46,698 research outputs found
Towards Exascale Scientific Metadata Management
Advances in technology and computing hardware are enabling scientists from
all areas of science to produce massive amounts of data using large-scale
simulations or observational facilities. In this era of data deluge, effective
coordination between the data production and the analysis phases hinges on the
availability of metadata that describe the scientific datasets. Existing
workflow engines have been capturing a limited form of metadata to provide
provenance information about the identity and lineage of the data. However,
much of the data produced by simulations, experiments, and analyses still need
to be annotated manually in an ad hoc manner by domain scientists. Systematic
and transparent acquisition of rich metadata becomes a crucial prerequisite to
sustain and accelerate the pace of scientific innovation. Yet, ubiquitous and
domain-agnostic metadata management infrastructure that can meet the demands of
extreme-scale science is notable by its absence.
To address this gap in scientific data management research and practice, we
present our vision for an integrated approach that (1) automatically captures
and manipulates information-rich metadata while the data is being produced or
analyzed and (2) stores metadata within each dataset to permeate
metadata-oblivious processes and to query metadata through established and
standardized data access interfaces. We motivate the need for the proposed
integrated approach using applications from plasma physics, climate modeling
and neuroscience, and then discuss research challenges and possible solutions
Global-Scale Resource Survey and Performance Monitoring of Public OGC Web Map Services
One of the most widely-implemented service standards provided by the Open
Geospatial Consortium (OGC) to the user community is the Web Map Service (WMS).
WMS is widely employed globally, but there is limited knowledge of the global
distribution, adoption status or the service quality of these online WMS
resources. To fill this void, we investigated global WMSs resources and
performed distributed performance monitoring of these services. This paper
explicates a distributed monitoring framework that was used to monitor 46,296
WMSs continuously for over one year and a crawling method to discover these
WMSs. We analyzed server locations, provider types, themes, the spatiotemporal
coverage of map layers and the service versions for 41,703 valid WMSs.
Furthermore, we appraised the stability and performance of basic operations for
1210 selected WMSs (i.e., GetCapabilities and GetMap). We discuss the major
reasons for request errors and performance issues, as well as the relationship
between service response times and the spatiotemporal distribution of client
monitoring sites. This paper will help service providers, end users and
developers of standards to grasp the status of global WMS resources, as well as
to understand the adoption status of OGC standards. The conclusions drawn in
this paper can benefit geospatial resource discovery, service performance
evaluation and guide service performance improvements.Comment: 24 pages; 15 figure
Integration of Exploration and Search: A Case Study of the M3 Model
International audienceEffective support for multimedia analytics applications requires exploration and search to be integrated seamlessly into a single interaction model. Media metadata can be seen as defining a multidimensional media space, casting multimedia analytics tasks as exploration, manipulation and augmentation of that space. We present an initial case study of integrating exploration and search within this multidimensional media space. We extend the M3 model, initially proposed as a pure exploration tool, and show that it can be elegantly extended to allow searching within an exploration context and exploring within a search context. We then evaluate the suitability of relational database management systems, as representatives of today’s data management technologies, for implementing the extended M3 model. Based on our results, we finally propose some research directions for scalability of multimedia analytics
Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources
Apache Calcite is a foundational software framework that provides query
processing, optimization, and query language support to many popular
open-source data processing systems such as Apache Hive, Apache Storm, Apache
Flink, Druid, and MapD. Calcite's architecture consists of a modular and
extensible query optimizer with hundreds of built-in optimization rules, a
query processor capable of processing a variety of query languages, an adapter
architecture designed for extensibility, and support for heterogeneous data
models and stores (relational, semi-structured, streaming, and geospatial).
This flexible, embeddable, and extensible architecture is what makes Calcite an
attractive choice for adoption in big-data frameworks. It is an active project
that continues to introduce support for the new types of data sources, query
languages, and approaches to query processing and optimization.Comment: SIGMOD'1
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A linked data-driven & service-oriented architecture for sharing educational resources
The two fundamental aims of managing educational resources are to enable resources to be reusable and interoperable and to enable Web-scale sharing of resources across learning communities. Currently, a variety of approaches have been proposed to expose and manage educational resources and their metadata on the Web. These are usually based on heterogeneous metadata standards and schemas, such as IEEE LOM or ADL SCORM, and diverse repository interfaces such as OAI-PMH or SQI. Also, there is still a lack of usage of controlled vocabularies and available data sets that could replace the widespread use of unstructured text for describing resources. On the other hand, the Linked Data approach has proven that it offers a set of successful principles that have the potential to alleviate the aforementioned issues. In this paper, we introduce an architecture and prototype which is fundamentally based on (a) Linked Data principles and (b) Service-orientation to resolve the integration issues for sharing educational resources
CHORUS Deliverable 3.4: Vision Document
The goal of the CHORUS Vision Document is to create a high level vision on audio-visual search engines in order to give guidance to the future R&D work in this area and to highlight trends and challenges in this domain. The vision of CHORUS is strongly connected to the CHORUS Roadmap Document (D2.3). A concise document integrating the outcomes of the two deliverables will be prepared for the end of the project (NEM Summit)
Hierarchical video surveillance architecture: a chassis for video big data analytics and exploration
There is increasing reliance on video surveillance systems for systematic derivation, analysis and interpretation of the data needed for predicting, planning, evaluating and implementing public safety. This is evident from the massive number of surveillance cameras deployed across public locations. For example, in July 2013, the British Security Industry Association (BSIA) reported that over 4 million CCTV cameras had been installed in Britain alone. The BSIA also reveal that only 1.5% of these are state owned. In this paper, we propose a framework that allows access to data from privately owned cameras, with the aim of increasing the efficiency and accuracy of public safety planning, security activities, and decision support systems that are based on video integrated surveillance systems. The accuracy of results obtained from government-owned public safety infrastructure would improve greatly if privately owned surveillance systems ‘expose’ relevant video-generated metadata events, such as triggered alerts and also permit query of a metadata repository. Subsequently, a police officer, for example, with an appropriate level of system permission can query unified video systems across a large geographical area such as a city or a country to predict the location of an interesting entity, such as a pedestrian or a vehicle. This becomes possible with our proposed novel hierarchical architecture, the Fused Video Surveillance Architecture (FVSA). At the high level, FVSA comprises of a hardware framework that is supported by a multi-layer abstraction software interface. It presents video surveillance systems as an adapted computational grid of intelligent services, which is integration-enabled to communicate with other compatible systems in the Internet of Things (IoT)
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