Parallel and distributed clustering framework for big spatial data mining

Clustering techniques are very attractive for identifying and extracting patterns of interests from datasets. However, their application to very large spatial datasets presents numerous challenges such as high-dimensionality, heterogeneity, and high complexity of some algorithms. Distributed clustering techniques constitute a very good alternative to the Big Data challenges (e.g., Volume, Variety, Veracity, and Velocity). In this paper, we developed and implemented a Dynamic Parallel and Distributed clustering (DPDC) approach that can analyse Big Data within a reasonable response time and produce accurate results, by using existing and current computing and storage infrastructure, such as cloud computing. The DPDC approach consists of two phases. The first phase is fully parallel and it generates local clusters and the second phase aggregates the local results to obtain global clusters. The aggregation phase is designed in such a way that the final clusters are compact and accurate while the overall process is efficient in time and memory allocation. DPDC was thoroughly tested and compared to well-known clustering algorithms BIRCH and CURE. The results show that the approach not only produces high-quality results but also scales up very well by taking advantage of the Hadoop MapReduce paradigm or any distributed system

Pengembangan Aplikasi Web Berbasis Cloud Menggunakan Framework Untuk Pengelompokan Hasil Pencarian Teks

Cloud computing atau dikenal dengan komputasi awan merupakan gabungan pemanfaatan teknologi komputer dalam suatu jaringan berbasis internet. Salah satu pengembangan teknologi tersebut adalah mesin pencari informasi. Tugas akhir ini membuat aplikasi mesin pencari menggunakan framework CodeIgniter dan API untuk mengambil data yang ada di internet dan mengelompokan data tersebut menjadi beberapa kategori, yaitu buku, jurnal, gambar(JPG), gambar(GIF) dan video. Sistem yang dibuat dengan teknologi cloud ini memiliki keakuratan 100% kesesuaian isi dengan kategori buku dari 200 data, jurnal dari 111 data, gambar(JPG) dari 293 data dan video dari 23 data, serta 44% untuk kategori gambar(GIF) dari 30 data. Kemudian jenis keyword yang digunakan juga berpengaruh terhadap kesesuain pencarian dan kecepatan pencarian. Keyword dengan Bahasa Inggris memiliki rata-rata 96% informasi yang dihasilkan sesuai dengan keyword dan rata-rata kecepatan 45.276 detik dalam satu kali pencarian, sedangkan Bahasa Indonesia memiliki rata-rata 66% informasi yang dihasilkan sesuai dengan keyword dan rata-rata kecepatan 28.46 detik dalam satu kali pencarian. ======================================================================================== Cloud computing is known by the combined utilization of computer technology within a network with cloud-based development. One of the technological development is the search engine information. The final project is to make the search engine application using CodeIgniter framework and API for retrieving data that is in the cloud and the data are grouped into several categories, namely books, journals, videos, GIF and JPG. Systems with cloud technology has 100% accuracy of the suitability of the contents by category of books with 200 data, journals 111 data, video with 23 data, JPG with 293 data and 44% for the category of GIF with 30 data. Then type the keyword that is used also affects the accuracy of search and search speed. Keyword with the English Language has an average of 96% of the information produced in accordance with keyword and an average speed of 45,276 seconds in a single search, while the Indonesian Language has an average of 66% of the information produced in accordance with keyword and an average speed of 28.46 seconds in a single search