Three success stories about compact data structures

2020 ◽  
Vol 63 (11) ◽  
pp. 64-65
Author(s):  
Diego Arroyuelo ◽  
José Fuentes-Sepülveda ◽  
Diego Seco
Keyword(s):  
Data Structures ◽  
Success Stories ◽  
Compact Data Structures

When Edge Computing Meets Compact Data Structures

2021 ◽  
Author(s):  
Zheng Li ◽  
Diego Seco ◽  
Jose Fuentes-Sepulveda
Keyword(s):  
Data Structures ◽  
Edge Computing ◽  
Compact Data Structures

Compact Data Structures to Represent and Query Data Warehouses into Main Memory

2018 ◽  
Vol 16 (9) ◽  
pp. 2328-2335 ◽  
Author(s):  
Cristian Vallejos ◽  
Monica Caniupan ◽  
Gilberto Gutierrez
Keyword(s):  
Data Structures ◽  
Main Memory ◽  
Data Warehouses ◽  
Compact Data Structures

scholarly journals Tool for SPARQL Querying over Compact RDF Representations

2021 ◽  
Vol 7 (1) ◽  
pp. 33
Author(s):  
Delfina Ramos-Vidal ◽  
Guillermo de Bernardo
Keyword(s):  
Data Storage ◽  
Data Structures ◽  
Low Level ◽  
Level Data ◽  
Query Engine ◽  
Compact Data Structures ◽  
High Level

We present an architecture for the efficient storing and querying of large RDF datasets. Our approach seeks to store RDF datasets in very little space while offering complete SPARQL functionality. To achieve this, our proposal was built over HDT, an RDF serialization framework, and its interaction with the Jena query engine. We propose a set of modifications to this framework in order to incorporate a range of space-efficient compact data structures for data storage and access, while using high-level capabilities to answer more complicated SPARQL queries. As a result, our approach provides a standard mechanism for using low-level data structures in complicated query situations requiring SPARQL searches, which are typically not supported by current solutions.

scholarly journals A Fun Application of Compact Data Structures to Indexing Geographic Data

2010 ◽  
pp. 77-88 ◽  
Author(s):  
Nieves R. Brisaboa ◽  
Miguel R. Luaces ◽  
Gonzalo Navarro ◽  
Diego Seco
Keyword(s):  
Data Structures ◽  
Geographic Data ◽  
Compact Data Structures

Computing voxelised representations of macromolecular surfaces

2016 ◽  
Vol 32 (3) ◽  
pp. 407-432 ◽  
Author(s):  
Sebastian Daberdaku ◽  
Carlo Ferrari
Keyword(s):  
Data Structures ◽  
Region Growing ◽  
Molecular Surface ◽  
Large Molecules ◽  
Computational Costs ◽  
Compact Data Structures ◽  
Workload Distribution ◽  
Surface Representations ◽  
Macromolecular Surfaces ◽  
Balanced Workload

Voxel-based representations of surfaces have received a lot of interest in bioinformatics and computational biology as a simple and effective way of representing geometrical and physicochemical properties of proteins and other biomolecules. Processing such surfaces for large molecules can be challenging, as space-demanding data structures with associated high computational costs are required. In this paper, we present a methodology for the fast computation of voxelised macromolecular surface representations (namely the van der Waals, solvent-accessible and solvent-excluded surfaces). The proposed method implements a spatial slicing procedure on top of compact data structures to efficiently calculate the three molecular surface representations at high-resolutions, in parallel. The spatial slicing protocol ensures a balanced workload distribution and allows the computation of the solvent-excluded surface with minimal synchronisation and communication between processes. This is achieved by adapting a multi-step region-growing EDT algorithm. At each step, distance values are first calculated independently for every slice, then, a small portion of the borders’ information is exchanged between adjacent slices. Very little process communication is also required in the pocket detection procedure, where the algorithm distinguishes surface portions belonging to solvent-accessible pockets from cavities buried inside the molecule. Experimental results are presented to validate the proposed approach.

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