RDF Storage and Querying

2018 ◽  
pp. 415-433 ◽  
Author(s):  
Jingwei Cheng ◽  
Z. M. Ma ◽  
Qiang Tong
Keyword(s):  
Query Processing ◽  
Query Language ◽  
Web Resources ◽  
Storage And Retrieval ◽  
Language Constructs

RDF plays an important role in representing Web resources in a natural and flexible way. As the amount of RDF datasets increasingly growing, storing and querying theses data have attracted the attention of more and more researchers. In this chapter, we first make a review of approaches for query processing of RDF datasets. We categorize existing methods as two classes, those making use of RDBMS to implement the storage and retrieval, and those devising their own native storage schemas. They are called Relational RDF Stores and Native Stores respectively. Secondly, we survey some important extensions of SPARQL, standard query language for RDF, which extend the expressing power of SPARQL to allow more sophisticated language constructs that meet the needs from various application scenarios.

RDF Storage and Querying

2015 ◽  
pp. 460-478
Author(s):  
Jingwei Cheng ◽  
Z. M. Ma ◽  
Qiang Tong
Keyword(s):  
Query Processing ◽  
Query Language ◽  
Web Resources ◽  
Storage And Retrieval ◽  
Language Constructs

RDF plays an important role in representing Web resources in a natural and flexible way. As the amount of RDF datasets increasingly growing, storing and querying theses data have attracted the attention of more and more researchers. In this chapter, we first make a review of approaches for query processing of RDF datasets. We categorize existing methods as two classes, those making use of RDBMS to implement the storage and retrieval, and those devising their own native storage schemas. They are called Relational RDF Stores and Native Stores respectively. Secondly, we survey some important extensions of SPARQL, standard query language for RDF, which extend the expressing power of SPARQL to allow more sophisticated language constructs that meet the needs from various application scenarios.

DocBase

2013 ◽  
pp. 365-393
Author(s):  
Arijit Sengupta ◽  
Ramesh Venkataraman
Keyword(s):  
Query Processing ◽  
Query Language ◽  
Formal Model ◽  
Query Languages ◽  
Prototype System ◽  
Storage And Retrieval ◽  
Visual Query Formulation ◽  
Visual Query ◽  
Nested Relations ◽  
Entity Relationship

This chapter introduces a complete storage and retrieval architecture for a database environment for XML documents. DocBase, a prototype system based on this architecture, uses a flexible storage and indexing technique to allow highly expressive queries without the necessity of mapping documents to other database formats. DocBase is an integration of several techniques that include (i) a formal model called Heterogeneous Nested Relations (HNR), (ii) a conceptual model XER (Extensible Entity Relationship), (ii) formal query languages (Document Algebra and Calculus), (iii) a practical query language (Document SQL or DSQL), (iv) a visual query formulation method with QBT (Query By Templates), and (v) the DocBase query processing architecture. This paper focuses on the overall architecture of DocBase including implementation details, describes the details of the query-processing framework, and presents results from various performance tests. The paper summarizes experimental and usability analyses to demonstrate its feasibility as a general architecture for native as well as embedded document manipulation methods.

DocBase

2011 ◽  
Vol 22 (4) ◽  
pp. 30-56
Author(s):  
Arijit Sengupta ◽  
Ramesh Venkataraman
Keyword(s):  
Query Processing ◽  
Query Language ◽  
Formal Model ◽  
Query Languages ◽  
Prototype System ◽  
Storage And Retrieval ◽  
Visual Query Formulation ◽  
Visual Query ◽  
Nested Relations ◽  
Entity Relationship

This article introduces a complete storage and retrieval architecture for a database environment for XML documents. DocBase, a prototype system based on this architecture, uses a flexible storage and indexing technique to allow highly expressive queries without the necessity of mapping documents to other database formats. DocBase is an integration of several techniques that include (i) a formal model called Heterogeneous Nested Relations (HNR), (ii) a conceptual model XER (Extensible Entity Relationship), (ii) formal query languages (Document Algebra and Calculus), (iii) a practical query language (Document SQL or DSQL), (iv) a visual query formulation method with QBT (Query By Templates), and (v) the DocBase query processing architecture. This paper focuses on the overall architecture of DocBase including implementation details, describes the details of the query-processing framework, and presents results from various performance tests. The paper summarizes experimental and usability analyses to demonstrate its feasibility as a general architecture for native as well as embedded document manipulation methods.

Framework for GeoSpatial Query Processing by Integrating Cassandra With Hadoop

2019 ◽  
pp. 353-388
Author(s):  
S. Vasavi ◽  
Mallela Padma Priya ◽  
Anu A. Gokhale
Keyword(s):  
Response Time ◽  
Query Processing ◽  
Geospatial Data ◽  
Distributed Data ◽  
Nosql Databases ◽  
Storage And Retrieval ◽  
Query Response Time ◽  
Partitioning Algorithm ◽  
Hadoop Framework ◽  
Partitioning Technique

We are moving towards digitization and making all our devices, such as sensors and cameras, connected to internet, producing bigdata. This bigdata has variety of data and has paved the way to the emergence of NoSQL databases, like Cassandra, for achieving scalability and availability. Hadoop framework has been developed for storing and processing distributed data. In this chapter, the authors investigated the storage and retrieval of geospatial data by integrating Hadoop and Cassandra using prefix-based partitioning and Cassandra's default partitioning algorithm (i.e., Murmur3partitioner) techniques. Geohash value is generated, which acts as a partition key and also helps in effective search. Hence, the time taken for retrieving data is optimized. When users request spatial queries like finding nearest locations, searching in Cassandra database starts using both partitioning techniques. A comparison on query response time is made so as to verify which method is more effective. Results show the prefix-based partitioning technique is more efficient than Murmur3 partitioning technique.

Framework for GeoSpatial Query Processing by Integrating Cassandra With Hadoop

2018 ◽  
pp. 1-41
Author(s):  
S. Vasavi ◽  
Mallela Padma Priya ◽  
Anu A. Gokhale
Keyword(s):  
Response Time ◽  
Query Processing ◽  
Geospatial Data ◽  
Distributed Data ◽  
Nosql Databases ◽  
Storage And Retrieval ◽  
Query Response Time ◽  
Partitioning Algorithm ◽  
Hadoop Framework ◽  
Partitioning Technique

We are moving towards digitization and making all our devices, such as sensors and cameras, connected to internet, producing bigdata. This bigdata has variety of data and has paved the way to the emergence of NoSQL databases, like Cassandra, for achieving scalability and availability. Hadoop framework has been developed for storing and processing distributed data. In this chapter, the authors investigated the storage and retrieval of geospatial data by integrating Hadoop and Cassandra using prefix-based partitioning and Cassandra's default partitioning algorithm (i.e., Murmur3partitioner) techniques. Geohash value is generated, which acts as a partition key and also helps in effective search. Hence, the time taken for retrieving data is optimized. When users request spatial queries like finding nearest locations, searching in Cassandra database starts using both partitioning techniques. A comparison on query response time is made so as to verify which method is more effective. Results show the prefix-based partitioning technique is more efficient than Murmur3 partitioning technique.

Efficient Storage and Retrieval of XML Documents Using XQuery

2013 ◽  
Vol 779-780 ◽  
pp. 1685-1688 ◽  
Author(s):  
Yu Bin Chiu ◽  
Huei Huang Chen ◽  
Chu Yen Liu ◽  
Shih Chih Chen ◽  
Chung Wen Hung
Keyword(s):  
Query Language ◽  
Management Systems ◽  
Xml Databases ◽  
Storage And Retrieval ◽  
Critical Data ◽  
Xml Documents ◽  
Efficient Storage ◽  
Data Interchange ◽  
Relational Database Management ◽  
Relational Database Management Systems

XML has already been the standard of data interchange on the Internet. Nowadays, a large amount of data is represented in XML format. However, most of the critical data in businesses are still stored in relational database management systems. It is difficult to query XML databases because of its textual format. This research intends to tackle this problem, and we proposed a system to manage XML documents that could be queried by the query language XQuery. XML documents are stored in relational format and the XQuery expressions are translated into appropriate SQL queries. The results of the SQL queries are transformed into XML documents. Comparing with LegoDB System, our system reduces processing time to search a relation configuration and proposes a better translating and executing technique which is more efficiently.

Evaluation of a Model-driven Knowledge Storage and Retrieval IDE for Interactive HRI Systems

2019 ◽  
Vol 13 (02) ◽  
pp. 207-227 ◽  
Author(s):  
Norman Köster ◽  
Sebastian Wrede ◽  
Philipp Cimiano
Keyword(s):  
Data Storage ◽  
User Study ◽  
Query Language ◽  
Storage System ◽  
Human Robot Interaction ◽  
Storage And Retrieval ◽  
Model Driven ◽  
Long Term Storage ◽  
Model Driven Software Development

Efficient storage and querying of long-term human–robot interaction data requires application developers to have an in-depth understanding of the involved domains. Creating syntactically and semantically correct queries in the development process is an error prone task which can immensely impact the interaction experience of humans with robots and artificial agents. To address this issue, we present and evaluate a model-driven software development approach to create a long-term storage system to be used in highly interactive HRI scenarios. We created multiple domain-specific languages that allow us to model the domain and seamlessly embed its concepts into a query language. Along with corresponding model-to-model and model-to-text transformations, we generate a fully integrated workbench facilitating data storage and retrieval. It supports developers in the query design process and allows in-tool query execution without the need to have prior in-depth knowledge of the domain. We evaluated our work in an extensive user study and can show that the generated tool yields multiple advantages compared to the usual query design approach.

QUERYING METABOLISM UNDER DIFFERENT PHYSIOLOGICAL CONSTRAINTS

2010 ◽  
Vol 08 (02) ◽  
pp. 247-293 ◽  
Author(s):  
ALI CAKMAK ◽  
GULTEKIN OZSOYOGLU ◽  
RICHARD W. HANSON
Keyword(s):  
Query Processing ◽  
Physiological Condition ◽  
Query Language ◽  
Visual Representations ◽  
Living Cells ◽  
Physiological Conditions ◽  
Physiological Constraints ◽  
Environmental Perturbations ◽  
Wide Range

Metabolism is a representation of the biochemical principles that govern the production, consumption, degradation, and biosynthesis of metabolites in living cells. Organisms respond to changes in their physiological conditions or environmental perturbations (i.e. constraints) via cooperative implementation of such principles. Querying inner working principles of metabolism under different constraints provides invaluable insights for both researchers and educators. In this paper, we propose a metabolism query language (MQL) and discuss its query processing. MQL enables researchers to explore the behavior of the metabolism with a wide-range of predicates including dietary and physiological condition specifications. The query results of MQL are enriched with both textual and visual representations, and its query processing is completely tailored based on the underlying metabolic principles.

Intelligent Querying Techniques for Sensor Data Fusion

2010 ◽  
pp. 213-233 ◽  
Author(s):  
Shi-Kuo Chang ◽  
Gennaro Costagliola ◽  
Erland Jungert ◽  
Karin Camara
Keyword(s):  
Data Fusion ◽  
Query Processing ◽  
Query Language ◽  
Query Languages ◽  
Multimedia Data ◽  
Sensor Data ◽  
Multiple Sources ◽  
Sensor Data Fusion ◽  
Temporal Query Language ◽  
Processing Techniques

Sensor data fusion imposes a number of novel requirements on query languages and query processing techniques. A spatial/temporal query language called SQL has been proposed to support the retrieval of multimedia information from multiple sources and databases. This chapter investigates intelligent querying techniques including fusion techniques, multimedia data transformations, interactive progressive query building and SQL query processing techniques using sensor data fusion. The authors illustrate and discuss tasks and query patterns for information fusion, provide a number of examples of iterative queries and show the effectiveness of SQL in a command-action scenario.

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