Visual composition of data sources by end users

2014 ◽  
Author(s):  
Carmelo Ardito ◽  
M. Francesca Costabile ◽  
Giuseppe Desolda ◽  
Rosa Lanzilotti ◽  
Maristella Matera ◽  
...  
Keyword(s):  
End Users ◽  
Data Sources ◽  
Visual Composition

Scientific Web Intelligence

2011 ◽  
pp. 995-999
Author(s):  
Mike Thelwall
Keyword(s):  
Data Mining ◽  
End Users ◽  
Research Field ◽  
Data Sources ◽  
Web Pages ◽  
Web Intelligence ◽  
Research Fields ◽  
Patterns Of Communication ◽  
The Impact ◽  
Citation Databases

Scientific Web Intelligence (SWI) is a research field that combines techniques from data mining, Web intelligence, and scientometrics to extract useful information from the links and text of academic-related Web pages using various clustering, visualization, and counting techniques. Its origins lie in previous scientometric research into mining off-line academic data sources such as journal citation databases. Typical scientometric objectives are either evaluative (assessing the impact of research) or relational (identifying patterns of communication within and among research fields). From scientometrics, SWI also inherits a need to validate its methods and results so that the methods can be justified to end users, and the causes of the results can be found and explained.

Big Data Warehouse Automatic Design Methodology

Big Data ◽  
2016 ◽  
pp. 454-492
Author(s):  
Francesco Di Tria ◽  
Ezio Lefons ◽  
Filippo Tangorra
Keyword(s):  
Big Data ◽  
Data Warehouse ◽  
Design Process ◽  
Hybrid Approach ◽  
End Users ◽  
Structured Data ◽  
Data Sources ◽  
The One ◽  
Definition Of ◽  
Big Data Warehouse

Traditional data warehouse design methodologies are based on two opposite approaches. The one is data oriented and aims to realize the data warehouse mainly through a reengineering process of the well-structured data sources solely, while minimizing the involvement of end users. The other is requirement oriented and aims to realize the data warehouse only on the basis of business goals expressed by end users, with no regard to the information obtainable from data sources. Since these approaches are not able to address the problems that arise when dealing with big data, the necessity to adopt hybrid methodologies, which allow the definition of multidimensional schemas by considering user requirements and reconciling them against non-structured data sources, has emerged. As a counterpart, hybrid methodologies may require a more complex design process. For this reason, the current research is devoted to introducing automatisms in order to reduce the design efforts and to support the designer in the big data warehouse creation. In this chapter, the authors present a methodology based on a hybrid approach that adopts a graph-based multidimensional model. In order to automate the whole design process, the methodology has been implemented using logical programming.

scholarly journals A Review of Accessing Big Data with Significant Ontologies

2020 ◽  
Vol 3 (2) ◽  
pp. 67
Author(s):  
Jumah Y.J Sleeman ◽  
Jehad Abdulhamid Hammad
Keyword(s):  
Big Data ◽  
Data Access ◽  
End Users ◽  
Ontology Mapping ◽  
Query Answering ◽  
Data Sources ◽  
Relational Data ◽  
Direct Access ◽  
Previous Step

Ontology Based Data Access (OBDA) is a recently proposed approach which is able to provide a conceptual view on relational data sources. It addresses the problem of the direct access to big data through providing end-users with an ontology that goes between users and sources in which the ontology is connected to the data via mappings. We introduced the languages used to represent the ontologies and the mapping assertions technique that derived the query answering from sources. Query answering is divided into two steps: (i) Ontology rewriting, in which the query is rewritten with respect to the ontology into new query; (ii) mapping rewriting the query that obtained from previous step reformulating it over the data sources using mapping assertions. In this survey, we aim to study the earlier works done by other researchers in the fields of ontology, mapping and query answering over data sources.

scholarly journals Architecture for Virtualization and Collaboration in Edge Computing: an implementation based on FIWARE building blocks

2020 ◽  
Author(s):  
Marcelo Pitanga Alves ◽  
Flávia Coimbra Delicato ◽  
Igor Leão Dos Santos ◽  
Paulo F. Pires
Keyword(s):  
Energy Efficiency ◽  
Fault Tolerance ◽  
Physical Environment ◽  
Building Blocks ◽  
End Users ◽  
Edge Computing ◽  
Data Sources ◽  
Low Latency ◽  
Iot Applications ◽  
Network Latency

Edge Computing is a novel paradigm that allows moving the computation closer to the end-users and/or data sources. In this paper, we present a three-tier architecture for virtualization and collaboration of Virtual Nodes that leverages the Edge tier to meet those emerging IoT applications that demand requirements such as low latency, geo-localization, and energy efficiency. Besides the Edge tier, our implementation is based on the mix of lightweight virtualization and microservices using the building blocks from the FIWARE platform to interact with the physical environment. Furthermore, we presented two experiments to assess our architecture under severe and realistic conditions, regarding the network latency and fault-tolerance.

scholarly journals Infrastructure as Software in Micro Clouds at the Edge

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7001
Author(s):  
Miloš Simić ◽  
Goran Sladić ◽  
Miroslav Zarić ◽  
Branko Markoski
Keyword(s):  
Real Time ◽  
Data Preprocessing ◽  
End Users ◽  
Cloud Services ◽  
Data Sources ◽  
Specific Location ◽  
Proposed Model ◽  
New Challenges ◽  
Real Time Applications ◽  
Novel Applications

Edge computing offers cloud services closer to data sources and end-users, making the foundation for novel applications. The infrastructure deployment is taking off, bringing new challenges: how to use geo-distribution properly, or harness the advantages of having resources at a specific location? New real-time applications require multi-tier infrastructure, preferably doing data preprocessing locally, but using the cloud for heavy workloads. We present a model, able to organize geo-distributed nodes into micro clouds dynamically, allowing resource reorganization to best serve population needs. Such elasticity is achieved by relying on cloud organization principles, adapted for a different environment. The desired state is specified descriptively, and the system handles the rest. As such, infrastructure is abstracted to the software level, thus enabling “infrastructure as software” at the edge. We argue about blending the proposed model into existing tools, allowing cloud providers to offer future micro clouds as a service.

Big Data Warehouse Automatic Design Methodology

2013 ◽  
pp. 115-149 ◽  
Author(s):  
Francesco Di Tria ◽  
Ezio Lefons ◽  
Filippo Tangorra
Keyword(s):  
Big Data ◽  
Data Warehouse ◽  
Design Process ◽  
Hybrid Approach ◽  
End Users ◽  
Structured Data ◽  
Data Sources ◽  
The One ◽  
Definition Of ◽  
Big Data Warehouse

Traditional data warehouse design methodologies are based on two opposite approaches. The one is data oriented and aims to realize the data warehouse mainly through a reengineering process of the well-structured data sources solely, while minimizing the involvement of end users. The other is requirement oriented and aims to realize the data warehouse only on the basis of business goals expressed by end users, with no regard to the information obtainable from data sources. Since these approaches are not able to address the problems that arise when dealing with big data, the necessity to adopt hybrid methodologies, which allow the definition of multidimensional schemas by considering user requirements and reconciling them against non-structured data sources, has emerged. As a counterpart, hybrid methodologies may require a more complex design process. For this reason, the current research is devoted to introducing automatisms in order to reduce the design efforts and to support the designer in the big data warehouse creation. In this chapter, the authors present a methodology based on a hybrid approach that adopts a graph-based multidimensional model. In order to automate the whole design process, the methodology has been implemented using logical programming.

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