A query application for On-Line Analytical Processing (OLAP) examines various kinds of data stored in a Data Warehouse (DW). There have been no systematic studies that look at the impact of query optimizations on performance and energy consumption in relational and NoSQL databases. Indeed, due to a lack of precise power calculation techniques in various databases and queries, the energy activity of several basic database operations is mostly unknown, as are the queries themselves, which are very complicated, extensive, and exploratory. As a result of the rapidly growing size of the DW system, query response times are regularly increasing. To improve decision-making performance, the response time of such queries should be as short as possible. To resolve these issues, multiple materialized views from individual database tables have been collected, and queries have been handled. Similarly, due to overall maintenance and storage expenses, as well as the selection of an optimal view set to increase the data storage facility’s efficacy, materializing all conceivable views is not viable. Thus, to overcome these issues, this paper proposed the method of energy-aware query optimization and processing, on materialized views using enhanced simulated annealing (EAQO-ESA). This work was carried out in four stages. First, a Simulated Annealing (SA) based meta-heuristic approach was used to pre-process the query and optimize the scheduling performance. Second, the optimal sets of views were materialized, resulting in enhanced query response efficiency. Third, the authors assessed the performance of the query execution time and computational complexity with and without optimization. Finally, based on processing time, efficiency, and computing cost, the system’s performance was validated and compared to the traditional technique.
Humans often express themselves through facial expressions. Deep learning techniques are used as an efficient system application process in research on the advancement of artificial intelligence technology in human-computer interactions. As an illustration, let’s say someone tries to communicate by using facial expressions. Some people who see it occasionally cannot foresee the expression or emotion it may evoke. Psychology includes study and evaluation of inferences in interpreting a person’s or group of people’s emotions when interacting in order to recognize emotions or facial expressions. Indeed, a convolutional neural networks (CNN) model may be learned to assess images and recognize facial expressions. This study suggests developing a system that can classify and forecast facial emotions using feature extraction and real-time Convolution Neural Network (CNN) technology from the OpenCV library. We have chosen FER 2013 Dataset as the main dataset for our study. Face detection, extraction of facial features, and facial emotion categorization are the three key procedures that make up the research that was implemented.
Customers always have to compare product prices and offers across several websites when they go to purchase a specific item. The solution seeks to address the fore mentioned issue. The comparison that Google Search now offers is focused more on text search than anything else. The pricing and any available coupons or discounts on that website are not listed. This paper proposes a solution- a price-deals-lister, a microservice based website which scrapes the various e-commerce websites and get the deals available, stores them in database and later the request processor unit will take the data from the database according to the user’s request and show it to the user. Customers and retailers alike will benefit from this initiative because it allows them to quickly and easily access all the data with just one click. Anyone can access the website and compare the offers found on other e-commerce websites. In view of the Market analysis, Shopkeepers can use a website to verify the current market price of a product, especially retailers that must offer their goods with tight margins. After that, they can raisethe product’s price in their store and sell it for a fair amount to make a profit. Shop owners can learn about various lucrative offers that are currently being offered for a specific product and utilize that knowledge to better serve their customers. Producers can research the products that consumers are most interested in and concentrate on making those products. Additionally, by providing the finest deals to the clients, they might consider various strategies for increasing the profitability of the products.
Buying furniture has been a daunting task for every family. Checklist associated with buying furniture is a long one and matching of the furniture item in their house is a very important aspect. The customers have a hard task in imagining the matching of the furniture item in their house. Here the concept of Augmented Reality comes to the aid of customers. With the help of augmented reality the customer can actually view how the furniture will be look at their house and based on that make intelligent purchase. This application will be beneficial to the furniture sellers as they will be able to market their products in a better and more efficient way. Thus the sellers will be able to provide better service to customers and grow their business. Intended to build a UNITY3d model based android application that will help customers visualize the furniture item set at their place. A sample booklet for the furniture will be provided to the customers by the seller. This booklet will have all the images of furniture item sets and customer can scan this and view the 3d model of the items at their home and can check which item suits them most. The use of Vuforia is very important along with the unity 3d as Vuforia stores the marker images which will be used as the marker for projecting the 3d model of the furniture item sets.
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