Epidemic Information Diffusion: A Simple Solution to Support Community-based Recommendations in P2P Overlays

Epidemic protocols proved to be very efficient solutions for supporting dynamic and complex information diffusion in highly dis- tributed computing infrastructures, like P2P environments. They are useful bricks for building and maintaining virtual network topologies, in the form of overlay networks as well as to support pervasive diffusion of information when it is injected into the network. This paper proposes a simple architecture exploiting the features of epidemic approaches to foster a collaborative percolation of information between computing nodes belonging to the network aimed at building a system that groups similar users and spread useful information among them.Comment: 8 pages, 2 figure

A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing

Data Grids have been adopted as the platform for scientific communities that need to share, access, transport, process and manage large data collections distributed worldwide. They combine high-end computing technologies with high-performance networking and wide-area storage management techniques. In this paper, we discuss the key concepts behind Data Grids and compare them with other data sharing and distribution paradigms such as content delivery networks, peer-to-peer networks and distributed databases. We then provide comprehensive taxonomies that cover various aspects of architecture, data transportation, data replication and resource allocation and scheduling. Finally, we map the proposed taxonomy to various Data Grid systems not only to validate the taxonomy but also to identify areas for future exploration. Through this taxonomy, we aim to categorise existing systems to better understand their goals and their methodology. This would help evaluate their applicability for solving similar problems. This taxonomy also provides a "gap analysis" of this area through which researchers can potentially identify new issues for investigation. Finally, we hope that the proposed taxonomy and mapping also helps to provide an easy way for new practitioners to understand this complex area of research.Comment: 46 pages, 16 figures, Technical Repor

From Social Data Mining to Forecasting Socio-Economic Crisis

Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It also appears appropriate to build a network of Crisis Observatories. They can be imagined as laboratories devoted to the gathering and processing of enormous volumes of data on both natural systems such as the Earth and its ecosystem, as well as on human techno-socio-economic systems, so as to gain early warnings of impending events. Reality mining provides the chance to adapt more quickly and more accurately to changing situations. Further opportunities arise by individually customized services, which however should be provided in a privacy-respecting way. This requires the development of novel ICT (such as a self- organizing Web), but most likely new legal regulations and suitable institutions as well. As long as such regulations are lacking on a world-wide scale, it is in the public interest that scientists explore what can be done with the huge data available. Big data do have the potential to change or even threaten democratic societies. The same applies to sudden and large-scale failures of ICT systems. Therefore, dealing with data must be done with a large degree of responsibility and care. Self-interests of individuals, companies or institutions have limits, where the public interest is affected, and public interest is not a sufficient justification to violate human rights of individuals. Privacy is a high good, as confidentiality is, and damaging it would have serious side effects for society.Comment: 65 pages, 1 figure, Visioneer White Paper, see http://www.visioneer.ethz.c

Recommender Systems for Online and Mobile Social Networks: A survey

Recommender Systems (RS) currently represent a fundamental tool in online services, especially with the advent of Online Social Networks (OSN). In this case, users generate huge amounts of contents and they can be quickly overloaded by useless information. At the same time, social media represent an important source of information to characterize contents and users' interests. RS can exploit this information to further personalize suggestions and improve the recommendation process. In this paper we present a survey of Recommender Systems designed and implemented for Online and Mobile Social Networks, highlighting how the use of social context information improves the recommendation task, and how standard algorithms must be enhanced and optimized to run in a fully distributed environment, as opportunistic networks. We describe advantages and drawbacks of these systems in terms of algorithms, target domains, evaluation metrics and performance evaluations. Eventually, we present some open research challenges in this area

Efficient opportunistic routing in dense mobile networks

The usage of smartphones is nowadays ubiquitous. Their simultaneous support for longand short-range communication has enabled the deployment of opportunistic, device-todevice networks, which exploit human mobility to enable and facilitate communication and content exchange among peer devices. Devices connect to each other without human intervention, potentially with the assistance of the cellular network provider. The underlying network topology constantly changes, depending on the mobility patterns of the participating mobile devices. Mobile devices support various technologies for discovering their location; GPS is very accurate but it works only outdoors and is power-hungry, whereas location discovery based on nearby announced SSIDs and/or the current cell ID is less accurate but power-friendly. Indoor localisation is much more challenging; approaches that are based on inertial sensors and dead reckoning, along with deployed beacons and pre-calculated signal strength maps have been proposed. In this thesis, we develop GeoHawk, a routing protocol for dense mobile networks that support opportunistic communication and content dissemination among mobile devices in crowded events. The driving use case has been the Grand Mosque, the largest mosque in the world located at the heart of the city of Makkah in Saudi Arabia. During the Ramadan and Hajj, viii the Grand Mosque can get extremely crowded, with anticipated number of visitors close to 2.5 million, after the current expansion work is completed. The proposed protocol incorporates a novel distributed localisation technique that can be used in conjunction with the protocol, when GPS is not available. GeoHawk deals with the very high density of users/devices by heavily aggregating routing information using Bloom filters. Identifiers of mobile devices that reside within specific geographical regions are disseminated in the network in the form of Bloom filters. Said geographical regions are dynamically created and destroyed; their size evolves to reflect the uncertainty in the topology, due to mobility and potential inaccuracies of the underlying location estimation mechanism. Bloom filters are also decayed to reflect information ageing. Devices exchange routing information with their neighbours and announce aggregated information (i.e. Bloom filters) in messages that propagate towards specific directions and reach distant areas of the opportunistic network. Data is then disseminated (and replicated through a simple but efficient ticketing mechanism) towards directions where the information about the existence of the destination node is stronger. Upon reaching the best-known region for the destination node, a message is either flooded, if the belief that the node resides in the region is strong (as indicated by a belief threshold), or, in the opposite case, redirected to a randomly selected region. The distributed localisation algorithm is a novel synthesis of existing techniques, including Pedestrian Dead Reckoning, estimated location sharing and particle filtering. Our approach can provide reasonable errors in the estimation, which allow the routing protocol to effectively deliver messages to destination nodes. We evaluate GeoHawk using extensive experimentation in the ONE simulator. We have developed mobility models that approximate the user behaviour in the targeted use ix cases and communication environments. We have experimented with a large variety of configuration parameters that affect the behaviour of the proposed protocol and recorded its performance in terms of message delivery ratio and latency as well as induced network overhead. We show that the GeoHawk’s performance is superior to baseline protocols, namely Epidemic, PRoPHET and WSR

Wiki: A Technology for Conversational Knowledge Management and Group Collaboration

Wikis (from wikiwiki, meaning fast in Hawaiian) are a promising new technology that supports conversational knowledge creation and sharing. A Wiki is a collaboratively created and iteratively improved set of web pages, together with the software that manages the web pages. Because of their unique way of creating and managing knowledge, Wikis combine the best elements of earlier conversational knowledge management technologies, while avoiding many of their disadvantages. This article introduces Wiki technology, the behavioral and organizational implications of Wiki use, and Wiki applicability as groupware and help system software. The article concludes that organizations willing to embrace the Wiki way with collaborative, conversational knowledge management systems, may enjoy better than linear knowledge growth while being able to satisfy ad-hoc, distributed knowledge needs

Maintaining privacy for a recommender system diagnosis using blockchain and deep learning.

The healthcare sector has been revolutionized by Blockchain and AI technologies. Artificial intelligence uses algorithms, recommender systems, decision-making abilities, and big data to display a patient's health records using blockchain. Healthcare professionals can make use of Blockchain to display a patient's medical records with a secured medical diagnostic process. Traditionally, data owners have been hesitant to share medical and personal information due to concerns about privacy and trustworthiness. Using Blockchain technology, this paper presents an innovative model for integrating healthcare data sharing into a recommender diagnostic computer system. Using the model, medical records can be secured, controlled, authenticated, and kept confidential. In this paper, researchers propose a framework for using the Ethereum Blockchain and x-rays as a mechanism for access control, establishing hierarchical identities, and using pre-processing and deep learning to diagnose COVID-19. Along with solving the challenges associated with centralized access control systems, this mechanism also ensures data transparency and traceability, which will allow for efficient diagnosis and secure data sharing