RDF data retrieval in structured format using aggregate function and keyword search in MashQL

2013 ◽  
Author(s):  
S. Medhe ◽  
D.A. Phalke
Keyword(s):  
Keyword Search ◽  
Data Retrieval ◽  
Aggregate Function ◽  
Rdf Data

scholarly journals A Linear and Monotonic Strategy to Keyword Search over RDF Data

2013 ◽  
pp. 338-353 ◽  
Author(s):  
Roberto De Virgilio ◽  
Antonio Maccioni ◽  
Paolo Cappellari
Keyword(s):  
Keyword Search ◽  
Rdf Data

Privacy-Preserving Outsourced Similarity Search

2014 ◽  
Vol 25 (3) ◽  
pp. 48-71 ◽  
Author(s):  
Stepan Kozak ◽  
David Novak ◽  
Pavel Zezula
Keyword(s):  
Similarity Search ◽  
Data Privacy ◽  
System Analysis ◽  
Keyword Search ◽  
Evaluation Criteria ◽  
Similarity Index ◽  
Data Retrieval ◽  
Privacy Preserving ◽  
Scientific Data ◽  
Sensitive Data

The general trend in data management is to outsource data to 3rd party systems that would provide data retrieval as a service. This approach naturally brings privacy concerns about the (potentially sensitive) data. Recently, quite extensive research has been done on privacy-preserving outsourcing of traditional exact-match and keyword search. However, not much attention has been paid to outsourcing of similarity search, which is essential in content-based retrieval in current multimedia, sensor or scientific data. In this paper, the authors propose a scheme of outsourcing similarity search. They define evaluation criteria for these systems with an emphasis on usability, privacy and efficiency in real applications. These criteria can be used as a general guideline for a practical system analysis and we use them to survey and mutually compare existing approaches. As the main result, the authors propose a novel dynamic similarity index EM-Index that works for an arbitrary metric space and ensures data privacy and thus is suitable for search systems outsourced for example in a cloud environment. In comparison with other approaches, the index is fully dynamic (update operations are efficient) and its aim is to transfer as much load from clients to the server as possible.

scholarly journals Enhanced Effective and Privacy Preserving Multi Keyword Search over Encrypted Data in Cloud Storage Using Blowfish Algorithm

2021 ◽  
Vol 12 (2) ◽  
pp. 2845-2853
Author(s):  
Ch. Chakradhara Rao, Et. al.
Keyword(s):  
Keyword Search ◽  
Data Retrieval ◽  
Specific Information ◽  
Authentication Code ◽  
Privacy And Security ◽  
Online Data ◽  
Comparison Results ◽  
Algorithm Comparison ◽  
Cloud Server ◽  
User Data

The data owner administers their data to the public cloud due to regulatory. Even this data encoded once it easily transmitted to the cloud. In order to ensure the privacy and security, cloud subscribers need a very different type of online data. This specific information must be germane to the recipient's query. The user to inspect the query too in the cloud with umpteen keywords can accomplish this. With the intention to defend the privacy of data, confidential data encoded before subcontracting by the metadata owner, attempting to make the traditional and efficient plaintext keyword search tactic pointless. Therefore, it is crucial to explore a secure data search service for encrypted user data. Due to the growing of massive number of digital users and legal documents in the cloud, umpteen keywords forced in the search request and documents returned in the order of their relevance to these keywords.Cloud assistance end-user demands cloud information by various paternoster inquiry, whichlabelled as umpteenkeywordstratified exploration over scrambled information. In the cloud server, all the client inquiries exchanged. Server looks through the significant matter by utilizing the harmonic equivalency and sends the applicable outcomes to the client. Information got from cloud server is in the scrambled configuration. To get to control to the client,Information proprietor furnishes with key for unscrambling of information. Message Authentication Code(MAC) calculation is utilized to check and confirm trustworthiness of information. Thusly this paper depicts the method of giving security to redistribute information on cloud and checking the trustworthiness of information using blowfish algorithm. Comparison results shows the improvement in efficiency of Data Retrieval and Time efficiency.

scholarly journals RDF keyword search using multiple indexes

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1861-1873 ◽  
Author(s):  
Xiaoqing Lin ◽  
Fu Zhang ◽  
Danling Wang ◽  
Jingwei Cheng
Keyword(s):  
Dynamic Programming ◽  
Keyword Search ◽  
Dynamic Programming Algorithm ◽  
Programming Algorithm ◽  
Path Index ◽  
Shortest Distance ◽  
Data Graph ◽  
Rdf Data ◽  
Entity Relationship ◽  
Entity Relationships

Since SPARQL has been the standard language for querying RDF data, keyword search based on keywords-to-SPARQL translation attracts more intention. However, existing keyword search based on keywords-to-SPARQL translation have limitations that the schema used for keyword-to-SPARQL translation is incomplete so that wrong or incomplete answers are returned and advantages of indexes are not fully taken. To address the issues, an inter-entity relationship summary (ER-summary) is constructed by distilling all the inter-entity relationships of RDF data graph. On ER-summary, we draw circles around each vertex with a given radius r and in the circles we build the shortest property path index (SP-index), the shortest distance index (SD-index) and the r-neighborhoods index by using dynamic programming algorithm. Rather than searching for top-k subgraphs connecting all the keywords centered directly as most existing methods do, we use these indexes to translate keyword queries into SPARQL queries to realize exchanging space for time. Extensive experiments show that our approach is efficient and effective.

Web Service Discovery Using Bio-Inspired Holistic Matching Based Linked Data Clustering Model for RDF Data

2019 ◽  
Vol 12 ◽  
Author(s):  
Manish Kumar Mehrotra ◽  
Suvendu Kanungo
Keyword(s):  
Semantic Web ◽  
Web Service ◽  
Data Clustering ◽  
Linked Data ◽  
Service Discovery ◽  
Data Retrieval ◽  
Data Representation ◽  
Web Service Discovery ◽  
Clustering Model ◽  
Rdf Data

: Resource description framework (RDF) is the de-facto standard language model for semantic data representation on Semantic Web. Designing an efficient management of RDF data with huge volume and efficient querying techniques are the primary research areas in semantic web. So far, several RDF management methods have been offered with data storage designs and query processing algorithms for data retrieval. We propose a Bio-inspired Holistic Matching based Linked Data Clustering (BHM-LDC) which works based on RDF data storing, clustering the linked data and web service discovery. Initially the BHM-LDC algorithm store the RDF dataset as graph based linked data. Then, an Integrated Holistic Entity Matching based Distributed Genetic Algorithm (IHEM-DGA) is proposed to cluster the linked data. Finally, modified sub-graph matching based Web Service Discovery Algorithm uses the clustered triples to find the best web services. The performance of the proposed web service discovery approach is established by business RDF dataset.

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