Optimization Mode Research of Weighted Undirected Graph

An algorithm is described for constructing a weighted undirected graph structure, based on the principle of directed acyclic graph, used the improved Floyd algorithm to improve the multi-node version of the evolution of information in order to release as an indicator of difference, to improve the reliability of multiple versions of data storage and multi-version storage and query efficiency. Theory and examples show that efficiency is improved using the optimization model in multi-node information system.

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Induced acyclic subgraphs in random digraphs: Improved bounds

Discrete Mathematics & Theoretical Computer Science ◽

10.46298/dmtcs.2795 ◽

2010 ◽

Vol DMTCS Proceedings vol. AM,... (Proceedings) ◽

Author(s):

Kunal Dutta ◽

C. R. Subramanian

Keyword(s):

Lower Bound ◽

Upper Bound ◽

Directed Acyclic Graph ◽

Undirected Graph ◽

Upper And Lower Bounds ◽

Slight Improvement ◽

Acyclic Orientations ◽

Acyclic Graph ◽

Random Digraphs ◽

International Audience

International audience Given a simple directed graph $D = (V,A)$, let the size of the largest induced directed acyclic graph $\textit{(dag)}$ be denoted by $mas(D)$. Let $D \in \mathcal{D}(n,p)$ be a $\textit{random}$ instance, obtained by choosing each of the $\binom{n}{2}$ possible undirected edges independently with probability $2p$ and then orienting each chosen edge independently in one of two possible directions with probabibility $1/2$. We obtain improved bounds on the range of concentration, upper and lower bounds of $mas(D)$. Our main result is that $mas(D) \geq \lfloor 2\log_q np - X \rfloor$ where $q = (1-p)^{-1}, X=W$ if $p \geq n^{-1/3+\epsilon}$ ($\epsilon > 0$ is any constant), $X=W/(\ln q)$ if $p \geq n^{-1/2}(\ln n)^2$, and $W$ is a suitably large constant. where we have an $O(\ln \ln np/\ln q)$ term instead of $W$. This improves the previously known lower bound with an $O(\ln \ln np/\ln q)$ term instead of $W$. We also obtain a slight improvement on the upper bound, using an upper bound on the number of acyclic orientations of an undirected graph. We also analyze a polynomial-time heuristic to find a large induced dag and show that it produces a solution whose size is at least $\log _q np + \Theta (\sqrt{\log_q np})$.

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COMPARISON OF BLOCKCHAIN TECHNOLOGY AND DIRECTED ACYCLIC GRAPH DURING DATA STORAGE AND PROCESSING IN A DISTRIBUTED REGISTRY

Bulletin of National Technical University KhPI Series System Analysis Control and Information Technologies ◽

10.20998/2079-0023.2020.01.18 ◽

2020 ◽

Vol 0 (1 (3)) ◽

pp. 106-116

Author(s):

Yaroslav Oleksandrovych Kliuchka ◽

Olexander Vitaliyovych Shmatko

Keyword(s):

Data Storage ◽

Directed Acyclic Graph ◽

Acyclic Graph ◽

Blockchain Technology

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IoT-enabled directed acyclic graph in spark cluster

Journal of Cloud Computing Advances Systems and Applications ◽

10.1186/s13677-020-00195-6 ◽

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.

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Risk identification and modeling for blockchain-enabled container shipping

International Journal of Physical Distribution & Logistics Management ◽

10.1108/ijpdlm-01-2020-0036 ◽

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.

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DBE2 – Management of experimental data for the VANTED system

Journal of Integrative Bioinformatics ◽

10.1515/jib-2011-162 ◽

2011 ◽

Vol 8 (2) ◽

pp. 85-94

Author(s):

Hendrik Mehlhorn ◽

Falk Schreiber

Keyword(s):

Experimental Data ◽

Information System ◽

Data Integration ◽

Data Management ◽

Data Storage ◽

Controlled Vocabulary ◽

Biological Experiment ◽

Experiment Data ◽

Measurement Units ◽

Persistent Data

Summary DBE2 is an information system for the management of biological experiment data from different data domains in a unified and simple way. It provides persistent data storage, worldwide accessibility of the data and the opportunity to load, save, modify, and annotate the data. It is seamlessly integrated in the VANTED system as an add-on, thereby extending the VANTED platform towards data management. DBE2 also utilizes controlled vocabulary from the Ontology Lookup Service to allow the management of terms such as substance names, species names, and measurement units, aiming at an eased data integration.

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