Optimasi Proses Klasterisasi di MySQL DBMS dengan Mengintegrasikan Algoritme MIC-Kmeans Menggunakan Bahasa SQL dalam Stored Procedure

<p>Proses klasterisasi data di <em>DBMS</em> akan lebih efisien jika dilakukan langsung di dalam <em>DBMS</em> itu sendiri karena <em>DBMS</em> mendukung untuk pengelolaan data yang baik. <em>SQL-Kmeans</em> merupakan salah satu metode yang sebelumnya telah digunakan untuk mengintegrasikan algoritme klasterisasi <em>K-means</em> ke dalam <em>DBMS</em> menggunakan <em>SQL</em>. Akan tetapi, metode ini juga membawa kelemahan dari algoritme <em>K-means</em> itu sendiri yaitu lamanya iterasi untuk mencapai konvergen dan keakuratan hasil klasterisasi yang belum optimal akibat dari proses inisialisasi <em>centroid</em> awal secara acak. Algoritme <em>Median Initial Centroid (MIC)-Kmeans</em> merupakan pengembangan dari algoritme <em>K-means</em> yang bisa memberikan solusi optimal dalam menentukan awal <em>centroid</em> yang berdampak pada keakuratan dan lamanya iterasi. Dengan keunggulan yang dimiliki algoritme <em>MIC-Kmeans</em>, maka dalam penelitian ini dipilih sebagai alternatif algoritme yang diintegrasikan dalam proses klasterisasi data secara langsung di <em>DBMS</em> menggunakan <em>SQL</em>. Proses integrasinya meliputi 4 tahap yaitu tahap inisialisasi tabel <em>dataset</em>, tahap pemetaan algoritme <em>MIC-Kmeans</em> pada <em>SQL</em> dan tabel <em>dataset</em>, tahap perancangan <em>SQL </em>untuk tiap hasil pemetaan dan tahap implementasi rancangan <em>SQL</em> dalam <em>MySQL</em> <em>stored procedure</em>. Hasil pengujian menunjukkan bahwa metode <em>SQL MIC-Kmeans</em> bisa mengurangi 43% jumlah iterasi dan mengurangi 39% waktu yang dibutuhkan dari metode <em>SQL-Kmeans</em> untuk mencapai konvergen. Selain itu, nilai rata-rata <em>silhouette coefficient </em>metode <em>SQL MIC-Kmeans</em> adalah 0,79 dan masuk dalam kategori <em>strong structure</em> (nilai rentang 0,7 sampai 1). Sedangkan nilai rata-rata <em>silhouette coefficient </em>metode <em>SQL-Kmeans </em>adalah<em> </em>0,68<em> </em>dan masuk dalam kategori <em>medium structure </em>(nilai rentang 0,5 sampai 0,7).</p><p class="Judul2"><strong><em>Abstract</em></strong></p><p class="Judul2"><em>The process of data clustering in the DBMS will be more efficient because the DBMS supports good data management. SQL-Kmeans is a method that has been used to integrate K-means clustering algorithms into DBMS using SQL. However, it carries the weakness of the K-means algorithm itself in the duration of iterations to reach convergence and the accuracy of clustering due to the centroid initialization process randomly. Median Initial Centroid (MIC)-Kmeans algorithm is a development of the K-means algorithm that can provide the optimal solution in determining the initial centroid which has an impact on the accuracy and duration of iterations. With the advantages of the MIC-Kmeans algorithm, the method was chosen as an alternative algorithm to be integrated in the DBMS using SQL for a clustering. The integration process includes 4 stages, there are dataset initialization, SQL algorithm mapping and dataset table, SQL design for each mapping result, and implementation SQL in the MySQL stored procedure. The test results show that the SQL MIC-Kmeans method can reduce 43% the number of iterations and reduce 39% of the time required from the SQL-Kmeans method to reach convergence. In addition, the average value of the coefficient SQL MIC-Kmeans method is 0.79 and categorized as strong structure (value ranges from 0.7 to 1). While, the average value of the coefficient SQL-Kmeans method is 0.68 and categorized as medium structure (value ranges from 0.5 to 0.7).</em></p>

ANALYSIS ON COGNITIVE SOCIAL DATA FUSION

Big data stands for effective handling of large amount of data, research, mining, intelligence. In social media large amount of data uploaded every.Social media handle large amount of data like photo, video, songs and so many using big data. When it comes for big data, a large amount of data should be effectively handled. Big data face various challenges like clustering of data, visualizing, data representation, data processing, pattern mining, tracking of data and analysing behaviour of users. In this paper the Emoji in messages are decoded and Unicode will be set. Based on the Emoji the user interest can be understood in a better way. Then another part involves the replacement of repeated data by using the map Reduce algorithm. Mapping of data with key values used to reduce the size of storage.

3D Data Denoising via Nonlocal Means Filter by Using Parallel GPU Strategies

Nonlocal Means (NLM) algorithm is widely considered as a state-of-the-art denoising filter in many research fields. Its high computational complexity leads researchers to the development of parallel programming approaches and the use of massively parallel architectures such as the GPUs. In the recent years, the GPU devices had led to achieving reasonable running times by filtering, slice-by-slice, and 3D datasets with a 2D NLM algorithm. In our approach we design and implement a fully 3D NonLocal Means parallel approach, adopting different algorithm mapping strategies on GPU architecture and multi-GPU framework, in order to demonstrate its high applicability and scalability. The experimental results we obtained encourage the usability of our approach in a large spectrum of applicative scenarios such as magnetic resonance imaging (MRI) or video sequence denoising.

Compact serialization of Prolog terms (with catalan skeletons, cantor tupling and Gödel numberings)

We describe a compact serialization algorithm mapping Prolog terms to natural numbers of bit-sizes proportional to the memory representation of the terms. The algorithm is a ‘no bit lost’ bijection, as it associates to each Prolog term a unique natural number and each natural number corresponds to a unique syntactically well-formed term.To avoid an exponential explosion resulting from bijections mapping term trees to natural numbers, we separate the symbol content and the syntactic skeleton of a term that we serialize compactly using a ranking algorithm for Catalan families.A novel algorithm for the generalized Cantor bijection between ${\mathbb{N}$ and ${\mathbb{N}$k is used in the process of assigning polynomially bounded Gödel numberings to various data objects involved in the translation.

The Description and Relation of WS-CDL and BPEL

WS-CDL (Web Service Choreography Description Language) is a language to describe multiple party how to work with together to accomplish a work in the context of SOA. BEPL (Business Process Execution Language) can get the same point, but they are from different view. WS-CDL is from a global view, which describes how multiple parties communicate with each other. BPEL is from a point of view of a single role who participates to manage the process of the work. Usually these two ways work together to describe and implement the business process. But WS-CDL has more advantages to achieve the most important goal of SOA-flexibility. So, W3C gives a suggestion to create an algorithm mapping from WS-CDL to BPEL; this chapter describes such a way to accomplish this.