Data Lifecycle Management:

2012 ◽  
pp. 397-400
Author(s):  
Will Hires
Keyword(s):  
Data Lifecycle ◽  
Lifecycle Management

scholarly journals Architecting an Enterprise Data Lake, A Covid19 Case Study

2021 ◽  
pp. 174-181
Author(s):  
Bushra ◽  
◽  
Mohsin Ali Memon ◽  
Salahuddin Saddar
Keyword(s):  
Metadata Management ◽  
Business Analytics ◽  
Data Governance ◽  
Business Decision ◽  
Central Platform ◽  
Rate Of Increase ◽  
Access Controls ◽  
Data Lifecycle ◽  
Lifecycle Management

Data is increasing at an enormous rate every day. Traditionally data has resided in silosacross any organization,so it’s difficult to have a complete picture for data driven business decision making. Data lake addresses the problem of rate of increase of data by providing “schema on read”, better integration and cheaper storage. It also solves the data silos problemby providing a central platform for a variety of data housing needs. However, implementing a data lake becomes challenging as the implementation needs to address the additional needs like metadata management, data discovery, data governance, data lifecycle management, security and centralized access controls mechanisms. This paper intends to provide a comprehensive architecture of data lake to address these challenges. We have also conducted and documented our experiments with publicly available datasets about COVID19 to validate the design and applicability of the proposed architecture for business analytics purposes.

scholarly journals Data Lifecycle Management in Precision Agriculture Supported by Information and Communication Technology

Agronomy ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1648
Author(s):  
Konstantinos Demestichas ◽  
Emmanouil Daskalakis
Keyword(s):  
Information And Communication Technology ◽  
Communication Technology ◽  
Precision Agriculture ◽  
Environmentally Sustainable ◽  
Related Data ◽  
Information And Communication ◽  
Management Concept ◽  
Data Lifecycle ◽  
Lifecycle Management

The role of agriculture in environmental degradation and climate change has been at the center of a long-lasting and controversial debate. This situation combined with the expected growth in crop demand and the increasing prices of fertilizers and pesticides has made the need for a more resource-efficient and environmentally sustainable agriculture more evident than ever. Precision agriculture (PA), as a relatively new farming management concept, aims to improve crop performance as well as to reduce the environmental footprint by utilizing information about the temporal and the spatial variability of crops. Information and communication technology (ICT) systems have influenced and shaped every part of modern life, and PA is no exception. The current paper conducts a literature review of prominent ICT solutions, focusing on their role in supporting different phases of the lifecycle of PA-related data. In addition to this, a data lifecycle model was developed as part of a novel categorization approach for the analyzed solutions.

scholarly journals BIOMIST: A Platform for Biomedical Data Lifecycle Management of Neuroimaging Cohorts

2017 ◽  
Vol 3 ◽  
Author(s):  
Marianne Allanic ◽  
Pierre-Yves Hervé ◽  
Cong-Cuong Pham ◽  
Myriam Lekkal ◽  
Alexandre Durupt ◽  
...  
Keyword(s):  
Biomedical Data ◽  
Data Lifecycle ◽  
Lifecycle Management

scholarly journals Data Lifecycle Management in Smart Building using Wireless Sensors Networks

2017 ◽  
Vol 50 (1) ◽  
pp. 12944-12949 ◽  
Author(s):  
Kais Mekki ◽  
William Derigent ◽  
Eric Rondeau ◽  
André Thomas
Keyword(s):  
Wireless Sensors ◽  
Smart Building ◽  
Data Lifecycle ◽  
Lifecycle Management ◽  
Wireless Sensors Networks

scholarly journals INGV data lifecycle management system performances during Mw 6.0 2016 Amatrice earthquake sequence

2016 ◽  
Vol 59 ◽  
Author(s):  
Stefano Pintore ◽  
Fabrizio Bernardi ◽  
Andrea Bono ◽  
Peter Danecek ◽  
Licia Faenza ◽  
...  
Keyword(s):  
Central Italy ◽  
Magnitude Estimate ◽  
Small Towns ◽  
Automated System ◽  
Civil Protection ◽  
Origin Time ◽  
Epicentral Area ◽  
Management Procedures ◽  
Data Lifecycle ◽  
Lifecycle Management

At 01:36:32 UTC on August 24, 2016 an earthquake of magnitude 6.0 occurred in Central Italy, affecting many small towns and municipalities in the Lazio, Umbria, Marche and Abruzzo regions. The event caused severe damages, many victims and 299 fatalities. Only 21 seconds after the beginning of the earthquake, the first automatic location of this earthquake was available and stored in our earthquakes database. The first magnitude estimate followed 68 seconds after the origin time. Few seconds later the INGV seismologists on duty in accordance to the agreed protocols provided the first alert to the Italian Civil Protection Department (Dipartimento di Protezione Civile, DPC) and thereby triggered the seismic emergency protocol. Subsequently, they elaborated the data in order to produce the first manually reviewed hypocenter, which was published on the Institute’s website at 01:53:18 UTC. The sequence following this mainshock generated thousands of earthquakes in the epicentral area, which the INGV automated localization system processed and detected along with the usual seismic activity in the rest of the Italian territory. In this paper we analyze the behavior of the automated system and of the data lifecycle management procedures in such extraordinary conditions. In particular we want to measure the capability of the system to manage the huge data flow, in terms of frequency and size of seismic events and its ability to remain fairly responsive and accurate in accomplishing its duty in the expected time. This will help us to identify potential problems and to suggest necessary improvements to better serve the INGV mission for Civil Protection.<br /><br id="tinymce" class="mceContentBody " />

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