Designing and Implementing a Distributed Database for a Small Multi-Outlet Business

Data is a fundamental and necessary element for businesses. During their operations they generate a certain amount of data that they need to capture, store, and later on retrieve when required. Databases provide the means to store and effectively retrieve data. Such a database can help a business improve its services, be more competitive, and ultimately increase its profits. In this paper, the system requirements of a distributed database are researched for a movie rental and sale store that has at least two outlets in different locations besides the main one. This project investigates the different stages of such a database, namely, the planning, analysis, decision, implementation and testing

SAW-TOPSIS Implementation To Determine An Appropriate DBMS Software

Selection an appropriate Database Management Software, is a crucial part to ensure operational excellence businesses firm. Database management software used to organize and manage the company’s data so that they can be efficiently accessed and used to improve operational and decision quality. However, a senior manager as decision maker sometimes lacks the comprehensive knowledge to choose a suitable database management software which meets with business needs. Then, The manager determines a database management software based on a consultant or vendor offer. On the other hand, a consultant or vendor has an interest in to sell their product, so they tend to lead manager to choose their product even though it is not fulfilling business needs. We present a decision support application to help the manager to select an appropriate database management software (DBM) for their company, using Simple Additive Weighting (SAW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. We observe SQL Server, MySQL, Oracle, DB2, and PostgreSQL as five top database management software and investigate the detail about cost, storage capacity, security, supported the operating system and supported programming language as key criteria to select best database management software from their official website. Then, we combining SAW and TOPSIS method to choose the best appropriate DBM software based on user requirement through computation program and validate our application performance includes the user interface, usability and accuracy result to 50 database engineers expert as respondent. The results are as follows; 1) 86 % of respondents are satisfied with application user interface, 2) 94% are happy with application usability and 3) 86% are pleased with the accuracy of the computation. Overall, this study provides a decision support application to determine an appropriate database management software based on business needs by combining SAW and TOPSIS methods

Desain Pola Struktur Mapping Schema untuk Sinkronisasi dan Integrasi Multidatabase Terdistribusi dalam Mengelola Data Epidemiologi

Pengaruh globalisasi terhadapap Perusahaan yang semakin luas area bisnisnya, maka organisasi menuntut inovasi teknologi informasi yang dapat mengelola peningkatan jumlah data dan sekalabilitas jarak transaksi (antara sistem aplikasi dengan database maupun antar database itu sendiri). Seperti pada pengelolaan data epidemiologi kesehatan, dimana sumber data tersebar pada database yang ada pada berbaga ilokasi rumah sakit dan poliklinik pada suatu wilayah kabupaten atau suatu kota tertentu. Permasalahannya database sumber (source) bersifat heterogen sehingga mengalami potensi konflik (kesulitan) dalam melakukan integrasi menuju pada pusat data epidemiologi (target) pada dinas kesehatan. Potensi konflik yang terjadi adalah ketidak seragaman skema relasi (konflik skema), ketidak akuratan isi (konflik data). Untuk itu dalam integrasi memerlukan analisis database sumber yang bersifat heterogen dengan melakukan strukturisasi dan sinkronisasi sebagai persiapan integrasi data. Dengan permasalahan integrasi antar database distribusi tersebut maka dalam penelitian ini bertujuan mendesain arsitektur database terdistribusi dengan metode replikasi yang akan diimplementasikan pada integrasi database epidemiologi sehingga akan didapatkan sebuah arsitektur database terdistribusi yang bisa mengatasi ketersediaan data pada sistem surveilans terpadu (SST). Untuk mengatasi masalah tersebut maka diperlukan suatu pengembangan arsitektur basis data terdistribusi untuk pola sinkronisasi dan integrasi dengan metode replikasi mapping schema. Metode replikasi mapping schema generator merupakan kemajuan dari rekayasa konsep teknologi DDBMS (Distributed Database Management System) yang mampu melakukan sinkronisasi (captures, routes, transforms) dan integrasi data yang bersifat heterogen secara real-time. Tujuan jangka panjang dalam penelitian ini adalah merancang teknologi dan aplikasi dalam mengembangkan teknik integrasi data dari berbagai ragam aplikasi dan database tanpa harus menyeragamkan aplikasi dan database yang sudah ada. Dengan demikian schema local dapat dipertahankan dalam mendapatkan schema global melalui teori rekayasa sinkronisasi dan integrasi basis data. Sedangkan target khusus yang akan dicapai adalah memperoleh model arsitektur database tersebar untuk integrasi data yang dapat diterapkan dalam mengelola dan mengembangkan sistem informasi epidemiologi terintegrasi pada dinas kesehatan dengan metode replikasi mapping schema . Metode penelitian yang digunakan adalah dengan studi literatur dan studi lapangan. Setelah melakukan studi awal kegiatan penelitian dilanjutkan dengan observasi dan studi pustaka, analisa permasalahan dalam pernacangan arsitektur database. Tahapan berikutnaya adalah melakukan desain pola integrasi database dan dilakukan uji integrasi dan replikasi untuk mendapatkan kesimpulan integrasi antar database heterogen. Hasil dalam kasus penelitian ini adalah integrasi antar dua relasi yang terjadi konflik (surveila_rs_A dan data_center_SST) menggunakan mapping schema (relasi_map_ICD_X)

PerfXplain: Debugging MapReduce Job Performance

While users today have access to many tools that assist in performing large scale data analysis tasks, understanding the performance characteristics of their parallel computations, such as MapReduce jobs, remains difficult. We present PerfXplain, a system that enables users to ask questions about the relative performances (i.e., runtimes) of pairs of MapReduce jobs. PerfXplain provides a new query language for articulating performance queries and an algorithm for generating explanations from a log of past MapReduce job executions. We formally define the notion of an explanation together with three metrics, relevance, precision, and generality, that measure explanation quality. We present the explanation-generation algorithm based on techniques related to decision-tree building. We evaluate the approach on a log of past executions on Amazon EC2, and show that our approach can generate quality explanations, outperforming two naive explanation-generation methods.Comment: VLDB201

Don't hold my data hostage - A case for client protocol redesign

Transferring a large amount of data from a database to a client program is a surprisingly expensive operation. The time this requires can easily dominate the query execution time for large result sets. This represents a significant hurdle for external data analysis, for example when using statistical software. In this paper, we explore and analyse the result set serialization design space. We present experimental results from a large chunk of the database market and show the inefficiencies of current approaches. We then propose a columnar serialization method that improves transmission performance by an order of magnitude