Evaluating dynamically evolving mobile-based social networks

The increasing capabilities of mobile phones enable them to participate in different type of web-based systems. One of the most popular systems are social networks. The phonebooks of the mobile devices also represent social relationships of the owner. This can be used for discovering additional relations in social networks. Following this line of thought, mobile-based social networks can be created by enabling a synchronization mechanism between phonebooks of the users and the social network. This mechanism detects similarities between phonebook contacts and members of the network. Users can accept or ignore these similarities. After acceptance, identity links are formed. If a member changes her or his personal detail, it will be propagated automatically into the phonebooks, via identity links after considering privacy settings. Estimating the total number of these identity links is a key issue from scalability and performance point of view in such networks. We have implemented a mobile-based social network, called Phonebookmark and examined the structure of the network during a test period of the system. We have found, that the distribution of identity links of the users follows a power law. Based on this, we propose a model for estimating the total number of identity links in the dynamically evolving network. We verify the model by measurements and we also prove the accuracy of the model mathematically. For this we use the fact, that the number of identity links of each user (and thus, the value of the random variable modeling it) is bounded linearly by the number of members Nm of the network. Then we show, that the variance of the random variable is Θ(Nm3-β), where 2 0, Pr[X = x] = c∙x-β, if x ≤ Nm and Pr[X = x] = 0 otherwise. The model and the results can be used in general when the distribution shows similar behavior

Digital Ecosystems: Ecosystem-Oriented Architectures

We view Digital Ecosystems to be the digital counterparts of biological ecosystems. Here, we are concerned with the creation of these Digital Ecosystems, exploiting the self-organising properties of biological ecosystems to evolve high-level software applications. Therefore, we created the Digital Ecosystem, a novel optimisation technique inspired by biological ecosystems, where the optimisation works at two levels: a first optimisation, migration of agents which are distributed in a decentralised peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. The Digital Ecosystem was then measured experimentally through simulations, with measures originating from theoretical ecology, evaluating its likeness to biological ecosystems. This included its responsiveness to requests for applications from the user base, as a measure of the ecological succession (ecosystem maturity). Overall, we have advanced the understanding of Digital Ecosystems, creating Ecosystem-Oriented Architectures where the word ecosystem is more than just a metaphor.Comment: 39 pages, 26 figures, journa

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page