of an organization. This proposal considers that, typically, an organization's information is distributed in peers, and that in each peer, this information could be represented through a different ontology. As each peer of an organization needs to exchange information, peer-to-peer mappings are created to bridge these ontologies. Based on these mappings, this paper proposes a set of heuristics, which are used to generate the EO. These heuristics have been incorporated into the OntoEmerge system, a prototype developed to facilitate the creation of an initial organization ontology. In order to evaluate such a system and the heuristics behind it, some experiments have been performed. A quantitative and qualitative analysis of these experiments is also presented in this paper. This approach presents encouraging results, and this fact can be considered as a starting point for the creation of organizational ontologies. [ABSTRACT FROM AUTHOR]

This paper explains the key role played by windowing in the preliminary analysis of multifactor and multivariate (MFMV) databases. The explanation is based on the general case of a database featuring quantitative or qualitative measurement variables and a hyperparallelepipedic structure in which the directions correspond to the factors. In order to maintain the MFMV aspects of this data structure, the windowing approach as described in this paper does not reduce the information as much as most of the basic non-windowing summarizing procedures using the standard statistical indicators. First, the data in each cell of the hyperparallelepiped are transformed into membership values that can be averaged over factors, such as time or individuals. Then, these membership values may be potentially investigated into with several graphic techniques; for this paper, multiple correspondence analysis (MCA) was chosen. The presentation fall into two parts. First, a didactic example based on a simulated data set describes the approach in comparison with more traditional approaches, and then a real data set, with multidimensional signals recorded for 34 subjects in 15 experimental overtaking situations, is used to demonstrate the power of the “space windowing/MCA” pair on a large real database. Next, the discussion section weighs out the pros and the cons of using space windowing to perform a preliminary analysis of a large MFMV database in studies of human component systems. [ABSTRACT FROM PUBLISHER]

In the intelligence community, the existence of a malicious insider poses a severe threat to information, to the actual analytic process, and, ultimately, to any decision-making process relying on such information and analyses. An analyst with malicious intent can create irreversible short-term, as well as long-term, damage that is hard to detect. In this paper, we propose a novel methodology that detects malicious analysts who attempt to manipulate decision makers' perceptions through their intelligence reports. This detection method relies on each analyst's working style, which we assume to be consistent from task to task. In order to measure an analyst's degree of consistency, we employ a user-modeling technique that automatically builds a computational model of each analyst based on observation of their activities. We hypothesize that inconsistency is mainly caused by malicious actions. Therefore, the detection method evaluates how consistent an analyst is across different tasks and raises an alert if any significantly large inconsistency is detected. A normalization procedure is employed which allows us to compare across a group of analysts and is shown to reduce noise and amplify inconsistency that is due to malicious actions. We show that this improves detection performance. Our experiments demonstrate the effectiveness of our approach in detecting malicious insiders. In the experiments, the percentage of malicious insiders grouped with legitimate ones is varied, and results are collected with and without normalization in order to provide a comprehensive analysis of our approach. [ABSTRACT FROM PUBLISHER]

An electronic marketplace (e-marketplace) is a common business information space populated with many entities of different system types. Each of them has its own context of how to process activities. This leads to heterogeneous e-marketplace activities, which are difficult to make interoperable and inferred from one entity to another. This study solves this problem by proposing a concept of separation strategy and implementing it through providing a semantic inference engine with a novel inference algorithm. The solution, called the RuleXPM approach, enables one to semantically infer a next e-marketplace activity across multiple contexts/domains. Experiments show that the cross-context/cross-domain semantic inference is achievable. This paper is an understanding of many aspects related to heterogeneous activity inference. [ABSTRACT FROM PUBLISHER]

Ontology mapping is a prerequisite for achieving heterogeneous data integration on the Semantic Web. The vision of the Semantic Web implies that a large number of ontologies present on the web need to be aligned before one can make use of them, for example, a question answering on the Semantic Web. At the same time, these ontologies can be used as domain-specific background knowledge by the ontology mapping systems to increase the mapping precision. However, these ontologies can differ in representation, quality, and size that pose different challenges to ontology mapping. In this paper, we analyze these challenges and introduce a multiagent ontology mapping framework that has been designed to operate effectively in the Semantic Web environment. [ABSTRACT FROM AUTHOR]

The imperfect nature of context in ambient intelligence environments, and the special characteristics of the entities that possess and share the available context information render contextual reasoning a very challenging task. The accomplishment of this task requires formal models that handle the involved entities as autonomous logic-based agents, and provide methods for handling the imperfect and distributed nature of context. We propose a solution based on the multi-context systems (MCS) formalism, in which local context knowledge of ambient agents is encoded in rule theories (contexts), and information flow between agents is achieved through mapping rules, associating concepts used by different contexts. To handle the imperfect nature of context, we extend MCS with non-monotonic features—local defeasible theories, defeasible mappings, and a preference ordering on the system contexts. In this paper, we present the novel representation model, called contextual defeasible logic, describe how its elements are used to derive distributed conclusions through a proof theory, and propose an algorithm for distributed query evaluation that implements the proof theory of contextual defeasible logic. The application of the proposed approach in a scenario from the ambient intelligence domain demonstrates how its distinct features overcome the challenges imposed by the special characteristics of ambient intelligence environments. [ABSTRACT FROM AUTHOR]

Fit represents a central concept for organizational design, but extant research maintains a static focus on fit, a focus that is incommensurate with the fundamentally dynamic nature of organizations and their environments. Most key organizational environments are inherently dynamic; hence, the corresponding organizational designs required for fit are necessarily dynamic too. The problem is, the dynamics of fit are not addressed well by extant theory in organization and management sciences. Alternatively, organizations can be viewed as systems of purposeful design, and designing organizations to maintain fit and respond to dynamic environments over time may be informed well by theory and practice in engineering fields where such design is well established. In this paper, we abstract to the level of airplane design, and we utilize the dynamical language and integrated system of concepts, definitions, and interrelationships from the engineering field Aerodynamics to extend organization and management sciences and address the problem of organizational design in a dynamic context. We begin with a focused summary of the literature regarding the nature of organizational fitness. We then outline a conceptual model adapted to organizational design from Aerodynamics, and we summarize the key aerodynamics concepts stability and maneuverability to inform our conceptualization in terms of both airplane and organization design. This paper enables us to articulate a set of propositions and measures that form a basis for empirical testing. This paper also reveals important, dynamic organizational design tradeoffs and implications, and it shows how such conceptualization can elucidate new insights via comparison with and extension to extant theory. [ABSTRACT FROM PUBLISHER]

This paper proposes an algorithm, called sequence prediction via enhanced episode discovery (SPEED), to predict inhabitant activity in smart homes. SPEED is a variant of the sequence prediction algorithm. It works with the episodes of smart home events that have been extracted based on the on –off states of home appliances. An episode is a set of sequential user activities that periodically occur in smart homes. The extracted episodes are processed and arranged in a finite-order Markov model. A method based on prediction by partial matching (PPM) algorithm is applied to predict the next activity from the previous history. The result shows that SPEED achieves an 88.3% prediction accuracy, which is better than LeZi Update, Active LeZi, IPAM, and C4.5. [ABSTRACT FROM PUBLISHER]

A context-aware system handling time-critical applications must satisfy explicit end-to-end time constraints which are highly dependent on the correlation between the dynamically changing contexts. To effectively deal with the timing constraints of context-aware systems, a methodology must be developed specifying the causal relationship between the contexts and timing aspect. To date, this issue has not yet been adequately addressed. In this paper, we propose a new modeling methodology which effectively represents the timing variation of contexts by extending interval timed colored Petri net. We also propose a technique determining if a service is able to satisfy the timing requirement. The effectiveness of the proposed approach is demonstrated using a collision avoidance service provided to moving vehicles. [ABSTRACT FROM PUBLISHER]

Product ecosystem design entails complex user experience (UX) that involves interactions among multiple users, products, and the ambience. This paper aims to capture causal relationships between UX and design elements and in turn to provide decision support to product ecosystem analysis. A fuzzy reasoning Petri net is developed to deal with the uncertainty, complexity, and dynamics associated with UX modeling. Reasoning of diverse constructs of UX is embedded in the fuzzy production rules that are derived from self-report UX data based on rough set mining. A fuzzy reasoning algorithm is implemented to perform parallel inference by multicriteria rules and to simulate most likely UX under different ambient factors. A case study of subway station UX design demonstrates the potential of product ecosystem FRPN formulation. [ABSTRACT FROM PUBLISHER]

Nowadays, we are witnesses of a transformation in the e-learning arena. This transformation has different drivers involving all the actors in the learning value chain, from final users to learning institutions through technology providers. All those actors share a common goal: making the learning processes more effective through the information and communication technologies. This is happening through the promotion of a paradigm shift from content-centered to process-centered solutions. In this paper, we present the results from the European Learning Grid Infrastructure project concerning models, processes, and services supported by a service-oriented software architecture for creating dynamic and adaptive virtual organizations for learning using Grid technologies. [ABSTRACT FROM AUTHOR]

Social tagging in online communities has become an important method for reflecting classified thoughts of individual users. A number of social Web sites provide tagging functionalities and also offer folksonomies within or across the sites. However, it is practically not easy to find users' interests based on such folksonomies. In this paper, we provide a novel approach for clustering user-centric interests by analyzing tagging practices of individual users. To do this, we collect Really Simple Syndication data from blogosphere, find conceptual clusters using formal concept analysis, and then evaluate the significance of these clusters. The results of the empirical evaluation show that we can effectively recommend different collections of tags to an individual or a set of users. [ABSTRACT FROM AUTHOR]

A significant obstacle for service robots is the execution of complex tasks in real environments. For example, it is not easy for service robots to find objects that are partially observable and are located at a place which is not identical but near the place where the robots saw them previously. To overcome the challenge effectively, robot knowledge represented as a semantic network can be extremely useful. This paper presents an ontology-based unified robot knowledge framework that integrates low-level data with high-level knowledge for robot intelligence. This framework consists of two sections: knowledge description and knowledge association. Knowledge description includes comprehensively integrated robot knowledge derived from low-level knowledge regarding perceptual features, part objects, metric maps, and primitive behaviors, as well as high-level knowledge about perceptual concepts, objects, semantic maps, tasks, and contexts. Knowledge association uses logical inference with both unidirectional and bidirectional rules. This characteristic enables reasoning to be performed even when only a partial information is available. The experimental results that demonstrate the advantages of using the proposed knowledge framework are also presented. [ABSTRACT FROM PUBLISHER]

In home-based care, reliable contextual information of remotely monitored patients should be generated by correctly recognizing the activities to prevent hazardous situations of the patient. It is difficult to achieve a higher confidence level of contextual information for several reasons. First, low-level data from multisensors have different degrees of uncertainty. Second, generated contexts can be conflicting, even though they are acquired by simultaneous operations. We propose the static evidential fusion process (SEFP) as a context-reasoning method. The context-reasoning method processes sensor data with an evidential form based on the Dezert–Smarandache theory (DSmT). The DSmT approach reduces ambiguous or conflicting contextual information in multisensor networks. Moreover, we compare SEFP based on DSmT with traditional fusion processes such as Bayesian networks and the Dempster–Shafer theory to understand the uncertainty analysis in decision making and to show the improvement of the DSmT approach compared to the others. [ABSTRACT FROM PUBLISHER]