1,486 research outputs found
The Vadalog System: Datalog-based Reasoning for Knowledge Graphs
Over the past years, there has been a resurgence of Datalog-based systems in
the database community as well as in industry. In this context, it has been
recognized that to handle the complex knowl\-edge-based scenarios encountered
today, such as reasoning over large knowledge graphs, Datalog has to be
extended with features such as existential quantification. Yet, Datalog-based
reasoning in the presence of existential quantification is in general
undecidable. Many efforts have been made to define decidable fragments. Warded
Datalog+/- is a very promising one, as it captures PTIME complexity while
allowing ontological reasoning. Yet so far, no implementation of Warded
Datalog+/- was available. In this paper we present the Vadalog system, a
Datalog-based system for performing complex logic reasoning tasks, such as
those required in advanced knowledge graphs. The Vadalog system is Oxford's
contribution to the VADA research programme, a joint effort of the universities
of Oxford, Manchester and Edinburgh and around 20 industrial partners. As the
main contribution of this paper, we illustrate the first implementation of
Warded Datalog+/-, a high-performance Datalog+/- system utilizing an aggressive
termination control strategy. We also provide a comprehensive experimental
evaluation.Comment: Extended version of VLDB paper
<https://doi.org/10.14778/3213880.3213888
Reasoning & Querying – State of the Art
Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF
Learning a Partitioning Advisor with Deep Reinforcement Learning
Commercial data analytics products such as Microsoft Azure SQL Data Warehouse
or Amazon Redshift provide ready-to-use scale-out database solutions for
OLAP-style workloads in the cloud. While the provisioning of a database cluster
is usually fully automated by cloud providers, customers typically still have
to make important design decisions which were traditionally made by the
database administrator such as selecting the partitioning schemes.
In this paper we introduce a learned partitioning advisor for analytical
OLAP-style workloads based on Deep Reinforcement Learning (DRL). The main idea
is that a DRL agent learns its decisions based on experience by monitoring the
rewards for different workloads and partitioning schemes. We evaluate our
learned partitioning advisor in an experimental evaluation with different
databases schemata and workloads of varying complexity. In the evaluation, we
show that our advisor is not only able to find partitionings that outperform
existing approaches for automated partitioning design but that it also can
easily adjust to different deployments. This is especially important in cloud
setups where customers can easily migrate their cluster to a new set of
(virtual) machines
AT-GIS: highly parallel spatial query processing with associative transducers
Users in many domains, including urban planning, transportation, and environmental science want to execute analytical queries over continuously updated spatial datasets. Current solutions for largescale spatial query processing either rely on extensions to RDBMS, which entails expensive loading and indexing phases when the data changes, or distributed map/reduce frameworks, running on resource-hungry compute clusters. Both solutions struggle with the sequential bottleneck of parsing complex, hierarchical spatial data formats, which frequently dominates query execution time. Our goal is to fully exploit the parallelism offered by modern multicore CPUs for parsing and query execution, thus providing the performance of a cluster with the resources of a single machine. We describe AT-GIS, a highly-parallel spatial query processing system that scales linearly to a large number of CPU cores. ATGIS integrates the parsing and querying of spatial data using a new computational abstraction called associative transducers(ATs). ATs can form a single data-parallel pipeline for computation without requiring the spatial input data to be split into logically independent blocks. Using ATs, AT-GIS can execute, in parallel, spatial query operators on the raw input data in multiple formats, without any pre-processing. On a single 64-core machine, AT-GIS provides 3Ă— the performance of an 8-node Hadoop cluster with 192 cores for containment queries, and 10Ă— for aggregation queries
Survey of time series database technology
This report has been prepared by Epimorphics Ltd. as part of the ENTRAIN project (NERC grant number NE/S016244/1) which is a feasibility project within the “NERC Constructing a Digital Environment Strategic Priorities Fund Programme”. The Centre for Ecology and Hydrology(CEH) is a research organisation focusing on land and freshwater ecosystems and their interaction with the atmosphere. The organization manages a number of sensor networks to monitor the environment, and also handles large databases of 3rd party data (e.g. river flows measured by the Environment Agency and equivalents in Scotland and Wales). Data from these networks is stored and made available to users, both internally (through direct query of databases, and externally via web-services). The ENTRAIN project aims to address a number of issues in relation to sensor data storage and integration, using a number of hydrological datasets to help define use cases: COSMOS-UK (a network of ~50 sites measuring soil moisture and meteorological variables at 1-30 minute resolutions); the CEH Greenhouse Gas (GHG) network (~15 sites measuring sub-second fluxes of gases and moisture, subsequently processed up to 30-minute aggregations); the Thames Initiative (a database of weekly and hourly water quality samples from sites around the Thames basin). In addition this report considers the UK National River Flow Archive, a database of daily river flows and catchment rainfall derived by regional environmental agencies from 15-minute measurements of river levels and flows. CEH commissioned this report to survey alternative technologies for storing sensor data that scale better, could manage larger data volumes more easily and less expensively, and that might be readily deployed on different infrastructures
Accessible Data Curation and Analytics for International-Scale Citizen Science Datasets
The Covid Symptom Study, a smartphone-based surveillance study on COVID-19
symptoms in the population, is an exemplar of big data citizen science. Over
4.7 million participants and 189 million unique assessments have been logged
since its introduction in March 2020. The success of the Covid Symptom Study
creates technical challenges around effective data curation for two reasons.
Firstly, the scale of the dataset means that it can no longer be easily
processed using standard software on commodity hardware. Secondly, the size of
the research group means that replicability and consistency of key analytics
used across multiple publications becomes an issue. We present ExeTera, an open
source data curation software designed to address scalability challenges and to
enable reproducible research across an international research group for
datasets such as the Covid Symptom Study dataset
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