Social Innovation And Social Entrepreneurship Through Big Data: Developing A Reseach Agenda

The power of big data and their applications are evident through the enormous attention they have received over the past few years, with the majority of the research focusing on solving technical and business problems. However, the challenge remains on how to harness the potential of big data in order to come up with innovative solutions on various societal problems. Big data have the potential to change the way that entrepreneurs as well as the other stakeholders of a social ecosystem take decisions, and develop new paths to create social innovation by taking data-driven decisions. Nonetheless, there is limited understanding on how social ecosystems need to change to embrace the advancement that big data entail. There is a need to institutionalize innovation through big data and social entrepreneurship, and examine how to successfully exploit big data towards achieving social good and sustainable change. We suggest building on the current state of the art, and go beyond it by merging and extending the findings with insights from the different stakeholders involved in the social innovation process. Further, we propose developing and testing a framework of best practices, that will be used as a roadmap by interested parties to successfully employ big data for social innovation, through the development of prototype applications which will clearly showcase the impact of big data on addressing societal challenges. This position paper concludes with research questions and challenges for future studies in the area

Analysis on the Causes and Countermeasures of Network Security Threats in the Era of Big Data

In the context of the information age, computer network technology continues to innovate and plays an increasingly prominent role in social production and life, but correspondingly, it also brings a series of security risks. Due to the openness of the Internet, information exchange and communication in cyberspace may lead to cyber crime, resulting in leakage and damage of important data and information. Big data is a product born in response to the explosive growth of network data scale, and it is particularly crucial to do a good job in computer network protection under big data, which is highly valued by all sectors of society. Therefore, grasping the characteristics of the big data era in the new era, promoting innovation and upgrading of computer network security protection technology, has profound significance for ensuring user information security and social harmony and stable development. Big data is a new stage in the development of information technology, and it is also a new situation in the development of information technology. Based on information technology, it mainly involves data and data processing. The era of big data has greatly changed the living, learning, and working conditions of modern society. The research object of this paper is the network security threat in the era of big data. By analyzing the cases of network security threats in the era of big data, this paper puts forward strategies and suggestions to solve the problems based on big data theory and technology

A planetary nervous system for social mining and collective awareness

We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level. Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering questions about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network. The PNS we foresee is the key tool for individual and collective awareness for the knowledge society. We need such a tool for everyone to become fully aware of how powerful is the knowledge of our society we can achieve by leveraging our wisdom as a crowd, and how important is that everybody participates both as a consumer and as a producer of the social knowledge, for it to become a trustable, accessible, safe and useful public good.Seventh Framework Programme (European Commission) (grant agreement No. 284709

Expertise and Trust-Aware Social Web Service Recommendation

With the increasing number of Web services, the personalized recommendation of Web services has become more and more important. Fortunately, the social network popularity nowadays brings a good alternative for social recommendation to avoid the data sparsity problem that is not treated very well in the collaborative filtering approach. Since the social network provides a big data about the users, the trust concept has become necessary to filter this abundance and to foster the successful interactions between the users. In this paper, we firstly propose a trusted friend detection mechanism in a social network. The dynamic of the users’ interactions over time and the similarity of their interests have been considered. Secondly, we propose a Web service social recommendation mechanism which considers the expertise of the trusted friends according to their past invocation histories and the active user’s query. The experiments of each mechanism produced satisfactory results

Data Anonymization: K-anonymity Sensitivity Analysis

These days the digitization process is everywhere, spreading also across central governments and local authorities. It is hoped that, using open government data for scientific research purposes, the public good and social justice might be enhanced. Taking into account the European General Data Protection Regulation recently adopted, the big challenge in Portugal and other European countries, is how to provide the right balance between personal data privacy and data value for research. This work presents a sensitivity study of data anonymization procedure applied to a real open government data available from the Brazilian higher education evaluation system. The ARX k-anonymization algorithm, with and without generalization of some research value variables, was performed. The analysis of the amount of data / information lost and the risk of re-identification suggest that the anonymization process may lead to the under-representation of minorities and sociodemographic disadvantaged groups. It will enable scientists to improve the balance among risk, data usability, and contributions for the public good policies and practices.info:eu-repo/semantics/publishedVersio