ОРГАНИЗАЦИЯ ХРАНИЛИЩ ДАННЫХ ДЛЯ РАСПРЕДЕЛЕННОЙ СИСТЕМЫ АНАЛИЗА ВИБРАЦИОННЫХ СИГНАЛОВ

Article covers the methods of vibration signals long implementations analysis as well as complex analysis of large amounts of signal data. Several architectural pattern applications of signal and analytical data warehouses for decision support system that evaluates the vibration characteristics of the objects are proposed. The ways of extending and embedding third-party functionality in MS SQL Server engine are recommended.Рассмотрены способы организации хранения длинных реализаций вибрационных сигналов, а также способы комплексного анализа больших объемов сигнальных данных. Приведены способы и методы организации сигнального и аналитического хранилищ данных для системы поддержки принятия решений, оценивающей вибрационные характеристики исследуемых объектов. Рассмотрены способы расширения и встраивания сторонней функциональности в СУБД MS SQL Server

A Blackboard Integration of Manufacturing Databases Using an Intelligent Interface.

The explosion of computer applications into the world of manufacturing along functional lines has produced the often mentioned islands of automation. Although many issues and problems are involved in interfacing and integrating the databases that serve these applications, we can extract valuable data from these independent systems to provide important information to decision makers. This research resulted in the development of MIMIR (Multiple Integrated Manufacturing Information Resources), a decision support system based upon the blackboard architecture. The blackboard architecture extends the common expert system design to include multiple expert systems, termed Knowledge Sources (KS\u27s), which combine to solve problems too diverse or complex for conventional expert systems. Extending the architecture, MIMIR uniquely adds Data Sources (DS\u27s) to the conventional KS\u27s for problem decomposition and solution. Developed in Common LISP and CLOS, MIMIR can answer basic questions about the data in the remote databases and generate multiple queries for more complex questions. Seven partitions in MIMIR\u27s blackboard allow KS\u27s and DS\u27s to focus on specific levels of the problem decomposition. MIMIR relies on an intelligent interface to translate an internal LISP-based SQL-like query into a valid SQL query string. This query is then be submitted to an external relational database and results are returned to the blackboard environment. While MIMIR is currently limited to SQL-accessible relational databases, the architecture can be extended to support interfaces to other data formats

Improving the Deductive System DES with Persistence by Using SQL DBMS's

This work presents how persistent predicates have been included in the in-memory deductive system DES by relying on external SQL database management systems. We introduce how persistence is supported from a user-point of view and the possible applications the system opens up, as the deductive expressive power is projected to relational databases. Also, we describe how it is possible to intermix computations of the deductive engine and the external database, explaining its implementation and some optimizations. Finally, a performance analysis is undertaken, comparing the system with current relational database systems.Comment: In Proceedings PROLE 2014, arXiv:1501.0169

On-line analytical processing

On-line analytical processing (OLAP) describes an approach to decision support, which aims to extract knowledge from a data warehouse, or more specifically, from data marts. Its main idea is providing navigation through data to non-expert users, so that they are able to interactively generate ad hoc queries without the intervention of IT professionals. This name was introduced in contrast to on-line transactional processing (OLTP), so that it reflected the different requirements and characteristics between these classes of uses. The concept falls in the area of business intelligence.Peer ReviewedPostprint (author's final draft

Using Visualization to Support Data Mining of Large Existing Databases

In this paper. we present ideas how visualization technology can be used to improve the difficult process of querying very large databases. With our VisDB system, we try to provide visual support not only for the query specification process. but also for evaluating query results and. thereafter, refining the query accordingly. The main idea of our system is to represent as many data items as possible by the pixels of the display device. By arranging and coloring the pixels according to the relevance for the query, the user gets a visual impression of the resulting data set and of its relevance for the query. Using an interactive query interface, the user may change the query dynamically and receives immediate feedback by the visual representation of the resulting data set. By using multiple windows for different parts of the query, the user gets visual feedback for each part of the query and, therefore, may easier understand the overall result. To support complex queries, we introduce the notion of approximate joins which allow the user to find data items that only approximately fulfill join conditions. We also present ideas how our technique may be extended to support the interoperation of heterogeneous databases. Finally, we discuss the performance problems that are caused by interfacing to existing database systems and present ideas to solve these problems by using data structures supporting a multidimensional search of the database