The Experts below are selected from a list of 98466 Experts worldwide ranked by ideXlab platform
Shafiq Ahmad - One of the best experts on this subject based on the ideXlab platform.
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big Data Analytics in industrial iot using a concentric computing model
IEEE Communications Magazine, 2018Co-Authors: Muhammad Habib Ur Rehman, E Ahmed, Ibrar Yaqoob, Ibrahim Abaker Targio Hashem, Muhammad Imran, Shafiq AhmadAbstract:The unprecedented proliferation of miniaturized sensors and intelligent communication, computing, and control technologies have paved the way for the development of the Industrial Internet of Things. The IIoT incorporates machine learning and massively parallel distributed systems such as clouds, clusters, and grids for big Data storage, processing, and Analytics. In IIoT, end devices continuously generate and transmit Data streams, resulting in increased network traffic between device-cloud communication. Moreover, it increases in-network Data transmissions. requiring additional efforts for big Data processing, management, and Analytics. To cope with these engendered issues, this article first introduces a novel concentric computing model (CCM) paradigm composed of sensing systems, outer and inner gateway processors, and central processors (outer and inner) for the deployment of big Data Analytics applications in IIoT. Second, we investigate, highlight, and report recent research efforts directed at the IIoT paradigm with respect to big Data Analytics. Third, we identify and discuss indispensable challenges that remain to be addressed for employing CCM in the IIoT paradigm. Lastly, we provide several future research directions (e.g., real-time Data Analytics, Data integration, transmission of meaningful Data, edge Analytics, real-time fusion of streaming Data, and security and privacy).
Shane Dawson - One of the best experts on this subject based on the ideXlab platform.
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loop a learning Analytics tool to provide teachers with useful Data visualisations
Proceedings of the 32nd Annual Conference of the Australasian Society for Computers in Learning and Tertiary Education (ASCILITE 2015), 2015Co-Authors: Linda Corrin, Dragan Gasevic, Aneesha Bakharia, Paula De Barba, Gregor Kennedy, Shane Dawson, Lori Lockyer, David J Williams, Scott CopelandAbstract:One of the great promises of learning Analytics is the ability of digital systems to generate meaningful Data about students’ learning interactions that can be returned to teachers. If provided in appropriate and timely ways, such Data could be used by teachers to inform their current and future teaching practice. In this paper we showcase the learning Analytics tool, Loop, which has been developed as part of an Australian Government Office of Learning and Teaching project. The project aimed to develop ways to deliver learning Analytics Data to academics in a meaningful way to support the enhancement of teaching and learning practice. In this paper elements of the tool will be described. The paper concludes with an outline of the next steps for the project including the evaluation of the effectiveness of the tool.
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numbers are not enough why e learning Analytics failed to inform an institutional strategic plan
Educational Technology & Society, 2012Co-Authors: Leah P Macfadyen, Shane DawsonAbstract:Learning Analytics offers higher education valuable insights that can inform strategic decision-making regarding resource allocation for educational excellence. Research demonstrates that learning management systems (LMSs) can increase student sense of community, support learning communities and enhance student engagement and success, and LMSs have therefore become core enterprise component in many universities. We were invited to undertake a current state analysis of enterprise LMS use in a large research-intensive university, to provide Data to inform and guide an LMS review and strategic planning process. Using a new e-learning Analytics platform, combined with Data visualization and participant observation, we prepared a detailed snapshot of current LMS use patterns and trends and their relationship to student learning outcomes. This paper presents selected Data from this "current state analysis" and comments on what it reveals about the comparative effectiveness of this institution's LMS integration in the service of learning and teaching. More critically, it discusses the reality that the institutional planning process was nonetheless dominated by technical concerns, and made little use of the intelligence revealed by the Analytics process. To explain this phenomenon we consider theories of change management and resistance to innovation, and argue that to have meaningful impact, learning Analytics proponents must also delve into the socio-technical sphere to ensure that learning Analytics Data are presented to those involved in strategic institutional planning in ways that have the power to motivate organizational adoption and cultural change.
Gillian L Schauer - One of the best experts on this subject based on the ideXlab platform.
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results of a feasibility and acceptability trial of an online smoking cessation program targeting young adult nondaily smokers
Journal of Environmental and Public Health, 2012Co-Authors: Carla J Berg, Gillian L SchauerAbstract:Despite increases in nondaily smoking among young adults, no prior research has aimed to develop and test an intervention targeting this group. Thus, we aimed to develop and test the feasibility, acceptability, and potential effectiveness of an online intervention targeting college student nondaily smokers. We conducted a one-arm feasibility and acceptability trial of a four-week online intervention with weekly contacts among 31 college student nondaily smokers. We conducted assessments at baseline (B), end of treatment (EOT), and six-week followup (FU). We maintained a 100% retention rate over the 10-week period. Google Analytics Data indicated positive utilization results, and 71.0% were satisfied with the program. There were increases (P < .001) in the number of people refraining from smoking for the past 30 days and reducing their smoking from B to EOT and to FU, with additional individuals reporting being quit despite recent smoking. Participants also increased in their perceptions of how bothersome secondhand smoke is to others (P < .05); however, no other attitudinal variables were altered. Thus, this intervention demonstrated feasibility, acceptability, and potential effectiveness among college-aged nondaily smokers. Additional research is needed to understand how nondaily smokers define cessation, improve measures for cessation, and examine theoretical constructs related to smoking among this population.
Ankur Chattopadhyay - One of the best experts on this subject based on the ideXlab platform.
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a middle school module for introducing Data mining big Data ethics and privacy using rapidminer and a hollywood theme
Technical Symposium on Computer Science Education, 2018Co-Authors: Amber Dryer, Nicole Walia, Ankur ChattopadhyayAbstract:Today's organizations, including online businesses, use the art of Data-driven decision-making i.e. business-intelligence (BI) to benefit from all the Data out in the open. Given the current market demand for BI skill-sets, including the knowledge of different sources and tools for Data-collection plus processing, today's youth need a basic understanding of Data-driven intelligence, and an awareness of big-Data related ethics and privacy. However, there has been limited research and development work towards designing an effective educational module in this regard at the K-12 level. We intend to address this particular limitation by presenting a uniquely engaging middle-school learning module based upon a combination of useful topics, like Data-mining, predictive-Analytics, Data-visualization, big-Data, ethics and privacy, using the free RapidMiner software-tool. The novelty of our module lies in the use of a GUI-based visual hands-on platform (RapidMiner), a Hollywood movie-theme based educational activity, as well as an added focus on big-Data ethics and privacy, and its conceptual mapping to the NSA-GenCyber security-first principles. We discuss and analyze the survey Data obtained from over hundred participants through several offerings of our module as an educational workshop through our Google-IgniteCS and NSA-GenCyber programs. The collected learning-Analytics Data indicate that our module can become a simple yet effective means for introducing Data-mining, big-Data, ethical and privacy issues, and GenCyber security-first principles at the middle-school level. Our results show prospects of motivating middle-school participants towards further learning of topics in Data-science, Data-ethics and Data-security, which is necessary today in a variety of professions.
Andries Smith - One of the best experts on this subject based on the ideXlab platform.
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the semantic knowledge graph a compact auto generated model for real time traversal and ranking of any relationship within a domain
arXiv: Information Retrieval, 2016Co-Authors: Trey Grainger, Khalifeh Aljadda, Mohammed Korayem, Andries SmithAbstract:This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from underlying corpus statistics. As a result, any combination of nodes can have edges to any other nodes materialize and be scored to reveal latent relationships between the nodes. This provides numerous benefits: the knowledge graph can be built automatically from a real-world corpus of Data, new nodes - along with their combined edges - can be instantly materialized from any arbitrary combination of preexisting nodes (using set operations), and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph. Such a system has widespread applications in areas as diverse as knowledge modeling and reasoning, natural language processing, anomaly detection, Data cleansing, semantic search, Analytics, Data classification, root cause analysis, and recommendations systems. The main contribution of this paper is the introduction of a novel system - the Semantic Knowledge Graph - which is able to dynamically discover and score interesting relationships between any arbitrary combination of entities (words, phrases, or extracted concepts) through dynamically materializing nodes and edges from a compact graphical representation built automatically from a corpus of Data representative of a knowledge domain.
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the semantic knowledge graph a compact auto generated model for real time traversal and ranking of any relationship within a domain
IEEE International Conference on Data Science and Advanced Analytics, 2016Co-Authors: Trey Grainger, Khalifeh Aljadda, Mohammed Korayem, Andries SmithAbstract:This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from underlying corpus statistics. As a result, any combination of nodes can have edges to any other nodes materialize and be scored to reveal latent relationships between the nodes. This provides numerous benefits: the knowledge graph can be built automatically from a real-world corpus of Data, new nodes - along with their combined edges - can be instantly materialized from any arbitrary combination of preexisting nodes (using set operations), and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph. Such a system has widespread applications in areas as diverse as knowledge modeling and reasoning, natural language processing, anomaly detection, Data cleansing, semantic search, Analytics, Data classification, root cause analysis, and recommendations systems. The main contribution of this paper is the introduction of a novel system - the Semantic Knowledge Graph - which is able to dynamically discover and score interesting relationships between any arbitrary combination of entities (words, phrases, or extracted concepts) through dynamically materializing nodes and edges from a compact graphical representation built automatically from a corpus of Data representative of a knowledge domain. The source code for our Semantic Knowledge Graph implementation is being published along with this paper to facilitate further research and extensions of this work.