Scalable RDF Data Compression using X10

The Semantic Web comprises enormous volumes of semi-structured data elements. For interoperability, these elements are represented by long strings. Such representations are not efficient for the purposes of Semantic Web applications that perform computations over large volumes of information. A typical method for alleviating the impact of this problem is through the use of compression methods that produce more compact representations of the data. The use of dictionary encoding for this purpose is particularly prevalent in Semantic Web database systems. However, centralized implementations present performance bottlenecks, giving rise to the need for scalable, efficient distributed encoding schemes. In this paper, we describe an encoding implementation based on the asynchronous partitioned global address space (APGAS) parallel programming model. We evaluate performance on a cluster of up to 384 cores and datasets of up to 11 billion triples (1.9 TB). Compared to the state-of-art MapReduce algorithm, we demonstrate a speedup of 2.6-7.4x and excellent scalability. These results illustrate the strong potential of the APGAS model for efficient implementation of dictionary encoding and contributes to the engineering of larger scale Semantic Web applications

Allelic polymorphism of Ovar-DRB1 exon2 gene and parasite resistance in two dairy sheep breeds

The Ovar-DRB1 gene locus is one of the most polymorphic genes of the Major Histocompatibility Complex (Ovar-MHC) and holds a functional role to antigen presentation. The aim of this study was: a) to describe the Ovar-DRB1 locus variability in two dairy Greek sheep breeds and b) to investigate associations between this variability with resistance to gastrointestinal parasitosis. Blood and faecal samples were collected from 231 and 201 animals of Arta and Kalarrytiko breeds, respectively. The identification of alleles was performed using the sequence–base method. Faecal egg counting (FEC) of the gastrointestinal parasites and measures of blood plasma pepsinogen levels were performed in order to evaluate parasitological parameters. From this study in the overall examined animals, thirty-nine Ovar-DRB1 alleles were identified, among them, ten new alleles, reported for the first time in the literature. In Arta breed a total of twenty-four alleles were found. Among the detected alleles, ten were breed specific and five were new. Regarding the Kalarrytiko breed, twenty-nine alleles were found, fifteen of them were unique and nine were new. The studied breeds differed in their allelic profile, with only 12 common from the total of 134 different recorded genotypes. A higher number of animals with high parasitic load and high plasma pepsinogen values were found in Kalarrytiko. Associations between Ovar-DRB1 alleles with FEC values were found with certain heterozygous genotypes to present significantly reduced FEC values. The large number of detected alleles with low frequencies and the fact that the majority of animals were heterozygous, make hard to find strong association

Explainable Human-in-the-loop Dynamic Data-Driven Digital Twins

Digital Twins (DT) are essentially Dynamic Data-driven models that serve as real-time symbiotic "virtual replicas" of real-world systems. DT can leverage fundamentals of Dynamic Data-Driven Applications Systems (DDDAS) bidirectional symbiotic sensing feedback loops for its continuous updates. Sensing loops can consequently steer measurement, analysis and reconfiguration aimed at more accurate modelling and analysis in DT. The reconfiguration decisions can be autonomous or interactive, keeping human-in-the-loop. The trustworthiness of these decisions can be hindered by inadequate explainability of the rationale, and utility gained in implementing the decision for the given situation among alternatives. Additionally, different decision-making algorithms and models have varying complexity, quality and can result in different utility gained for the model. The inadequacy of explainability can limit the extent to which humans can evaluate the decisions, often leading to updates which are unfit for the given situation, erroneous, compromising the overall accuracy of the model. The novel contribution of this paper is an approach to harnessing explainability in human-in-the-loop DDDAS and DT systems, leveraging bidirectional symbiotic sensing feedback. The approach utilises interpretable machine learning and goal modelling to explainability, and considers trade-off analysis of utility gained. We use examples from smart warehousing to demonstrate the approach.Comment: 10 pages, 1 figure, submitted to the 4th International Conference on InfoSymbiotics/Dynamic Data Driven Applications Systems (DDDAS2022

A Dynamic Data-Driven Simulation Approach for Preventing Service Level Agreement Violations in Cloud Federation

The new possibility of accessing an infinite pool of computational resources at a drastically reduced price has made cloud computing popular. With the increase in its adoption and unpredictability of workload, cloud providers are faced with the problem of meeting their service level agreement (SLA) claims as demonstrated by large vendors such as Amazon and Google. Therefore, users of cloud resources are embracing the more promising cloud federation model to ensure service guarantees. Here, users have the option of selecting between multiple cloud providers and subsequently switching to a more reliable one in the event of a provider’s inability to meet its SLA. In this paper, we propose a novel dynamic data-driven architecture capable of realising resource provision in a cloud federation with minimal SLA violations. We exemplify the approach with the aid of case studies to demonstrate its feasibility. Keywords