Risk Spillover Network Structure Learning for Correlated Financial Assets: A Directed Acyclic Graph Approach

2021 ◽  
Author(s):  
Xiaokang Wang ◽  
Huiwen Wang ◽  
Zhichao Wang ◽  
Shan Lu ◽  
Ying Fan
Keyword(s):  
Network Structure ◽  
Directed Acyclic Graph ◽  
Structure Learning ◽  
Financial Assets ◽  
Acyclic Graph

scholarly journals Network causality structures among Bitcoin and other financial assets: A directed acyclic graph approach

2018 ◽  
Vol 70 ◽  
pp. 203-213 ◽  
Author(s):  
Qiang Ji ◽  
Elie Bouri ◽  
Rangan Gupta ◽  
David Roubaud
Keyword(s):  
Directed Acyclic Graph ◽  
Financial Assets ◽  
Acyclic Graph

scholarly journals IoT-enabled directed acyclic graph in spark cluster

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Jahwan Koo ◽  
Nawab Muhammad Faseeh Qureshi ◽  
Isma Farah Siddiqui ◽  
Asad Abbas ◽  
Ali Kashif Bashir
Keyword(s):  
Distributed Computing ◽  
Real Time ◽  
Directed Acyclic Graph ◽  
Random Access ◽  
Heterogeneous Data ◽  
The Body ◽  
Computing Environment ◽  
Time Data ◽  
Acyclic Graph ◽  
Sensory Data

Abstract Real-time data streaming fetches live sensory segments of the dataset in the heterogeneous distributed computing environment. This process assembles data chunks at a rapid encapsulation rate through a streaming technique that bundles sensor segments into multiple micro-batches and extracts into a repository, respectively. Recently, the acquisition process is enhanced with an additional feature of exchanging IoT devices’ dataset comprised of two components: (i) sensory data and (ii) metadata. The body of sensory data includes record information, and the metadata part consists of logs, heterogeneous events, and routing path tables to transmit micro-batch streams into the repository. Real-time acquisition procedure uses the Directed Acyclic Graph (DAG) to extract live query outcomes from in-place micro-batches through MapReduce stages and returns a result set. However, few bottlenecks affect the performance during the execution process, such as (i) homogeneous micro-batches formation only, (ii) complexity of dataset diversification, (iii) heterogeneous data tuples processing, and (iv) linear DAG workflow only. As a result, it produces huge processing latency and the additional cost of extracting event-enabled IoT datasets. Thus, the Spark cluster that processes Resilient Distributed Dataset (RDD) in a fast-pace using Random access memory (RAM) defies expected robustness in processing IoT streams in the distributed computing environment. This paper presents an IoT-enabled Directed Acyclic Graph (I-DAG) technique that labels micro-batches at the stage of building a stream event and arranges stream elements with event labels. In the next step, heterogeneous stream events are processed through the I-DAG workflow, which has non-linear DAG operation for extracting queries’ results in a Spark cluster. The performance evaluation shows that I-DAG resolves homogeneous IoT-enabled stream event issues and provides an effective stream event heterogeneous solution for IoT-enabled datasets in spark clusters.

Risk identification and modeling for blockchain-enabled container shipping

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Son Nguyen ◽  
Peggy Shu-Ling Chen ◽  
Yuquan Du
Keyword(s):  
Design Methodology ◽  
Directed Acyclic Graph ◽  
Risk Identification ◽  
Container Shipping ◽  
Content Type ◽  
Acyclic Graph ◽  
Operational Risks ◽  
Systems Security ◽  
Event Risk ◽  
Potential Risks

PurposeAlthough being considered for adoption by stakeholders in container shipping, application of blockchain is hindered by different factors. This paper investigates the potential operational risks of blockchain-integrated container shipping systems as one of such barriers.Design/methodology/approachLiterature review is employed as the method of risk identification. Scientific articles, special institutional reports and publications of blockchain solution providers were included in an inclusive qualitative analysis. A directed acyclic graph (DAG) was constructed and analyzed based on network topological metrics.FindingsTwenty-eight potential risks and 47 connections were identified in three groups of initiative, transitional and sequel. The DAG analysis results reflect a relatively well-connected network of identified hazardous events (HEs), suggesting the pervasiveness of information risks and various multiple-event risk scenarios. The criticality of the connected systems' security and information accuracy are also indicated.Originality/valueThis paper indicates the changes of container shipping operational risk in the process of blockchain integration by using updated data. It creates awareness of the emerging risks, provides their insights and establishes the basis for further research.

Multi-scale features for heartbeat classification using directed acyclic graph CNN

2018 ◽  
Vol 32 (7-8) ◽  
pp. 613-628 ◽  
Author(s):  
Zahra Golrizkhatami ◽  
Shahram Taheri ◽  
Adnan Acan
Keyword(s):  
Directed Acyclic Graph ◽  
Heartbeat Classification ◽  
Acyclic Graph ◽  
Multi Scale
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