5,983 research outputs found
EAGLE—A Scalable Query Processing Engine for Linked Sensor Data
Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio–temporal correlations. Most semantic approaches do not have spatio–temporal support. Some of them have attempted to provide full spatio–temporal support, but have poor performance for complex spatio–temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio–temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio–temporal computing in the linked sensor data context.EC/H2020/732679/EU/ACTivating InnoVative IoT smart living environments for AGEing well/ACTIVAGEEC/H2020/661180/EU/A Scalable and Elastic Platform for Near-Realtime Analytics for The Graph of Everything/SMARTE
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
From Information to Sense-Making: Fetching and Querying Semantic Repositories
Information, its gathering, sharing, and storage, is growing at a very rapid rate. Information turned into knowledge leads to sense- making. Ontologies, and their representations in RDF, are increasingly being used to turn information into knowledge. This paper describes how to leverage the power of ontologies and semantic repositories to turn today’s glut of information into sense-making. This would enable better applications to be built making users’ lives easier and more effective
Storage Solutions for Big Data Systems: A Qualitative Study and Comparison
Big data systems development is full of challenges in view of the variety of
application areas and domains that this technology promises to serve.
Typically, fundamental design decisions involved in big data systems design
include choosing appropriate storage and computing infrastructures. In this age
of heterogeneous systems that integrate different technologies for optimized
solution to a specific real world problem, big data system are not an exception
to any such rule. As far as the storage aspect of any big data system is
concerned, the primary facet in this regard is a storage infrastructure and
NoSQL seems to be the right technology that fulfills its requirements. However,
every big data application has variable data characteristics and thus, the
corresponding data fits into a different data model. This paper presents
feature and use case analysis and comparison of the four main data models
namely document oriented, key value, graph and wide column. Moreover, a feature
analysis of 80 NoSQL solutions has been provided, elaborating on the criteria
and points that a developer must consider while making a possible choice.
Typically, big data storage needs to communicate with the execution engine and
other processing and visualization technologies to create a comprehensive
solution. This brings forth second facet of big data storage, big data file
formats, into picture. The second half of the research paper compares the
advantages, shortcomings and possible use cases of available big data file
formats for Hadoop, which is the foundation for most big data computing
technologies. Decentralized storage and blockchain are seen as the next
generation of big data storage and its challenges and future prospects have
also been discussed
Towards a Cloud-Based Service for Maintaining and Analyzing Data About Scientific Events
We propose the new cloud-based service OpenResearch for managing and
analyzing data about scientific events such as conferences and workshops in a
persistent and reliable way. This includes data about scientific articles,
participants, acceptance rates, submission numbers, impact values as well as
organizational details such as program committees, chairs, fees and sponsors.
OpenResearch is a centralized repository for scientific events and supports
researchers in collecting, organizing, sharing and disseminating information
about scientific events in a structured way. An additional feature currently
under development is the possibility to archive web pages along with the
extracted semantic data in order to lift the burden of maintaining new and old
conference web sites from public research institutions. However, the main
advantage is that this cloud-based repository enables a comprehensive analysis
of conference data. Based on extracted semantic data, it is possible to
determine quality estimations, scientific communities, research trends as well
the development of acceptance rates, fees, and number of participants in a
continuous way complemented by projections into the future. Furthermore, data
about research articles can be systematically explored using a content-based
analysis as well as citation linkage. All data maintained in this
crowd-sourcing platform is made freely available through an open SPARQL
endpoint, which allows for analytical queries in a flexible and user-defined
way.Comment: A completed version of this paper had been accepted in SAVE-SD
workshop 2017 at WWW conferenc
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