Cloud, edge and Internet of Things (IoT) technologies have emerged to overcome the challenges involved in sharing computational resources and information services. Within generic cloud systems, two models have been identified as having widespread applicability: computation clouds and data clouds. A data cloud is cloud computing that aims to manage, unify and operate multiple data workloads. Many current applications generate datasets consisting of petabytes (PB) of information. Managing large datasets is a complex issuel; in particular, datasets associated with many applications can be distributed widely in geographical terms, particularly in IoT systems. Edge and IoT systems are facing new challenges with increased complexity, making scalability an important issue that will affect the performance of the system. Data replication services are widely accepted techniques to improve availability and fault tolerance, and to improve the data access time. Current replication services, however, often exhibit an increase in response time, reflecting the problems associated with the ever-increasing size of databases. This paper proposes a prediction model to predict replica locations using the files’ access profile, which feeds the neural networks with the access and location behavior (file profile) to minimize the overhead of transferring large volumes of data, which slows down the system and requires careful management. This new model has shown high accuracy and low overheads. The result shows a significant improvement in total task execution time using the proposed model for locating files by 16.34% and 30.45%; in addition, the results show bandwidth improvement by 24.7% and 49.4% compared to the user profile prediction model and replica service model without prediction, respectively. Consequently, the proposed algorithm can improve data access speed, reduce data access latency and decrease bandwidth consumption.

https://www.mdpi.com/2079-9292/11/16/2531/pdf?version=1660839189

Intelligent Replica Selection in Edge and IoT Environments Using Artificial Neural Networks

Abstrak - Sistem temu kembali informasi (information retrieval system) merupakan sistem yang digunakan untuk menemukan informasi yang relevan dengan kebutuhan dari penggunanya, dengan menerapkan sistem tersebut permasalahan pencarian informasi dokumen skripsi bisa memberikan hasil yang relevan sesuai kebutuhan pengguna. Terdapat dua proses utama dalam sistem temu kembali informasi yaitu indexing dan retrieval. Proses indexing adalah proses untuk memberikan bobot pada kata dalam dokumen, metode pembobotan pada penelitian ini menggunakan metode pembobotan TF-IDF. Proses retrieval adalah proses untuk menghitung kemiripan query terhadap dokumen, perhitungan kemiripan menggunakan konsep vector space model dengan mencari nilai cosine similarity. Tujuan dari penelitian ini adalah untuk mengembangkan dan mengimplementasikan pengindeksan otomatis untuk membangun sistem pencarian dokumen di dalam sebuah system penyimpanan dokumen dengan konsep temu-kembali informasi. Kata kunci : Information retrieval,Term Frequncy Inverse Document Frequency, Vector Space Model.

/pdf/penerapan-sistem-temu-kembali-informasi-pada-kumpulan-15zt4635o1.pdf

Penerapan sistem temu kembali informasi pada kumpulan dokumen skripsi

Time series data are an extremely important type of data in the real world. Time series data gradually accumulate over time. Due to the dynamic growth in time series data, they tend to have higher dimensions and large data scales. When performing cluster analysis on this type of data, there are shortcomings in using traditional feature extraction methods for processing. To improve the clustering performance on time series data, this study uses a recurrent neural network (RNN) to train the input data. First, an RNN called the long short-term memory (LSTM) network is used to extract the features of time series data. Second, pooling technology is used to reduce the dimensionality of the output features in the last layer of the LSTM network. Due to the long time series, the hidden layer in the LSTM network cannot remember the information at all times. As a result, it is difficult to obtain a compressed representation of the global information in the last layer. Therefore, it is necessary to combine the information from the previous hidden unit to supplement all of the data. By stacking all the hidden unit information and performing a pooling operation, a dimensionality reduction effect of the hidden unit information is achieved. In this way, the memory loss caused by an excessively long sequence is compensated. Finally, considering that many time series data are unbalanced data, the unbalanced K-means (UK-means) algorithm is used to cluster the features after dimensionality reduction. The experiments were conducted on multiple publicly available time series datasets. The experimental results show that LSTM-based feature extraction combined with the dimensionality reduction processing of the pooling technology and cluster processing for imbalanced data used in this study has a good effect on the processing of time series data.

/pdf/a-novel-long-and-short-term-memory-network-with-time-series-2d3fuzysoc.pdf

A Novel Long- and Short-Term Memory Network with Time Series Data Analysis Capabilities

There is a lot of data that does not have a pattern and unbalanced that is difficult to classify, such as the total population of each country in the world is very varied especially when compared with the number of refugees from each of these countries, China and India numbered more than 2 billion people but the number of refugees is only 0, 01%, while Syria around 70% of the 18 million more residents are refugees. By using the K-Means algorithm, we can group countries that have similar characteristics of population and number of refugees, the average percentage of refugees to the population in each cluster is the character of the cluster. The methodology used in this study are: measures the distance of the data using the Euclidean distance formula, runs the K-Means algorithm, calculate the percentage value of each cluster and find conclusions from the characteristics of the clusters formed. We found how machine learning made a pattern of data without political and social issues, the result is K-Means describing machine learning can grouped every country in cluster that make sense.

Unbalanced Data Clustering with K-Means and Euclidean Distance Algorithm Approach Case Study Population and Refugee Data

Framework Design of Information Retrieval System for Official Letter Using Extraction of Geometry Feature Method

Hiba Utama buses still use conventional methods in ordering tickets and searching for the departure schedule of a prospective passenger, namely through outlets/counters and via telephone only. This method is less effective both in terms of time and cost because there can be a discrepancy between the customer's wishes in terms of departure time or even the tickets have sold out. So that information applications and ordering bus tickets are needed that can provide clear information and are not limited by distance and time by using mobile media. This study aims to build information applications and order bus tickets based on Android. The application development stage includes analysis, system design, implementation, and testing. Methods of data collection using the method of literature study, and observation. Android-based bus ticket information and booking applications built with the Java programming language. Android-based bus ticket information and booking applications that are made to provide bus information and ticket bookings. So that it can make it easier for customers to access information and order bus tickets due to the level of effectiveness and efficiency of the function of the mobile itself. The results of system testing show that this application is feasible and can be used as a tool to provide information and to order Hiba Utama bus tickets.

/pdf/design-of-android-based-online-ticketing-system-application-kncqp3ti.pdf

Design of Android-based Online Ticketing System Application on Hiba Utama Bus Using Location Based Service (LBS)

Health Service is a place to organize health efforts. Not all health service locations are known to the public, both those from Tasikmalaya City and those from outside the city. This is due to a lack of information about health services in the City of Tasikmalaya. Progress and development of the times, as currently the use of technology is needed to be able to provide information quickly, where the use of technology can be accessed anytime and wherever the user is. The use of Information Technology such as using an Android-based Smartphone Application can be used to help citizens to find a location and find out the route to the desired health service. This study aims to build an Android-based Health Service Search application as a source of information to find out the location and routes to service locations. health services in Tasikmalaya City. The research method used is the waterfall process model. Implementation of health service applications using Javascript programming with Android Studio, Location Based Service, and maps sourced from the Google Maps API. The results of the research are in the form of Android-based Tasikmalaya City health service applications that help make it easier for Tasikmalaya City residents and outside the region to obtain information about Health Services and routes from the user's location to the desired Health Service location in Tasikmalaya City with the help of GPS.

/pdf/android-based-health-service-center-search-application-in-2krkv4iv.pdf

Android-Based Health Service Center Search Application in Tasikmalaya City Using Location Based Service (LBS) Method

Bima is one of the cities in West Nusa Tenggara which has various tourist objects. Apart from nature tourism, there are also many other tours such as modern water tourism that provides water parks, historical tours, religious tours and culinary tours. So far, recommendations for tourist destinations are usually word of mouth, mass media, browsing¸ and social media. This makes not all tourist objects can be visited, due to lack of information and the location of tourist objects that are scattered so that it is difficult to reach by public transportation. This research offers an android application that can provide convenience to tourists who want to visit tourist attractions. This application provides information about existing tourist objects starting from location information, facilities, opening and closing hours as well as guides for reaching locations using the Location Based Service method. Every tourist can download the application and run it on a smartphone. Application users can select tourist objects by category and the system will provide guide facilities to the tourist sites they want to visit. This application is expected to be a solution to the problems above and become a media for tourism promotion in the city of Bima.

/pdf/tourism-search-application-within-the-bima-city-locale-of-3htbdln5.pdf

Tourism Search Application within the Bima City Locale of West Nusa Tenggara Utilizing the Android-Based Location-Based Benefit Strategy Utilizing Visual Studio Code

NM Faizah

Papers

Unbalanced Data Clustering with K-Means and Euclidean Distance Algorithm Approach Case Study Population and Refugee Data

Framework Design of Information Retrieval System for Official Letter Using Extraction of Geometry Feature Method

Design of Android-based Online Ticketing System Application on Hiba Utama Bus Using Location Based Service (LBS)

Android-Based Health Service Center Search Application in Tasikmalaya City Using Location Based Service (LBS) Method

Tourism Search Application within the Bima City Locale of West Nusa Tenggara Utilizing the Android-Based Location-Based Benefit Strategy Utilizing Visual Studio Code