Ranked Paths Research Articles

GEViTRec: Data Reconnaissance Through Recommendation Using a Domain-Specific Visualization Prevalence Design Space.

Genomic Epidemiology (genEpi) is a branch of public health that uses many different data types including tabular, network, genomic, and geographic, to identify and contain outbreaks of deadly diseases. Due to the volume and variety of data, it is challenging for genEpi domain experts to conduct data reconnaissance; that is, have an overview of the data they have and make assessments toward its quality, completeness, and suitability. We present an algorithm for data reconnaissance through automatic visualization recommendation, GEViTRec. Our approach handles a broad variety of dataset types and automatically generates visually coherent combinations of charts, in contrast to existing systems that primarily focus on singleton visual encodings of tabular datasets. We automatically detect linkages across multiple input datasets by analyzing non-numeric attribute fields, creating a data source graph within which we analyze and rank paths. For each high-ranking path, we specify chart combinations with positional and color alignments between shared fields, using a gradual binding approach to transform initial partial specifications of singleton charts to complete specifications that are aligned and oriented consistently. A novel aspect of our approach is its combination of domain-agnostic elements with domain-specific information that is captured through a domain-specific visualization prevalence design space. Our implementation is applied to both synthetic data and real Ebola outbreak data. We compare GEViTRec's output to what previous visualization recommendation systems would generate, and to manually crafted visualizations used by practitioners. We conducted formative evaluations with ten genEpi experts to assess the relevance and interpretability of our results. Code, Data, and Study Materials Availability: https://github.com/amcrisan/GEVitRec.

A new fog-based routing strategy (FBRS) for vehicular ad-hoc networks

Vehicular ad-hoc network (VANET) plays a significant role in future intelligent transportation systems. The main objective of vehicular ad hoc networks (VANETs) is to improve driver safety and traffic efficiency. Many researchers proposed different schemes to improve communication efficiency. It is quite challenging where vehicles’ speed, Direction, and density of neighbors on the move are not consistent. Although several routing protocols have been introduced to manage data exchange among vehicles in VANETS, they still suffer from many drawbacks such as lost packets or time penalties. This paper introduced a new Fog Based Routing Strategy, which constructs a reliable system of adaptive, stable, and efficient routing networks. FBRS consists of two main phases: System Setup Phase (SSP) and System Operation Phase (SOP). SSP creates a cluster network, collects its nodes’ data, mining routes between them, and ranking paths using Dijkstra’s algorithm into a simplified table. Although, SOP generates a reliable route between the request of any two nodes for a communication channel and maintains the route against any simultaneous crashes. Recent VANET routing protocols have been compared against FBRS. Experimental results have proven the outperforming of the proposed algorithm against recent routing protocols in terms of packet delivery ratio and routing overhead.

Unraveling energy–water nexus paths in urban agglomeration: A case study of Beijing–Tianjin–Hebei

Energy and water are closely intertwined within economic sectors in urban systems. The direct and indirect linkages between them in economic systems, termed the energy–water nexus, have been widely studied from production and consumption perspectives. However, the step-by-step linkages from initial production to final consumption remain unclear, particularly the indirect linkages. In this paper, we develop a multiregional energy–water nexus path model based on multiregional input–output analysis and structural path analysis. The results show that the top 50 ranking paths of water-related energy induced by rural household, urban household, and government consumption account for 84%, 82%, and 90%, respectively, of total flows, whereas the corresponding figures for energy-related water are 57%, 58% and 76%, respectively. The proportion of the top 50 paths within total paths is much lower for energy-related water than is the case for water-related energy. Sectoral flows within Beijing–Tianjin–Hebei account for 87% of the total, whereas cross-regional flows only account for 13%. By comparing the energy–water nexus from production, consumption, and supply chain perspectives, we aim to identify the critical, yet often overlooked, energy–water nexus paths and transmission sectors to enhance coordinated energy–water management in urban systems.

Product sustainable design for carbon footprint during product life cycle

Product low carbon design plays a significant part in reducing carbon emissions during the product life cycle and achieving sustainable development. Aiming at the uncertainty of the carbon footprint caused by the unknown decision of product material solution, this paper proposes a low carbon design approach that uses the multigraph and colour marking to solve the problem of one edge assigned with multiple weights and the constraint problem of vertex combination in the decision space of product low carbon design. After a weighted edge-coloured graph (WECG) is proposed for low carbon design, the multi-stage product low carbon design model for product life cycle is discussed in detail. An algorithm of searching the alternative set of optimal design is also put forward in detail, including to create the directed WECG, to define the complete path, to find the ideal solution set using breadth-first search, to judge the ideal solution set by colouring the graph to obtain the feasible paths, and to rank paths in the ideal solution set in ascending order. The low carbon design of a robot is given as an example to demonstrate the methodology.

Path-Sensitive Oracle Data Selection via Static Analysis

A test oracle is a procedure that is used during testing to determine whether software behaves correctly or not. One of most important tasks for a test oracle is to choose oracle data (the set of variables monitored during testing) to observe. However, most literature on test oracles has focused either on formal specification generation or on automated test oracle construction, whereas little work exists for supporting oracle data selection. In this paper, we present a path-sensitive approach, PSODS (path-sensitive oracle data selection), to automatically select oracle data for use by expected value oracles. PSODS ranks paths according to the possibility that potential faults may exist in them, and the ranked paths help testers determine which oracle data should be considered first. To select oracle data for each path, we introduce quantity and quality analysis of oracle data, which use static analysis to estimate oracle data for their substitution capability and fault-detection capability. Quantity analysis can reduce the number of oracle data. Quality analysis can rank oracle data based on their fault-detection capability. By using quantity and quality analysis, PSODS reduces the cost of oracle construction and improves fault-detection efficiency and effectiveness. We have implemented our approach and applied it to a real-world project. The experimental results show that PSODS is efficient in helping testers construct test oracles. Moreover, the oracle datasets produced by our approach are more effective and efficient than output-only oracles at detecting faults.

Context-Aware Path Ranking in Road Networks

Ranking paths becomes an increasingly important functionality in many transportation services, where multiple paths connecting a source-destination pair are offered to drivers. We study ranking such paths under specific contexts. More specifically, we model ranking as a regression problem where we assign a ranking score to each path with the help of historical trajectories. To solve the regression problem, we first propose an effective training data enriching method to obtain a compact and diversified set of training paths using historical trajectories. Next, we propose a multi-task learning framework that considers features representing both candidate paths and contexts. Specifically, a road network embedding is proposed to embed paths into feature vectors by considering both road network topology and spatial properties. By modeling different departure times as a temporal graph, graph embedding is used to embed departure times. The objective function not only considers the discrepancies on ranking scores but also the reconstruction errors of the spatial properties of the paths, which in turn improves the final ranking estimation. Empirical studies on a substantial trajectory data set offer insight into the designed properties of the proposed framework and indicating that it is effective and practical in real world settings.

Developing Efficient Implementations of Shortest Paths and Page Rank Algorithms for NEC SX-Aurora TSUBASA Architecture

The main goal of this paper is to demonstrate that the newest generation of NEC SX-Aurora TSUBASA architecture can perform large-scale graph processing extremely efficiently. This paper proposes approaches, which can be used for the development of high-performance vector-oriented implementations of page rank and shortest paths algorithms, including vectorised graph storage format, efficient vector-friendly graph traversals, optimised cache-aware memory accesses and efficient load-balancing. The developed implementations are optimised according to the most important features and properties of SX-Aurora architecture, which allows them achieve up to 15 times better performance compared to the optimised Intel Skylake parallel implementations and up to 5 times better performance compared to NVGRAPH library implementations for Pascal GPU architecture.

Reconstruction and signal propagation analysis of the Syk signaling network in breast cancer cells.

The ability to build in-depth cell signaling networks from vast experimental data is a key objective of computational biology. The spleen tyrosine kinase (Syk) protein, a well-characterized key player in immune cell signaling, was surprisingly first shown by our group to exhibit an onco-suppressive function in mammary epithelial cells and corroborated by many other studies, but the molecular mechanisms of this function remain largely unsolved. Based on existing proteomic data, we report here the generation of an interaction-based network of signaling pathways controlled by Syk in breast cancer cells. Pathway enrichment of the Syk targets previously identified by quantitative phospho-proteomics indicated that Syk is engaged in cell adhesion, motility, growth and death. Using the components and interactions of these pathways, we bootstrapped the reconstruction of a comprehensive network covering Syk signaling in breast cancer cells. To generate in silico hypotheses on Syk signaling propagation, we developed a method allowing to rank paths between Syk and its targets. We first annotated the network according to experimental datasets. We then combined shortest path computation with random walk processes to estimate the importance of individual interactions and selected biologically relevant pathways in the network. Molecular and cell biology experiments allowed to distinguish candidate mechanisms that underlie the impact of Syk on the regulation of cortactin and ezrin, both involved in actin-mediated cell adhesion and motility. The Syk network was further completed with the results of our biological validation experiments. The resulting Syk signaling sub-networks can be explored via an online visualization platform.

CAECP and CRPD: Classification by Aggregating Essential Contrast Patterns, and Contrast Ranked Path Diagrams

In this paper, we propose a class predictive model for categorical data; this model uses the contrast patterns (CPs) that characteristically appear in a specific class. In a previous study, Morita and Nishiguchi [(2013). Classification model using contrast patterns. In the 15th International Conference on Enterprise Information Systems, Vol. 1, pp. 336–341] proposed a classification model using contrast patterns (CACP). CACP has been applied to practical data and successfully used to construct effective models [Morita, H, Y Shirai and Y Nakamoto (2013). Analysis method for flash marketing incorporating diversification on purchasing. In Annual Conference of Japan Society for Management Information 2013 Autumn, pp. 329–332 (in Japanese); Nishiguchi, M (2014). Classification model using contrast patterns and GRASP. Journal of Information Assurance & Security, 9(5), 235–243]. In their research, we can see that a large number of CPs are required to construct a high-performance model that can be applied to real business data. In terms of knowledge management, the model must provide highly accurate predictions, and offer high readability for their implementations. Such models provide a significant amount of knowledge. To find such knowledge, it is important to select a necessary subset of CPs and to construct a simple model, while maintaining the performance of the model. Further, we propose a visualisation method called Contrast Ranked Path Diagram (CRPD) that allows us to interpret the relationships among the CPs. In computational experiments, we initially apply our method to well-known benchmark problems. From the results, we show that our proposed model outperforms existing methods in terms of the necessary number of CPs and prediction accuracy. Subsequently, we apply our method to actual data from a Japanese retailer, and show the usefulness of the data for business applications.

Ranking paths in statistical energy analysis models with non-deterministic loss factors

Finding the contributions of transmission paths in statistical energy analysis (SEA) models has become an established valuable tool to detect and remedy vibro-acoustic problems. Paths are identified in SEA according to Craik׳s definition and recently, very efficient methods have been derived to rank them in the framework of graph theory. However, up to date classification schemes have only considered the mean values of loss factors for path comparison, their variance being ignored. This can result in significant errors in the final results. In this work it is proposed to address this problem by defining stochastic biparametric SEA graphs whose edges are assigned both, mean and variance values. Paths between subsystems are then compared according to a proposed cost function that accounts for the stochastic nature of loss factors. For an efficacious ranking of paths, the stochastic SEA graph is converted to an extended deterministic SEA graph where fast classification deterministic algorithms can be applied. The importance of nonneglecting the influence of the variance in path ranking is illustrated by means of some academic numerical examples.

NetPathMiner: R/Bioconductor package for network path mining through gene expression.

NetPathMiner is a general framework for mining, from genome-scale networks, paths that are related to specific experimental conditions. NetPathMiner interfaces with various input formats including KGML, SBML and BioPAX files and allows for manipulation of networks in three different forms: metabolic, reaction and gene representations. NetPathMiner ranks the obtained paths and applies Markov model-based clustering and classification methods to the ranked paths for easy interpretation. NetPathMiner also provides static and interactive visualizations of networks and paths to aid manual investigation. The package is available through Bioconductor and from Github at http://github.com/ahmohamed/NetPathMiner.

Ranking paths in stochastic time-dependent networks

In this paper we address optimal routing problems in networks where travel times are both stochastic and time-dependent. In these networks, the best route choice is not necessarily a path, but rather a time-adaptive strategy that assigns successors to nodes as a function of time. Nevertheless, in some particular cases an origin–destination path must be chosen a priori, since time-adaptive choices are not allowed. Unfortunately, finding the a priori shortest path is an NP-hard problem.In this paper, we propose a solution method for the a priori shortest path problem, and we show that it can be easily extended to the ranking of the first K shortest paths. Our method exploits the solution of the time-adaptive routing problem as a relaxation of the a priori problem. Computational results are presented showing that, under realistic distributions of travel times and costs, our solution methods are effective and robust.

Probabilistic inference and ranking of gene regulatory pathways as a shortest-path problem

BackgroundSince the advent of microarray technology, numerous methods have been devised to infer gene regulatory relationships from gene expression data. Many approaches that infer entire regulatory networks. This produces results that are rich in information and yet so complex that they are often of limited usefulness for researchers. One alternative unit of regulatory interactions is a linear path between genes. Linear paths are more comprehensible than networks and still contain important information. Such paths can be extracted from inferred regulatory networks or inferred directly. Since criteria for inferring networks generally differs from criteria for inferring paths, indirect and direct inference of paths may achieve different results.ResultsThis paper explores a strategy to infer linear pathways by converting the path inference problem into a shortest-path problem. The edge weights used are the negative log-transformed probabilities of directness derived from the posterior joint distributions of pairwise mutual information between gene expression levels. Directness is inferred using the data processing inequality. The method was designed with two goals. One is to achieve better accuracy in path inference than extraction of paths from inferred networks. The other is to facilitate priorization of interactions for laboratory validation. A method is proposed for achieving this by ranking paths according to the joint probability of directness of each path's edges. The algorithm is evaluated using simulated expression data and is compared to extraction of shortest paths from networks inferred by two alternative methods, ARACNe and a minimum spanning tree algorithm.ConclusionsDirect path inference appears to achieve accuracy competitive with that obtained by extracting paths from networks inferred by the other methods. Preliminary exploration of the use of joint edge probabilities to rank paths is largely inconclusive. Suggestions for a better framework for such comparisons are discussed.

How to allocate review tasks for robust ranking

In the process of reviewing and ranking projects by a group of reviewers, the allocation of the subset of projects to each reviewer has major impact on the robustness of the outcome ranking. We address here this problem where each reviewer is assigned, out of the list of all projects, a subset of up to k projects. Each individual reviewer then ranks and compares all pairs of k projects. The k-allocation problem is to determine an allocation of up to k projects to each reviewer, that lie within the expertise set of the reviewer, so that the resulting union of reviewed projects has certain desirable properties. The k-complete problem is a k-allocation with the property that all pairs of projects have been compared by at least one reviewer. A k-complete allocation is desirable as otherwise there may be projects that were not compared by any reviewer, leading to possible adverse properties in the outcome ranking. When a k-complete allocation cannot be achieved, one might settle for other properties. One basic requirement is that each pair of projects is comparable via a ranking path which is a sequence of pairwise rankings of projects implying a comparison of all pairs on the path. A k-allocation with a ranking path between each pair is the connectivity-k-aloc. Since the robustness of relative comparisons deteriorates with increased length of the ranking path, another goal is that between each pair of projects there will be at least one ranking path that has at most two hops or q hops for fixed values of q. An alternative means for increasing the robustness of the ranking is to use a k-allocation with at least p disjoint ranking paths between each pair. We model all these problems as graph problems. We demonstrate that the connectivity-k-aloc problem is polynomially solvable, using matroid intersection; we prove that the k-complete problem is NP-hard unless k = 2; and we provide approximation algorithms for a related optimization problem. All other variants are shown to be NP-complete for all values of k ≥ 2.