RDF-TR: Exploiting structural redundancies to boost RDF compression

The number and volume of semantic data have grown impressively over the last decade, promoting compression as an essential tool for RDF preservation, sharing and management. In contrast to universal compressors, RDF compression techniques are able to detect and exploit specific forms of redundancy in RDF data. Thus, state-of-the-art RDF compressors excel at exploiting syntactic and semantic redundancies, i.e., repetitions in the serialization format and information that can be inferred implicitly. However, little attention has been paid to the existence of structural patterns within the RDF dataset; i.e. structural redundancy. In this paper, we analyze structural regularities in real-world datasets, and show three schema-based sources of redundancies that underpin the schema-relaxed nature of RDF. Then, we propose RDF-Tr (RDF Triples Reorganizer), a preprocessing technique that discovers and removes this kind of redundancy before the RDF dataset is effectively compressed. In particular, RDF-Tr groups subjects that are described by the same predicates, and locally re-codes the objects related to these predicates. Finally, we integrate RDF-Tr with two RDF compressors, HDT and k2-triples. Our experiments show that using RDF-Tr with these compressors improves by up to 2.3 times their original effectiveness, outperforming the most prominent state-of-the-art techniques

SMART-KG: Hybrid Shipping for SPARQL Querying on the Web

While Linked Data (LD) provides standards for publishing (RDF) and (SPARQL) querying Knowledge Graphs (KGs) on the Web, serving, accessing and processing such open, decentralized KGs is often practically impossible, as query timeouts on publicly available SPARQL endpoints show. Alternative solutions such as Triple Pattern Fragments (TPF) attempt to tackle the problem of availability by pushing query processing workload to the client side, but suffer from unnecessary transfer of irrelevant data on complex queries with large intermediate results. In this paper we present smart-KG, a novel approach to share the load between servers and clients, while significantly reducing data transfer volume, by combining TPF with shipping compressed KG partitions. Our evaluations show that smart-KG outperforms state-of-the-art client-side solutions and increases server-side availability towards more cost-effective and balanced hosting of open and decentralized KGs

Localized self-heating in large arrays of 1D nanostructures

One dimensional (1D) nanostructures offer a promising path towards highly efficient heating and temperature control in integrated microsystems. The so called self-heating effect can be used to modulate the response of solid state gas sensor devices. In this work, efficient self-heating was found to occur at random networks of nanostructured systems with similar power requirements to highly ordered systems (e.g. individual nanowires, where their thermal efficiency was attributed to the small dimensions of the objects). Infrared thermography and Raman spectroscopy were used to map the temperature profiles of films based on random arrangements of carbon nanofibers during self-heating. Both the techniques demonstrate consistently that heating concentrates in small regions, the here-called 'hot-spots'. On correlating dynamic temperature mapping with electrical measurements, we also observed that these minute hot-spots rule the resistance values observed macroscopically. A physical model of a random network of 1D resistors helped us to explain this observation. The model shows that, for a given random arrangement of 1D nanowires, current spreading through the network ends up defining a set of spots that dominate both the electrical resistance and power dissipation. Such highly localized heating explains the high power savings observed in larger nanostructured systems. This understanding opens a path to design highly efficient self-heating systems, based on random or pseudo-random distributions of 1D nanostructures

A prevalent mutation with founder effect in Spanish Recessive Dystrophic Epidermolysis Bullosa families

<p>Abstract</p> <p>Background</p> <p>Recessive Dystrophic Epidermolysis Bullosa (RDEB) is a genodermatosis caused by more than 500 different mutations in the <it>COL7A1 </it>gene and characterized by blistering of the skin following a minimal friction or mechanical trauma.</p> <p>The identification of a cluster of RDEB pedigrees carrying the c.6527insC mutation in a specific area raises the question of the origin of this mutation from a common ancestor or as a result of a hotspot mutation. The aim of this study was to investigate the origin of the c.6527insC mutation.</p> <p>Methods</p> <p>Haplotypes were constructed by genotyping nine single nucleotides polymorphisms (SNPs) throughout the <it>COL7A1 </it>gene. Haplotypes were determined in RDEB patients and control samples, both of Spanish origin.</p> <p>Results</p> <p>Sixteen different haplotypes were identified in our study. A single haplotype cosegregated with the c.6527insC mutation.</p> <p>Conclusion</p> <p>Haplotype analysis showed that all alleles carrying the c.6527insC mutation shared the same haplotype cosegregating with this mutation (<b><it>CCGCTCAAA_6527insC</it></b>), thus suggesting the presence of a common ancestor.</p

Localized self-heating in large arrays of 1D nanostructures

One dimensional (1D) nanostructures offer a promising path towards highly efficient heating and temperature control in integrated microsystems. The so called self-heating effect can be used to modulate the response of solid state gas sensor devices. In this work, efficient self-heating was found to occur at random networks of nanostructured systems with similar power requirements to highly ordered systems (e.g. individual nanowires, where their thermal efficiency was attributed to the small dimensions of the objects). Infrared thermography and Raman spectroscopy were used to map the temperature profiles of films based on random arrangements of carbon nanofibers during self-heating. Both the techniques demonstrate consistently that heating concentrates in small regions, the here-called 'hot-spots'. On correlating dynamic temperature mapping with electrical measurements, we also observed that these minute hot-spots rule the resistance values observed macroscopically. A physical model of a random network of 1D resistors helped us to explain this observation. The model shows that, for a given random arrangement of 1D nanowires, current spreading through the network ends up defining a set of spots that dominate both the electrical resistance and power dissipation. Such highly localized heating explains the high power savings observed in larger nanostructured systems. This understanding opens a path to design highly efficient self-heating systems, based on random or pseudo-random distributions of 1D nanostructures