scholarly journals Commentary - Methods to find all the edges on any of the shortest paths between two given nodes of a directed acyclic graph

2021 ◽  
Vol 7 ◽  
Author(s):  
Deepak Ahire ◽  
Omkar Jadhav
Keyword(s):  
Dynamic Programming ◽  
Graph Theory ◽  
Directed Acyclic Graph ◽  
Shortest Paths ◽  
The Other ◽  
Use Case ◽  
Counting Problems ◽  
Acyclic Graph ◽  
Questions And Answers ◽  
Community Portal

This article puts forth all the existing methods proposed by the various authors of the Stack Exchange community to find all the edges on any shortest path between two given nodes of a directed acyclic graph. For a directed acyclic graph with N number of nodes, an exponential number of paths are possible between any two given nodes and, thus, it is not feasible to compute every path and find the shortest ones in polynomial time to generate a set of all edges that contribute or make any of the shortest paths. The methods discussed in this article are not limited only to this specific use case, but have a much broader scope in graph theory, dynamic programming and counting problems. Generally, various other questions and answers, raised on the community portal having similar scope to those that the users specifically seek, do not receive sufficient hits and, hence, enough attention and votes for various reasons worth contemplating. Therefore, this article also aims to highlight the various scopes of the methods discussed in this article and acknowledge the efforts of the authors, moderators and contributors of the Stack Exchange community for their expertise and time to write precise answers and share their opinions and advice. Finally, it also appeals to all the other beneficiaries in the community to use their privileges responsibly and upvote the posted answers, if they helped solve their queries, as one upvote is free of cost.

scholarly journals Uncertainty-aware workflow migration among edge nodes based on blockchain

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Zhanyang Xu ◽  
Qingfan Geng ◽  
Hao Cao ◽  
Chuanjian Wang ◽  
Xihua Liu
Keyword(s):  
Genetic Algorithm ◽  
Graph Theory ◽  
Energy Consumption ◽  
Directed Acyclic Graph ◽  
Processing Time ◽  
Resource Provisioning ◽  
Workflow Scheduling ◽  
Acyclic Graph ◽  
Time And Energy ◽  
Workflow Tasks

Abstract Workflow is one of the most typical applications in distributed computing, which makes a variety of complex computing work orderly. However, assigning workflow tasks to nodes in the process of multi-node collaboration is still a challenge, because there are some unpredictable emergencies, i.e., uncertainty, in the process of workflow scheduling. The paper proposes a blockchain-powered resource provisioning (BPRP) method to solve the above problems. Technically, we use the directed acyclic graph in the graph theory to represent the workflow task and optimize the workflow scheduling strategy in the presence of uncertainty. The processing time and energy consumption of workflow tasks are also optimized by using non-dominated sorting genetic algorithm III (NSGA-III). Finally, we carry out experimental simulations to verify the effectiveness of the proposed method.

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.

An efficient route design for solid waste collection using graph theory and the algorithm of the traveling agent in dynamic programming

2021 ◽  
Vol 193 (5) ◽  
Author(s):  
Moisés Filiberto Mora Murillo ◽  
Walter Alfredo Mora Murillo ◽  
Luis Xavier Orbea Hinojosa ◽  
Arlys Michel Lastre Aleaga ◽  
Gabriel Estuardo Cevallos Uve ◽  
...  
Keyword(s):  
Dynamic Programming ◽  
Graph Theory ◽  
Solid Waste ◽  
Waste Collection ◽  
Solid Waste Collection ◽  
Route Design ◽  
Efficient Route

scholarly journals Rule based Classification of Graph Theory Concepts by Use Case Analysis

2016 ◽  
Vol 9 (48) ◽  
Author(s):  
Anuja Bokhare ◽  
P. S. Metkewar ◽  
R. S. Walse
Keyword(s):  
Graph Theory ◽  
Case Analysis ◽  
Use Case ◽  
Rule Based

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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