Self-Optimizing Database Architecture

Over the most recent decades, analysts and database service providers have fabricated devices to help DBAs (Database Administrators) in various parts of framework tuning and the actual design of the database. Most of this past work, regardless, is fragmented on the grounds that it expects people to come up with an official agreement or judgement about any modifications to the data in the database and fix issues after they happen rather than preventing such cases from taking place or adjusting to these changes automatically. What is required for a really "self-driving" database management system (DBMS) is another way of approaching this that is intended for independent activity and automatic decision making. This is different from prior endeavors since all angles of this framework are constrained by a coordinated arranging part that not just enhance the framework for the current responsibility, but in addition to this, it also predicts future responsibility that might take place and prepares itself for such not-so-common occurrences and adjusts to them as required while keeping the efficiency of the operations as close to normal as possible. With this, the DBMS can uphold all the past tuning procedures without requiring a human to decide the right way and proper opportunity to use them. It likewise empowers new advancements that are significant for current DBMSs (Database Management System), which are impractical today because of the fact that the intricacy of overseeing these frameworks has outperformed the abilities of human specialists who are supposed to tune them and make changes when required. Keywords: Database Management System, Database Administrator, Forecasting, Long Short-Term Memory, Recurrent Neural Networks

Sistem Rekomendasi Pembelian Produk Kesehatan pada E-Commerce ABC berbasis Graph Database Amazon Neptune menggunakan Metode Hybrid Content-Collaborative Filtering

Recommendation System of Health Product Purchasing at ABC E-Commerce System based on Amazon Neptune’s Graph Database using Hybrid ContentCollaborative Filtering Method.Health products purchased by society, either in drugstores or pharmacies may vary according to their needs. ABC e-commerce is a Business to Business (B2B)-based e-commerce owned by PT XYZ. As a health product sales system from distributors to drug stores/pharmacies, they still do not have a health product purchase recommendation system yet. The recommendation system is needed to provide recommendations of health products for the customers. Amazon Neptune is implemented in this research to build a health product recommendation system. The hybrid contentcollaborative filtering method is used to generate complete recommendations based on content attributes and user habits. The datasets were product data, product categories, customers, product principals, and data of products trading. This research produces a health products recommendations model at ABC e-commerce with android based using web services. The implementation can provide recommendations of health products that can be accessed in real-time by customers.Keywords: health products, recommendation systems, graph database, Amazon Neptune, hybrid content-collaborative filteringAbstrak. Produk kesehatan yang dibeli masyarakat, melalui toko obat/apotek, dapat berbeda sesuai kebutuhan. E-commerce ABC berbasis Business to Business (B2B) milik PT XYZ sebagai sistem penjualan produk kesehatan dari distributor kepada toko obat/apotek belum memiliki sistem rekomendasi pembelian produk kesehatan. Sistem rekomendasi sebagai pengembangan fitur e-commerce ABC diperlukan untuk memberikan rekomendasi produk kesehatan yang sesuai dengan keadaan setiap pelanggan. Amazon Neptune sebagai graph database service yang dapat mengelola relasi dalam data yang saling terhubung, digunakan dalam penelitian untuk membangun sistem rekomendasi produk kesehatan. Metode hybrid content-collaborative filtering digunakan untuk menghasilkan rekomendasi yang lengkap berdasarkan atribut konten dan kebiasaan pengguna. Dataset yang digunakan meliputi data produk, kategori produk, pelanggan, principal, serta data jual-beli produk di e-commerce ABC. Penelitian ini menghasilkan model rekomendasi produk kesehatan yang diimplementasikan pada e-commerce ABC berbasis Android menggunakan web service. Implementasi tersebut memberikan rekomendasi produk kesehatan yang dapat diakses secara real-time oleh pelanggan pada saat menggunakan ecommerce ABC.Kata Kunci: produk kesehatan, sistem rekomendasi, graph database, Amazon Neptune, hybrid content-collaborative filtering

Development of air quality mobile tools for observation

Mobile weather stations are needed because of their better coverage balance than stationary stations. Center for Climate Risk and Opportunity Management in Southeast Asia Pacific (CCROM-SEAP) of Bogor Agricultural University (Institut Pertanian Bogor or IPB University) developed a low-cost mini observation system using Espressif ESP32 DOIT Development Kit V1 module, which based on the internet of things (IoT) to monitor real-time meteorological elements (such as temperature, humidity, and pressure), CO2, PM2.5, and PM10 concentration for Bogor (Center of Bogor City). With Firebase (database service by Google) integration, the system records data every 2 minutes and sent automatically to Firebase. We also create an unpublished android application called ServMo for exporting JSON to CSV format. The results show this system has a good performance for real-time monitoring purposes for a better balance of measurements coverage.

Progress toward a holistic land and marine surface meteorological database and a call for additional contributions.

<p>Historical observational climate records are crucial in understanding climatic variability, extreme past weather and climate events and allowing us to make informed decisions for better societal adaptation to climate change. Historical observations are also a key component to derive reanalysis products and evaluate climate models.The management of both marine and land historical datasets has been highly fragmented, leading to diverse data holdings held by multiple institutions. Consequently, it is necessary to confront the challenges of: a plethora of distinct data formats; gross duplication of records with differing identifiers, names; and in many cases varying geo-location information. Within available land and marine data holdings there are greatly differing levels of completeness, data quality checks and data processing applied. There are further issues with limited data discovery metadata and sometimes a distinct lack of traceability to the underlying original data source. In light of these issues, we have produced The C3S Global Land and Marine Observations Database which is part of the Copernicus Climate Change Service, making climate data and information more easily accessible to support adaptation and mitigation policies of the European Union and the wider global community  This talk outlines progress of the Global Land and Marine Observations Database service in securing data sources and introduces the data deposit component. We present details of land based data holdings inventoried, highlighting priority needs in terms of periods, regions and Essential Climate Variables (ECVs) where additional land based data could bring most benefit. These holdings are being iteratively merged and integrated to best meet user needs and are served to the user via the Copernicus Climate Data Store (CDS). Details of the current land based data release are also presented in this talk. The secure Data Deposit Service enables any data provider to share additional data and metadata with the service. We encourage all data owners to share their data with the C3S service via our Data Upload Server. All unique and relevant data acquired or submitted will be also archived at the NOAA National Centers for Environmental Information World Data Center for Meteorology, Asheville, North Carolina, USA and used in their data base curation efforts which are being jointly developed.</p>

ProMoCell and ProModb: web services for analyzing interaction-based functionally localized protein modules in a cell

The modular organization of a cell which can be determined by its interaction network allows us to understand a mesh of cooperation among the functional modules. Therefore, cellular level identification of functional modules aids in understanding the functional and structural characteristics of the biological network of a cell and also assists in determining or comprehending the evolutionary signal. We develop ProMoCell that performs real-time web scraping for generating clusters of the cellular level functional units of an organism. ProMoCell constructs the Protein Locality Graphs and clusters the cellular level functional units of an organism by utilizing experimentally verified data from various online sources. Also, we develop ProModb, a database service that houses precomputed whole-cell protein-protein interaction network-based functional modules of an organism using ProMoCell. Our web service is entirely synchronized with the KEGG pathway database and allows users to generate spatially localized protein modules for any organism belonging to the KEGG genome using its real-time web scraping characteristics. Hence, the server will host as many organisms as is maintained by the KEGG database. Our web services provide the users a comprehensive and integrated tool for an efficient browsing and extraction of the spatial locality-based protein locality graph and the functional modules constructed by gathering experimental data from several interaction databases and pathway maps. We believe that our web services will be beneficial in pharmacological research, where a novel research domain called modular pharmacology has initiated the study on the diagnosis, prevention, and treatment of deadly diseases using functional modules.

Design and Implementation of a Mobile Library Management System for Improving Service Delivery

A mobile library management system provides a more efficient way of managing library processes and rendering effective library services irrespective of time and place. This research work aimed to develop a Mobile Library Management System for the Nile University of Nigeria Library to overcome the challenges that hinder the librarians from managing the library processes on the go. The android mobile library management system was developed using Android Studio, HTML, CSS, PHP, and MySQLi database. Service Responsibility Table was used in eliciting and documenting the user’s requirements for the library management system. The proposed Android-based mobile library system was evaluated through a survey by the librarians. The evaluation has shown that the proposed system is capable of complementing the existing library management systems.

Mapping the intellectual structure of GIS-T field (2008–2019): a dynamic co-word analysis

Using geographic information systems (GIS) widely for dealing with transportation problems (is well-known as GIS-T), has made it nessasary for researchers to discover the current state-of-the-art and predict the trends of future research. This paper aims to contribute to a better understanding of GIS-T research area from a longitudinal perspective, over the period 2008–2019. A co-word analysis was used to illustrate all the underlying subfields of GIS-T based on published papers in the Web of Science (WoS) database service. The main knowledge areas representing the intellectual structure of GIS-T including (a) sustainability, (b) health, (c) planning and management, and (d) methods and tools, were detected. Finally, in order to illustrate the structure and development of the identified clusters, two-dimensional maps and strategic diagrams for each period were drawn. This study is the first attempt to employ a text mining method so as to detect the conceptual structure of GIS-T research area from a complex and interdisciplinary literature.

On the Performance of Cloud Services and Databases for Industrial IoT Scalable Applications

In the Industry 4.0 the communication infrastructure is derived from the Internet of Things (IoT), and it is called Industrial IoT or IIoT. Smart objects deployed on the field collect a large amount of data which is stored and processed in the Cloud to create innovative services. However, differently from most of the consumer applications, the industrial scenario is generally constrained by time-related requirements and its needs for real-time behavior (i.e., bounded and possibly short delays). Unfortunately, timeliness is generally ignored by traditional service provider, and the Cloud is treated as a black box. For instance, Cloud databases (generally seen as “Database as a service”—DBaaS) have unknown or hard-to-compare impact on applications. The novelty of this work is to provide an experimental measurement methodology based on an abstract view of IIoT applications, in order to define some easy-to-evaluate metrics focused on DBaaS latency (no matter the actual implementation details are). In particular, the focus is on the impact of DBaaS on the overall communication delays in a typical IIoT scalable context (i.e., from the field to the Cloud and the way back). In order to show the effectiveness of the proposed approach, a real use case is discussed (it is a predictive maintenance application with a Siemens S7 industrial controller transmitting system health status information to a Cloudant DB inside the IBM Bluemix platform). Experiments carried on in this use case provide useful insights about the DBaaS performance: evaluation of delays, effects of involved number of devices (scalability and complexity), constraints of the architecture, and clear information for comparing with other implementations and for optimizing configuration. In other words, the proposed evaluation strategy helps in finding out the peculiarities of Cloud Database service implementations.

A Proposed IoT Smart Trap using Computer Vision for Sustainable Pest Control in Coffee Culture

The Internet of Things (IoT) is emerging as a multi-purpose technology with enormous potential for improving the quality of life in several areas. In particular, IoT has been applied in agriculture to make it more sustainable ecologically. For instance, electronic traps have the potential to perform pest control without any pesticide. In this paper, a smart trap with IoT capabilities that uses computer vision to identify the insect of interest is proposed. The solution includes 1) an embedded system with camera, GPS sensor and motor actuators; 2) an IoT middleware as database service provider, and 3) a Web application to present data by a configurable heat map. The demonstration of proposed solution is exposed and the main conclusions are the perception about pest concentration at the plantation and the viability as alternative pest control against traditional control based on pesticides.