344 results about "Cause analysis" patented technology

A method of root cause analysis in a virtual machine environment includes receiving a plurality of events from a system monitoring the virtualized environment. The events may include alarms or alerts, such as alarms or alerts associated with a resource reaching or exceeding a threshold. The capacity manager consumes these events and performs event correlation to produce a set of correlated events. The capacity manager performs a root cause analysis on the set of correlated events to identify one or more root causes. The capacity manager further performs an impact analysis to determine how the root cause impacts the system, such as other virtual machines, hosts or resource in the virtual environment. Based on the root cause and impact analysis, the capacity manager makes one or more recommendations to address issues with or to improve the operations and / or performance of the virtualized environment.

A root cause analysis engine uses event durations and gradual deletion of events to improve analysis accuracy and reduce the number of required calculations. Matching ratios of relevant rules are recalculated every time notification of an event is received. The calculation results are held in a rule memory in the analysis engine. Each event has a valid duration, and when the duration has expired, that event is deleted from the rule memory. Events held in the rule memory can be deleted without affecting other events held in the rule memory. The analysis engine can then re-calculate the matching ratio of each rule by only performing the re-calculation with respect to affected rules related to the deleted event. The calculation cost can be reduced because analysis engine processes events incrementally or decrementally. Analysis engine can determine the most possible conclusion even if one or more condition elements were not true.

The invention discloses a log management and fault diagnosis method and device under multiple hosts. The method comprises fault log collection, fault log analysis and fault log correlation analysis. Fault log collection comprises collecting fault logs of all hardware and software in the cluster and storing the logs in a log server uniformly. Fault log analysis comprises filtering the fault logs, extracting log template information and classifying the logs according to log types. Fault log correlation analysis comprises performing fault reason analysis by using log analysis results and combining time windows, clustering the related faults caused by the same fault into a category and trying to find the root of the type of the faults. The method and the device are capable of analyzing the system operation condition effectively, assisting administrators to process system faults and improving the fault type determination accuracy.

The present invention is directed to computer-based methods and system to perform root-cause analysis on an industrial process. The methods and system load process data for an industrial process from a historian database and build a hybrid first-principles and inferential model. The methods and system then executes the hybrid model to generate KPIs for the industrial process using the loaded process variables. The methods and system then selects a subset of the KPIs to represent an event occurring in the industrial process, and divides the data for the subset into multiple subset of time series. The system and methods select time intervals from the time series based on the data variability in the selected time intervals and perform a cross-correlation between the loaded process variables and the selected time interval, resulting in a cross-correlation score for each loaded process variable. The methods and system then select precursor candidates from the loaded process variables based on the cross-correlation scores and execute a parametric model for performing quantitative analysis of the selected precursor candidates, resulting in a strength of correlation score for each precursor candidate. The methods and system select root-cause variables from the selected precursor candidates based on the strength of correlation scores for analyzing the root-cause of the event.

A system and method for using event management root cause analysis to depict resource dependencies are provided. With the system and method, since the root cause analysis of known event management systems provides a mechanism for defining interactions of enterprise components for root cause analysis purposes, this information is used as a basis for obtaining enterprise component relationship information that may be used by other management applications. That is, the system and method monitor the application of correlation rules to event notifications from various enterprise components and determine relationships of the various enterprise components based on the results of the application of the correlation rules. These relationships may then be stored in a data structure representing the system environment and may be used by other management systems or displayed to the user in a graphical format that graphically represents the system environment.

Multivariate analysis can be performed to determine whether a computing environment is encountering a business disruption (e.g., relatively long end-user response times) or other problem. Cluster analysis (comparing more recent data with a particular cluster of good operating data), predictive modeling, or other suitable multivariate analysis can be used. A probable cause analysis may be performed in conjunction with the multivariate analysis. A probable cause analysis may be used when one or more abnormal instruments, abnormal components, abnormal load patterns, suspicious actions (such as resource provisioning or deprovisioning activities), software or hardware updates or failures, recent changes to the computing environment (component provisioning, change of a control, etc.), or any combination thereof. The probable cause analysis can include ranking potential causes based on likelihood, and such ranking can include statistical analysis, policy violations, recent changes to the computing environment, or any combination thereof.

Root cause analysis is augmented by providing optimized inputs to root cause analysis systems or the like. Such optimized inputs can be generated from causality graphs by creating sub-graphs, finding and removing cycles, and reducing the complexity of the input. Optimization of inputs enables a root cause analysis system to reduce the number of iterative cycles that are required to execute probable cause analysis, among other things. In one instance, cycle removal eliminates perpetuation of errors throughout a system being analyzed.

A technique determines which configuration change(s) caused an application invocation failure of a computer application without the need for a knowledge database. To determine which configuration change is the most likely cause, the cause analysis program (121) checks other computers (102) that have experienced the same configuration changes. The cause analysis program checks and counts the application invocation results before and after each configuration change is done. If the same configuration changes are found in the other computers, the program checks whether each configuration change caused or cured the same problem in that computer. The program counts the similar cases for all of the computers. Subsequently, the program calculates the ratio of those instances involving a change from success to failure and the ratio of those instances involving a change from failure to success out of all instances for each configuration change.

The invention relates to a method and a system of fault diagnosis of a rail transit based on an SVM (Support Vector Machine). The method comprises the following steps of collecting historical monitoring data and real-time monitoring data of the rail transit and transmitting the historical monitoring data and the real-time monitoring data to a data analysis server to carry out preprocessing, feature selection, data vectoring and model training, computing, analyzing and classifying the real-time monitoring data according to a classification model which is obtained through the historical monitoring data, judging whether a fault exists and obtaining a reason for generating the fault. The system comprises a data collection component, a data storage component, a data preprocessing component, a feature selection component, a data vectoring component, a model training component and a real-time data analysis component. According to the method and the system, manual judgment and analysis of the fault in mass monitoring signals can be replaced by an automatic monitoring manner, so that a large amount of labor cost and the time for analyzing the reason of the fault can be reduced for providing a time guarantee for subsequent jobs, such as maintenance and rescue.

The invention provides an automatic drive vehicle test scene construction method based on traffic accident case deconstruction. The method comprises the following steps of (1) extracting scene elements used for testing an automatic drive vehicle by analyzing characteristics of a real traffic scene; (2) arranging a test scene of the automatic drive vehicle, thereby generating test cases used for testing the automatic driving vehicle; and (3) arranging a test scene collaborative control system. The scene elements and typical features are analyzed and extracted through clustering analysis, and important parameters in the real traffic scene are converted into test parameters in combination with cause analysis, so that an automatic drive urban complex traffic scene safety adaptability test evaluation method suitable for industrial development and third-party certification evaluation is constructed.

The invention relates to a jam cause analysis method and device, computer equipment and a memory medium. The method comprises the steps of obtaining traffic flow data of a road; preprocessing the traffic flow data; establishing a jam evaluation index system according to the preprocessed traffic flow data; identifying frequent jam road sections and import jam road sections; for the frequent jam road sections and the import jam road sections, establishing a jam feature description matrix; analyzing the jam feature description matrix through adoption of a cause analysis model; obtaining an attribute classified decision tree according to an analysis result; and feeding back the attribute classified decision tree. According to the method, the device, the computer equipment and the media medium,main factors resulting in jam are discovered accurately, analysis efficiency is improved, and loss rustling from analysis inaccuracy is reduced.

Method and apparatus for identifying a cause for a response time problem for a transaction in a distributed computing system that includes a central server and a plurality of subsystems. Data is stored at each subsystem relating to sub-transactions of transactions performed by the subsystems. When a problem is discovered in connection with the completion of a particular transaction, each subsystem of the plurality of subsystems that was involved in the particular transaction is identified, and both instance data relating to all of the sub-transactions of the particular transaction stored at each identified subsystem and current hourly aggregate data stored at each identified subsystem is forwarded to the central server. Root-Cause Analysis is then performed using the forwarded instance data and aggregate data to identify the particular subsystem that caused the transaction problem.

The invention discloses a root fault cause analysis method and analysis device relating to the data mining and network management field, solving the problems that in the current root fault cause analysis process, a great deal of manpower and time are used to analyze weblogs, the root fault cause analysis efficiency is relatively low, and the network fault cannot be eliminated timely. The method comprises: determining the fault time point of a network device; obtaining the first log information set generated by the network device in the first time period; wherein the first log information set comprises M kinds of log information; analyzing every kind of log information in the M kinds of log information according to a preset analysis strategy, obtaining N kinds of root cause logs in the M kinds of log information; wherein the root cause logs are log information generated when the network device generates the fault, N is more than or equal to 1 and is less than or equal to M, the preset analyzing strategy is: preset fault generation rule when the network device fault is generated; and determining the cause of generating the network device fault according to the N kinds of root cause logs.

Embodiments of the invention utilize advanced statistical data analytics to predict and provide recommendations for root-cause analysis for service access QoE issues in networks, such as 3G / 4G networks. Using FCAPS data as predictor variables, embodiments are configured to set up the problem as a predictive regression or classification problem to estimate service access QoE related indicators. Some embodiments perform training and tuning of various non-linear statistical modelling algorithms, based for example on tree and ensemble methods, using network deregistration information from RAN logs.

Conventionally, there is a technique to conduct a cause analysis in which a single evaluation axis, such as a performance time, is used to configure a problem point network. However, in particular, in a system to visualize a management problem in which problems are intricately-intertwined with one another, information on multiple evaluation axes, collected from a business system, are utilized, and units are different for respective evaluation axes. Therefore, it is difficult to simply specify a cause of problem occurrence. In view of this situation, an object of the present invention is to extract and present factor which has a significant influence on the problem occurrence. The present invention is directed to an apparatus which stores causal relation information specifying the causal relation and the relation level between the indexes, business goal achievement level information, and an influence level of the causal relation, calculates the influence level by using the business goal achievement level information and the relation level of the causal relation, specifies as the causal index the index having a large influence, and generates screen information for displaying a graph and / or table relating to the causal index.

The invention discloses an alarm positioning method based on data analysis. The method comprises the following steps: obtaining alarm historical information, and preprocessing the alarm historical information to generate an alarm event description in a unified format; dividing the alarm event description into multiple event sets according to a preset time window; performing incidence relation calculation on the divided alarm events; and performing root cause analysis according to the calculated incidence relation between the alarm events to generate a cause analysis result causing the alarm events. By adoption of the alarm positioning method disclosed by the invention, operation and maintenance engineers can be liberated from the massively repeated alarm information, the relationship between the alarms is grasped quickly and intuitively, thereby not only reducing the workload of the operation and maintenance engineers, but also providing reference for the operation and maintenance works, reducing the working difficulty and improving the working efficiency of the operation and maintenance works. In addition, the invention further discloses an alarm positioning apparatus and device based on data analysis, and a computer storage medium, which have the above technical advantages.

In a computer system that comprises multiple target computers and an analysis computer, one or more first target computers, in which a predetermined application has been installed and invoked, send a log comprising information of multiple configuration changes that have been made prior to invoking the predetermined application to the analysis computer, and the analysis computer receives the log and computes, for each type of configuration change and based on the log, an invocation failure rate which is a percentage at which the invocation of the predetermined application fails subsequent to the configuration change. Then, a second target computer receives, from the analysis computer, first information comprising an invocation failure rate for each type of configuration change related to the predetermined application, and based on the invocation failure rate, displays the type of configuration change that is the cause of the failure of the predetermined application invocation.

The present invention discloses a data abnormal fluctuation detecting method and apparatus. The method comprises: obtaining to-be-detected data, wherein the to-be-detected data comprises at least one piece of index data; according to a data abnormal fluctuation detecting method, determining at least one piece of abnormal fluctuation index data in the at least one piece of index data; and carrying out analysis on the abnormal fluctuation index data respectively according to corresponding preset dimension information, and generating a corresponding abnormal fluctuation analysis result, the preset dimension information comprises dimensions of the abnormal fluctuation index data, and at least one dimension index data corresponding to each dimension. According to the technical scheme provided by embodiments of the present invention, technical problems in the prior art that abnormal fluctuation detection and analysis time is long, a cost is high and an accuracy rate is low due to a data analyst determining whether each index data in the to-be-detected data occurs abnormal fluctuation and analyzing causes of abnormal fluctuation are solved, and efficiency of abnormal fluctuation detection accuracy and abnormal fluctuation cause analysis is improved.

Even when a user does not know a structure of a computer readable program, a technique capable of analyzing log information is provided. A log analyzing method for analyzing log information outputted when a program is executed by a computer is comprised of: a step for acquiring log information outputted when a program is executed to thereby read the acquired log information; a step for accepting a user designation condition indicative of log information which is extracted; a step for extracting information from the log information based upon the accepted user designation condition, and for judging as to whether or not each of operations while the program is executed can satisfy an initial condition thereof so as to analyze a cause of an error occurred while the program is executed; and a step for providing a result of the error cause analysis to a user.

An apparatus and a method for supporting Computer-Aided Diagnosis (CAD) are provided. The apparatus includes a cause analyzer configured to analyze a cause of a reliability of a diagnosis result of a medical image being lower than a level, the medical image being captured using a probe, and a guide information generator configured to generate guide information of an operation of the probe based on the analyzed cause.

The disclosure relates to technology for processing data sets to generate data rules for the data sets in a communications network. A first set of data including key quality indicators (KQIs) indicative of a quality of service and a second set of data including key performance indicators (KPIs) indicative of a performance level are received. The first data set and the second data set are categorized using a first value into a plurality of KQI groups and a second value into a plurality of KPI groups, respectively. Each of the KQI and KPI groups are identified with a label. Each of the KQI and KPI groups identified with a same label are processed by application of association rule learning to generate the data rules. The data rules model a relationship between the KQIs and the KPIs by calculating association frequencies.

The embodiment of the invention discloses a network fault detection method and a network fault detection device, applied to the field of communication technologies. In the method of the embodiment, a fault detection server acquires network communication information of a client in a content delivery network system, then the network communication information is matched with a preset fault model library, if the network communication information is matched with description information of a first fault model, namely values of a plurality of parameters in the network communication information are respectively in value ranges of corresponding parameters in the description information of the first fault model, it determines that a communication fault accords with the first fault model, and warning is sent to a corresponding content delivery network node. Compared with manual fault cause analysis in the prior art, the method provided by the embodiment of the invention can learn the fault the type of the current communication fault by matching collected information with the fault model library, so that a fault cause can be rapidly and accurately acquired by analyzing.

The invention relates to a construction method for an urban disaster prevention and fighting informationalized rescue command comprehensive intelligent platform. The method organically integrates the telemetry and remote-sensing devices for video monitoring and fire detection and alarm, the CHINANET, and the acoustic and optical analysis and data transmission techniques. Through the establishment of a local area network which is connected with the CHINANET and is specialized in disaster prevention and rescue handling, the platform realizes the real-time acquisition of a great amount of disaster area information (including the information about the disaster occurrence process) in a most economical mode within the shortest time, shares the real-time information with related experts and technical staff for analysis and treatment in a safe mode, and provides a brand-new informationalized solution for the disaster cause analysis, process recording and rescue handling. The invention also provides a practical solution for the wider practical and daily application of the system. Simultaneously, the method related to data acquisition and processing also breaks an inherent data source, acquisition and transmission mode of the optical analysis.

The system thereof comprises test unit, communication server, supervision terminal, database server, analyzing and processing server, analyzing terminal and communication network for connecting said devices. The method based on said system comprises: collecting data; preprocessing data; diagnosis and estimation; statistic; trend analysis, effect-cause analysis and rule adjustment. The invention supports the full test of voice quality for GSM900 / 1800 / GPRS,CDMAIS95 / 1X / EVDO.

The invention provides a transformer region low-voltage cause analysis method and system. The method comprises the following steps: to begin with, according to collected transformer region network parameters and collected transformer region operation parameters, establishing transformer region network topology and component models thereof; then, calculating a transformer region low-voltage quantitative evaluation index--low-voltage index under the current state through load flow calculation; on the basis of the current state of the transformer region, through a variable parameter method, adjusting various factors closely related to the low voltage gradually till the corresponding indexes return to normal, and calculating transformer region low-voltage indexes when adjusting various factorstill the corresponding indexes return to normal respectively; and calculating the difference values between the transformer region low-voltage indexes obtained when adjusting the various factors tillthe corresponding indexes return to normal and the low-voltage index obtained in the step 4), and comparing the difference values, the smaller the difference value being, the higher the dependency between the factors and the low voltage being, and thus main cause of the transformer region low voltage can be obtained. The method is simple and high in operability, and can provide targeted decisionbasis for low-voltage treatment.

The invention discloses a city group ecological space damage identification and evaluation method. The method includes: analyzing and identifying important landscape space patterns such as city groupecological source land and ecological galleries; on the basis, overlapping an urban group landscape pattern comprehensive index and an ecological service function conversion equivalent evaluation result brought by land utilization change to obtain urban group ecosystem damage space distribution; furthermore, combining on-site investigation and classification combing to summarize the typical ecological damage space type of the urban group, and carrying out cause analysis and damage state evaluation.

The invention relates to a power distribution network failure cause analyzing method based on an expert system and the D-S evidence theory. The method comprises the steps that information needed by power distribution network failure cause analysis and the source of the information are determined firstly, and the needed information comprises failure information and non-failure information; then, primary failure cause analysis is carried out through the expert system, and primary conclusions are obtained; finally, each primary conclusion is adopted as a basic trust distributive function, the basic trust distributive functions are integrated into a final basic trust distributive function through the combination mode of the D-S evidence theory, the final basic trust distributive function is the final conclusion, and the power distribution network failure cause is obtained. According to the method, the most precise conclusion of the failure cause is obtained according to various kinds of information based on reasonable derivation, therefore, the power supply reliability of a power distribution network can be overall improved, and economic benefits can be improved.

Cloud-based integrated yield / equipment data processing system for collecting and analyzing integrated tool-related data (cause data) and material-related data (effect data) pertaining to at least one material processing tool and at least one material is disclosed. In an embodiment, the tool-related data is correlated with the material-related data and the correlated tool-related data and material-related data is employed by logic to perform, using a cloud computing approach, at least one of root-cause analysis, prediction model building and tool control / optimization.

The invention relates to an equipment alarm message remote management method which comprises the following steps: a terminal management platform receives an alarm notification message sent by a gateway when monitoring that monitoring parameters are beyond a corresponding alarm threshold range, and the alarm notification message at least contains an alarm event number and relevant alarm parameters; and the terminal management platform transmits an alarm response message used for confirming the receiving of the message to the gateway and carries out fault positioning and fault cause analysis according to the alarm event number and the relevant alarm parameters. The invention also relates to an equipment alarm message remote management system, and also provides an equipment fault alarm message remote management proposal in accordance with the existing TR069 protocol system, which not only conducively complements the TR069 protocol, but also brings convenience for the operation and maintenance in the existing network and reduces the cost load brought by equipment improvement.