Pengujian Optimization dan Non-Optimization Query Metode Topsis untuk Menentukan Tingkat Kerusakan Sektor Bencana Alam

Volume data yang sangat besar dari tim surveyor Perencanaan dan Pengendalian Penanganan Bencana(P3B) menciptakan masalah yang luas dan beragam sehingga dapat menghabiskan sumber daya sistem dan waktu pemrosesan yang terbilang lama. Oleh karena itu penelitian ini mengusulkan solusi dengan melakukan Optimasi query pada metode TOPSIS yang diimplementasikan pada sistem pendukung kepeutusan untuk menentukan tingkat kerusakan pasca bencana. Berdasarkan 3 kali uji coba dengan jumlah data yang berbeda-beda yaitu ujicoba ke-1 menggunakan 114 data, ujicoba ke-2 sebanyak 228 data dan ujicoba ke-3 menggunakan 334 data. Selain itu, setiap ujicoba dilakukan lagi pengukuran re-spons time sebanyak 3 kali maka didapatkan hasil rata-rata (average) response time dari masing-masing langkah metode TOPSIS. Didapati bahwa hasil dari tahapan perangkingan menggunakan query optimiza-tion lebih cepat 0.00076 dibandingakan dengan qury non-optimization. Sehingga dapat di simpulkan bahwa response time yang didapat query optimization pada setiap langkah metode TOPSIS pada sistem pendukung keputusan kerusakan sektor pasca bencana alam lebih kecil dibandingkan dengan response time pada query non-optimization.

Ranked Deep Web Page Detection Using Reinforcement Learning and Query Optimization

This paper proposes a novel algorithm based on reinforcement learning-entitled asynchronous advantage actor-critic (A3C). Overflow queries are optimized to crawl the ranked deep web. A3C assigns the reward and penalty to the various queries. Queries are derived from the domain-based taxonomy that helps to fill the search forms. Overflow queries are the collection of queries that match with more than k number of results and only top k matched results are retrieved. Low ranked documents beyond k results are not accessible and lead to low coverage. Overflow queries are optimized to convert into non-overflow queries based on the proposed technique and lead to more coverage. As of yet, no research work has been explored by using A3C with taxonomy in the domain of ranked deep web. The experimental results show that the proposed technique outperforms the three other techniques (i.e., document frequency, random query, and high frequency) in terms of average improvement metric by 26%, 69%, and 92%, respectively.

Research on Query Optimization of Classic Art Database Based on Artificial Intelligence and Edge Computing

With the changes and development of the social era, my country’s classic art is slowly being lost. In order to more effectively inherit and preserve classic art, the collection and sorting of classic art data through modern information technology has become a top priority. Database storage is a good way. However, as the amount of data grows, the requirements for computing processing power and query speed for massive amounts of data and information are also increasing day by day. Faced with this problem, this article is aimed at studying the optimization of database queries through effective algorithms to improve the efficiency of data query. Based on the traditional database query optimization algorithm, this article improves on the traditional algorithm and proposes a semi-join query optimization algorithm, which reduces the number of connection cards and the number of columns and uses the number of blocks that participate in the semi-link algorithm connection and preconnection preview and selection. And other functions reduce the size of the participating block, and the connection sent between sites reduces the cost of sending between networks. The graph data query optimization algorithm is used to optimize the graph data query in the database to reduce the extra task overhead and improve the system performance. The experimental results of this paper show that through the data query optimization algorithm of this paper, the additional task overhead is reduced by 19%, the system performance is increased by 22%, and the data query efficiency is increased by 31%.

Intensional and Extensional Views in DL-Lite Ontologies

The use of virtual collections of data is often essential in several data and knowledge management tasks. In the literature, the standard way to define virtual data collections is via views, i.e., virtual relations defined using queries. In data and knowledge bases, the notion of views is a staple of data access, data integration and exchange, query optimization, and data privacy. In this work, we study views in Ontology-Based Data Access (OBDA) systems. OBDA is a powerful paradigm for accessing data through an ontology, i.e., a conceptual specification of the domain of interest written using logical axioms. Intuitively, users of an OBDA system interact with the data only through the ontology's conceptual lens. We present a novel framework to express natural and sophisticated forms of views in OBDA systems and introduce fundamental reasoning tasks for these views. We study the computational complexity of these tasks and present classes of views for which these tasks are tractable or at least decidable.

Optimization of correlate subquery based on distributed database

Subquery is widely used in database. It can be divided into related subquery and non-related subquery according to whether it is dependent on the table of the parent query. For related subqueries, it is necessary to take a tuple from the parent query before executing the subquery, that is, the content of the subquery needs to be repeatedly operated. Disk access costs of this strategy is very big, in the distributed database, because of data communication overhead, in the parent query yuan set is too low efficiency, therefore, for the class sub queries, on the basis of the optimization of the existing query strategy, combining with the characteristics of distributed database, put forward by the subquery on to join queries, eliminate redundant clauses in the subquery, eliminate accumulation function method based on distributed database query optimization strategy, and the effectiveness of the present optimization strategy is verified by experiment.