A Service-Oriented Foundation for Big Data

This paper provides a service-oriented foundation for big data. The foundation has two parts. Part 1 reveals 10 big characteristics of big data. Part 2 presents a service-oriented framework for big data. The framework has fundamental, technological, and socio-economic levels. The fundamental level has four big fundamental characteristics of big data: big volume, big velocity, big variety, and big veracity. The technological level consists of three big technological characteristics of big data: Big intelligence, big analytics, big infrastructure. The socioeconomic level has three big socioeconomic characteristics of big data: big service, big value, and big market. The article looks at each level of the proposed framework from a service-oriented perspective. The multi-level framework will help organizations and researchers understand how the 10 big characteristics relate to big opportunities, big challenges, and big impacts arising from big data. The proposed approach in this paper might facilitate the research and development of big data, big data analytics, business intelligence, and business analytics.

InaCORS-BIG Service For Practice Survey

Badan Informasi Geospasial (BIG) is a government agency engaged in the field of Geospatial Information (IG). BIG provides several services that can be accessed by the general public and related to IG. One of the services provided by BIG is the Indonesia Continuously Operating Reference System (InaCORS). InaCORS is divided into various services, as Rinex data services, Online Post-Processing, RTK NTrip, and Mobile InaCORS. This study aims to determine the effectiveness and accuracy of InaCORS services for GNSS surveys using the Rapid Static method and Network RTK. The rapid static survey data is processed using online post-processing services and Network RTK (iMax, Max, and Nearest) will use InaCORS points as a base reference. This study also uses the results of the Total Station tool as comparison data. The results showed that the average value of the difference between the rapid static coordinates and the TS observations was dN = 0.353 m dE = 0.180 m and dZ = 0.233 m, while the Network RTK and TS coordinates were dN = 0.408 m dE = 0.184 m and dZ = 0.176 m.

A Service-Oriented Foundation for Big Data

This paper provides a service-oriented foundation for big data. The foundation has two parts. Part 1 reveals 10 big characteristics of big data. Part 2 presents a service-oriented framework for big data. The framework has fundamental, technological, and socio-economic levels. The fundamental level has four big fundamental characteristics of big data: big volume, big velocity, big variety, and big veracity. The technological level consists of three big technological characteristics of big data: Big intelligence, big analytics, big infrastructure. The socioeconomic level has three big socioeconomic characteristics of big data: big service, big value, and big market. The article looks at each level of the proposed framework from a service-oriented perspective. The multi-level framework will help organizations and researchers understand how the 10 big characteristics relate to big opportunities, big challenges, and big impacts arising from big data. The proposed approach in this paper might facilitate the research and development of big data, big data analytics, business intelligence, and business analytics.

School Management Application Using iOS

Recently, the mobile application become a big service that’s make users easy manage the data over the server. The Application consist several sections. First section the Front End used is Swift Language in Xcode platform with MySQL and web server. Second section the Back End used is MySQL. In this paper there are several modules such as Data Entry module, Data Records module. These modules are further divided in to sub modules. That is Class Setup, Student Setup, Teacher Setup, Student Attendance, Subject Setup, Examination Setup and Exam Details are in Data Entry module. Student Details, Teacher Records, Student Attendance are in the Data Records module. These modules give way in managing the organization efficiently. So, this project helps in efficient management of human resource inside the organization. Also, it consumes less time consumption. The main and important benefit of this proposed Application is that it is very much user friendly and accurate. So the employees and the administrators feel so much comfortable to work with it. Also in all the modules the regularly updated information are very much useful when they are extracted.