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29 results for “Causal analysis”
Figure 2. Causal loop diagram for the problem situation-An Efficient Expert System Generator for Qualitative Feed-Back Loop Analysis
<p>The problem situation could be represented in the following form of a causal loop diagram<br> as shown in Figure 2.</p>
Dataset Artifact for paper "Root Cause Analysis for Microservice System based on Causal Inference: How Far Are We?"
<p>Artifacts for the paper titled <strong><em>Root Cause Analysis for Microservice System based on Causal Inference: How Far Are We?</em></strong>.</p> <p>This artifact repository contains 9 compressed folders, as follows: </p> <table> <tbody> <tr> <td><strong>ID</strong></td> <td><strong>File Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>1</td> <td>syn_circa.zip</td> <td>CIRCA10, and CIRCA50 datasets for Causal Discovery</td> </tr> <tr> <td>2</td> <td>syn_rcd.zip</td> <td>RCD10, and RCD50 datasets for Causal Discovery</td> </tr> <tr> <td>3</td> <td>syn_causil.zip</td> <td>CausIL10, and CausIL50 datasets for Causal Discovery</td> </tr> <tr> <td>4</td> <td>rca_circa.zip</td> <td>CIRCA10, and CIRCA50 datasets for RCA</td> </tr> <tr> <td>5</td> <td>rca_rcd.zip</td> <td>RCD10, and RCD50 datasets for RCA</td> </tr> <tr> <td>6</td> <td>online-boutique.zip</td> <td>Online Boutique dataset for RCA</td> </tr> <tr> <td>7</td> <td>sock-shop-1.zip</td> <td>Sock Shop 1 dataset for RCA</td> </tr> <tr> <td>8</td> <td>sock-shop-2.zip</td> <td>Sock Shop 2 dataset for RCA</td> </tr> <tr> <td>9</td> <td>train-ticket.zip</td> <td>Train Ticket dataset for RCA</td> </tr> </tbody> </table> <p>Each zip file contains the generated/collected data from the corresponding data generator or microservice benchmark systems (e.g., online-boutique.zip contains metrics data collected from the Online Boutique system). </p> <p><strong>Details about the generation of our datasets</strong></p> <p><em>1. Synthetic datasets</em></p> <p>We use three different synthetic data generators from three previous RCA studies [15, 25, 28] to create the synthetic datasets: CIRCA, RCD, and CausIL data generators. Their mechanisms are as follows:<br><br>1. CIRCA datagenerator [28] generates a random causal directed acyclic graph (DAG) based on a given number of nodes and edges. <span>From this DAG, time series data for each node is generated using a </span><span>vector auto-regression (VAR) model. A fault is injected into a node </span><span>by altering the noise term in the VAR model for two timestamps. <br></span><span><br>2. RCD data generator [25] uses the pyAgrum package [3] to generate </span><span>a random DAG based on a given number of nodes, subsequently </span><span>generating discrete time series data for each node, with values ranging from 0 to 5. A fault is introduced into a node by changing its </span><span>conditional probability distribution.<br><br>3. CausIL data generator [15] generates causal graphs and time series data that simulate </span><span>the behavior of microservice systems. It first constructs a DAG of </span><span>services and metrics based on domain knowledge, then generates </span><span>metric data for each node of the DAG using regressors trained on </span><span>real metrics data. Unlike the CIRCA and RCD data generators, the </span><span>CausIL data generator does not have the capability to inject faults.<br><br></span>To create our synthetic datasets, we first generate 10 DAGs whose nodes range from 10 to 50 for each of the synthetic data generators. Next, we generate fault-free datasets using these DAGs with different seedings, resulting in 100 cases for the CIRCA and RCD generators and 10 cases for the CausIL generator. We then create faulty datasets by introducing ten faults into each DAG and generating the corresponding faulty data, yielding 100 cases for the CIRCA and RCD data generators. The fault-free datasets (e.g. `syn_rcd`, `syn_circa`) are used to evaluate causal discovery methods, while the faulty datasets (e.g. `rca_rcd`, `rca_circa`) are used to assess RCA methods. </p> <p><em>2. Data collected from benchmark microservice systems </em></p> <p>We deploy three popular benchmark microservice systems: Sock Shop [6], Online Boutique [4], and Train Ticket [8], on a four-node Kubernetes cluster hosted by AWS. Next, we use the Istio service mesh [2] with Prometheus [5] and cAdvisor [1] to monitor and collect resource-level and service-level metrics of all services, as in previous works [ 25 , 39, 59 ]. To generate traffic, we use the load generators provided by these systems and customise them to explore all services with 100 to 200 users concurrently. We then introduce five common faults (CPU hog, memory leak, disk IO stress, network delay, and packet loss) into five different services within each system. Finally, we collect metrics data before and after the fault injection operation. An overview of our setup is presented in the Figure below.</p> <p></p> <p><strong>Code</strong></p> <p>The code to reproduce the experimental results in the paper is available at <a href="https://github.com/phamquiluan/RCAEval">https://github.com/phamquiluan/RCAEval</a>.</p> <p><strong>References</strong></p> <p>As in our paper.</p>
Data from: Quantification of collective behaviour via causality analysis
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Datasets for Insights into prismatic loop formation in irradiated Fe-Cr alloys from hypothesis-driven active learning and causal analysis
<p>Datasets for irradiated Fe-Cr alloys are collected from the experimental reports on dislocation loop type and dislocation density. We have constructed a data set from experimental literature containing 182 data points. To address such challenges to predict dislocation density, we have implemented a three-step ML approach as listed in the following:</p> <div> <div> <div> <ul> <li> <p>impute dataset to fill in the missing data to construct a predictive model using the RF regression algorithm. </p> </li> <li> <p>generate functionalized features and evaluate feature importance using the predictive model</p> </li> <li> <p>use the physics-based important functionalized features as hypotheses (physics-augmented GP models) in a hypothesis-driven active learning scheme to learn and predict dislocation density for all alloys. </p> </li> </ul> </div> </div> </div>
Causal HRSI Dataset: Human-Robot Spatial Interaction Dataset for Causal Analysis from Mobile Platforms
<h2>Causal HRSI Dataset: Human-Robot Spatial Interaction Dataset for Causal Analysis from Mobile Platforms</h2> <div>The dataset captures a Human-Robot Spatial Interaction (HRSI) scenario between a person and the TIAGo robot. It focuses specifically on human-goal and human-robot spatial interaction in an indoor environment, captured from the perspective of a 3D Velodyne VLP-16 LiDAR mounted on the TIAGo robot. It includes:</div> <ul> <li>rosbags containing: Velodyne LiDAR point clound, robot and human state (position, orientation and velocities);</li> <li>CSV files containing trajectories of the person and the robot generated by post-processing the rosbags;</li> <li>the map of the environment extracted from the TIAGo robot.</li> </ul> <p><strong>15 participants</strong> took part in the experiment, with the dataset capturing <strong>5 minutes of HRSI motion for each participant</strong>.</p> <h3>Experiment Description</h3> <p>The experiment and data collection occurred in a laboratory room of the University of Lincoln (UK), measuring 5 x 8.2m. <br>Fifteen participants (6 females, aged between 25 and 55) took part in the experiment. Seven of them were used to work with a robot. They were required to walk between four goal positions and avoid the robot if a cross occurs. A predefined rectangular path was set for the TIAGo robot to navigate along the room and generate frequent interactions with the participants.</p> <p>The experimental procedure can be described as follows. Each participant started from one of the four target positions. The next target position was randomly chosen by the participant, who then started moving towards it. Upon reaching the goal position, the participant stopped there and randomly chose the next goal, repeating the process for 5 minutes. In this experimental setting, the robot was considered by the participant as an obstacle to avoid while walking towards their target positions.</p> <h3>Directory Structure</h3> <p>Dataset<br>|<br>|____Map: folder containing the map of the environment extracted from the TIAGo robot<br>|<br>|____RosBags: forder containing the rosbag for each partipant<br>| |____A1.bag<br>| |____A2.bag<br>| |____A3.bag<br>| |____A4.bag<br>| |____A5.bag<br>| |____A6.bag<br>| |____A7.bag<br>| |____A8.bag<br>| |____A9.bag<br>| |____A10.bag<br>| |____A11.bag<br>| |____A12.bag<br>| |____A13.bag<br>| |____A14.bag<br>| |____A15.bag<br>|<br>|____Trajectories: postprocessed trajectories extracted for the rosbag files <br> |____A1_traj.csv<br> |____A2_traj.csv<br> |____A3_traj.csv<br> |____A4_traj.csv<br> |____A5_traj.csv<br> |____A6_traj.csv<br> |____A7_traj.csv<br> |____A8_traj.csv<br> |____A9_traj.csv<br> |____A10_traj.csv<br> |____A11_traj.csv<br> |____A12_traj.csv<br> |____A13_traj.csv<br> |____A14_traj.csv<br> |____A15_traj.csv</p>
Dataset for "Towards Centrality and Causality based Vulnerability Propagation Analysis"
<ul> <li> <p><strong><code>aggregated_data.json</code></strong>: This file contains the processed data generated after loading the Goblin Weaver tool. It includes all CVEs, CWEs, and other relevant attributes. Statistical analyses, particularly vulnerability analyses, are performed on this dataset.</p> </li> <li> <p><strong><code>graph_nodes_edges.pkl</code></strong>: This file stores the parsed GraphML-format graph, divided into chunks of nodes and edges to optimize memory usage during computations. All centrality-based measurements are conducted using this file.</p> </li> <li> <p><strong><code>cve_data.csv</code></strong>: This file captures features of nodes along with their one-hop neighbors. It includes attributes such as <code>whether_cve_exists</code>, <code>cve_exists</code>, and <code>cve_num</code>, as well as the source and target nodes involved.</p> </li> <li> <p><strong><code>cve_2_siblings_data.csv</code></strong>: This file documents features of nodes with their two-hop neighbors. It includes attributes like <code>whether_cve_exists</code>, <code>cve_exists</code>, and <code>cve_num</code>, along with details of source nodes and their two-hop neighbor nodes.</p> </li> <li><code><strong>fea_matrix.csv</strong>:</code><code>This file records the matrix of every node based on transformed five types of attributes, including missrelease (freshness missrelease), outdays(freshness outdatedTimeinMs), popularity, speed, and severity. It has been used for correlation analysis. </code></li> </ul>
ClearCausal: Cross Layer Causal Analysis for Automatic Microservice Performance Debugging
<p>Dataset for the paper: <em>ClearCausal: Cross Layer Causal Analysis for Automatic Microservice Performance Debugging.</em></p> <div> </div>
Causal Analysis of Google Code Jam Contest Data
<p>This archive is the replication package for the paper:</p> <p>Carlo A. Furia, Richard Torkar, Robert Feldt: <em>Towards Causal Analysis of Empirical Software Engineering Data — The Impact of Programming Languages on Coding Competitions</em>. <a href="https://arxiv.org/abs/2301.07524">arXiv:2301.07524</a>. January 2023.</p>
Analysis and Figures from "Causal network inference from gene transcriptional time-series response to glucocorticoids"
<p>Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately enabling regulatory network re-engineering. Network inference from transcriptional time-series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time-series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance and additionally infers whether the causal effects are activating or inhibitory. We apply BETS to transcriptional time-series data of 2,768 differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2,768 genes and 31,945 directed edges (FDR <= 0.2). We validate inferred causal network edges using two external data sources: overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is available as an open source software package at https://github.com/lujonathanh/BETS</p> <p>This upload documents the analysis and figure files that support each numerical claim of the manuscript. Full Progeny.xlsx lists out the relevant code and files for each numerical claim of the manuscript, assuming the home folder of port-from-della</p>
Data and code for Causality analysis and prediction of riverine algal blooms by combining empirical dynamic modeling and machine learning techniques
<p>Hydrological data (including daily water levels, flow velocities, and streamflow discharges) from two hydrological stations, the Hankou Station in the Yangtze River (YR) and the Hanchuan Station in the Han River (HR), were obtained from Hubei Province Hydrology and Water Resources Center.</p> <p>Water quality data (i.e., total nitrogen (TOTN), total phosphorus (TOTP), and water temperature in the Han River) and algae densities at three sections (Baihezui, Qinduankou and Zongguan) were acquired from the Yangtze River Basin Ecological and Environmental Supervision Authority. </p> <p><span>The R script(s) for machine learning models can also be found at <a href="../api/records/10901736/draft/files/Code%20for%20machine%20learning%20classification%20model.R/content" target="_blank" rel="noopener noreferrer">Code for machine learning classification model.R</a>.</span></p> <p> </p>
Testing of NBIA Genes: Analysis of Genetic Heterogeneity and Validation of Mitochondrial Markers for Assessing Causality of Sequence Variants.
ClinicalTrials.gov study NCT05615571. IPD Sharing: NO. Countries: 1. Publications: 1.
REBECCA-3 Study, Research on Breast Cancer Induced Chronic Conditions Supported by Causal Analysis of Multi-source Data
ClinicalTrials.gov study NCT06435091. IPD Sharing: Not stated. Countries: 1. Publications: 1.
CHANCE: Causal hazard analysis for natural clinical experiments of therapies in uncommon diseases
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Causality analysis, path analyses and models between Chlorophyll a and environmental factors
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Data from: Is there a causal link between knee loading and knee osteoarthritis progression? A systematic review and meta-analysis of cohort studies and randomised trials
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Variant-to-function analysis of the childhood obesity chr12q13 locus implicates rs7132908 as a causal variant within the 3’ UTR of FAIM2 (snATAC-Seq)
GEO Series GSE241593. Homo sapiens. 8 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Variant-to-function analysis of the childhood obesity chr12q13 locus implicates rs7132908 as a causal variant within the 3’ UTR of FAIM2 (RNA-Seq)
GEO Series GSE241050. Homo sapiens. 32 samples. Type: Expression profiling by high throughput sequencing.
Variant-to-function analysis of the childhood obesity chr12q13 locus implicates rs7132908 as a causal variant within the 3’ UTR of FAIM2 (snRNA-Seq)
GEO Series GSE241594. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
RNA-Seq analysis of maize lines resistant and susceptible to Cercospora zeina, causal organism of Grey leafspot disease
GEO Series GSE99005. Zea mays. 6 samples. Type: Expression profiling by high throughput sequencing.
Single nuclei RNAseq analysis of HD mouse model and human brain reveals impaired 1 oligodendrocyte maturation and potential causal regulators
GEO Series GSE180928. Homo sapiens. 32 samples. Type: Expression profiling by high throughput sequencing.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.