I'm developing an AI tool to find known equipments' errors and find new patterns of failure. This log file is time based and has known  messages (information and error).I'm using a JavaScript library Event drops to show the data in a soft way,but my real job and doubts are how to train the AI to find the known patterns and find new possible patterns. I have some requirements:1 - The tool have to don't depends of extra environment installation or the less possible (the perfect scenario is run the tool entire on the browser in standalone mode);2 - Possibility to make the pattern analyzer fragmented,a kind of modularity,one module per error;Which are the recommended kind of algorithm to do this ( Neural network, genetic algorithm, etc)? Exist something to work using JavaScript? If not what is the best language to make this AI?
Design AI for log file analysis
machine learning;ai design;training
Given how general the question is, I'll just provide a general  approach that could be adapted or modified into achieving something  more specific.By log file analysis, I am going to assume that this is in reference to web logs and tracking. And since I don't know what type of analysis you wish to achieve, I'll also assume that you are attempting a classification problem, or something similar to it.Which are the recommended kind of algorithm to do this ( Neural network, genetic algorithm, etc)? The most general approach is to select a supervised learning/classification algorithm and have it learn the function or type of analysis you wish to achieve by training it with known examples of data patterns. By this, I mean, select an algorithm capable of mapping or classifying unknown web logs or tracking data patterns to a predefined set of categories. These categories might be time spent, scrolling, visits per page, visits per user, and so forth.Those categories are probably the basics, and they could easily be done manually. So for a more in depth process you could attempt analysing any correlations in the data by utilising an approach similar to bagging. Bagging or bootstrap aggregating is a type of algorithm used in ensemble learning. Ensemble learning refers to the approach of applying or using multiple algorithms to achieve better predictive/classification performance.Bagging relies on the process of obtaining replicas of the training data set (web logs, tracking data, etc.) and training the classifiers on these replicas.So essentially,Different training data subsets are obtained randomly from the entiretraining dataset.Each training data subset is used to train a different classifier.Individual classifiers are then combined by taking a simple majorityvote of their decisions, where the chosen majority vote    results inthe ensemble decision.With trying to identify any correlation between page visits. Take a sufficiently large population of size N - large enough to ensure that any form of sampling and resampling from the population does not repeatedly result in any substantial overlaps. From the population, sample a subset of it, train a classifier on this subset sample, resample the population by replacing the log visits in the previous subset with new log visits from the population, and train again. Repeat this process for all the classifiers in the ensemble with different subset samples. Eventually, each classifier should determine their own correlation or measurable trait regarding the log vists in the population. The ensemble algorithm then determines what this correlation or measurable trait is by considering the majority classification determined by the classifiers composing the ensemble.Hint ;So simply,Identify what you are trying to measure or analyse.Generate data for training in which that measurable trait can bediscovered.Specify the measurable trait as a parameter.Select a number of sufficiently similar supervised learningalgorithms.Train each of these algorithms on the training data.If successful, identify or define more parameters and just repeatthe above.what is the best language to make this AI?Just have a look at python library for log file analysis.And also Scala for pattern matching inline with recognition,functional programming and strong for static type system.Note I think the key step is defining or redefining this problem as a supervised learning problem and going from there.also have a look at this logs