Skipnet-Octree Based Indexing Technique for Cloud Database Management System

2018 ◽  
Vol 13 (3) ◽  
pp. 1-13 ◽  
Author(s):  
Shweta Malhotra ◽  
Mohammad Najmud Doja ◽  
Bashir Alam ◽  
Mansaf Alam
Keyword(s):  
Query Processing ◽  
Double Layer ◽  
Database Management System ◽  
Multidimensional Data ◽  
Data Repositories ◽  
Cloud Data ◽  
Data Indexing ◽  
Indexing Structure ◽  
Inverted Indexing ◽  
Indexing Technique

This article describes how data indexing plays a very crucial role in query processing. Systems based on traditional indexes like B-tree, R-tree, Bitmap, inverted indexing techniques are not suitable for efficient query evaluation as these systems are based on simple key-value pair and used only for point queries. In cloud data repositories, point queries are not sufficient for query as a cloud consists of multidimensional data. For multidimensional query processing, many techniques have been developed. In this article, a dynamic double layer indexing structure with the help of a Skipnet overlay for global indexing and an Octree index technique for local indexing has been proposed. It has been concluded from the experiments that Skipnet-Octree performs better than the previous double-layer indexing technique for complex queries.

INDEXING FUZZY NUMERICAL DATA WITH A B+ TREE FOR FAST RETRIEVAL USING NECESSITY-MEASURED FLEXIBLE CONDITIONS

2009 ◽  
Vol 17 (supp01) ◽  
pp. 1-23 ◽  
Author(s):  
CARLOS D. BARRANCO ◽  
JESÚS R. CAMPAÑA ◽  
JUAN M. MEDINA
Keyword(s):  
Query Processing ◽  
Numerical Data ◽  
Experimental Results ◽  
Fuzzy Data ◽  
Hilbert Curve ◽  
Indexing Structure ◽  
Fuzzy Database ◽  
Indexing Technique ◽  
Current Systems

This paper proposes an indexing procedure for improving the performance of query processing on a fuzzy database. It focuses on the case when a necessity-measured atomic flexible condition is imposed on the values of a fuzzy numerical attribute. The proposal is to apply a classical indexing structure for numerical crisp data, a B +-tree combined with a Hilbert curve. The use of such a common indexing technique makes its incorporation into current systems straightforward. The efficiency of the proposal is compared with that of another indexing procedure for similar fuzzy data and flexible query types. Experimental results reveal that the performance of the proposed method is similar and more stable than that of its competitor.

scholarly journals A Hybrid Spatial Indexing Structure of Massive Point Cloud Based on Octree and 3D R*-Tree

2021 ◽  
Vol 11 (20) ◽  
pp. 9581
Author(s):  
Wei Wang ◽  
Yi Zhang ◽  
Genyu Ge ◽  
Qin Jiang ◽  
Yang Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Index Structure ◽  
Spatial Indexing ◽  
Cloud Data ◽  
Neighbor Search ◽  
Nearest Neighbor Searching ◽  
Indexing Structure ◽  
Balanced Tree

The spatial index structure is one of the most important research topics for organizing and managing massive 3D Point Cloud. As a point in Point Cloud consists of Cartesian coordinates (x,y,z), the common method to explore geometric information and features is nearest neighbor searching. An efficient spatial indexing structure directly affects the speed of the nearest neighbor search. Octree and kd-tree are the most used for Point Cloud data. However, Octree or KD-tree do not perform best in nearest neighbor searching. A highly balanced tree, 3D R*-tree is considered the most effective method so far. So, a hybrid spatial indexing structure is proposed based on Octree and 3D R*-tree. In this paper, we discussed how thresholds influence the performance of nearest neighbor searching and constructing the tree. Finally, an adaptive way method adopted to set thresholds. Furthermore, we obtained a better performance in tree construction and nearest neighbor searching than Octree and 3D R*-tree.

EPOS-Norway Portal

2021 ◽  
Author(s):  
Jan Michalek ◽  
Kuvvet Atakan ◽  
Christian Rønnevik ◽  
Helga Indrøy ◽  
Lars Ottemøller ◽  
...  
Keyword(s):  
Web Services ◽  
Visual Analysis ◽  
Earth Science ◽  
National Research Council ◽  
Geophysical Data ◽  
Multidimensional Data ◽  
Data Sets ◽  
Complex Data ◽  
Submarine Landslides ◽  
Data Repositories

<p>The European Plate Observing System (EPOS) is a European project about building a pan-European infrastructure for accessing solid Earth science data, governed now by EPOS ERIC (European Research Infrastructure Consortium). The EPOS-Norway project (EPOS-N; RCN-Infrastructure Programme - Project no. 245763) is a Norwegian project funded by National Research Council. The aim of the Norwegian EPOS e‑infrastructure is to integrate data from the seismological and geodetic networks, as well as the data from the geological and geophysical data repositories. Among the six EPOS-N project partners, four institutions are providing data – University of Bergen (UIB), - Norwegian Mapping Authority (NMA), Geological Survey of Norway (NGU) and NORSAR.</p><p>In this contribution, we present the EPOS-Norway Portal as an online, open access, interactive tool, allowing visual analysis of multidimensional data. It supports maps and 2D plots with linked visualizations. Currently access is provided to more than 300 datasets (18 web services, 288 map layers and 14 static datasets) from four subdomains of Earth science in Norway. New datasets are planned to be integrated in the future. EPOS-N Portal can access remote datasets via web services like FDSNWS for seismological data and OGC services for geological and geophysical data (e.g. WMS). Standalone datasets are available through preloaded data files. Users can also simply add another WMS server or upload their own dataset for visualization and comparison with other datasets. This portal provides unique way (first of its kind in Norway) for exploration of various geoscientific datasets in one common interface. One of the key aspects is quick simultaneous visual inspection of data from various disciplines and test of scientific or geohazard related hypothesis. One of such examples can be spatio-temporal correlation of earthquakes (1980 until now) with existing critical infrastructures (e.g. pipelines), geological structures, submarine landslides or unstable slopes.  </p><p>The EPOS-N Portal is implemented by adapting Enlighten-web, a server-client program developed by NORCE. Enlighten-web facilitates interactive visual analysis of large multidimensional data sets, and supports interactive mapping of millions of points. The Enlighten-web client runs inside a web browser. An important element in the Enlighten-web functionality is brushing and linking, which is useful for exploring complex data sets to discover correlations and interesting properties hidden in the data. The views are linked to each other, so that highlighting a subset in one view automatically leads to the corresponding subsets being highlighted in all other linked views.</p>

An Envisioned Approach for Modeling and Supporting User-Centric Query Activities on Data Warehouses

2013 ◽  
Vol 9 (2) ◽  
pp. 89-109 ◽  
Author(s):  
Marie-Aude Aufaure ◽  
Alfredo Cuzzocrea ◽  
Cécile Favre ◽  
Patrick Marcel ◽  
Rokia Missaoui
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Information Retrieval ◽  
Query Processing ◽  
Data Warehouse ◽  
Multidimensional Data ◽  
Data Warehouses ◽  
Cooperative Query Answering ◽  
User Centric ◽  
Use Of Knowledge

In this vision paper, the authors discuss models and techniques for integrating, processing and querying data, information and knowledge within data warehouses in a user-centric manner. The user-centric emphasis allows us to achieve a number of clear advantages with respect to classical data warehouse architectures, whose most relevant ones are the following: (i) a unified and meaningful representation of multidimensional data and knowledge patterns throughout the data warehouse layers (i.e., loading, storage, metadata, etc); (ii) advanced query mechanisms and guidance that are capable of extracting targeted information and knowledge by means of innovative information retrieval and data mining techniques. Following this main framework, the authors first outline the importance of knowledge representation and management in data warehouses, where knowledge is expressed by existing ontology or patterns discovered from data. Then, the authors propose a user-centric architecture for OLAP query processing, which is the typical applicative interface to data warehouse systems. Finally, the authors propose insights towards cooperative query answering that make use of knowledge management principles and exploit the peculiarities of data warehouses (e.g., multidimensionality, multi-resolution, and so forth).

Towards a Generic Security Framework for Cloud Data Management Environments

2012 ◽  
Vol 3 (1) ◽  
pp. 17-34 ◽  
Author(s):  
Alexandra Carpen-Amarie ◽  
Alexandru Costan ◽  
Catalin Leordeanu ◽  
Cristina Basescu ◽  
Gabriel Antoniu
Keyword(s):  
Data Management ◽  
Denial Of Service ◽  
Management Systems ◽  
Security Level ◽  
Security Framework ◽  
Data Repositories ◽  
Data Management Systems ◽  
Cloud Data ◽  
Management Platform ◽  
Cloud Data Management

Providing an adequate security level in Cloud Environments is currently an extremely active research area. More specifically, malicious behaviors targeting large-scale Cloud data repositories (e.g., Denial of Service attacks) may drastically degrade the overall performance of such systems and cannot be detected by typical authentication mechanisms. This article proposes a generic security management framework allowing providers of Cloud data management systems to define and enforce complex security policies. This security framework is designed to detect and stop a large array of attacks defined through an expressive policy description language and to be easily interfaced with various data management systems. The authors show that they can efficiently protect a data storage system by evaluating the security framework on top of the BlobSeer data management platform. The authors evaluate the benefits of preventing a DoS attack targeted towards BlobSeer through experiments performed on the Grid’5000 testbed.

scholarly journals A Simple Semantic-Based Data Storage Layout for Querying Point Clouds

2020 ◽  
Vol 9 (2) ◽  
pp. 72 ◽  
Author(s):  
Sami El-Mahgary ◽  
Juho-Pekka Virtanen ◽  
Hannu Hyyppä
Keyword(s):  
Data Storage ◽  
Point Cloud ◽  
Point Clouds ◽  
Database Management System ◽  
Programming Approach ◽  
Point Cloud Data ◽  
Data Layout ◽  
Cloud Data ◽  
Semantic Point ◽  
Relational Database Management

The importance of being able to separate the semantics from the actual (X,Y,Z) coordinates in a point cloud has been actively brought up in recent research. However, there is still no widely used or accepted data layout paradigm on how to efficiently store and manage such semantic point cloud data. In this paper, we present a simple data layout that makes use the semantics and that allows for quick queries. The underlying idea is especially suited for a programming approach (e.g., queries programmed via Python) but we also present an even simpler implementation of the underlying technique on a well known relational database management system (RDBMS), namely, PostgreSQL. The obtained query results suggest that the presented approach can be successfully used to handle point and range queries on large points clouds.

Optimizing Aggregate Query Processing in Cloud Data Warehouses

2014 ◽  
pp. 1-12 ◽  
Author(s):  
Swathi Kurunji ◽  
Tingjian Ge ◽  
Xinwen Fu ◽  
Benyuan Liu ◽  
Amrith Kumar ◽  
...  
Keyword(s):  
Query Processing ◽  
Data Warehouses ◽  
Cloud Data ◽  
Aggregate Query
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