RDBMS Technology Research Articles

Desain dan Implementasi Platform Manajemen Historis Harga Saham dengan Kurasi Data dan Analisis Teknikal

Saat ini penelitian terkait saham didominasi oleh topik prediksi harga saham. Untuk melakukan prediksi harga saham dibutuhkan data historis harga saham. Banyak provider yang menyediakan data tersebut namun tidak semuanya gratis. Beberapa provider yang menyediakan data historis harga saham secara gratis adalah Yahoo Finance, Google Finance, Stooq, dan National Stock Exchange (NSE) India. Namun, seringkali terdapat perbedaan data antar provider baik dari sisi ketersediaan, konten, format data, dan sebagainya. Sehingga, investor perlu mengambil data dari beberapa provider untuk diperbandingkan guna mendapatkan hasil analisis yang optimal. Oleh karena itu, penelitian ini membangun platform manajemen data historis harga saham yang melakukan kurasi data dari keempat sumber data tersebut sekaligus dilengkapi dengan analisis teknikal untuk memudahkan analisis investasi. Dalam penelitian ini ditemukan bahwa kurasi data historis harga saham untuk keempat sumber data tersebut sudah dapat dilakukan dengan teknologi RDBMS sebagai databasenya. Namun, terdapat kelemahan yaitu kurang fleksibel apabila diperlukan penambahan sumber data dengan data yang tidak terstruktur atau memiliki kolom berbeda-beda. Meskipun demikian, dengan data dari keempat sumber data yang telah terintegrasi, analisis teknikal dapat memberikan gambaran yang lebih luas terhadap tren pergerakan harga dengan memperbandingkan hasil analisis untuk setiap sumber data. 
 Kata kunci – historis harga saham, kurasi data, rdbms, analisis teknikal, moving average

Scaling up the performance of more powerful Datalog systems on multicore machines

Extending RDBMS technology to achieve performance and scalability for queries that are much more powerful than those of SQL-2 has been the goal of deductive database research for more than thirty years. The $$\mathcal {D}e\mathcal {A}\mathcal {L}\mathcal {S}$$DeALS system has made major progress toward this goal, by (1) Datalog extensions that support the more powerful recursive queries needed in advanced applications, and (2) superior performance for both traditional recursive queries and those made possible by the new extensions, while (3) delivering competitive performance with commercial RDBMSs on non-recursive queries. In this paper, we focus on the techniques used to support the in-memory evaluation of Datalog programs on multicore machines. In $$\mathcal {D}e\mathcal {A}\mathcal {L}\mathcal {S}$$DeALS, a Datalog program is represented as an AND/OR tree, and multiple copies of the same AND/OR tree are used to access the tables in the database concurrently during the parallel evaluation. We describe compilation techniques that (1) recognize when the given program is lock-free, (2) transform a locking program into a lock-free program, and (3) find an efficient parallel plan that correctly evaluates the program while minimizing the use of locks and other overhead required for parallel evaluation. Extensive experiments demonstrate the effectiveness of the proposed techniques.

Query from examples

In this paper, we propose a new approach, called Query from Examples (QFE), to help non-expert database users construct SQL queries. Our approach, which is designed for users who might be unfamiliar with SQL, only requires that the user is able to determine whether a given output table is the result of his or her intended query on a given input database. To kick-start the construction of a target query Q , the user first provides a pair of inputs: a sample database D and an output table R which is the result of Q on D. As there will be many candidate queries that transform D to R , QFE winnows this collection by presenting the user with new database-result pairs that distinguish these candidates. Unlike previous approaches that use synthetic data for such pairs, QFE strives to make these distinguishing pairs as close to the original ( D,R ) pair as possible. By doing so, it seeks to minimize the effort needed by a user to determine if a new database-result pair is consistent with his or her desired query. We demonstrate the effectiveness and efficiency of our approach using real datasets from SQLShare, a cloud-based platform designed to help scientists utilize RDBMS technology for data analysis.

A survey of mapreduce based parallel processing technologies

Along with the increasing Big Data challenges, the MapReduce based systems are extensively welcomed, because of their remarkable simplicity and scalability. However, from the first day MapReduce is proposed, its argument with parallel DBMSs never stops, as it over-focuses on the scalability but overlooks the efficiency. Accordingly, extended systems are proposed in order to improve the performance on the limited scale clusters. In the meantime, traditional RDBMS technologies like structured data model, transaction, SQL, etc. are also getting more attention. This paper reviews such systems, from Google and also the third parties, trying to indicate the directions for the future research.

Knowledge-based system for rainfed natu tobacco (Nicotiana tabacum) germplasam

Tobacco (Nicotiana tabacum) is a commercial crop in many countries. Most of them are maintaining large number of germplasm collections without computer databases. Accessing the large amounts of plant genetic resource data requires the development of germplasm databases. Tobacco germplasm are of different ‘types’, with large numbers of accessions in each type. Among the different tobacco types grown in India, rainfed natu tobacco is an indigenous type mainly used for domestic consumption. Based on the data available on various parameters of rainfed natu tobacco accessions, a knowledge-based system for rainfed natu tobacco germplasm was developed with user-friendly menus using RDBMS technology. This software allows the users to store, update and retrieve the germplasm data of all tobacco types on various parameters and acts as a decision-making tool. This database can be kept in the web server for global accessing.

Spider: managing clinical data of cancer patients treated through a multidisciplinary approach by a palm based system

Background: The complexity of modern oncology, based on multi-disciplinary management of cancer patients, results in critical amounts of data, leading to problems in managing and sharing information. Methods: Spider is a multi-user system, based on integrated palm technology, created to facilitate data recording, managing and sharing, through Intra-Internet connection. By palms or PCs, data are collected directly at the place where information is generated. Every health professional can edit, modify and display all of the patient's data according to his/her operational level. A powerful engine enables Spider’s users to create series of cancer patients’ appointments linked to one another by specified time intervals and save them as “Protocols”. Applying a protocol to the patient, the system schedules a wave of appointments and alerts keeping the correlation with time intervals previously specified by specialists. XML technology is integrated with traditional RDBMS technology to build the Electronic Patient File (EPF) updated during each patient’s admission or consultation, including any new diagnostic/therapeutic events and collective decisions. The system automatically produces all clinical documents routinely in use (discharge letters, exams’ requests, etc.). Results: Spider’s different archives include 4387 patients (Prostate, n=849; Lung, n=1596; Rectum, n=1541; Head & Neck, n=291; Cervix, n=110). The EPF includes specific modules: staging, surgery, chemotherapy, hormonotherapy, radiotherapy, toxicity, pathology, follow-up and clinical summary. Spider Hospitalization displays the ward map and important details of patients occupying each single bed. Conclusions: Spider makes data capture easier and accurate. The availability of large amounts of information accelerates outcome analysis and improves cancer research.

A Web services-based framework for building componentized digital libraries

We present a new Web services-based framework for building componentized digital libraries (DLs). We particularly demonstrate how traditional RDBMS technology can be easily deployed to support several common digital library services. Configuration and customization of the framework to build specialized systems is supported by a wizard-like tool which is based on a generic metamodel for DLs. Such a tool implements a workflow process that segments the DL design tasks into well-defined steps and drives the designer along these steps. Both the framework and the configuration tool are evaluated in terms of several performance and usability criteria. Our experimental evaluation demonstrates the feasibility and superior performance of our framework, as well as the effectiveness of the wizard tool for setting up DLs.

A Multidisciplinary Decision Support System for Forest Fire Crisis Management

A wildland fire is a serious threat for forest ecosystems in Southern Europe affecting severely and irreversibly regions of significant ecological value as well as human communities. To support decision makers during large-scale forest fire incidents, a multidisciplinary system has been developed that provides rational and quantitative information based on the site-specific circumstances and the possible consequences. The system's architecture consists of several distinct supplementary modules of near real-time satellite monitoring and fire forecast using an integrated framework of satellite Remote Sensing, GIS, and RDBMS technologies equipped with interactive communication capabilities. The system may handle multiple fire ignitions and support decisions regarding dispatching of utilities, equipment, and personnel that would appropriately attack the fire front. The operational system was developed for the region of Penteli Mountain in Attika, Greece, one of the mountain areas in the country most hit by fires. Starting from a real fire incident in August 2000, a scenario is presented to illustrate the effectiveness of the proposed approach.

A GIS based operational system for wildland fire crisis management II. System architecture and case studies

The most important characteristic of major forest fire incidents, from a societal perspective, is their potentiality to seriously and irreversibly damage regions of significant natural beauty. In addition, the extent and severity of such incidents may significantly affect the population and the environment of the adjacent areas. Following a forest fire event, effort should be made to limit such effects. Management decisions should be based on rational and quantitative information based on the site specific circumstances and the possible consequences. To produce such information we have developed an operational system for managing large-scale wildland fire incidents. Its architecture involves an integrated framework of GIS and RDBMS technology systems equipped with interactive communication capabilities. The operational system was developed for Windows 98 platforms, for the region of Penteli Mountain Attika. The fire simulation tool of the operational system that was based on a fuzzy/neural system for the estimation of fire spread and a corresponding discrete contour propagation model for estimating fire consequences as a function of influencing factors such as terrain characteristics, vegetation type and density and meteorological conditions was presented in a related work [Appl. Math. Model. 28(2004), this issue]. Several case studies are presented to illustrate the effectiveness of the proposed approach.

Development of an emission inventory system from transport in Greece

In Greece, to date, a detailed emission inventory database from transport is not available. The Greek Ministry for the Environmental Physical Planning and Public Works has financed an “Emission Inventory System from Transport” (EIST). The EIST aims to be the first detailed and well-structured national emission database from transport (road, rail, air and sea transport and also off-road activities), focusing on the best-input datasets available, the most advanced pollutant emission algorithms and the highest spatial and temporal resolutions. The EIST can also be used as a decision support system (DSS) for environmental planning and development, as it is possible to examine the environmental effects of various “emission scenarios” which result from the application of different environmental measures and policies. The EIST was developed in distributed software fashion technology for “Windows 2000”. Its architecture involves an integrated framework of GIS and RDBMS technology systems equipped with interactive communication capabilities between peripheral software tools. In the present paper, the structure of the system is presented with emphasis on the emission calculation methodology applied. In order to illustrate the functionalities of the system, emission results, primarily derived from the geographical information system, are presented and some initial conclusions regarding the emissions from the transport sector in Greece are drawn.

A Database System to Advance Subsurface Sensing and Imaging

The CenSSIS Image Database System is a scientific database that enables effective collaboration, scientific data sharing and accelerates fundamental research. We describe a state-of-the-art system that uses the Oracle RDBMS and J2EE technologies to provide remote, Internet-based data access. The system incorporates efficient submission and retrieval of images and metadata, indexing of metadata for efficient searching, and complex relational query capabilities.

An operational centre for managing major chemical industrial accidents

The most important characteristic of major chemical accidents, from a societal perspective, is their tendency to produce off-site effects. The extent and severity of the accident may significantly affect the population and the environment of the adjacent areas. Following an accident event, effort should be made to limit such effects. Management decisions should be based on rational and quantitative information based on the site specific circumstances and the possible consequences. To produce such information we have developed an operational centre for managing large-scale industrial accidents. Its architecture involves an integrated framework of geographical information system (GIS) and RDBMS technology systems equipped with interactive communication capabilities. The operational centre was developed for Windows 98 platforms, for the region of Thriasion Pedion of West Attica, where the concentration of industrial activity and storage of toxic chemical is immense within areas of high population density. An appropriate case study is given in order to illuminate the use and necessity of the operational centre.

Towards the building of a dense-region-based OLAP system

On-line analytical processing (OLAP) has become a very useful tool in decision support systems built on data warehouses. Relational OLAP (ROLAP) and multidimensional OLAP (MOLAP) are two popular approaches for building OLAP systems. These two approaches have very different performance characteristics: MOLAP has good query performance but bad space efficiency, while ROLAP can be built on mature RDBMS technology but it needs sizable indices to support it. Many data warehouses contain many small clustered multidimensional data ( dense regions), with sparse points scattered around in the rest of the space. For these databases, we propose that the dense regions be located and separated from the sparse points. The dense regions can subsequently be represented by small MOLAPs, while the sparse points are put in a ROLAP table. Thus the MOLAP and ROLAP approaches can be integrated in one structure to build a high performance and space efficient dense-region-based data cube. In this paper, we define the dense region location problem as an optimization problem and develop a chunk scanning algorithm to compute dense regions. We prove a lower bound on the accuracy of the dense regions computed. Also, we analyze the sensitivity of the accuracy on user inputs. Finally, extensive experiments are performed to study the efficiency and accuracy of the proposed algorithm.