Data mining technologies in a distributed medical information system

Proposed a method for solving the problem of identifying hidden relationships in hard-to-structure data that have an implicit character is considered using information mining. Proposed decision trees, the effectiveness of which is illustrated by a specific example. The use of OLAP analysis systems on data presented using in the form of a real or virtual hypercube’s information is an effective tool for the effectiveness of the management for medical monitoring

Implementasi Sistem Business Intelligence Untuk Data Penelitian di Perguruan Tinggi

Data didalam sebuah perguruan tinggi merupakan aset, aset yang senantiasa berkembang dan membutuhkan pengelolaan secara khusus, baik dari sisi pemanfaatannya maupun dari sisi penyimpanannya. Pembangunan business intelligence merupakan salah satu cara untuk dapat mengekstrak informasi penting di sebuah perguruan tinggi. Kondisi sistem yang berjalan saat ini di Perguruan Tinggi XYZ yaitu laporan tentang data-data penelitian belum mempunyai database dan hanya dihasilkan langsung dengan file yang berbentuk excel, dan setiap program studi mempunyai format yang berbeda-beda. Oleh karena itu dibutuhkan sistem business intelligence yang perancangannya meliputi perancangan data warehouse, perancangan olap analysis, perancangan report, dan perancangan dashboard. Penelitian ini menghasilkan sebuah sistem business intelligence di Perguruan Tinggi XYZ yang sangat membantu didalam mengumpulkan data-data penelitian sehingga dapat dianalisis. Report yang dirancang sangat membantu didalam membuat laporan penelitian dan dapat disesuaikan dengan kebutuhan, sehingga masalah yang sering terjadi dalam hal kebergantungan dalam perolehan laporan diharapkan tidak terjadi kembali. Dashboard penelitian yang dirancang membantu pimpinan didalam menganalisis data untuk mempelajari tren penelitian yang dilakukan di Perguruan Tinggi XYZ, serta dapat dijadikan untuk mendukung pengambilan keputusan dan nantinya dapat juga sebagai pengukur kinerja dosen.

Deductive Data Warehouses

This chapter presents the concept of “deductive data warehouses.” Deductive data warehouses rely on deductive databases but use a data warehouse in the background instead of a database. The authors show how Datalog, as a logic programming language, can be used to perform on-line analytical processing (OLAP) analysis on data. For that purpose, a small data warehouse has been implemented. Furthermore, they propose and briefly discuss “Datalog by example” as a visual front-end tool for posing Datalog queries to deductive data warehouses.

Research of the possibilities of application of the Data Warehouse in the construction area

Today, in information technologies, the direction associated with the use of Data Warehouse (DW) is evolving very dynamically. Using DW, it is possible to implement two types of data analysis: OLAP-analysis: a set of technologies for the rapid processing of data presented as a multidimensional cube; Data Mining is an intelligent, deep analysis of data to detect previously unknown, practically useful patterns (in our case, the construction area). It is noted, that of all the methods used in technology Data Mining, cluster analysis is especially useful for the construction area. At present, the role of DW has increased, significantly due to the fact, that many methods and approaches of Data Mining have formed the basis of a new, promising method of Big Data. We will specify that, that Data processing from the Data Warehouse with the help of technology Big Data, allows to deduce researches in a building area to the higher level. The purpose of this work is to research of the possibilities of application of the Data Warehouse in the construction area. The article suggests the new approach to data analysis in the construction area, based on the use of Big Data technology and elements of OLAP - analysis. In the section “Discussion” is considering the possibility of the new promising business in the construction field, based on the application of Data Warehouse and technology Big Data.

OLAP on multidimensional text databases: Topic network cube and its applications

Multidimensional text data contains both structured attributes and unstructured text. Unlike the traditional numerical data, it is not straightforward to apply online analytical processing on multidimensional text data. Although some OLAP methods such as topic cube have been proposed in order to effectively utilize its structured information and valuable text data, these methods cant tell the relations of topicwords. Considering that topics usually consist of several subtopics and each subtopic usually contains some topic words, we here use a topic network manner, in which related topic words are connected, to express the complex relations of topics. This paper introduces a new concept of topic network cube to perform OLAP analysis on multidimensional text databases. Firstly, we propose a method called GL-LDA based on Gibbs sampling outputs of Labeled LDA to measure the relations between topic words. Secondly, we give a storagemodel of topic network cubewhich can efficiently generate topic network using GL-LDA. Thirdly, we show how to perform OLAP analysis on topic network cube. Experimental results show that we can analyze multidimensional text databases in different granularity easily and effectively using just a few simple SQL statements, and the output network provides rich and useful information of topics.