Generation of Explicit Knowledge from Empirical Data through Pruning of Trainable Neural Networks

This paper presents a generalized technology of extraction of explicit knowledge from data. The main ideas are 1) maximal reduction of network complexity (not only removal of neurons or synapses, but removal all the unnecessary elements and signals and reduction of the complexity of elements), 2) using of adjustable and flexible pruning process (the pruning sequence shouldn't be predetermined - the user should have a possibility to prune network on his own way in order to achieve a desired network structure for the purpose of extraction of rules of desired type and form), and 3) extraction of rules not in predetermined but any desired form. Some considerations and notes about network architecture and training process and applicability of currently developed pruning techniques and rule extraction algorithms are discussed. This technology, being developed by us for more than 10 years, allowed us to create dozens of knowledge-based expert systems. In this paper we present a generalized three-step technology of extraction of explicit knowledge from empirical data.Comment: 9 pages, The talk was given at the IJCNN '99 (Washington DC, July 1999

Status of the DIRAC Project: overview and recent developments

International audienceMultiple research user communities need to put in common infrastructures their computing resources in order to boost the efficiency of their usage. Various grid infrastructures are trying to help the new users to start doing computations by providing services facilitating access to distributed computing resources. The DIRAC project is providing software for creating and operating such services. Multiple DIRAC installations are functional now in various countries. The project is rapidly evolving by providing access to new types of computing and storage resources. It uses new technologies to ensure better, more scalable and more reactive control of the system. New services for massive computations and data operations are available. In this contribution the overview of the project as well as new recent developments will be presented

DIRAC Data Management System

International audienceDIRAC Project is developing software for building distributed computing systems for the needs of research communities. It provides a complete solution covering both Workload Management and Data Management tasks of accessing computing and storage resources. The Data Management subsystem (DMS) of DIRAC includes all the necessary components to organize distributed data of a given scientific community. The central component of the DMS is the File Catalog (DFC) service. It allows to build a logical File System of DIRAC presenting all the distributed storage elements as a single entity for the users with transparent access to the data. The Metadata functionality of the DFC service is provided to classify data with user defined tags. This can be used for an efficient search of the data necessary for a particular analysis. The DMS supports all the usual data management tasks of uploading and downloading, replication, removal files, etc. A special attention is paid to the bulk data operations involving large numbers of files. Automation of data operations driven by new data registrations is also possible. In this contribution we will make an overview of the DIRAC Data Management System and will give examples of its usage by several research communities

DIRAC Infrastructure for Distributed Analysis

DIRAC is the LHCb Workload and Data Management system for Monte Carlo simulation, data processing and distributed user analysis. Using DIRAC, a variety of resources may be integrated, including individual PC's, local batch systems and the LCG grid. We report here on the progress made in extending DIRAC for distributed user analysis on LCG. In this paper we describe the advances in the workload management paradigm for analysis with computing resource reservation by means of Pilot Agents. This approach allows DIRAC to mask any inefficiencies of the underlying Grid from the user thus increasing the effective performance of the distributed computing system. The modular design of DIRAC at every level lends the system intrinsic flexibility. The possible strategy for the evolution of the system will be discussed. The DIRAC API consolidates new and existing services and provides a transparent and secure way for users to submit jobs to the Grid. Jobs find their input data by interrogating the LCG File Catalogue which the LCG Resource Broker also uses to determine suitable destination sites. While it may be exploited directly by users, it also serves as the interface for the GANGA Grid front-end to perform distributed user analysis for LHCb. DIRAC has been successfully used to demonstrate distributed data analysis on LCG for LHCb. The system performance results are presented and the experience gained is discussed

Using of microservice architecture in the implementation of electronic information and educational environment

The article deals experience of development, implementation and using of electronic information and educational environment that developed on the basis of microservice architecture at the Russian State Vocational Pedagogical UniversityВ статье рассматривается опыт разработки, внедрения и использования электронной информационно-образовательной среды, построенной на основе микросервисной архитектуры, в Российском государственном профессионально-педагогическом университет