scholarly journals Improving Scalability of Java Archive Search Engine through Recursion Conversion And Multithreading

2016 ◽  
Vol 10 (1) ◽  
pp. 15 ◽  
Author(s):  
Oscar Karnalim
Keyword(s):  
Vector Space ◽  
Search Engine ◽  
Processing Time ◽  
Vector Space Model ◽  
Semantic Relatedness ◽  
Low Rank ◽  
Space Model ◽  
Before And After ◽  
Strongly Connected ◽  
Rank Vector

Based on the fact that bytecode always exists on Java archive, a bytecode based Java archive search engine had been developed [1, 2]. Although this system is quite effective, it still lack of scalability since many modules apply recursive calls and this system only utilizes one core (single thread). In this research, Java archive search engine architecture is redesigned in order to improve its scalability. All recursion are converted to iterative forms although most of these modules are logically recursive and quite difficult to convert (e.g. Tarjan’s strongly connected component algorithm). Recursion conversion can be conducted by following its respective recursive pattern. Each recursion is broke down to four parts (before and after actions of current and its children) and converted to iteration with the help of caller reference. This conversion mechanism improves scalability by avoiding stack overflow error caused by method calls. System scalability is also improved by applying multithreading mechanism which successfully cut off its processing time. Shorter processing time may enable system to handle larger data. Multithreading is applied on major parts which are indexer, vector space model (VSM) retriever, low-rank vector space model (LRVSM) retriever, and semantic relatedness calculator (semantic relatedness calculator also involves multiprocess). The correctness of both recursion conversion and multithread design are proved by the fact that all implementation yield similar result.

scholarly journals SISTEM TEMU KEMBALI INFORMASI PADA DOKUMEN DENGAN METODE VECTOR SPACE MODEL

2017 ◽  
Vol 9 (1) ◽  
pp. 74
Author(s):  
Irmawati Irmawati
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Search Engine ◽  
Vector Space Model ◽  
Space Model

Informasi saat ini sangat mudah didapatkan dengan memanfaatkan fasilitas internet dimanapun dan kapanpun. Di sisi lain informasi yang didapat dari  search engine merupakan semua hal yang berkaitan dengan kata kunci yang dicari. Hal ini menyebabkan pengguna terpaksa menyaring untuk mendapatkan dokumen yang relevan. Oleh karena itu diperlukan cara untuk mengelompokkan banyaknya informasi yang tersedia, yang dibutuhkan pengguna sehingga memudahkan pengguna untuk mendapatkan dokumen yang diinginkan. Pada penelitian ini diusulkan suatu solusi dari permasalahan tersebut dengan mengembangkan metode ilmu pencarian yang dikenal dengan temu-kembali informasi (information retrieval) dan metode Vector Space Model (VSM). Pada metode Vector Space Model (VSM) beberapa dokumen online akan diindeks dan diurutkan berdasarkan bobot dari kata pencarian yang terdapat di dalam dokumen online tersebut. Salah satu algoritma pembobotannya adalah algoritma tf-idf yang dipengaruhi oleh frekuensi kemunculan kata pada tiap dokumen online dan frekuensi dari dokumen online yang memiliki kata tersebut.

An Improved Method of Judging the Theme Relativity Based on Vector Space Model in Vertical Search Engine

2013 ◽  
Vol 411-414 ◽  
pp. 106-109 ◽  
Author(s):  
Ya Heng Ren
Keyword(s):  
Vector Space ◽  
Search Engine ◽  
Vector Space Model ◽  
Improved Method ◽  
New Model ◽  
Space Model ◽  
Vertical Search ◽  
Vertical Search Engine ◽  
The Web

Vertical Search Engine provides a professional search compared with the traditional search engine. All of the data searched by vertical search engine is relative with some one theme, which is decided by users. Usually Vector Space Model is used for judging the relativity between data in the web and the decided theme. But when elements of the theme appear repeatedly, their order is not considered by Vector Space Model. Adding a new element, the Evolved Vector Space Model is provided. The experiments show that the new model has fixed the problem and have a better performance in judging relativity.

scholarly journals SISTEM TEMU KEMBALI INFORMASI PADA DOKUMEN DENGAN METODE VECTOR SPACE MODEL

2017 ◽  
Vol 9 (1) ◽  
pp. 74
Author(s):  
Irmawati Irmawati
Keyword(s):  
Information Retrieval ◽  
Vector Space ◽  
Search Engine ◽  
Vector Space Model ◽  
Space Model

Informasi saat ini sangat mudah didapatkan dengan memanfaatkan fasilitas internet dimanapun dan kapanpun. Di sisi lain informasi yang didapat dari  search engine merupakan semua hal yang berkaitan dengan kata kunci yang dicari. Hal ini menyebabkan pengguna terpaksa menyaring untuk mendapatkan dokumen yang relevan. Oleh karena itu diperlukan cara untuk mengelompokkan banyaknya informasi yang tersedia, yang dibutuhkan pengguna sehingga memudahkan pengguna untuk mendapatkan dokumen yang diinginkan. Pada penelitian ini diusulkan suatu solusi dari permasalahan tersebut dengan mengembangkan metode ilmu pencarian yang dikenal dengan temu-kembali informasi (information retrieval) dan metode Vector Space Model (VSM). Pada metode Vector Space Model (VSM) beberapa dokumen online akan diindeks dan diurutkan berdasarkan bobot dari kata pencarian yang terdapat di dalam dokumen online tersebut. Salah satu algoritma pembobotannya adalah algoritma tf-idf yang dipengaruhi oleh frekuensi kemunculan kata pada tiap dokumen online dan frekuensi dari dokumen online yang memiliki kata tersebut.

scholarly journals Development and evaluation of a biomedical search engine using a predicate-based vector space model

2013 ◽  
Vol 46 (5) ◽  
pp. 929-939 ◽  
Author(s):  
Myungjae Kwak ◽  
Gondy Leroy ◽  
Jesse D. Martinez ◽  
Jeffrey Harwell
Keyword(s):  
Vector Space ◽  
Search Engine ◽  
Vector Space Model ◽  
Space Model

An Ontology Based Model for Document Clustering

2012 ◽  
pp. 199-215
Author(s):  
Sridevi U. K. ◽  
Nagaveni N.
Keyword(s):  
Vector Space ◽  
Domain Knowledge ◽  
Clustering Algorithm ◽  
Document Clustering ◽  
Vector Space Model ◽  
Search Space ◽  
Space Model ◽  
Before And After ◽  
Document Collection ◽  
Improved Performance

Clustering is an important topic to find relevant content from a document collection and it also reduces the search space. The current clustering research emphasizes the development of a more efficient clustering method without considering the domain knowledge and user’s need. In recent years the semantics of documents have been utilized in document clustering. The discussed work focuses on the clustering model where ontology approach is applied. The major challenge is to use the background knowledge in the similarity measure. This paper presents an ontology based annotation of documents and clustering system. The semi-automatic document annotation and concept weighting scheme is used to create an ontology based knowledge base. The Particle Swarm Optimization (PSO) clustering algorithm can be applied to obtain the clustering solution. The accuracy of clustering has been computed before and after combining ontology with Vector Space Model (VSM). The proposed ontology based framework gives improved performance and better clustering compared to the traditional vector space model. The result using ontology was significant and promising.

scholarly journals Rancang Bangun Tabloid Online Bestari dengan Fitur Pencarian berbasis Search Engine Teknologi menggunakan Metode Vector Space Model

Repositor ◽  
2020 ◽  
Vol 2 (5) ◽  
pp. 611
Author(s):  
Mentari Mas'ama Safitri ◽  
Nur Hayatin ◽  
Yufis Azhar
Keyword(s):  
College Students ◽  
Vector Space ◽  
Search Engine ◽  
Vector Space Model ◽  
Academic Community ◽  
Cosine Similarity ◽  
Space Model ◽  
Search Feature

AbstrakBestari merupakan lembaga pers mahasiswa yang merupakan media utama untuk menyuarakan dan mendokumentasikan berbagai kegiatan yang dilakukan sivitas akademika Universitas Muhammadiyah Malang. Bestari juga memliki tabloid online yang dapat diakses oleh mahasiswa, namun tabloid online Bestari Universitas Muhammadiyah Malang saat ini belum memiliki fitur pencarian, sehingga pengguna kesulitan untuk mendapatkan informasi sesuai dengan yang diinginkan. Berdasarkan masalah tersebut pembangunan aplikasi Tabloid Online Bestari Universitas Muhammadiyah Malang dengan fitur pencarian berbasis search engine ini bertujuan untuk memberikan kemudahan kepada pengguna khususnya mahasiswa Universitas Muhammadiyah Malang dalam melakukan pencarian.Pada studi kasus ini metode Vector Space Model  digunakan untuk memodelkan kumpulan berita dan keyword dari user dalam bentuk vektor yang telah di beri bobot dengan menggunakan metode pembobotan TF-IDF, kemudian akan di hitung kedekatan dari masing-masing dokumen dengan keyword dari user menggunakan cosine similarity. AbstractBestari is collage students’ press agency that is the main media to show their opinion and document any activities that have already done by academic community of UMM. Bestari also has online tabloid that can be accessed by college students, but Bestari tabloid UMM does not currently have a search feature so the user difficult to get information needed. According to this problem, the developing of Tabloid Online Bestari UMM application that completed search feature with search engine based is purposed to give solution to the users especially for colledge students of UMM. This study used Vector Space Model (VSM) method to collect the news and keyword from the users in the form of vector that has been given scale used TF-IDF method, then the correlation of keyword with the document will be counted by using cosine similarity

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