Multi connection query optimization in data warehouse dependent on multiple linear regression algorithm

One of the prevalent fish commodities in the community is clarias which is marked by high demand. High demand makes clarias farmers need an effective aquaculture strategy to assemble the consumption needs of clarias in the community. One strategy that needs to be noticed is the determination of the appropriate feed size so that the feeding is maximized. Currently, farmers have difficulty in determining the right size of feed, as a result, many uneaten feed remains are found. Therefore, an analysis of determining the size of the feed is needed that is adjusted to the width of the clarias's mouth so that feeding can carry out optimally. This study analyzed determining feed size in clarias using the Multiple Linear Regression algorithm. This study uses 150 data, namely training data totaling 105 and testing data totaling 45. The result of the coefficient of determination obtained is 81.40 and has an intercept value of -0.094. Measurement of the performance of the Multiple Linear Regression algorithm equation using Mean Absolute Percentage Error shows a result of 21%. From the results of the analysis above, it concluded that the determination of feed size affects the optimal growth of clarias.

Based on bioimpedance spectroscopy technology, a method of automatically identifying the cell suspension concentration is proposed. This method combines multiple linear regression algorithm and bioimpedance spectroscopy technology, which can identify the concentration of cell suspension quickly and accurately. Firstly, a strategy of random distribution of cell locations is proposed to simulate the true existence of cells. Secondly, 2400 groups of normal, cancerous and mixed cell models with different concentrations are generated by numerical simulation and their bioimpedance spectroscopy data are calculated.Thirdly, the multiple linear regression algorithm (MLR), support vector machine (SVM), and gradient boosting regression algorithm (GBR) are used to identify the concentration of cancerous cells. The simulation results show that the MLR is the best regression model for cell suspension concentration identification and its average goodness of fit and mean square error are 0.9997 and 0.0008respectively. Finally, the MLR is applied to the identification of red blood cell suspensions with different concentrations, the experimental results show that the average goodness of fit and mean square error are 0.9998 and 0.0079, respectively, indicating that this method has a greater ability to identify cell suspension concentrations.

A data warehouse can store very large amounts of data that should be processed in parallel in order to achieve reasonable query execution times. The MapReduce programming model is a very convenient way to process large amounts of data in parallel on commodity hardware clusters. A very popular query used in data warehouses is star-join. In this paper, we present a fast and efficient star-join query execution algorithm built on top of a MapReduce framework called Hadoop. By using dynamic filters against dimension tables, the algorithm needs a single scan of the fact table, which means a significant reduction of input/output operations and computational complexity. Also, the algorithm requires only two MapReduce iterations in total-one to build the filters against dimension tables and one to scan the fact table. Our experiments show that the proposed algorithm performs much better than the existing solutions in terms of execution time and input/output. Copyright © 2014 John Wiley & Sons, Ltd.

Decision support applications require complex queries, e.g., multi way joins defining on huge warehouses usually modelled using star schemas, i.e., a fact table and a set of data dimensions (Papadomanolakis & Ailamaki, 2004). Star schemas have an important property in terms of join operations between dimensions tables and the fact table (i.e., the fact table contains foreign keys for each dimension). None join operations between dimension tables. Joins in data warehouses (called star join queries) are particularly expensive because the fact table (the largest table in the warehouse by far) participates in every join and multiple dimensions are likely to participate in each join. To speed up star join queries, many optimization structures were proposed: redundant structures (materialized views and advanced index schemes) and non redundant structures (data partitioning and parallel processing). Recently, data partitioning is known as an important aspect of physical database design (Sanjay, Narasayya & Yang, 2004; Papadomanolakis & Ailamaki, 2004). Two types of data partitioning are available (Özsu & Valduriez, 1999): vertical and horizontal partitioning. Vertical partitioning allows tables to be decomposed into disjoint sets of columns. Horizontal partitioning allows tables, materialized views and indexes to be partitioned into disjoint sets of rows that are physically stored and usually accessed separately. Contrary to redundant structures, data partitioning does not replicate data, thereby reducing storage requirement and minimizing maintenance overhead. In this paper, we concentrate only on horizontal data partitioning (HP). HP may affect positively (1) query performance, by performing partition elimination: if a query includes a partition key as a predicate in the WHERE clause, the query optimizer will automatically route the query to only relevant partitions and (2) database manageability: for instance, by allocating partitions in different machines or by splitting any access paths: tables, materialized views, indexes, etc. Most of database systems allow three methods to perform the HP using PARTITION statement: RANGE, HASH and LIST (Sanjay, Narasayya & Yang, 2004). In the range partitioning, an access path (table, view, and index) is split according to a range of values of a given set of columns. The hash mode decomposes the data according to a hash function (provided by the system) applied to the values of the partitioning columns. The list partitioning splits a table according to the listed values of a column. These methods can be combined to generate composite partitioning. Oracle currently supports range-hash and range-list composite partitioning using PARTITION - SUBPARTITION statement. The following SQL statement shows an example of fragmenting a table Student using range partitioning.

This paper proposes the compression of data in Relational Database Management Systems (RDBMS) using existing text compression algorithms. Although the technique proposed is general, we believe it is particularly advantageous for the compression of medium size and large dimension tables in data warehouses. In fact, dimensions usually have a high number of text attributes and a reduction in their size has a big impact in the execution time of queries that join dimensions with fact tables. In general, the high complexity and long execution time of most data warehouse queries make the compression of dimension text attributes (and possible text attributes that may exist in the fact table, such as false facts) an effective approach to speed up query response time. The proposed approach has been evaluated using the well-known TPC-H benchmark and the results show that speed improvements greater than 40% can be achieved for most of the queries.KeywordsText AttributeData CompressionData WarehouseCompression AlgorithmEfficient CompressionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Data mining, often also called knowledge discovery in database (KDD), is an activity that includes collecting, using historical data to find order, patterns or relationships in large data sets. Outputs from data mining can be used to improve future decision making. Problems that often occur in BPDASHL are estimation problems such as weather, difficulties in planting, lack of labor, lack of experience in tree nurseries, and different soil conditions. Another problem that is found in agencies is that they do not have a system to predict estimated tree seedlings orders every year so that a method is needed, namely the Multiple Linear Regression Algorithm. So with this was made the Application of Data Mining To Predict Ordering Tree Seeds With Multiple Linear Regression. Multiple Linear Regression Algorithms which are methods that support estimating or predicting order targets for the coming period. Algorithm testing is done using SPSS software. From the results of the research that has been done, it can help BPDASHL to make it easier to predict the ordering of seeds using SPSS Software

Data warehouse is designed for answering analytical queries. Data warehouse saves historical data. In the data warehouse, the response time to analytical queries is long. So reducing the response time is a critical problem. There are a lot of algorithms to solve the problem. Some of them, materialize frequent views. The previously posed queries have important information that will be used in the future. This paper proposes an algorithm for view materialization. The proposed algorithm finds proper views using previous queries and materializes them. The views are able to answer future queries. The view selection algorithm has four steps. At first, it clusters previous queries by SOM method. Then frequent queries are found by Apriori algorithm. In the third step the problem is converted to 0/1 knapsack equations and finally, optimal queries are joined to create only one view for each cluster. This paper improves the first and third step. This paper uses the SOM algorithm for clustering previous queries in the first step and it solves the 0/1 knapsack equations according to shuffled frog leaping algorithm in the third step. Experimental results show that it improves the previous view selection algorithms according to response time and storage space factor.

A data warehouse provides information for analytical processing, decision making and data mining tools. As the concept of real-time enterprise evolves, the synchronism between transactional data and data warehouses, statically implemented, has been redefined. Traditional data warehouse systems have static structures of their schemas and relationships between data, and therefore are not able to support any dynamics in their structure and content. Their data is only periodically updated because they are not prepared for continuous data integration. For real-time enterprises with needs in decision support purposes, real-time data warehouses seem to be very promising. In this paper we present a methodology on how to adapt data warehouse schemas and user-end OLAP queries for efficiently supporting real-time data integration. To accomplish this, we use techniques such as table structure replication and query predicate restrictions for selecting data, to enable continuously loading data in the data warehouse with minimum impact in query execution time. We demonstrate the efficiency of the method by analyzing its impact in query performance using benchmark TPC-H executing query workloads while simultaneously performing continuous data integration at various insertion time rates.

YouTube, as a new form of mass media in the advancement of increasingly sophisticated technology, caters to a diverse audience seeking daily information. Many YouTube users leverage this platform as a medium for creativity and income generation, such as through video blogs, educational videos, gaming videos, and various other content. Not all YouTubers can express themselves directly or through livestreaming; some use virtual 2D or 3D characters created with computer software, utilizing technologies such as Face Tracking and Hand Tracking. Hololive Production is a Japanese VTuber agency owned by the Japanese technology company Cover Corporation. Hololive Indonesia (Hololive ID) was established to produce Indonesian-language VTuber content and has garnered significant attention from fans in Indonesia and worldwide. VTuber content primarily revolves around video game topics, indicating a strong interest among VTuber fans in gaming-related content. Moreover, the analysis results show that multiplayer games enabling interaction among VTubers or even with viewers are highly favored, further supporting the popularity of gaming topics among VTuber enthusiasts. The Multiple Linear Regression algorithm has been widely used for sales predictions related to buyer patterns and trends. The performance measurement is influenced by the amount of data and the number of attributes.

The traction motor is the key equipment of the high-speed EMU (Electric Multiple Units), and most of its faults or performance degradation will cause the temperature to rise, so the operating temperature of the traction motor is an important basis for its fault prediction and health management. To solve the problems of large temperature deviation, long response time, and high false alarm rate in the current EMU traction motor temperature warning process, this paper discusses the fault prediction method of high-speed train traction motor based on multiple linear regression algorithm, which combines the interdisciplinary application of data science and railway engineering. Taking the peak stator temperature of the traction motor as the prediction target, the objective function between the predicted value and the multivariate factors such as the temperature of the driving end, the temperature of the non-driving end, the outside temperature, the running speed of the train, and the input power of the traction motor was constructed, and the fault prediction model of the traction motor was established. After data cleaning and outlier processing, analysis and application show that the model can effectively predict the peak temperature of the motor and provide support for the monitoring of the operating status of the EMU and the fault warning.

Solar power-based photovoltaic energy conversion could be considered one of the best sustainable sources of electric power generation. Thus, the prediction of the output power of the photovoltaic panel becomes necessary for its efficient utilization. The main aim of this paper is to predict the output power of solar photovoltaic panels using different machine learning algorithms based on the various input parameters such as ambient temperature, solar radiation, panel surface temperature, relative humidity and time of the day. Three different machine learning algorithms namely, multiple regression, support vector machine regression and gaussian regression were considered, for the prediction of output power, and compared on the basis of results obtained by different machine learning algorithms. The outcomes of this study showed that the multiple linear regression algorithm provides better performance with the result of mean absolute error, mean squared error, coefficient of determination and accuracy of 0.04505, 0.00431, 0.9981 and 0.99997 respectively, whereas the support vector machine regression had the worst prediction performance. Moreover, the predicted responses are in great understanding with the actual values indicating that the purposed machine learning algorithms are quite appropriate for predicting the output power of solar photovoltaic panels under different environmental conditions.

Why is the snowflake schema a good data warehouse design?

8 - Dimensions

Background: Experiments were conducted to analyze environmental parameters and growth-related factors to identify the most influential factors in estimating pig’s body weight (PBW) using artificial neural networks (ANNs) and multiple linear regression (MLR)-based models. Methods: Four different ANNs, including Cascade Forward Back-propagation (CFBP), Layer Recurrent (LR), Feed Forward Back-propagation (FFBP) and Elman (EL), and MLR models were developed to estimate the body weight of pigs. The current research was conducted for 92 days during the two experimental periods (2021–2022) in a pig barn. Result: The Levenberg-Marquardt training function, gradient descent weight and bias learning function, tan-sigmoid transfer function, and two hidden layers with 16 neurons in each layer were shown to be the most effective architecture of the FFBP model in predicting PBW. According to the sensitivity analysis, length of pig (LP) was the most influential factor in estimating the PBW for MLR/ANN models. However, the environmental parameters along with growth-related factors could not always be the same association with PBW. Therefore, further research on viable alternative breeds with different management conditions may be considered to evaluate MLR and ANN model’s performance.

The 800‐year sulfate record from Lomonosovfonna was analyzed by a novel multiple linear regression algorithm that attempts to explain sulfate variability in terms of other chemical species measured in the core and sulfur emission inventories. We use three statistical approaches to determine sulfate sources. We examine trends using singular spectrum analysis with confidence intervals, finding clear evidence that anthropogenic sources are important but not dominant; we use cross‐wavelet coherence to examine significant multidecadal covariance in terrestrial sulfate; but our main tool is multiple regression analysis of the sulfate dependency on other ions and anthropogenic emission inventories. Models are fitted in a moving time window of typically 50 years length, explaining 80% of the sulfate variance. A suite of model predictors are examined, and the variation in relative magnitudes of the model coefficients along the core can be used to infer variations in the strength of various sulfate sources. We observe large changes in sulfate sources at the end of the Little Ice Age associated with changes in Barents Sea marine productivity, changes in North Atlantic sulfate input and a long‐lasting period of disturbance caused by the large Laki volcanic eruption. Modeling sulfur emission inventories shows that western Europe contributes about 15% of the sulfate budget, with essentially no input from other regions, in contrast with predictions from global circulation models incorporating sulfur chemistry. Multidecadal cycles are mainly confined to the Little Ice Age and most likely associated with increased storminess and enhanced deposition of both marine aerosol and biogenic sulfate from the Atlantic Ocean. The model residuals do not show a normal distribution but display very large spikes. Unexpectedly, those significant at the 99% level can be closely matched to major volcanic eruptions using independent dating methods. The 20th century sulfate in the core is inventoried as sea salt (15%), terrestrial (10%), volcanic (5–10%), western European anthropogenic (10–25%), Barents Sea biogenic (20–40%), and Atlantic biogenic (10–15%).