PetaByte Ltd

Budapest, Hungary

PetaByte Ltd

Budapest, Hungary
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BasePort has developed a clusterable 100 TeraByte proof-of-concept unit which upon further funding and development will lead to the mid 2017 demonstration of the economical and power-efficient PetaByte storage prototype, with commercialization to follow in 2018. Baseport has developed a clusterable 100 TeraByte proof-of-concept unit which upon further funding and development will lead to the mid 2017 demonstration of the economical and power-efficient PetaByte storage prototype, with commercialization to follow in 2018. About BasePort Headquartered in London UK, BasePort Laboratories is a technical leader in healthcare data-management systems with combined strengths in hardware & operating system development, nVidia accelerated Deep Learning, Open-Source software integration and proprietary optimizations such as industry leading data compression. BasePort's unique hardware supports from small research groups up to equipping large data centres. BasePort’s systems are primarily engineered for the massive mega-PetaByte genomic data storage and processing requirements of Personalised Medicine healthcare strategies, where rapid data integration & analysis is required to enable accurate patient diagnosis, followed by decades of secure and reliable archival storage. Contact: b.bhullar@baseport.ch http://www.baseport.ch London, United Kingdom, May 15, 2017 --( PR.com )-- Secure data storage should be a priority of every organization, but existing options were evidently too expensive to implement, judging by the widespread lack of available backups in Healthcare, Utility, and other institutions in the recent Global Ransomeware attack. Relying on decryption does not guarantee the data has not been tampered with, whereas a mere restoration of the data from a recent backup is all that it takes to completely recover pristine data.Baseport has developed a clusterable 100 TeraByte proof-of-concept unit which upon further funding and development will lead to the mid 2017 demonstration of the economical and power-efficient PetaByte storage prototype, with commercialization to follow in 2018.About BasePortHeadquartered in London UK, BasePort Laboratories is a technical leader in healthcare data-management systems with combined strengths in hardware & operating system development, nVidia accelerated Deep Learning, Open-Source software integration and proprietary optimizations such as industry leading data compression. BasePort's unique hardware supports from small research groups up to equipping large data centres. BasePort’s systems are primarily engineered for the massive mega-PetaByte genomic data storage and processing requirements of Personalised Medicine healthcare strategies, where rapid data integration & analysis is required to enable accurate patient diagnosis, followed by decades of secure and reliable archival storage.Contact:b.bhullar@baseport.chhttp://www.baseport.ch Click here to view the list of recent Press Releases from BasePort


Gulyas L.,PetaByte Ltd | Gulyas L.,AITIA International Inc | Gulyas L.,Eötvös Loránd University | Kampis G.,PetaByte Ltd | And 2 more authors.
European Physical Journal: Special Topics | Year: 2013

We develop and analyze an agent-based model for the study of information propagation in dynamic contact networks. We represent information as a state of a node in a network that can be probabilistically transferred to an adjacent node within a single time step. The model is based on a closed (yet sufficiently large) population that can support processes of link generation and annihilation using different contact regimes. Our study is confined to the case of homogeneous contacts, where each agent establishes and breaks contacts in the same way. We consider information to be available for spreading in a fixed time window (i.e. finite memory). We find, surprisingly, that information transmission (measured as the proportion of informed nodes after a fixed number of time steps) is identical for dynamic preferential and random networks, but radically different for the associate mixing contact regime. We also find that the probability of transmission is, similarly counterintuitively, not a main driver of the process as opposed the the main network par maters determining contact lifetime and the turnover rate on connections. We discuss the explanation and the significance of these results in the light of the fundamental difference between dynamic and static (cumulative) networks. © 2013 EDP Sciences and Springer.


Gulyas L.,PetaByte Ltd | Gulyas L.,AITIA International Inc | Gulyas L.,Eötvös Loránd University | Kampis G.,PetaByte Ltd | And 4 more authors.
European Physical Journal: Special Topics | Year: 2013

Inspecting the dynamics of networks opens a new dimension in understanding the interactions among the components of complex systems. Our goal is to understand the baseline properties expected from elementary random changes over time, in order to be able to assess the various effects found in longitudinal data. We created elementary dynamic models from classic random and preferential networks. Focusing on edge dynamics, we defined several processes for changing networks of a fixed size. We applied simple rules, including random, preferential and assortative modifications of existing edges - or a combination of these. Starting from initial Erdos-Rényi networks, we examined various basic network properties (e.g., density, clustering, average path length, number of components, degree distribution, etc.) of both snapshot and cumulative networks (for various lengths of aggregation time windows). Our results provide a baseline for changes to be expected in dynamic networks. We found universalities in the dynamic behavior of most network statistics. Furthermore, our findings suggest that certain network properties have a strong, non-trivial dependence on the length of the sampling window. © 2013 EDP Sciences and Springer.

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