![]() Hasil citra sampai saat ini hanya tersimpan pada folder-folder dengan penamaan khusus. Seiring bertambahnya jumlah hasil citra yang dihasilkan satelit LAPAN-A2 dan LAPAN-A3 baik itu citra SpaceCam maupun citra LISA pada saat ini maka dibuat mesin pencari yang mampu mencari citra satelit dengan akurat. Search engine yaitu kombinasi perangkat keras dan perangkat lunak komputer yang disediakan oleh perusahaan tertentu melalui website yang telah ditentukan. In terms of architectural styles, microservice architecture, event-driven architecture, and service-based architecture are the most preferred architectural approaches for such a system. For the storage of a content aggregation system, replication and partitioning must be used to improve availability, latency, and scalability. To increase the performance of the proposed system, various caching methods, load balancers, and message queues should be actively used. The presented architecture aims to provide high availability, scalability for high query volumes, and big data performance. Finally, this paper presents the high-level architecture of a content aggregation system. ![]() The study also provides a detailed description of web crawling and fuzzy duplicate detection systems. The research covers the basic principles of content aggregation, like main criteria for data sampling, automation of aggregation processes, content copy strategies, and content aggregation approaches. It discusses such science and technical problems of content aggregation like web crawling, summarization, searching for fuzzy duplicates, methods of increasing, methods to reduce the delay between the publication of new content by the source and the appearance of its copy in the information aggregator, methods to increase the scalability and performance of similar systems. ![]() ![]() This research focuses on the main issues and approaches to creating content aggregation systems. As a proof of concept, we implement a prototype system using the Hadoop platform of our improved scheduling algorithm and conduct experimental studies to demonstrate the feasibility and performance of our approach. Finally, we describe the construction process of the classification index that is based on the training set by combining Hadoop and Lucene. On this basis, we propose a classification algorithm that follows the principle of document similarity and document classification algorithm and does not use training sets. Furthermore, we propose a classification index construction method using non-training sets, thereby improving the term frequency–inverse document frequency weight formula by introducing timeliness and entropy. The improved algorithm can avoid the execution of slow bulk task caused by non-uniform velocity nodes for Hadoop in a heterogeneous environment and can improve operating efficiency and stability. In this article, we use Hadoop to build a distributed computing platform that stores unstructured data and improve the Hadoop scheduling algorithm on the basis of the end time of slow tasks. Several problems should be addressed when obtaining effective access to massive amounts of unstructured data, such as data stored in scattered locations, differences in data access, and non-unified data formats. The rapid growth of unstructured data has become a key factor that drives the development of enterprises.
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