First experiments in cultural alignment repair (extended version)

euzenat2014cInternational audienceAlignments between ontologies may be established through agents holding such ontologies attempting at communicating and taking appropriate action when communication fails. This approach, that we call cultural repair, has the advantage of not assuming that everything should be set correctly before trying to communicate and of being able to overcome failures. We test here the adaptation of this approach to alignment repair, i.e., the improvement of incorrect alignments. For that purpose, we perform a series of experiments in which agents react to mistakes in alignments. The agents only know about their ontologies and alignments with others and they act in a fully decentralised way. We show that cultural repair is able to converge towards successful communication through improving the objective correctness of alignments. The obtained results are on par with a baseline of a priori alignment repair algorithms

Self-Enforcing Access Control for Encrypted RDF

The amount of raw data exchanged via web protocols is steadily increasing. Although the Linked Data infrastructure could potentially be used to selectively share RDF data with different individuals or organisations, the primary focus remains on the unrestricted sharing of public data. In order to extend the Linked Data paradigm to cater for closed data, there is a need to augment the existing infrastructure with robust security mechanisms. At the most basic level both access control and encryption mechanisms are required. In this paper, we propose a flexible and dynamic mechanism for securely storing and efficiently querying RDF datasets. By employing an encryption strategy based on Functional Encryption (FE) in which controlled data access does not require a trusted mediator, but is instead enforced by the cryptographic approach itself, we allow for fine-grained access control over encrypted RDF data while at the same time reducing the administrative overhead associated with access control management

H2O: A Hands-free Adaptive Store

Modern state-of-the-art database systems are designed around a single data storage layout. This is a fixed decision that drives the whole architectural design of a database system, i.e., row-stores, column-stores. However, none of those choices is a universally good solution; different workloads require different storage layouts and data access methods in order to achieve good performance. In this paper, we present the H2O system which introduces two novel concepts. First, it is flexible to support multiple storage layouts and data access patterns in a single engine. Second, and most importantly, it decides on-the-fly, i.e., during query processing, which design is best for classes of queries and the respective data parts. At any given point in time, parts of the data might be materialized in various patterns purely depending on the query workload; as the workload changes and with every single query, the storage and access patterns continuously adapt. In this way, H2O makes no a priori and fixed decisions on how data should be stored, allowing each single query to enjoy a storage and access pattern which is tailored to its specific properties. We present a detailed analysis of H2O using both synthetic benchmarks and realistic scientific workloads. We demonstrate that while existing systems cannot achieve maximum performance across all workloads, H2O can always match the best case performance without requiring any tuning or workload knowledge

An introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them

Targeted Chromosomal Insertion of Large DNA into the Human Genome by a Fiber-Modified High-Capacity Adenovirus-Based Vector System

A prominent goal in gene therapy research concerns the development of gene transfer vehicles that can integrate exogenous DNA at specific chromosomal loci to prevent insertional oncogenesis and provide for long-term transgene expression. Adenovirus (Ad) vectors arguably represent the most efficient delivery systems of episomal DNA into eukaryotic cell nuclei. The most advanced recombinant Ads lack all adenoviral genes. This renders these so-called high-capacity (hc) Ad vectors less cytotoxic/immunogenic than those only deleted in early regions and creates space for the insertion of large/multiple transgenes. The versatility of hcAd vectors is been increased by capsid modifications to alter their tropism and by the incorporation into their genomes of sequences promoting chromosomal insertion of exogenous DNA. Adeno-associated virus (AAV) can insert its genome into a specific human locus designated AAVS1. Trans- and cis-acting elements needed for this reaction are the AAV Rep78/68 proteins and Rep78/68-binding sequences, respectively. Here, we describe the generation, characterization and testing of fiber-modified dual hcAd/AAV hybrid vectors (dHVs) containing both these elements. Due to the inhibitory effects of Rep78/68 on Ad-dependent DNA replication, we deployed a recombinase-inducible gene switch to repress Rep68 synthesis during vector rescue and propagation. Flow cytometric analyses revealed that rep68-positive dHVs can be produced similarly well as rep68-negative control vectors. Western blot experiments and immunofluorescence microscopy analyses demonstrated transfer of recombinase-dependent rep68 genes into target cells. Studies in HeLa cells and in the dystrophin-deficient myoblasts from a Duchenne muscular dystrophy (DMD) patient showed that induction of Rep68 synthesis in cells transduced with fiber-modified and rep68-positive dHVs leads to increased stable transduction levels and AAVS1-targeted integration of vector DNA. These results warrant further investigation especially considering the paucity of vector systems allowing permanent phenotypic correction of patient-own cell types with large DNA (e.g. recombinant full-length DMD genes)

TripleProv: efficient processing of lineage queries in a native RDF store.

Given the heterogeneity of the data one can find on the Linked Data cloud, being able to trace back the provenance of query results is rapidly becoming a must-have feature of RDF systems. While provenance models have been extensively discussed in recent years, little attention has been given to the efficient implementation of provenance-enabled queries inside data stores. This paper introduces TripleProv: a new system extending a native RDF store to efficiently handle such queries. TripleProv implements two different storage models to physically co-locate lineage and instance data, and for each of them implements algorithms for tracing provenance at two granularity levels. In the following, we present the overall architecture of our system, its different lineage storage models, and the various query execution strategies we have implemented to efficiently answer provenance-enabled queries. In addition, we present the results of a comprehensive empirical evaluation of our system over two different datasets and workloads. Copyright is held by the International World Wide Web Conference Committee (IW3C2)

ArmaTweet: Detecting events by semantic tweet analysis

Armasuisse Science and Technology, the R&D; agency for the Swiss Armed Forces, is developing a Social Media Analysis (SMA) system to help detect events such as natural disasters and terrorist activity by analysing Twitter posts. The system currently supports only keyword search, which cannot identify complex events such as ‘politician dying’ or ‘militia terror act’ since the keywords that correctly identify such events are typically unknown. In this paper we present ArmaTweet, an extension of SMA developed in a collaboration between armasuisse and the Universities of Fribourg and Oxford that supports semantic event detection. Our system extracts a structured representation from the tweets’ text using NLP technology, which it then integrates with DBpedia and WordNet in an RDF knowledge graph. Security analysts can thus describe the events of interest precisely and declaratively using SPARQL queries over the graph. Our experiments show that ArmaTweet can detect many complex events that cannot be detected by keywords alone

ArmaTweet: Detecting events by semantic tweet analysis

Armasuisse Science and Technology, the RandD agency for the Swiss Armed Forces, is developing a Social Media Analysis (SMA) system to help detect events such as natural disasters and terrorist activity by analysing Twitter posts. The system currently supports only keyword search, which cannot identify complex events such as ‘politician dying’ or ‘militia terror act’ since the keywords that correctly identify such events are typically unknown. In this paper we present ArmaTweet, an extension of SMA developed in a collaboration between armasuisse and the Universities of Fribourg and Oxford that supports semantic event detection. Our system extracts a structured representation from the tweets’ text using NLP technology, which it then integrates with DBpedia and WordNet in an RDF knowledge graph. Security analysts can thus describe the events of interest precisely and declaratively using SPARQL queries over the graph. Our experiments show that ArmaTweet can detect many complex events that cannot be detected by keywords alone