Combination of self-organization mechanisms to enhance service discovery in open systems

Decentralized systems have emerged as an alternative to centralized approaches for dealing with dynamic requirements in new business models. These systems should provide mechanisms that contribute to flexibility and facilitate adaptation to changes in the environment. In this paper, we present two self-organization mechanisms for a decentralized service discovery system in order to improve its performance. These mechanisms are based on local actions of agents that only consider local information about queries they forward during the discovery process. The self-organization actions are chosen by each agent individually when the agent considers them to be appropriate. The actions are: remaining in the system, leaving the system, cloning, and changing structural relations with other agents. We have evaluated each self-organization mechanism separately but also the combination of the two as the environmental conditions in the service demand change. The results show that the proposed self-organization mechanisms considerably improve the performance of the service discovery systemDel Val Noguera, E.; Rebollo Pedruelo, M.; Botti Navarro, VJ. (2014). Combination of self-organization mechanisms to enhance service discovery in open systems. Information Sciences. 279:138-162. doi:10.1016/j.ins.2014.03.109S13816227

Using Structure Indices for Efficient Approximation of Network Properties

Statistics on networks have become vital to the study of relational data drawn from areas including bibliometrics, fraud detection, bioinformatics, and the Internet. Calculating many of the most important measures—such as betweenness centrality, closeness centrality, and graph diameter—requires identifying short paths in these networks. However, finding these short paths can be intractable for even moderate-size networks. We introduce the concept of a network structure index (NSI), a composition of (1) a set of annotations on every node in the network and (2) a function that uses the annotations to estimate graph distance between pairs of nodes. We present several varieties of NSIs, examine their time and space complexity, and analyze their performance on synthetic and real data sets. We show that creating an NSI for a given network enables extremely efficient and accurate estimation of a wide variety of network statistics on that network

An Overview of Search Strategies in Distributed Environments

[EN] Distributed systems are populated by a large number of heterogeneous entities that join and leave the systems dynamically. These entities act as clients and providers and interact with each other in order to get a resource or to achieve a goal. To facilitate the collaboration between entities the system should provide mechanisms to manage the information about which entities or resources are available in the system at a certain moment, as well as how to locate them in an e cient way. However, this is not an easy task in open and dynamic environments where there are changes in the available resources and global information is not always available. In this paper, we present a comprehensive vision of search in distributed environments. This review does not only considers the approaches of the Peer-to-Peer area, but also the approaches from three more areas: Service-Oriented Environments, Multi-Agent Systems, and Complex Networks. In these areas, the search for resources, services, or entities plays a key role for the proper performance of the systems built on them. The aim of this analysis is to compare approaches from these areas taking into account the underlying system structure and the algorithms or strategies that participate in the search process.Work partially supported by the Spanish Ministry of Science and Innovation through grants TIN2009-13839-C03-01, CSD2007-0022 (CONSOLIDER-INGENIO 2010), PROMETEO 2008/051, PAID-06-11-2048, and FPU grant AP-2008-00601 awarded to E. del Val.Del Val Noguera, E.; Rebollo Pedruelo, M.; Botti, V. (2013). An Overview of Search Strategies in Distributed Environments. Knowledge Engineering Review. 1-33. https://doi.org/10.1017/S0269888913000143S133Sigdel K. , Bertels K. , Pourebrahimi B. , Vassiliadis S. , Shuai L. 2005. A framework for adaptive matchmaking in distributed computing. In Proceedings of GRID Workshop.Prabhu S. 2007. 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Mobile Application Adoption by Young Adults: A Social Network Perspective

The use of mobile applications, defined as small programs that run on a mobile device and perform tasks ranging from banking to gaming and web browsing, is exploding. Within the past two years, the industry has grown from essentially nothing to a $2 billion marketplace, but adoption rates are still on the rise. Using network theory, this study examines how the adoption of mobile apps among young consumers is influenced by others in their social network. The results suggest that the likelihood of adoption and usage of mobile apps increases with their use by the consumer\u27s strongest relationship partner. In addition, the authors find marginal support for the hypothesis that the adoption of mobile apps will be more strongly influenced by a consumer\u27s social contacts (friends, compared to family members), possibly due to their closer similarity to the consumer. Managerial and theoretical implications are discussed

FLOCK THEORY: COOPERATION AND DECENTRALIZATION IN COMMUNICATION NETWORKS

Research has shown that decentralized organizations and groups perform better and have more satisfied members than centralized ones. Further, decentralized self-organizing groups are particularly superior when solving complex problems. Despite mounting research in support of decentralization, the means of how to foster and maintain a decentralized, coordinated group remains a particular problem for organizations. The current line of research proposes a theory of decentralized organizational communication, flock theory, and conducts preliminary tests of the theory. Grounded in literature from social networks, flock theory represents a theoretical model for the decentralized evolution of communicative systems. The flock model is then extended to integrate roadmap based flocking, bipartite networks, and findings from small world research to create a theory of cooperation, coordination, and navigation within decentralized communication networks. Empirical illustrations of flock theory are conducted via two studies on two different research-based organizations, as research organizations focus on complex problem solving and coordination of knowledge. Findings provide initial support for flock theory, confirm parallel research on decentralization, and indicate that research-based organizations may be different from traditional corporate organizations in several ways

Competitors and Cooperators: A Micro‐Level Analysis of Regional Economic Development Collaboration Networks

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90044/1/j.1540-6210.2011.02501.x.pd

Regional medical inter-institutional cooperation in medical provider network constructed using patient claims data from Japan

The aging world population requires a sustainable and high-quality healthcare system. To examine the efficiency of medical cooperation, medical provider and physician networks were constructed using patient claims data. Previous studies have shown that these networks contain information on medical cooperation. However, the usage patterns of multiple medical providers in a series of medical services have not been considered. In addition, these studies used only general network features to represent medical cooperation, but their expressive ability was low. To overcome these limitations, we analyzed the medical provider network to examine its overall contribution to the quality of healthcare provided by cooperation between medical providers in a series of medical services. This study focused on: i) the method of feature extraction from the network, ii) incorporation of the usage pattern of medical providers, and iii) expressive ability of the statistical model. Femoral neck fractures were selected as the target disease. To build the medical provider networks, we analyzed the patient claims data from a single prefecture in Japan between January 1, 2014 and December 31, 2019. We considered four types of models. Models 1 and 2 use node strength and linear regression, with Model 2 also incorporating patient age as an input. Models 3 and 4 use feature representation by node2vec with linear regression and regression tree ensemble, a machine learning method. The results showed that medical providers with higher levels of cooperation reduce the duration of hospital stay. The overall contribution of the medical cooperation to the duration of hospital stay extracted from the medical provider network using node2vec is approximately 20%, which is approximately 20 times higher than the model using strength