R2D: A FRAMEWORK FOR THE RELATIONAL TRANSFORMATION OF RDF DATA

The astronomical growth of the World Wide Web has resulted in data explosion that in turn has given rise to a need for data representation methodologies and standards to present required information in a rapid and automated manner. The Resource Description Framework (RDF) is one such standard proposed by W3C to address the above need. The ubiquitous acceptance of RDF on the Internet has resulted in the emergence of a new data storage paradigm, the RDF Graph Model, which, as with any data storage methodology, requires data modeling and visualization tools to aid with data management. This paper presents R2D (RDF-to-Database), a relational wrapper for RDF Data Stores, which aims to transform, at run-time, semi-structured RDF data into an equivalent domain-specific relational schema, thereby bridging the gap between RDF and RDBMS concepts and making the abundance of relational tools currently in the market available to the RDF Stores. The primary R2D functionalities and mapping constructs, the high-level system architecture, and deployment flowchart are presented along with algorithms and performance graphs for every stage of the transformation process and screenshots of a relational visualization tool using R2D as evidence of the feasibility of the proposed work.

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A Model for Performance-Intelligent Design Advisors

19th Design Automation Conference: Volume 1 — Mechanical System Dynamics; Concurrent and Robust Design; Design for Assembly and Manufacture; Genetic Algorithms in Design and Structural Optimization ◽

10.1115/detc1993-0314 ◽

1993 ◽

Author(s):

R. Grant Reed ◽

Robert H. Sturges

Keyword(s):

Intelligent Design ◽

Specific Design ◽

Design Information ◽

Domain Specific ◽

Computer Based ◽

Level Design ◽

And Performance ◽

High Level ◽

Level Performance ◽

Inputs And Outputs

Abstract We consider a design advisor to be performance-intelligent when its suggestions do not conflict with high level performance-related goals of the design under study. We address the problem of representing non-domain-specific design Information at a high level and describe coupling it to the inputs and outputs of design critics and their suggestion mechanisms. High level design Information represented in a function-based structure with linked allocations is shown to interact with a domain-specific design critic in three instances, viz.: allocation refinement, goal matching with a supported function, and performance-intelligent tradeoffs. Examples of manual and computer-based procedures are discussed.

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Towards Massive RDF Storage in NoSQL Databases

Advances in Data Mining and Database Management - Emerging Technologies and Applications in Data Processing and Management ◽

10.4018/978-1-5225-8446-9.ch013 ◽

2019 ◽

pp. 263-284 ◽

Cited By ~ 2

Author(s):

Zongmin Ma ◽

Li Yan

Keyword(s):

Data Storage ◽

Large Scale ◽

Future Research ◽

Nosql Databases ◽

Current State ◽

Data Store ◽

Rdf Data ◽

Description Framework ◽

Resource Description ◽

The Web

The resource description framework (RDF) is a model for representing information resources on the web. With the widespread acceptance of RDF as the de-facto standard recommended by W3C (World Wide Web Consortium) for the representation and exchange of information on the web, a huge amount of RDF data is being proliferated and becoming available. So, RDF data management is of increasing importance and has attracted attention in the database community as well as the Semantic Web community. Currently, much work has been devoted to propose different solutions to store large-scale RDF data efficiently. In order to manage massive RDF data, NoSQL (not only SQL) databases have been used for scalable RDF data store. This chapter focuses on using various NoSQL databases to store massive RDF data. An up-to-date overview of the current state of the art in RDF data storage in NoSQL databases is provided. The chapter aims at suggestions for future research.

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A Review of RDF Storage in NoSQL Databases

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Managing Big Data in Cloud Computing Environments ◽

10.4018/978-1-4666-9834-5.ch009 ◽

2016 ◽

pp. 210-229 ◽

Cited By ~ 2

Author(s):

Zongmin Ma ◽

Li Yan

Keyword(s):

Data Storage ◽

Large Scale ◽

Future Research ◽

Nosql Databases ◽

Current State ◽

Data Store ◽

Rdf Data ◽

Description Framework ◽

Resource Description ◽

The Web

The Resource Description Framework (RDF) is a model for representing information resources on the Web. With the widespread acceptance of RDF as the de-facto standard recommended by W3C (World Wide Web Consortium) for the representation and exchange of information on the Web, a huge amount of RDF data is being proliferated and becoming available. So RDF data management is of increasing importance, and has attracted attentions in the database community as well as the Semantic Web community. Currently much work has been devoted to propose different solutions to store large-scale RDF data efficiently. In order to manage massive RDF data, NoSQL (“not only SQL”) databases have been used for scalable RDF data store. This chapter focuses on using various NoSQL databases to store massive RDF data. An up-to-date overview of the current state of the art in RDF data storage in NoSQL databases is provided. The chapter aims at suggestions for future research.

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Human Cognitive Processes

Managing Cognitive Load in Adaptive Multimedia Learning ◽

10.4018/978-1-60566-048-6.ch001 ◽

2009 ◽

pp. 1-33

Author(s):

Slava Kalyuga

Keyword(s):

Working Memory ◽

Cognitive Architecture ◽

Human Cognition ◽

Long Term Memory ◽

Domain Specific ◽

Knowledge Based ◽

Domain Specific Knowledge ◽

And Performance ◽

Memory Knowledge ◽

High Level

One of the major components of our cognitive architecture, working memory, becomes overloaded if more than a few chunks of information are processed simultaneously. For example, we all experience this cognitive overload when trying to keep in memory an unfamiliar telephone number or add two four-digit numbers in the absence of a pen and paper. Similar in nature processing limitations of working memory represent a major factor influencing the effectiveness of human learning and performance, particularly in complex environments that require concurrent performance of multiple tasks. The learner prior domain-specific knowledge structures and associated levels of expertise are considered as means of reducing these limitations and guiding high-level knowledge-based cognitive activities. One of the most important results of studies in human cognition is that the available knowledge is a single most significant learner cognitive characteristic that influences learning and cognitive performance. Understanding the key role of long-term memory knowledge base in our cognition is important to the successful management of cognitive load in multimedia learning.

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High-End Storage

Integrated Information and Computing Systems for Natural, Spatial, and Social Sciences ◽

10.4018/978-1-4666-2190-9.ch007 ◽

2012 ◽

pp. 132-152

Author(s):

Isabel Schwerdtfeger

Keyword(s):

Real Time ◽

Data Storage ◽

High Performance ◽

Storage System ◽

Real Time Processing ◽

Storage Solutions ◽

Future Demands ◽

And Performance ◽

Extreme Scale ◽

High Level

This chapter discusses the challenges high-end storage solutions will have with future demands. Due to heavy end-user demands for real-time processing of data access, this need must be addressed by high-end storage solutions. But what type of high-end storage solutions address this need and are suitable to ensure high performance write and retrieval of data in real-time from high- end storage infrastructures, including read and write access from digital archives? For this reason, this chapter reviews a few disk and tape solutions as well as combined disk- and tape storage solutions. The review on the different storage solutions does not focus on compliance of data storage management, but on available commercial high-end systems, addressing scalability and performance requirements both for online storage and archives. High level requirements aid in identifying high-end storage system features and support Extreme Scale infrastructures for the amount of data that high-end storage systems will need to manage in future.

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Towards Interactive Analytics over RDF Graphs

Algorithms ◽

10.3390/a14020034 ◽

2021 ◽

Vol 14 (2) ◽

pp. 34 ◽

Cited By ~ 1

Author(s):

Maria-Evangelia Papadaki ◽

Nicolas Spyratos ◽

Yannis Tzitzikas

Keyword(s):

Query Language ◽

Proposed Model ◽

Aggregate Queries ◽

Rdf Data ◽

Description Framework ◽

Rdf Graphs ◽

High Level ◽

Resource Description ◽

Functional Algebra ◽

Continuous Accumulation

The continuous accumulation of multi-dimensional data and the development of Semantic Web and Linked Data published in the Resource Description Framework (RDF) bring new requirements for data analytics tools. Such tools should take into account the special features of RDF graphs, exploit the semantics of RDF and support flexible aggregate queries. In this paper, we present an approach for applying analytics to RDF data based on a high-level functional query language, called HIFUN. According to that language, each analytical query is considered to be a well-formed expression of a functional algebra and its definition is independent of the nature and structure of the data. In this paper, we investigate how HIFUN can be used for easing the formulation of analytic queries over RDF data. We detail the applicability of HIFUN over RDF, as well as the transformations of data that may be required, we introduce the translation rules of HIFUN queries to SPARQL and we describe a first implementation of the proposed model.

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DATA MODELING FOR OPERATION AND MAINTENANCE OF UTILITY NETWORKS: IMPLEMENTATION AND TESTING

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-vi-4-w1-2020-69-2020 ◽

2020 ◽

Vol VI-4/W1-2020 ◽

pp. 69-76

Author(s):

F. Fossatti ◽

G. Agugiaro ◽

L. olde Scholtenhuis ◽

A. Dorée

Keyword(s):

Asset Management ◽

Information Needs ◽

Management Practices ◽

Data Representation ◽

Domain Ontology ◽

National Standards ◽

Domain Specific ◽

Future Data ◽

Utility Network ◽

And Performance

Abstract. The organisational data models that support the information needs of utility network managers are proprietary and domain-specific, while the emerging national standards in this field often lack lifecycle data representation capabilities. However, multiple types of utility networks can be comprehensively represented with the free and open-source Utility Network Application Domain Extension (ADE) of the international standard CityGML. The Operation & Maintenance (O&M) Domain Ontology is a proposed extended version of the Utility Network ADE that allows for consistent and comprehensive processing, storage and exchange of O&M-related utility network data. So far, this ontology has not yet been implemented in a spatial-relational database. Consequently, the support it offers during routine utility asset management tasks has remained untested. This paper, therefore, tests the support of the O&M domain ontology for asset management and proposes a database implementation of this data model. To this end, it models and loads two utility networks from the campus of the University of Twente, the Netherlands. It tests the ontology’s support for asset management by simulating a street reconstruction project and retrieving necessary project information in relation to a utility’s (a) maintenance history and performance, and (b) site conditions and valve locations. Results show that the implemented model supports projects with rapid, comprehensive, and consistent information about semantic details of utilities. Such data needs yet to be collected and registered systematically to enable future data-driven asset management practices.

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A Review of RDF Storage in NoSQL Databases

Big Data ◽

10.4018/978-1-4666-9840-6.ch005 ◽

2016 ◽

pp. 85-104

Author(s):

Zongmin Ma ◽

Li Yan

Keyword(s):

Data Storage ◽

Large Scale ◽

Future Research ◽

Nosql Databases ◽

Current State ◽

Data Store ◽

Rdf Data ◽

Description Framework ◽

Resource Description ◽

The Web

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Storing massive Resource Description Framework (RDF) data: a survey

The Knowledge Engineering Review ◽

10.1017/s0269888916000217 ◽

2016 ◽

Vol 31 (4) ◽

pp. 391-413 ◽

Cited By ~ 21

Author(s):

Zongmin Ma ◽

Miriam A. M. Capretz ◽

Li Yan

Keyword(s):

Data Storage ◽

Resource Description Framework ◽

Relational Databases ◽

Query Language ◽

Current State ◽

Rdf Data ◽

Description Framework ◽

Resource Description ◽

Flexible Model ◽

The Web

AbstractThe Resource Description Framework (RDF) is a flexible model for representing information about resources on the Web. As a W3C (World Wide Web Consortium) Recommendation, RDF has rapidly gained popularity. With the widespread acceptance of RDF on the Web and in the enterprise, a huge amount of RDF data is being proliferated and becoming available. Efficient and scalable management of RDF data is therefore of increasing importance. RDF data management has attracted attention in the database and Semantic Web communities. Much work has been devoted to proposing different solutions to store RDF data efficiently. This paper focusses on using relational databases and NoSQL (for ‘not only SQL (Structured Query Language)’) databases to store massive RDF data. A full up-to-date overview of the current state of the art in RDF data storage is provided in the paper.

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The design and Performance of the Atlas Inner Detector Trigger in High Pileup Collisions at 13 Tev at the Large Hadron Collider

EPJ Web of Conferences ◽

10.1051/epjconf/201921401051 ◽

2019 ◽

Vol 214 ◽

pp. 01051

Author(s):

Julie Kirk

Keyword(s):

Data Storage ◽

Hadron Collider ◽

Level Trigger ◽

Physics Program ◽

Wide Range ◽

High Interaction ◽

Vital Component ◽

And Performance ◽

High Level ◽

Selection Of

The design and performance of the ATLAS Inner Detector (ID) trigger algorithms running online on the High Level Trigger (HLT) processor farm for 13 TeV LHC collision data with high pileup are discussed. The HLT ID tracking is a vital component in all physics signatures in the ATLAS trigger for the precise selection of the rare or interesting events necessary for physics analysis without overwhelming the offline data storage in terms of both size and rate. To cope with the high interaction rates expected in the 13 TeV LHC collisions, the ID trigger was redesigned during the 2013-15 long shutdown. The performance of the ID trigger in Run 2 data from 13 TeV LHC collisions has been excellent and exceeded expectations, even at the very high interaction multiplicities observed at the end of data-taking in 2017. The detailed efficiencies and resolutions of the ID trigger in a wide range of physics signatures are presented for the Run 2 data. The superb performance of the ID trigger algorithms in these extreme pileup conditions demonstrates how the ID tracking continues to lie at the heart of the trigger performance to enable the ATLAS physics program, and will continue to do so in the future.

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