79,326 research outputs found

    Ekstraksi Informasi Halaman Web dengan Memanfaatkan Mining Data Record

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    ABSTRAKSI: Sebagian besar dari informasi pada Web diisi di struktur obyek yang teratur, disebut dengan data record. Data record ini sangat penting karena mempresentasikan inti informasi dari halaman host-nya, misalnya daftar produk atau layanan. Me-mining data record untuk mengekstrak informasi dari halaman Web bertujuan untuk menyediakan nilai tambah suatu layanan. Pada Tugas Akhir ini diimplementasikan metode untuk mining data record pada halaman Web secara otomatis dengan menggunakan algoritma yang disebut MDR (Mining Data Records in Web Page). Teknik ini lebih efektif karena hanya berdasarkan pada dua pengamatan penting saja, yaitu mengamati data record yang berada pada halaman Web dan algoritma pencocokan string. Pada proses me-mining data record ini ada tiga langkah yang utama yaitu, membangun sebuah tag tree HTML dari halaman Web, mining data region pada halaman Web dengan menggunakan tag tree dan pencocokan string, dan mengidentifikasi data record dari setiap data region. Tahap analisis dan pengujian memberikan hasil bahwa algoritma MDR yang dibangun terbukti bisa mendapatkan data record pada halaman Web meskipun ada beberapa noise.Kata Kunci : Web Mining, tag tree HTML, data region, data record.ABSTRACT: A large amount of information on the Web is contained in regularly structured objects, which call data record. Data record are important because often present the essential information of it host pages, e.g, list of products and services. It is useful to mine such data record in order to extract information from web pages to provide value-added services. In this Final project is implemented method for mining data records in web pages automatic with use algorithm is called MDR (Mining Data Records in Web Pages). This technique is more effective because just based on two important observations i.e, observe data records in Web pages and a string matching algorithm. In the process of mining data records are three main steps, i.e, building a HTML tag tree of the page, mining data regions in the Web pages using the tag tree and string comparison, and identifiying data records from each data region. From implementation and analyse stage shown that MDR algorithm is built proved to can find out data rcords in Web pages though it is noise.Keyword: Web Mining, data mining, tag tree HTML, data region, data record

    WEBMINING: ISSUES

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    Web is an assortment of between related records on at any rate one web workers while web mining proposes dispensing with basic data from web information bases. Web mining is one of the information mining regions where information tunneling procedures are utilized for eliminating data from the web workers. The web information wires site pages, web joins, objects on the web an extraordinary arrangement logs. Web mining is utilized to understand the client lead, assess a specific site page dependent on the data which is dealt with in web log records. Web mining is assessed by utilizing information mining frameworks, unequivocally depiction, grouping, and joining rules. It has some steady zones or applications, for example, Electric conversation, E-learning, E-government, E-plans, E-vote based system, Electric trade, security, awful execution appraisal and advanced library. Recovering the significant site page from the web accommodatingly and appropriately changes into an inconvenient undertaking since web is contained unstructured information, which passes on the gigantic extent of data and expansion the multifaceted thought of regulating data from various web master gatherings. The assortment of material winds up being tricky, concentrate, and channel or assess the basic information for the clients. In this paper, to have dissected the essential considerations of web mining, assembling, cycles and issues. In addition, this task comparatively isolated the web mining research inconveniences

    Automatically Extract Information from Web Documents

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    The Internet could be considered to be a reservoir of useful information in textual form ā€” product catalogs, airline schedules, stock market quotations, weather forecast etc. There has been much interest in building systems that gather such information on a user\u27s behalf. But because these information resources are formatted differently, mechanically extracting their content is difficult. Systems using such resources typically use hand-coded wrappers, customized procedures for information extraction. Structured data objects are a very important type of information on the Web. Such data objects are often records from underlying databases and displayed in Web pages with some fixed templates. Mining data records in Web pages is useful because they typically present their host pages\u27 essential information, such as lists of products and services. Extracting these structured data objects enables one to integrate data/information from multiple Web pages to provide value-added services, e.g., comparative shopping, meta-querying and search. Web content mining has thus become an area of interest for many researchers because of the phenomenal growth of the Web contents and the economic benefits associated with it. However, due to the heterogeneity of Web pages, automated discovery of targeted information is still posing as a challenging problem

    ORegAnno: an open-access community-driven resource for regulatory annotation

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    ORegAnno is an open-source, open-access database and literature curation system for community-based annotation of experimentally identified DNA regulatory regions, transcription factor binding sites and regulatory variants. The current release comprises 30 145 records curated from 922 publications and describing regulatory sequences for over 3853 genes and 465 transcription factors from 19 species. A new feature called the ā€˜publication queueā€™ allows users to input relevant papers from scientific literature as targets for annotation. The queue contains 4438 gene regulation papers entered by experts and another 54 351 identified by text-mining methods. Users can enter or ā€˜check outā€™ papers from the queue for manual curation using a series of user-friendly annotation pages. A typical record entry consists of species, sequence type, sequence, target gene, binding factor, experimental outcome and one or more lines of experimental evidence. An evidence ontology was developed to describe and categorize these experiments. Records are cross-referenced to Ensembl or Entrez gene identifiers, PubMed and dbSNP and can be visualized in the Ensembl or UCSC genome browsers. All data are freely available through search pages, XML data dumps or web services at: http://www.oreganno.org

    MalStone: Towards A Benchmark for Analytics on Large Data Clouds

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    Developing data mining algorithms that are suitable for cloud computing platforms is currently an active area of research, as is developing cloud computing platforms appropriate for data mining. Currently, the most common benchmark for cloud computing is the Terasort (and related) benchmarks. Although the Terasort Benchmark is quite useful, it was not designed for data mining per se. In this paper, we introduce a benchmark called MalStone that is specifically designed to measure the performance of cloud computing middleware that supports the type of data intensive computing common when building data mining models. We also introduce MalGen, which is a utility for generating data on clouds that can be used with MalStone

    Rough Sets Clustering and Markov model for Web Access Prediction

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    Discovering user access patterns from web access log is increasing the importance of information to build up adaptive web server according to the individual userā€™s behavior. The variety of user behaviors on accessing information also grows, which has a great impact on the network utilization. In this paper, we present a rough set clustering to cluster web transactions from web access logs and using Markov model for next access prediction. Using this approach, users can effectively mine web log records to discover and predict access patterns. We perform experiments using real web trace logs collected from www.dusit.ac.th servers. In order to improve its prediction ration, the model includes a rough sets scheme in which search similarity measure to compute the similarity between two sequences using upper approximation
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