Combining Graph Exploration and Fragmentation for Scalable RDF Query Processing

2021 | Information Systems Frontiers | Citations: 0

Authors: Khelil, Abdallah; Mesmoudi, Amin; Galicia, Jorge; Bellatreche, Ladjel; Hacid, Mohand-Saïd; Coquery, Emmanuel

Abstract: The flexibility offered by the Resource Description Framework (RDF) has led it t ... Expand

Abstract: The flexibility offered by the Resource Description Framework (RDF) has led it to become a very popular standard for representing data with an undefined or variable schema using the concept of triples. Its success has resulted in many large scale multidisciplinary datasets, that have prompted the development of efficient RDF processing systems. Current approaches can be distinguished into two groups: the first, adopting the relational model storing the triples in tables, and the second creating data structures that model RDF data as a graph. The strategies of the first group are more easily scalable since they apply optimization strategies from the relational model like indexing and fragmentation. However, these approaches suffer many overheads when dealing with complex queries (e.g. compounded SPARQL graphs involving filters) persistent in existing applications. On the other hand, graph-based systems that use more complex data structures fail to efficiently manage the main memory and are not scalable in computer hardware with limited resources. In this paper, we propose a novel approach to perform queries (Basic Graph Patterns, Wildcards, Aggregations and Sorting) on RDF data. We propose to combine both RDF graph exploration with physical fragmentation of triples. In this work, we describe our graph-based storage and query evaluation models. Then, we detail the architecture of our system and we largely explain the strategy, based in the Volcano execution model, used to manage the main memory at query runtime. We conducted extensive experiments on synthetic and real datasets to evaluate the efficiency of our proposal. We compared our performance with a relational-based (Virtuoso), a graph-based (gStore) and an intensive-indexing (RDF-3X) approach. According to our evaluation, our system offers the best compromise between efficient query processing and scalability. Collapse

Topics: computer hardware Neo4j graph database structured query language database system

Methods: experiment computational algorithm leading question literature study

The Energy Consumption of Blockchain Technology: Beyond Myth

2020 | Business & Information Systems Engineering | Citations: 27

Authors: Sedlmeir, Johannes; Buhl, Hans Ulrich; Fridgen, Gilbert; Keller, Robert

Abstract: When talking about blockchain technology in academia, business, and society, fre ... Expand

Abstract: When talking about blockchain technology in academia, business, and society, frequently generalizations are still heared about its – supposedly inherent – enormous energy consumption. This perception inevitably raises concerns about the further adoption of blockchain technology, a fact that inhibits rapid uptake of what is widely considered to be a groundbreaking and disruptive innovation. However, blockchain technology is far from homogeneous, meaning that blanket statements about its energy consumption should be reviewed with care. The article is meant to bring clarity to the topic in a holistic fashion, looking beyond claims regarding the energy consumption of Bitcoin, which have, so far, dominated the discussion. Collapse

Topics: blockchain cryptocurrency Bitcoin computer hardware database system

An Ontology-based Approach for Software Architecture Recommendations

2017 | Americas Conference on Information Systems | Citations: 0

Authors: Bhat, Manoj; Shumaiev, Klym; Biesdorf, Andreas; Hohenstein, Uwe; Hassel, Michael; Matthes, Florian

Abstract: The design and development of sustainable software systems require software arch ... Expand

Abstract: The design and development of sustainable software systems require software architects to consider a variety of architectural solutions and their trade-offs. With the frequent introduction of new architectural methods and software solutions, as well as, due to time-to-market constraints faced by software architects, considering even a subset of alternative architectural solutions during the decision-making process is a challenge. In this paper, we propose a recommendation system that automatically annotates architectural elements in software architecture documents and then proposes a) alternative architectural solutions for the annotated elements and b) concrete software solutions to realize an architectural design decision. These annotations and recommendations are derived from the knowledge captured in a publicly available cross-domain ontology. The evaluation of the recommendation system indicates that our approach can effectively support software architects to consider alternative architectural solutions while making architectural design decisions. Collapse

Topics: knowledge representation recommender system enterprise architecture database system relational database

Methods: DBpedia ontological modelling machine learning design pattern existing ontologies