Efficient data retrieval using corrugated search technique

2012 ◽  
Vol 1 (2) ◽  
pp. 108-114
Author(s):  
Vishnupriya V ◽  
Shameema Parveen A ◽  
Rithra R ◽  
Shanmugasundaram M ◽  
Manimegalai R
Keyword(s):  
Data Retrieval ◽  
Search Technique ◽  
Efficient Data

Querying Multidimensional Data

2003 ◽  
pp. 252-281
Author(s):  
Leonardo Tininini
Keyword(s):  
Algebraic Approach ◽  
Evaluation Process ◽  
Data Retrieval ◽  
Data Cube ◽  
Multidimensional Data ◽  
Integration Policy ◽  
Multidimensional Database ◽  
Efficient Data ◽  
Speed Up ◽  
Logical Data

A powerful and easy-to-use querying environment is certainly one of the most important components in a multidimensional database, and its effectiveness is influenced by many other aspects, both logical (data model, integration, policy of view materialization, etc.) and physical (multidimensional or relational storage, indexes, etc.). As is evident, multidimensional querying is often based on the metaphor of the data cube and on the concepts of facts, measures, and dimensions. In contrast to conventional transactional environments, multidimensional querying is often an exploratory process, performed by navigating along the dimensions and measures, increasing/decreasing the level of detail and focusing on specific subparts of the cube that appear to be “promising” for the required information. In this chapter we focus on the main languages proposed in the literature to express multidimensional queries, particularly those based on: (i) an algebraic approach, (ii) a declarative paradigm (calculus), and (iii) visual constructs and syntax. We analyze the problem of evaluation, i.e., the issues related to the efficient data retrieval and calculation, possibly (often necessarily) using some pre-computed data, a problem known in the literature as the problem of rewriting a query using views. We also illustrate the use of particular index structures to speed up the query evaluation process.

scholarly journals A Conceptual Model for Art Criticism

2019 ◽  
pp. 138-157
Author(s):  
Maria Giovanna Mancini ◽  
Luigi Sauro
Keyword(s):  
Detailed Analysis ◽  
Conceptual Model ◽  
Art Criticism ◽  
Data Retrieval ◽  
Conceptual Modelling ◽  
Related Data ◽  
Efficient Data ◽  
Cidoc Crm

In this work, we present a detailed analysis of the different acceptations and practices of art criticism. This investigation underpins a novel conceptual modelling that extends Cidoc CRM and has been specifically designed to semantically annotate art criticism-related data and documents in order to enhance in this context interoperability and more efficient data retrieval.

scholarly journals An Optimum Approach for Preprocessing of Web User Query

2018 ◽  
Vol 7 (1) ◽  
pp. 8
Author(s):  
Sunny Sharma ◽  
Sunita Sunita ◽  
Arjun Kumar ◽  
Vijay Rana
Keyword(s):  
Search Engines ◽  
Critical Role ◽  
Data Retrieval ◽  
Raw Data ◽  
Internet Users ◽  
User Query ◽  
Stop Word ◽  
Optimum Approach ◽  
Efficient Data ◽  
The Web

<span lang="EN-US">The emergence of the Web technology generated a massive amount of raw data by enabling Internet users to post their opinions, comments, and reviews on the web. To extract useful information from this raw data can be a very challenging task. Search engines play a critical role in these circumstances. User queries are becoming main issues for the search engines. Therefore a preprocessing operation is essential. In this paper, we present a framework for natural language preprocessing for efficient data retrieval and some of the required processing for effective retrieval such as elongated word handling, stop word removal, stemming, etc. This manuscript starts by building a manually annotated dataset and then takes the reader through the detailed steps of process. Experiments are conducted for special stages of this process to examine the accuracy of the system.</span>

scholarly journals Mapping Sensitive Vegetation Communities in Mining Eco-space using UAV-LiDAR

2021 ◽  
Author(s):  
Bikram Banerjee ◽  
Simit Raval
Keyword(s):  
Longwall Mining ◽  
Signal To Noise Ratio ◽  
Cost Effective ◽  
Data Retrieval ◽  
Visual Assessment ◽  
High Signal ◽  
Fine Scale ◽  
Complex Environments ◽  
Mining Areas ◽  
Efficient Data

Near earth sensing from unmanned aerial vehicles or UAVs has emerged as a potential approach for fine-scale environmental monitoring. These systems provide a cost-effective and repeatable means to acquire remotely sensed images in unprecedented spatial detail and high signal-to-noise ratio. It is becoming increasingly possible to obtain both physiochemical and structural insights of the environment using state-of-art light detection and ranging (LiDAR) sensors integrated onto UAVs. Monitoring of sensitive environments, such as swamp vegetation in longwall mining areas is important, yet challenging due to their inherent complexities. Current practices for monitoring these remote and difficult environments are primarily ground-based. This is partly due to an absent framework and challenges of using UAV-based sensor systems in monitoring such sensitive environments. This research addresses the related challenges in the development of a LiDAR system including a workflow for mapping and potentially monitoring highly heterogeneous and complex environments. This involves the amalgamation of several design components, which include hardware integration, calibration of sensors, mission planning, and designing of a processing chain to generate usable datasets. It also includes the creation of new methodologies and processing routines to establish a pipeline for efficient data retrieval and generation of usable products. The designed systems and methods were applied on a peat swamp environment to obtain accurate geo-spatialised LiDAR point cloud. Performance of the LiDAR data was tested against ground-based measurements on various aspects including visual assessment for generation LiDAR metrices maps, canopy height model, and fine-scale mapping.

scholarly journals Bovine Genome Database: new annotation tools for a new reference genome

2019 ◽  
Author(s):  
Md Shamimuzzaman ◽  
Justin J Le Tourneau ◽  
Deepak R Unni ◽  
Colin M Diesh ◽  
Deborah A Triant ◽  
...  
Keyword(s):  
Genome Assembly ◽  
Genome Annotation ◽  
Reference Genome ◽  
Gene Annotation ◽  
Bovine Genome ◽  
Data Retrieval ◽  
Genome Database ◽  
Ruminant Species ◽  
Search Tool ◽  
Efficient Data

Abstract The Bovine Genome Database (BGD) (http://bovinegenome.org) has been the key community bovine genomics database for more than a decade. To accommodate the increasing amount and complexity of bovine genomics data, BGD continues to advance its practices in data acquisition, curation, integration and efficient data retrieval. BGD provides tools for genome browsing (JBrowse), genome annotation (Apollo), data mining (BovineMine) and sequence database searching (BLAST). To augment the BGD genome annotation capabilities, we have developed a new Apollo plug-in, called the Locus-Specific Alternate Assembly (LSAA) tool, which enables users to identify and report potential genome assembly errors and structural variants. BGD now hosts both the newest bovine reference genome assembly, ARS-UCD1.2, as well as the previous reference genome, UMD3.1.1, with cross-genome navigation and queries supported in JBrowse and BovineMine, respectively. Other notable enhancements to BovineMine include the incorporation of genomes and gene annotation datasets for non-bovine ruminant species (goat and sheep), support for multiple assemblies per organism in the Regions Search tool, integration of additional ontologies and development of many new template queries. To better serve the research community, we continue to focus on improving existing tools, developing new tools, adding new datasets and encouraging researchers to use these resources.

Study of Dynamic Multikeyword Text Search Techniques over Encrypted Data in Cloud

2017 ◽  
Vol 7 (8) ◽  
pp. 326
Author(s):  
Ankita Puri ◽  
Naveen Kumari
Keyword(s):  
Data Retrieval ◽  
Modern Technology ◽  
Inner Product ◽  
Data Outsourcing ◽  
Keyword Query ◽  
Cloud Data ◽  
Data Services ◽  
Cloud Server ◽  
Efficient Data ◽  
Ranked Search

Day by Day ,with the advancement of modern technology over cloud computing motivating the data owners to outsource their data to the cloud server like Amazon, Microsoft, Azure etc .With the help of data outsourcing ,the organization can provide reliable data services to their user without any management of the overhead concern. Suppose, a large number of users that are on cloud and large number of documents on cloud, Its important for the service provider to allow multi-keyword query and provided the result that meet efficient data retrieval needs. In this paper, for the first time, we define and solve the challenging problem of privacy preserving multi-keyword ranked search over encrypted cloud data (MRSE), and establish a set of strict privacy requirements for such a secure cloud data utilization system to become a reality. Among various multi-keyword semantics, we choose the efficient principle of “coordinate matching”, i.e., as many matches as possible, to capture the similarity between search query and data documents, and further use “inner product similarity” to quantitatively formalize such principle for similarity measurement.

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