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The work is devoted to problem of information exchange between elements and devices of a telecommunication network for laser technological complex. The laser complex is a combination of hardware and software designed to carry out operations based on the use of laser radiation. Laser complex structure provides a possibility of remote control through a client-server system built on the basis of TCP/IP protocol. The main requirements for industrial network of the complex are performance and predictability of information delivery time. To solve this problem, a probabilistic model for calculating the numerical characteristics of the distribution of requests timeout for external commands from control/monitoring nodes is proposed.

Data mining is an analytical process of knowledge discovery in large and complex data sets. Many studies wish to explore data, to find information so that knowledge can be obtained through the grouping process, classification, rules discovery, associations and data mining visualization which shows similarity. Periodic data often occurs in business applications and sciences that has big size, high dimension and continuously updated. The similarity in periodic data is based on several approaches. One of common approaches is to transform periodic series into other domains so that dimensions are reduced, followed by index mechanism. Many studies of time series do not give optimal result because limited to extracting data not able to represent time series and its pattern which is then change into rules. Rules can be found in time series data, but they are still constrained by over fitting and difficult to present. It causes time series data and non linier function of data mining decision can’t be optimal. The basic idea in the method proposed is to do periodic discretization for sub-sequential formation. These sub-sequences are grouped through a measure of similarity. The simple rule-finding technique is applied to obtain hidden rules in the temporal pattern. The optimal time series data expected to generate the uncertainty trend, previously unknown and can be used to make decisions or forecasting in the future.

Big data is a term of complex data and difficult to process. It consists of several characteristics called 6 Vs. Many applications generate huge data and grow rapidly in seconds. This kind of data comes from many sources such as social media, Internet of Things, log system, e-commerce and so on. This rapid data should be handled with a different approach in big data solutions. This paper proposes to create stream architecture for big data velocity with open source technologies such as Apache Kafka and NoSQL database. The implementation is to handle massive incoming data from social media with specific keywords from Twitter and ingested to NoSQL Database though stream architecture. Historical data then processed to gain valuable insight for better information.

The aim of this paper is to study the possibility of improving the gamma/hadron discrimination in extensive air showers. For this purpose, the identification of hadronic extensive air showers is carried out by means of the detection of muons in water Cherenkov detectors (WCDs). Machine learning algorithms have proven to be useful in a wide variety of fields, and due to their outstanding performance in problems involving complex data, Convolutional Neural Networks (CNNs) have been used in the analysis of the signals measured by the WCDs. Taking simulated events, different approaches were proposed attending to the balance of the classes in the training stage. The results obtained are promising and show that machine learning algorithms provide a powerful tool for muon detection and gamma/hadron discrimination to be considered in future gamma-rays detectors like The Southern Wide-field Gamma-ray Observatory (SWGO) to be built in South America.

Given the existing packet dimensionality reduction model, the simple distance hypothesis is only used as a simple assumption that there is a certain relationship between packet data. This document proposes to share information between packet data with relevant random measures as a priori. We explicitly calculated the Lévy measure of the mixed random measure and offered the inference steps of detailed parameter a posteriori. Compared with the traditional method, the grouping dimensionality reduction model can achieve faster convergence and can well maintain the original information of data. The experimental results on the public dataset show that the grouping dimensionality reduction model is an effective dimensionality reduction algorithm and can be employed to extract characteristics on big data.

The emerging era of big data for past few years has led to large and complex data which needed faster and better decision making. However, the small dataset problems still arise in a certain area which causes analysis and decision are hard to make. In order to build a prediction model, a large sample is required as a training sample of the model. Small dataset is insufficient to produce an accurate prediction model. This paper will review an artificial data generation approach as one of the solution to solve the small dataset problem.

Amazon S3 is a widely adopted web API for scalable cloud storage that could also fulfill storage requirements of the high-energy physics community. CERN has been evaluating this option using some key HEP applications such as ROOT and the CernVM filesystem (CvmFS) with S3 back-ends. In this contribution we present an evaluation of two versions of the Huawei UDS storage system stressed with a large number of clients executing HEP software applications. The performance of concurrently storing individual objects is presented alongside with more complex data access patterns as produced by the ROOT data analysis framework. Both Huawei UDS generations show a successful scalability by supporting multiple byte-range requests in contrast with Amazon S3 or Ceph which do not support these commonly used HEP operations. We further report the S3 integration with recent CvmFS versions and summarize the experience with CvmFS/S3 for publishing daily releases of the full LHCb experiment software stack.

The ATLAS experiment triggering system is distributed across large farms of approximately 6000 nodes. Online monitoring and data quality runs alongside this system. We have designed a mechanism that integrates the monitoring data from different nodes and makes it available for shift crews. This integration includes adding or averaging of histograms, summing of trigger rates, and combination of more complex data types across multiple monitoring processes. Performance milestones have been achieved which ensure the needs of early datataking will be met. We will present a detailed description of the architectural features and performance.