Semantic Caching Framework: An FPGA-Based Application for IoT Security Monitoring

Security monitoring is one subdomain of cybersecurity which aims to guarantee the safety of systems, continuously monitoring unusual events. The development of Internet Of Things leads to huge amounts of information, being heterogeneous and requiring to be efficiently managed. Cloud Computing provides software and hardware resources for large scale data management. However, performances for sequences of on-line queries on long term historical data may be not compatible with the emergency security monitoring. This work aims to address this problem by proposing a semantic caching framework and its application to acceleration hardware with FPGA for fast- and accurate-enough logs processing for various data stores and execution engines

Un marco para democratizar la minería de datos: propuesta inicial y retos

Movimientos como el de datos abiertos posibilitan que cada vez haya una mayor disponibilidad de datos accesibles para su reutilización. A pesar de que el número de herramientas analíticas que están a nuestra disposición crece cada día, lamentablemente ninguna permite realizar un proceso de extracción de conocimiento directo a usuarios con poca o nula experiencia en el uso de la estadística y de algoritmos de minería de datos. En este artículo se presenta una aproximación a un marco KaaS (Knowledge as a Service) que posibilite a usuarios no expertos la extracción de conocimiento a partir de un conjunto de datos. Se muestra que la propuesta es viable y se plantean los retos aún abiertos

Privacy in data service composition

In modern information systems different information features, about the same individual, are often collected and managed by autonomous data collection services that may have different privacy policies. Answering many end-users’ legitimate queries requires the integration of data from multiple such services. However, data integration is often hindered by the lack of a trusted entity, often called a mediator, with which the services can share their data and delegate the enforcement of their privacy policies. In this paper, we propose a flexible privacy-preserving data integration approach for answering data integration queries without the need for a trusted mediator. In our approach, services are allowed to enforce their privacy policies locally. The mediator is considered to be untrusted, and only has access to encrypted information to allow it to link data subjects across the different services. Services, by virtue of a new privacy requirement, dubbed k-Protection, limiting privacy leaks, cannot infer information about the data held by each other. End-users, in turn, have access to privacy-sanitized data only. We evaluated our approach using an example and a real dataset from the healthcare application domain. The results are promising from both the privacy preservation and the performance perspectives

Big Data – a step change for SDI?

The globally hyped notion of Big Data has increasingly influenced scientific and technical debates about the handling and management of geospatial information. Accordingly, we see a need to recall what has happened over the past years, to present the recent Big Data landscape from an infrastructural perspective and to outline the major implications for the SDI community. We primarily conclude that it would be too simple and naïve to consider only the technological aspects that are underpinning geospatial (web) services. Instead, we request SDI researchers, engineers, providers and consumers to develop new methodologies and capacities for dealing with (geo)spatial information as part of broader knowledge infrastructures