CLOUTIDY: A CLOUD-BASED SUPPLY CHAIN MANAGEMENT SYSTEM USING SEMAR AND BLOCKCHAIN SYSTEM

Supply chain management (SCM) system is an essential requirement for companies and manufacturers to collaborate in doing business. There are many techniques to manage supply chains, such as using Excel sheets and web-based applications. However, these techniques are ineffective, insecure, and prone to human error. In this paper, we propose CLOUTIDY, a cloud-based SCM system using SEMAR (Service Market) and Blockchain system. We modify JUGO architecture to develop SEMAR as a broker between users and cloud service providers. Also, we apply the Blockchain concept to store the activity log of the SCM system in a decentralized database. CLOUTIDY system can solve several common cases: service selection, resource provisioning, authentication and access control. Also, it improves the security of data by storing each activity log of the supply chain management system in the Blockchain system

MAPPING POTENTIAL ATTACKERS AGAINST NETWORK SECURITY USING LOCATION AWARE REACHABILITY QUERIES ON GEO SOCIAL DATA

Attacks on network security can happen anywhere. Using Geo-Social Networks (GSN), i.e., a graph that combines social network data and spatial information, we can find the potential attackers based on the given location. In answering the graph-based problems, Reachability Queries are utilized. It verifies the reachability between two nodes in the graph. This paper addresses a problem defined as follows: Given a geo-social grap

Pembangunan Algoritme Efisien untuk Menjawab K-Produk Paling Menjanjikan Pada Lingkungan Terdistribusi

Strategi pemilihan produk berbasis preferensi pelanggan banyak dipelajari pada lingkungan terpusat. Pada kenyataannya, pengelolaan dan penyimpanan data menjadi semakin terdistribusi. Beberapa strategi pemilihan produk yang sudah ada tidak dapat diimplementasikan secara langsung pada lingkungan terdistribusi karena harus membaca keseluruhan data. Salah satu pendekatan yang paling mudah adalah dengan mengumpulkan semua data dari setiap node menjadi satu untuk diproses. Namun, hal ini akan menyebabkan (1) biaya komunikasi yang tinggi dan (2) waktu pemrosesan kueri yang lama. Terinspirasi dengan strategi pemilihan produk k-Most Promising Products (KMPP), penelitian ini mempelajari permasalahan pemilihan produk menjanjikan pada lingkungan terdistribusi. Penelitian ini mengusulkan algoritme baru yang efisien untuk menjawab kueri kueri k-produk paling menjanjikan pada lingkungan terdistribusi (KMPPD) dengan mengusulkan strategi pemangkasan berbasis Anti-Dominance Region (ADR). Untuk menunjang strategi pemangkasan tersebut, penelitian ini mencoba menggabungkan struktur data Grid-based index dan R-Tree untuk meminimalkan biaya komputasi. Algoritme yang diusulkan juga memanfaatkan dua kueri skyline, yaitu dynamic skyline dan reverse skyline, untuk menghitung nilai kontribusi pasar masing-masing produk. Hasil pengujian menunjukkan bahwa kinerja algoritme KMPPD lebih efisien dibandingkan dengan algoritme KMPPD-RiG untuk menjawab kueri k-produk paling menjanjikan pada lingkungan terdistribusi. Ditambah lagi, algoritme KMPPD memiliki total waktu kueri 97% jauh lebih cepat dibandingkan algoritme Naïve dan algoritme KMPPD-RiG memiliki total waktu kueri 95% jauh lebih cepat dibandingkan algoritme Naïve. ================================================================================================== Product selection strategies based on customer preferences are widely studied in the centralized environment. However, the increasing rate of data generation causes data management and storage to be increasingly distributed. The existing product selection strategies cannot be directly applied in the distributed environment because the strategies must read the entire data. Intuitively, all data from each node in the network must be put together before processing. However, this naïve approach generates (1) high communication costs and (2) high query execution time. Inspired by the k-Most Promising Products (KMPP) product selection strategy, this study addresses the problem of selecting promising products in the distributed environment. This study proposes a new efficient algorithm for answering k-most promising products queries over distributed environments (KMPPD) by proposing an Anti-Dominance Region (ADR)-based pruning strategy. This study also proposes an index structure that combines R-Tree and Grid-based indexes to minimize computational costs. The proposed algorithm utilizes two skyline queries, i.e., dynamic skyline and reverse skyline, to calculate each product's market contribution value. The results show that the KMPPD algorithm performs more efficiently than the KMPPD-RiG algorithm to answer the k-most promising products over a distributed environment. Moreover, the KMPPD algorithm has a 97% faster query time than the Naïve approach, while the KMPPD-RiG algorithm has a 95% faster query time than the Naïve approach

Cloutidy: A Cloud-based Supply Chain Management System Using Semar and Blockchain System

Supply chain management (SCM) system is an essential requirement for companies and manufacturers to collaborate in doing business. There are many techniques to manage supply chains, such as using Excel sheets and web-based applications. However, these techniques are ineffective, insecure, and prone to human error. In this paper, we propose CLOUTIDY, a cloud-based SCM system using SEMAR (Service Market) and Blockchain system. We modify JUGO architecture to develop SEMAR as a broker between users and cloud service providers. Also, we apply the Blockchain concept to store the activity log of the SCM system in a decentralized database. CLOUTIDY system can solve several common cases: service selection, resource provisioning, authentication and access control. Also, it improves the security of data by storing each activity log of the supply chain management system in the Blockchain system

Mapping Potential Attackers Against Network Security Using Location Aware Reachability Queries On Geo Social Data

Attacks on network security can happen anywhere. Using Geo-Social Networks (GSN), i.e., a graph that combines social network data and spatial information, we can find the potential attackers based on the given location. In answering the graph-based problems, Reachability Queries are utilized. It verifies the reachability between two nodes in the graph. This paper addresses a problem defined as follows: Given a geo-social graph and a location area as a query point, we map potential attackers against network security using location-aware reachability queries. We employ the concepts of Reachability Minimum Bounding Rectangle (RMBR) and graph traversal algorithm, i.e., Depth-First Search (DFS), to answer the location-aware reachability queries. There are two kinds of the proposed solution, i.e., (1) RMBR-based solution map potential attackers by looking for intersecting RMBR values, and (2) Graph traversal-based solution map potential attackers by traversing the graph. We evaluate the performance of both proposed solutions using synthetic datasets. Based on the experimental result, the RMBR-based solution has much lower execution time and memory usage than the graph traversal-based solution