An adaptive plan-based approach to integrating semantic streams with remote RDF data

2016 ◽  
Vol 43 (6) ◽  
pp. 852-865 ◽  
Author(s):  
Sejin Chun ◽  
Jooik Jung ◽  
Seungmin Seo ◽  
Wonwoo Ro ◽  
Kyong-Ho Lee
Keyword(s):  
Large Scale ◽  
Streaming Data ◽  
High Rate ◽  
Execution Plan ◽  
Adaptive Process ◽  
Multiple Strategies ◽  
Static Data ◽  
Rdf Data ◽  
Description Framework ◽  
Remote Data

To satisfy a user’s complex requirements, Resource Description Framework (RDF) Stream Processing (RSP) systems envision the fusion of remote RDF data with semantic streams, using common data models to query semantic streams continuously. While streaming data are changing at a high rate and are pushed into RSP systems, the remote RDF data are retrieved from different remote sources. With the growth of SPARQL endpoints that provide access to remote RDF data, RSP systems can easily integrate the remote data with streams. Such integration provides new opportunities for mixing static (or quasi-static) data with streams on a large scale. However, the current RSP systems do not offer any optimisation for the integration. In this article, we present an adaptive plan-based approach to efficiently integrate sematic streams with the static data from a remote source. We create a query execution plan based on temporal constraints among constituent services for the timely acquisition of remote data. To predict the change of remote sources in real time, we propose an adaptive process of detecting a source update, forecasting the update in the future, deciding a new plan to obtain remote data and reacting to a new plan. We extend a SPARQL query with operators for describing the multiple strategies of the proposed adaptive process. Experimental results show that our approach is more efficient than the conventional RSP systems in distributed settings.

Characterising RDF data sets

2017 ◽  
Vol 44 (2) ◽  
pp. 203-229 ◽  
Author(s):  
Javier D Fernández ◽  
Miguel A Martínez-Prieto ◽  
Pablo de la Fuente Redondo ◽  
Claudio Gutiérrez
Keyword(s):  
Data Structures ◽  
Large Scale ◽  
Open Data ◽  
Structural Features ◽  
Data Sets ◽  
Data Set ◽  
Wide Range ◽  
Rdf Data ◽  
Description Framework ◽  
Resource Description

The publication of semantic web data, commonly represented in Resource Description Framework (RDF), has experienced outstanding growth over the last few years. Data from all fields of knowledge are shared publicly and interconnected in active initiatives such as Linked Open Data. However, despite the increasing availability of applications managing large-scale RDF information such as RDF stores and reasoning tools, little attention has been given to the structural features emerging in real-world RDF data. Our work addresses this issue by proposing specific metrics to characterise RDF data. We specifically focus on revealing the redundancy of each data set, as well as common structural patterns. We evaluate the proposed metrics on several data sets, which cover a wide range of designs and models. Our findings provide a basis for more efficient RDF data structures, indexes and compressors.

Towards Massive RDF Storage in NoSQL Databases

2019 ◽  
pp. 263-284 ◽  
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.

A Review of RDF Storage in NoSQL Databases

2016 ◽  
pp. 210-229 ◽  
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.

A Review of RDF Storage in NoSQL Databases

Big Data ◽  
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

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.

scholarly journals High-Rate Transition Metal-based Cathode Materials for Battery-Supercapacitor Hybrid Devices

2021 ◽  
Author(s):  
Cong Wang ◽  
Zehao Song ◽  
Pei Shi ◽  
Lin Lv ◽  
Houzhao Wan ◽  
...  
Keyword(s):  
Large Scale ◽  
Rapid Development ◽  
Electronic Devices ◽  
High Rate ◽  
Specific Power ◽  
Energy Storage Devices ◽  
Storage Devices ◽  
Electrochemical Devices ◽  
Hybrid Devices ◽  
Scale Grid

With the rapid development of portable electronic devices, electric vehicles and large-scale grid energy storage devices, it needs to reinforce specific energy and specific power of related electrochemical devices meeting...

scholarly journals Rapid evolution at the Drosophila telomere: transposable element dynamics at an intrinsically unstable locus

Genetics ◽  
2020 ◽  
Vol 217 (2) ◽  
Author(s):  
Michael P McGurk ◽  
Anne-Marie Dion-Côté ◽  
Daniel A Barbash
Keyword(s):  
Copy Number ◽  
Large Scale ◽  
Insertion Site ◽  
Rapid Evolution ◽  
Interspecific Variation ◽  
High Rate ◽  
Evolutionary Strategy ◽  
Control Mechanisms ◽  
Genetic Conflict ◽  
Number Variation

AbstractDrosophila telomeres have been maintained by three families of active transposable elements (TEs), HeT-A, TAHRE, and TART, collectively referred to as HTTs, for tens of millions of years, which contrasts with an unusually high degree of HTT interspecific variation. While the impacts of conflict and domestication are often invoked to explain HTT variation, the telomeres are unstable structures such that neutral mutational processes and evolutionary tradeoffs may also drive HTT evolution. We leveraged population genomic data to analyze nearly 10,000 HTT insertions in 85  Drosophila melanogaster genomes and compared their variation to other more typical TE families. We observe that occasional large-scale copy number expansions of both HTTs and other TE families occur, highlighting that the HTTs are, like their feral cousins, typically repressed but primed to take over given the opportunity. However, large expansions of HTTs are not caused by the runaway activity of any particular HTT subfamilies or even associated with telomere-specific TE activity, as might be expected if HTTs are in strong genetic conflict with their hosts. Rather than conflict, we instead suggest that distinctive aspects of HTT copy number variation and sequence diversity largely reflect telomere instability, with HTT insertions being lost at much higher rates than other TEs elsewhere in the genome. We extend previous observations that telomere deletions occur at a high rate, and surprisingly discover that more than one-third do not appear to have been healed with an HTT insertion. We also report that some HTT families may be preferentially activated by the erosion of whole telomeres, implying the existence of HTT-specific host control mechanisms. We further suggest that the persistent telomere localization of HTTs may reflect a highly successful evolutionary strategy that trades away a stable insertion site in order to have reduced impact on the host genome. We propose that HTT evolution is driven by multiple processes, with niche specialization and telomere instability being previously underappreciated and likely predominant.

ASSESSMENT OF LINUX' DATA PATH IMPLEMENTATIONS FOR DOWNLOAD AND STREAMING

2007 ◽  
Vol 17 (04) ◽  
pp. 465-481 ◽  
Author(s):  
PÅL HALVORSEN ◽  
TOM ANDERS DALSENG ◽  
CARSTEN GRIWODZ
Keyword(s):  
Operating Systems ◽  
Comprehensive Evaluation ◽  
Streaming Data ◽  
High Rate ◽  
Data Path ◽  
Power Budget ◽  
Data Movement ◽  
Technological Advances ◽  
Reduced Power Consumption ◽  
Streaming Systems

Distributed multimedia streaming systems are increasingly popular due to technological advances, and numerous streaming services are available today. On servers or proxy caches, there is a huge scaling challenge in supporting thousands of concurrent users that request delivery of high-rate, time-dependent data like audio and video, because this requires transfers of large amounts of data through several sub-systems within a streaming node. Unnecessary copy operations in the data path can therefore contribute significantly to the resource consumption of streaming operations. Despite previous research, off-the-shelf operating systems have only limited support for data paths that have been optimized for streaming. Additionally, system call overhead has grown with newer operating systems editions, adding to the cost of data movement. Frequently, it is argued that these issues can be ignored because of the continuing growth of CPU speeds. However, such an argument fails to take problems of modern streaming systems into account. The dissipation of heat generated by disks and high-end CPUs is a major problem of data centers, which would be alleviated if less power-hungry CPUs could be used. The power budget of mobile devices, which are increasingly used for streaming as well, is tight, and reduced power consumption an important issue. In this paper, we prove that these operations consume a large amount of resources, and we therefore revisit the data movement problem and provide a comprehensive evaluation of possible streaming data I/O paths in the Linux 2.6 kernel. We have implemented and evaluated several enhanced mechanisms and show how to provide support for more efficient memory usage and reduction of user/kernel space switches for content download and streaming applications. In particular, we are able to reduce the CPU usage by approximately 27% compared to the best approach without kernel modifications, by removing copy operations and system calls for a streaming scenario in which RTP headers must be added to stored data for sequence numbers and timing.

Neurofeedback for Opioid Dependent Patients in an Outpatient Setting: A Pilot Feasibility Study

2021 ◽  
Author(s):  
Helena A. Rempala ◽  
Justin A. Barterian
Keyword(s):  
Large Scale ◽  
The United States ◽  
Outpatient Setting ◽  
Opioid Use Disorder ◽  
High Rate ◽  
Treatment Center ◽  
Opioid Use ◽  
Treatment As Usual ◽  
Factors Affecting ◽  
Study Approach

Abstract Background: Neurofeedback (NF) has been described as “probably efficacious” when used in conjunction with other interventions for substance use disorders, including the most recent studies in population of individuals with opioid use disorder. Despite these promising outcomes, the seriousness of the opioid epidemic, and the high rate of relapse even with the most effective medication-assisted maintenance treatments NF continues to be an under-researched treatment modality. This article explores factors that affected the feasibility of adding Alpha/Theta Neurofeedback to treatment as usual for opioid dependence in an outpatient urban treatment center. The study strived to replicate previous research completed in Iran that found benefits of NF for opioid dependence.Methods: Out of approximately two dozen patients eligible for Alpha/Theta NF, about 60% (n=15) agreed to participate; however, only 2 participants completed treatment. The rates of enrollment in response to active treatment were monitored. Results: The 4 factors affecting feasibility were: 1) the time commitment required of participants, 2) ineffectiveness of standard incentives to promote participation, 3) delayed effects of training, and 4) the length and number of treatments required.Conclusion: The findings indicate a large scale study examining the use of NF for the treatment of opioid use disorder in the United States will likely be difficult to accomplish without modification to the traditional randomized control study approach and suggests challenges to the implementation of this treatment in an outpatient setting.

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