Multidimensional Dominance Drawings and Their Applications

In a dominance drawing Γ of a directed acyclic graph (DAG) G, a vertex v is reachable from a vertex u if, and only if all the coordinates of v are greater than or equal to the coordinates of u in Γ. Dominance drawings of DAGs are very important in many areas of research. They combine the aspect of drawing a DAG on the grid with the fact that the transitive closure of the DAG is apparently obvious by the dominance relation between grid points associated with the vertices. The smallest number d for which a given DAG G has a d-dimensional dominance drawing is called dominance drawing dimension, and it is NP-hard to compute. In this paper, we present efficient algorithms for computing dominance drawings of G with a number of dimensions respecting theoretical bounds. We first describe a simple algorithm that shows how to compute a dominance drawing of G from its compressed transitive closure. Next, we describe a more complicated algorithm, which is based on the concept of modular decomposition of G, and obtaining dominance drawings with a lower number of dimensions. Finally, we consider the concept of weak dominance, a relaxed version of the dominance, and we discuss interesting experimental results.

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An Advanced Sensor Placement Strategy for Small Leaks Quantification Using Lean Graphs

Water ◽

10.3390/w12123439 ◽

2020 ◽

Vol 12 (12) ◽

pp. 3439

Author(s):

Ary Mazharuddin Shiddiqi ◽

Rachel Cardell-Oliver ◽

Amitava Datta

Keyword(s):

Directed Acyclic Graph ◽

Water Distribution ◽

Sensor Placement ◽

Distribution Networks ◽

Experimental Results ◽

Water Distribution Networks ◽

Acyclic Graph ◽

Quantification Method ◽

Sensor Failures

Small leaks in water distribution networks have been a major problem both economically and environmentally, as they go undetected for years. We model the signature of small leaks as a unique Directed Acyclic Graph, called the Lean Graph, to find the best places for k sensors for detecting and locating small leaks. We use the sensors to develop dictionaries that map each leak signature to its location. We quantify leaks by matching out-of-normal flows detected by sensors against records in the selected dictionaries. The most similar records of the dictionaries are used to quantify the leaks. Finally, we investigate how much our approach can tolerate corrupted data due to sensor failures by introducing a subspace voting based quantification method. We tested our method on water distribution networks of literature and simulate small leaks ranging from [0.1, 1.0] liter per second. Our experimental results prove that our sensor placement strategy can effectively place k sensors to quantify single and multiple small leaks and can tolerate corrupted data up to some range while maintaining the performance of leak quantification. These outcomes indicate that our approach could be applied in real water distribution networks to minimize the loss caused by small leaks.

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An Improved Directed Acyclic Graph Support Vector Machines Based on Inter-Cluster Separability Measure in Feature Space

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.229-231.2002 ◽

2012 ◽

Vol 229-231 ◽

pp. 2002-2006

Author(s):

Qing Ai ◽

Ji Zhao ◽

Yu Ping Qin

Keyword(s):

Support Vector Machine ◽

Support Vector Machines ◽

Directed Acyclic Graph ◽

Feature Space ◽

Experimental Results ◽

Support Vector ◽

Acyclic Graph ◽

Vector Machines ◽

The Matrix ◽

Cluster Separability

For disadvantage of the methods that are used to evaluate inter-cluster separability measure, a novel separability measure is proposed and applied to directed acyclic graph support vector machine. The distance between cluster centers and distribution of samples in feature space are both considered by the algorithm. Firstly, use hyper-sphere support vector machine to obtain minimal bounding hyper-sphere of each cluster, according to the radius and centers of minimal bounding hyper-spheres, introduce the concept of inter-cluster separability measure in feature space, get the matrix of inter-cluster separability measure according to the concept, finally construct the directed acyclic graph according to the matrix. The experimental results show that the algorithm has higher classification precision, comparing with old directed acyclic graph support vector machine.

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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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