Enabling On-Demand Database Computing with MIT SuperCloud Database Management System

The MIT SuperCloud database management system allows for rapid creation and flexible execution of a variety of the latest scientific databases, including Apache Accumulo and SciDB. It is designed to permit these databases to run on a High Performance Computing Cluster (HPCC) platform as seamlessly as any other HPCC job. It ensures the seamless migration of the databases to the resources assigned by the HPCC scheduler and centralized storage of the database files when not running. It also permits snapshotting of databases to allow researchers to experiment and push the limits of the technology without concerns for data or productivity loss if the database becomes unstable.Comment: 6 pages; accepted to IEEE High Performance Extreme Computing (HPEC) conference 2015. arXiv admin note: text overlap with arXiv:1406.492

Resident database interfaces to the DAVID system, a heterogeneous distributed database management system

A methodology for building interfaces of resident database management systems to a heterogeneous distributed database management system under development at NASA, the DAVID system, was developed. The feasibility of that methodology was demonstrated by construction of the software necessary to perform the interface task. The interface terminology developed in the course of this research is presented. The work performed and the results are summarized

Database management and implementation for coal fire detection and monitoring in the Ruijgou Coalfield, Northwest China

Coal fire monitoring by aerospace remote sensing and GIS techniques is an advanced approach, which has been implemented in the Rujigou coalfield, northwest China. A database of several different types of data, including remote sensing images, results of geological surveys, etc., has been collected and assembled in a PC-based information system, which is being developed by the International Institute for Aerospace Survey and Earth Sciences (ITC). This coal fire monitoring and management information system (CoalMan) comprises of two main parts: one is the database with its management tools, and the tools for data processing and analysis form the other. This paper first introduces the structure of the CoalMan information system, presents the data types and contents of CoalMan database, then shows the data management methods. A complex data analysis package is outlined too, as the core of the information system

Implementation of a data management software system for SSME test history data

The implementation of a software system for managing Space Shuttle Main Engine (SSME) test/flight historical data is presented. The software system uses the database management system RIM7 for primary data storage and routine data management, but includes several FORTRAN programs, described here, which provide customized access to the RIM7 database. The consolidation, modification, and transfer of data from the database THIST, to the RIM7 database THISRM is discussed. The RIM7 utility modules for generating some standard reports from THISRM and performing some routine updating and maintenance are briefly described. The FORTRAN accessing programs described include programs for initial loading of large data sets into the database, capturing data from files for database inclusion, and producing specialized statistical reports which cannot be provided by the RIM7 report generator utility. An expert system tutorial, constructed using the expert system shell product INSIGHT2, is described. Finally, a potential expert system, which would analyze data in the database, is outlined. This system could use INSIGHT2 as well and would take advantage of RIM7's compatibility with the microcomputer database system RBase 5000

Deductive Optimization of Relational Data Storage

Optimizing the physical data storage and retrieval of data are two key database management problems. In this paper, we propose a language that can express a wide range of physical database layouts, going well beyond the row- and column-based methods that are widely used in database management systems. We use deductive synthesis to turn a high-level relational representation of a database query into a highly optimized low-level implementation which operates on a specialized layout of the dataset. We build a compiler for this language and conduct experiments using a popular database benchmark, which shows that the performance of these specialized queries is competitive with a state-of-the-art in memory compiled database system

Creating an Intelligent System for Bankruptcy Detection: Semantic data Analysis Integrating Graph Database and Financial Ontology

In this paper, we propose a novel intelligent methodology to construct a Bankruptcy Prediction Computation Model, which is aimed to execute a company’s financial status analysis accurately. Based on the semantic data analysis and management, our methodology considers the Semantic Database System as the core of the system. It comprises three layers: an Ontology of Bankruptcy Prediction, Semantic Search Engine, and a Semantic Analysis Graph Database

Innovative Evaluation System – IESM: An Architecture for the Database Management System for Mobile Application

As the mobile applications are constantly facing a rapid development in the recent years especially in the academic environment such as student response system [1-8] used in universities and other educational institutions; there has not been reported an effective and scalable Database Management System to support fast and reliable data storage and retrieval. This paper presents Database Management Architecture for an Innovative Evaluation System based on Mobile Learning Applications. The need for a relatively stable, independent and extensible data model for faster data storage and retrieval is analyzed and investigated. It concludes by emphasizing further investigation for high throughput so as to support multimedia data such as video clips, images and documents