Implementation of Data Mining Technology in Bonded Warehouse Inbound and Outbound Goods Trade

For the taxed goods, the actual freight is generally determined by multiplying the allocated freight for each KG and actual outgoing weight based on the outgoing order number on the outgoing bill. Considering the conventional logistics is insufficient to cope with the rapid response of e-commerce orders to logistics requirements, this work discussed the implementation of data mining technology in bonded warehouse inbound and outbound goods trade. Specifically, a bonded warehouse decision-making system with data warehouse, conceptual model, online analytical processing system, human-computer interaction module and WEB data sharing platform was developed. The statistical query module can be used to perform statistics and queries on warehousing operations. After the optimization of the whole warehousing business process, it only takes 19.1 hours to get the actual freight, which is nearly one third less than the time before optimization. This study could create a better environment for the development of China's processing trade.

A Simulation Model for Application Development in Data Warehouses

Software development projects have been blamed for being behind schedule, cost overruns, and the delivery of poor quality product. This paper presents a simulation model of a data warehouse to evaluate the feasibility of different software development controls and measures to better manage a software development lifecycle, and improve the performance of the launched software. This paper attempts to address the practical issue of code defects in each stage of data warehouse application development. The author has compared the defect removal rate of their previous project to the newly proposed enhanced project development life cycle that uses code inspection and code scorecard along with other phases of software development life cycle. Simulation results show that the code inspection and code score-carding have achieved a significant code defect reduction. This has also significantly improved the software development process and allowed for a flawless production execution. The author proposes this simulation model to a data warehouse application development process to enable developers to improve their current process.

Large Scale System for Social Media Data Warehousing

Social media data become an integral part in the business data and should be integrated into the decisional process for better decision making based on information which reflects better the true situation of business in any field. However, social media data are unstructured and generated in very high frequency which exceeds the capacity of the data warehouse. In this work, we propose to extend the data warehousing process with a staging area which heart is a large scale system implementing an information extraction process using Storm and Hadoop frameworks to better manage their volume and frequency. Concerning structured information extraction, mainly events, we combine a set of techniques from NLP, linguistic rules and machine learning to succeed the task. Finally, we propose the adequate data warehouse conceptual model for events modeling and integration with enterprise data warehouse using an intermediate table called Bridge table. For application and experiments, we focus on drug abuse events extraction from Twitter data and their modeling into the Event Data Warehouse.

Analisa Kategori Barang dengan Penjualan Terbanyak dalam Jangka Waktu 3 Bulan Menggunakan Data Warehouse

UD. Makmur Sejahtera sebagai salah satu distributor terbesar untuk barang kebutuhan sehari-hari di Manokwari Papua, memiliki data-data transaksi penjualan untuk setiap kategori barang dan jenis barang. Data-data ini masih tersimpan secara fisik dalam bentuk nota serta belum didigitalkan untuk dapat dimanfaatkan secara maksimal untuk membantu UD. Makmur Sejahtera meningkatkan penjualan. Penelitian ini memiliki ide dasar pemanfaatan data digital transaksi penjualan untuk mengetahui kategori barang mana yang memiliki penjualan terbanyak dalam kurun waktu tiga bulan (Juli 2020 hingga September 2020) melalui proses Extraction, Transformation, Loading (ETL) berbasis Pentaho Data Integration, untuk kemudian disimpan dalam bentuk data multi dimensi, dikategorikan, dan divisualisasikan menggunakan Tableau. Hasil pengujian menunjukkan bahwa komoditas beras merupakan kategori barang dengan penjualan terbanyak pada kurun waktu tiga bulan serta implementasi Data Warehouse sangat membantu UD. Makmur Sejahtera di dalam mencapai tujuan bisnis usahanya.

a Schema Extraction of Document-Oriented Database for Data Warehouse

The data warehouse is a very famous solution for analyzing business data from heterogeneous sources. Unfortunately, a data warehouse only can analyze structured data. Whereas, nowadays, thanks to the popularity of social media and the ease of creating data on the web, we are experiencing a flood of unstructured data. Therefore, we need an approach that can "structure" the unstructured data into structured data that can be processed by the data warehouse. To do this, we propose a schema extraction approach using Google Cloud Platform that will create a schema from unstructured data. Based on our experiment, our approach successfully produces a schema from unstructured data. To the best of our knowledge, we are the first in using Google Cloud Platform for extracting a schema. We also prove that our approach helps the database developer to understand the unstructured data better.

Implementasi ETL (Extract, Transform, Load) pada Data warehouse Penjualan Menggunakan Tools Pentaho

Perusahaan yang bergerak dibidang komersil perlu melakukan analisis kinerja penjualan. Dengan melakukan analisis kinerja penjualan, perusahaan dapat meningkatkan kinerja penjualannya. Salah satu cara melakukan analisis kinerja penjualan adalah dengan mengumpulkan data historis yang berkaitan dengan penjualan dan kemudian mengolah data tersebut sehingga menghasilkan informasi berupa hasil kinerja penjualan perusahaan yang dapat digunakan sebagai acuan dalam pengambilan keputusan dalam perusahaan. Penulis disini akan mencoba menganalisa sebuah data yang terkait dengan data penjualan yang ada pada sebuah Superstore di Negara Amerika Serikat, data-data yang dikumpulkan berikut terkait dengan penjualan seperti data produk, segment penjualan, transaksi penjualan, dan lain-lain. Setelah semua data yang dibutuhkan untuk membangun Data warehouse terkumpul, proses selanjutnya adalah ETL (Extract, Transform dan Load) data. Tools yang digunakan pada proses ETL ini yaitu Pentaho. Pada proses ekstraksi data ini meliputi 1 sumber data yaitu data penjualan dengan jenis file excel. Setelah melakukan proses ektraksi selanjutnya ada proses transformasi data yaitu melakukan beberapa perubahan terhadap data yang sudah diekstraksi agar lebih konsisten dan seragam sesuai dengan kebutuhan data warehouse. Setelah transformasi dilakukan, hasil akhir dari proses ETL tersebut berupa Data warehouse sederhana yang berisikan data Customer, Home Office dan Corporate, kemudian data tersebut dimasukkan ke dalam Data warehouse dan di tampilkan kedalam Database MySql dan file Microsoft Excel.

A Natural Language Interface to Relational Databases Using an Online Analytic Processing Hypercube

Structured Query Language (SQL) is commonly used in Relational Database Management Systems (RDBMS) and is currently one of the most popular data definition and manipulation languages. Its core functionality is implemented, with only some minor variations, throughout all RDBMS products. It is an effective tool in the process of managing and querying data in relational databases. This paper describes a method to effectively automate the conversion of a data query from a Natural Language Query (NLQ) to Structured Query Language (SQL) with Online Analytical Processing (OLAP) cube data warehouse objects. To obtain or manipulate the data from relational databases, the user must be familiar with SQL and must also write an appropriate and valid SQL statement. However, users who are not familiar with SQL are unable to obtain relevant data through relational databases. To address this, we propose a Natural Language Processing (NLP) model to convert an NLQ into an SQL query. This allows novice users to obtain the required data without having to know any complicated SQL details. The model is also capable of handling complex queries using the OLAP cube technique, which allows data to be pre-calculated and stored in a multi-dimensional and ready-to-use format. A multi-dimensional cube (hypercube) is used to connect with the NLP interface, thereby eliminating long-running data queries and enabling self-service business intelligence. The study demonstrated how the use of hypercube technology helps to increase the system response speed and the ability to process very complex query sentences. The system achieved impressive performance in terms of NLP and the accuracy of generating different query sentences. Using OLAP hypercube technology, the study achieved distinguished results compared to previous studies in terms of the speed of the response of the model to NLQ analysis, the generation of complex SQL statements, and the dynamic display of the results. As a plan for future work, it is recommended to use infinite-dimension (n-D) cubes instead of 4-D cubes to enable ingesting as much data as possible in a single object and to facilitate the execution of query statements that may be too complex in query interfaces running in a data warehouse. The study demonstrated how the use of hypercube technology helps to increase system response speed and process very complex query sentences.