scholarly journals Managing Sparse Spatio-Temporal Data in SAVIME: an Evaluation of the Ph-tree Index

2021 ◽  
Author(s):  
Stiw Herrera ◽  
Larissa Miguez da Silva ◽  
Paulo Ricardo Reis ◽  
Anderson Silva ◽  
Fabio Porto
Keyword(s):  
Sparse Data ◽  
Scientific Data ◽  
Efficient Implementation ◽  
Temporal Data ◽  
Indexing Structure ◽  
Tree Index ◽  
Spatio Temporal ◽  
Data Ingestion ◽  
Memory Indexing ◽  
Array Databases

Scientific data is mainly multidimensional in its nature, presenting interesting opportunities for optimizations when managed by array databases. However, in scenarios where data is sparse, an efficient implementation is still required. In this paper, we investigate the adoption of the Ph-tree as an in-memory indexing structure for sparse data. We compare the performance in data ingestion and in both range and punctual queries, using SAVIME as the multidimensional array DBMS. Our experiments, using a real weather dataset, highlights the challenges involving providing a fast data ingestion, as proposed by SAVIME, and at the same time efficiently answering multidimensional queries on sparse data.

United in Variety: The EarthServer Datacube Federation

2020 ◽  
Author(s):  
Peter Baumann
Keyword(s):  
Data Center ◽  
Data Centers ◽  
Distributed Data ◽  
Temporal Data ◽  
Atmospheric Data ◽  
Elevation Data ◽  
Technology Services ◽  
Spatio Temporal ◽  
Data Location ◽  
Array Databases

<p>Datacubes form an accepted cornerstone for analysis (and visualization) ready spatio-temporal data offerings. Beyond the multi-dimensional data structure, the paradigm also suggests rich services, abstracting away from the untractable zillions of files and products - actionable datacubes as established by Array Databases enable users to ask "any query, any time" without programming. The principle of location-transparent federations establishes a single, coherent information space.</p><p>The EarthServer federation is a large, growing data center network offering Petabytes of a critical variety, such as radar and optical satellite data, atmospheric data, elevation data, and thematic cubes like global sea ice. Around CODE-DE and DIASs an ecosystem of data has been established that is available to users as a single pool, in particular for efficient distributed data fusion irrespective of data location.</p><p>In our talk we present technology, services, and governance of this unique intercontinental line-up of data centers. A live demo will show dist<br>ributed datacube fusion.</p><p> </p>

scholarly journals Spatio-Temporal Range Search using K-DSTH Indexing Structure

2019 ◽  
Vol 9 (1) ◽  
pp. 3791-3797
Keyword(s):  
Social Networking Sites ◽  
Research Work ◽  
Hash Table ◽  
Temporal Data ◽  
Indexing Structure ◽  
Efficient Storage ◽  
Photo Sharing ◽  
Spatio Temporal ◽  
Temporal Objects ◽  
Storage Structures

A large amount of data which includes spatial and temporal information related to different fields like geography, satellite, medical or multimedia is generated and collected at an extraordinary scale. Such data is produced by satellites, mobile devices, emerging applications like social networking sites, photo sharing sites and many more. As the whole world is aware of the importance of such spatio-temporal data, a great amount of research work is evolving around efficient storage structures and algorithms to handle a variety of spatio-temporal queries. In this paper, the authors are introducing a novel spatio-temporal indexing structure k-dStH which is an extension of k-dSTHash indexing structure. It uses master hash table, local hash table and B-tree additionally which are based on timestamp values. The researchers also introduce an algorithm k-dStHSpaTempRangeSrch based on the proposed indexing structure to find spatio-temporal objects in given spatial range at particular temporal value. The performance analysis shows that the algorithm proposed by the authors is far more efficient as compared to brute force technique of searching for the spatio-temporal objects.

Spatial-temporal data collection process models using mobile sinks

2019 ◽  
Vol 942 (12) ◽  
pp. 22-28
Author(s):  
A.V. Materuhin ◽  
V.V. Shakhov ◽  
O.D. Sokolova
Keyword(s):  
Energy Consumption ◽  
Operation Time ◽  
Point Of View ◽  
Process Models ◽  
Temporal Data ◽  
Network Nodes ◽  
Mobile Sinks ◽  
Autonomous Operation ◽  
Data Collection Process ◽  
Spatio Temporal

Optimization of energy consumption in geosensor networks is a very important factor in ensuring stability, since geosensors used for environmental monitoring have limited possibilities for recharging batteries. The article is a concise presentation of the research results in the area of increasing the energy consumption efficiency for the process of collecting spatio-temporal data with wireless geosensor networks. It is shown that in the currently used configurations of geosensor networks there is a predominant direction of the transmitted traffic, which leads to the fact that through the routing nodes that are close to the sinks, a much more traffic passes than through other network nodes. Thus, an imbalance of energy consumption arises in the network, which leads to a decrease in the autonomous operation time of the entire wireless geosensor networks. It is proposed to use the possible mobility of sinks as an optimization resource. A mathematical model for the analysis of the lifetime of a wireless geosensor network using mobile sinks is proposed. The model is analyzed from the point of view of optimization energy consumption by sensors. The proposed approach allows increasing the lifetime of wireless geosensor networks by optimizing the relocation of mobile sinks.

scholarly journals From spatio-temporal data to chronological networks

2019 ◽  
Author(s):  
Didier A. Vega-Oliveros ◽  
Moshé Cotacallapa ◽  
Leonardo N. Ferreira ◽  
Marcos G. Quiles ◽  
Liang Zhao ◽  
...  
Keyword(s):  
Temporal Data ◽  
Spatio Temporal
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