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Artificial Intelligence: Splunk at Cox Automotive

#artificialintelligence

Splunk announced new versions of Splunk Enterprise, Splunk IT Service Intelligence (ITSI), Splunk Enterprise Security (ES) and Splunk User Behavior Analytics (UBA). These products leverage machine learning to speed-up and facilitate extracting insights from machine-generated data. Splunk was founded in 2003 and brought "big data" to Wall Street's attention with its 2012 IPO. It has always focused on machine-generated data and its platform captures, indexes and analyzes real-time data in a searchable repository, on premises or in the cloud. Machine learning extends the Splunk platform by adding outlier and anomaly detection, adaptive thresholding and predictive analytics capabilities, applying over 25 commonly-used machine learning algorithms or custom algorithms to build data models that forecast future events.


Artificial Intelligence: Splunk at Cox Automotive

#artificialintelligence

Splunk announced new versions of Splunk Enterprise, Splunk IT Service Intelligence (ITSI), Splunk Enterprise Security (ES) and Splunk User Behavior Analytics (UBA). These products leverage machine learning to speed-up and facilitate extracting insights from machine-generated data. Splunk was founded in 2003 and brought "big data" to Wall Street's attention with its 2012 IPO. It has always focused on machine-generated data and its platform captures, indexes and analyzes real-time data in a searchable repository, on premises or in the cloud. Machine learning extends the Splunk platform by adding outlier and anomaly detection, adaptive thresholding and predictive analytics capabilities, applying over 25 commonly-used machine learning algorithms or custom algorithms to build data models that forecast future events.


Call Detail Records Driven Anomaly Detection and Traffic Prediction in Mobile Cellular Networks

arXiv.org Artificial Intelligence

Mobile networks possess information about the users as well as the network. Such information is useful for making the network end-to-end visible and intelligent. Big data analytics can efficiently analyze user and network information, unearth meaningful insights with the help of machine learning tools. Utilizing big data analytics and machine learning, this work contributes in three ways. First, we utilize the call detail records (CDR) data to detect anomalies in the network. For authentication and verification of anomalies, we use k-means clustering, an unsupervised machine learning algorithm. Through effective detection of anomalies, we can proceed to suitable design for resource distribution as well as fault detection and avoidance. Second, we prepare anomaly-free data by removing anomalous activities and train a neural network model. By passing anomaly and anomaly-free data through this model, we observe the effect of anomalous activities in training of the model and also observe mean square error of anomaly and anomaly free data. Lastly, we use an autoregressive integrated moving average (ARIMA) model to predict future traffic for a user. Through simple visualization, we show that anomaly free data better generalizes the learning models and performs better on prediction task.


Argyle Data Points to Innovations in Machine Learning to Solve New Waves of Telco Fraud - insideBIGDATA

#artificialintelligence

Argyle Data, a leader in big data/machine learning analytics for mobile providers, has highlighted the role of supervised and unsupervised machine learning in detecting and preventing anomalous mobile traffic. The move comes as Argyle Data and Carnegie Mellon University (CMU) Silicon Valley's Department of Electrical and Computer Engineering prepare to publish a new research paper on anomaly detection, which will be presented at academic conferences during the first half of 2017. Global mobile fraud levels cost the industry an estimated U.S. 38 billion 2015 according to the latest CFCA survey. Most major attacks today are'fraud cocktails': unpredictable mixtures of several fraud types. The chief reason that operators are unable to detect complex new fraud is that approaches currently used to detect fraud in communications networks typically rely on static rules with pre-set thresholds, and can only detect known fraud types.


The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development

arXiv.org Machine Learning

As machine learning is applied more and more widely, data scientists often struggle to find or create end-to-end machine learning systems for specific tasks. The proliferation of libraries and frameworks and the complexity of the tasks have led to the emergence of "pipeline jungles" -- brittle, ad hoc ML systems. To address these problems, we introduce the Machine Learning Bazaar, a new approach to developing machine learning and AutoML software systems. First, we introduce ML primitives, a unified API and specification for data processing and ML components from different software libraries. Next, we compose primitives into usable ML programs, abstracting away glue code, data flow, and data storage. We further pair these programs with a hierarchy of search strategies -- Bayesian optimization and bandit learning. Finally, we create and describe a general-purpose, multi-task, end-to-end AutoML system that provides solutions to a variety of ML problem types (classification, regression, anomaly detection, graph matching, etc.) and data modalities (image, text, graph, tabular, relational, etc.). We both evaluate our approach on a curated collection of 431 real-world ML tasks and search millions of pipelines, and also demonstrate real-world use cases and case studies.