Journal articles: 'Data warehouse'

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Barahama, A. D., and R. Wardani. "Utilization Extract, Transform, Load For Developing Data Warehouse In Education Using Pentaho Data Integration." Journal of Physics: Conference Series 2111, no. 1 (November 1, 2021): 012030. http://dx.doi.org/10.1088/1742-6596/2111/1/012030.

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Abstract The utilization of data warehouses in various fields is an absolute necessity. A data warehouse is a database that contains large amounts of data that aims to help organizations, fields, and institutions specifically for decision making. Data warehouses can produce important information in the future. Loading data from various sources and processed through an ETL (Extract, Transform, Load) process that displays data consistently is the basis for creating a data warehouse architecture. The development of a data warehouse in education will provide significant benefits for the progress of education. Integration of data and processing results stored in the data warehouse can be the basis for evaluating better planning. Development of data warehouse adopt the multidimensional modelling method which consists of four stages: select the business process, declare the grain, select dimensions, and identify facts. This stage produces a data warehouse architecture and influences and contributes to the advanced information technology in education.

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Tan, Jun, and Hai Ming Zhao. "Construction of Data Warehouse Platform in Continual Quality Improvement." Applied Mechanics and Materials 519-520 (February 2014): 13–16. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.13.

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Aiming at improving product quality continually, we proposed an association rules mining system (ARMS) based on idea of PDCA cycling. Data warehouse is very useful for integrating heterogeneous database. Therefore, this paper designed a data warehouse platform as process data exchange module in ARMS. The role of data warehouse platform module is to integrate XML with enterprise process for realizing process data exchange among departments. In design of data warehosue, this paper chooses three-tier data warehouse structure and snowflake schema for indicating the complex relation between process data.

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Cravero Leal, Ania, Jose Norberto Mazón, and Juan Trujillo. "A business-oriented approach to data warehouse development." Ingeniería e Investigación 33, no. 1 (January 1, 2013): 59–65. http://dx.doi.org/10.15446/ing.investig.v33n1.37668.

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Several surveys have indicated that many data warehouses fail to meet business objectives or are outright failures. One reason for this is that requirement engineering is typically overlooked in real projects. This paper addresses data warehouse design from a business perspective by highlighting business strategy analysis, alignment between data warehouse objectives and a firm's strategy, goal-oriented information requirements' modelling and how an underlying multidimensional data warehouse model may be derived. A set of guidelines is provided allowing developers to design a data warehouse aligned with a prevailing business strategy. A classic case study is presented.

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Yang, Xiu Fang. "Key Technologies Analysis on Management System Data Warehouse." Applied Mechanics and Materials 644-650 (September 2014): 2925–28. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2925.

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In order to further analyze data warehouse’s application value in teaching management system, this paper first analyzes the disadvantages of previous teaching management system data extraction, illustrates the basic structure of data warehouse system, then discusses the establishment of three kinds of models of data warehouse, finally from the demand perspective of teaching management system, analyzes key technologies such as the design of data warehouse model of teaching management system, the upload of teaching data, data display and data warehouse interfaces.

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Haxhiu, Valdrin. "Decision making based on data analyses using data warehouses." International Journal of Business & Technology 6, no. 3 (May 1, 2018): 1–6. http://dx.doi.org/10.33107/ijbte.2018.6.3.04.

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Data warehouses are a collection of several databases, whose goal is to help different companies and corporations make important decisions about their activities. These decisions are taken from the analyses that are made to the data within the data warehouse. These data are taken from data that companies and corporations collect on daily basis from their branches that may be located in different cities, regions, states and continents. Data that are entered to data warehouses are historical data and they represent that part of data that is important for making decisions. These data go under a transformation process in order to accommodate with the structure of the objects within the databases in the data warehouse. This is done because the structure of the relational databases is not similar with the structure of the databases (multidimensional databases) within the data warehouse. The first ones are optimized for transactions on daily basis like: entering, changing, deleting and retrieving data through simple queries, the second ones are optimized for retrieving data through multidimensional queries, which enable us to extract important information. This information helps to make important decisions by learning which are the weak points and the strong points of the company, in order to invest more on the weak points and to strengthen the strong points, increasing the profits of the company. The goal of this paper is to treat data analyses for decision making from a data warehouse by using OLAP (online analytical processing) analysis. For this treatment we used the Analysis Services of Microsoft SQL Server 2016 platform. We analyzed the data of an IT Store with branches in different cities in Kosovo and came to a conclusion for some sales trends. This paper emphasizes the role of data warehouses in decision making.

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Hamad, Murtadha M., and Muhammed Abdul Raheem. "EVALUATION OF BITMAP INDEX USING PROTOTYPE DATA WAREHOUSE." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 2, no. 2 (April 30, 2012): 39–42. http://dx.doi.org/10.24297/ijct.v2i1.2614.

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Bitmap indices have become popular access methods for data warehouse applications and decision support systems with large amounts of read-mostly data. This paper could arrive a number of results such as ; Bitmap Index highly improves the performance of Query Answering in Data Warehouses, It highly increases the efficiency of Complex Query processing through using bitwise operations (AND, OR). A prototype of Data Warehouse â€œSTUDENTS DWâ€ has been built according to the conditions of W. Inomn of Data Warehouses. This prototype is built for student's information.

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Gluchowski, Peter. "Data Warehouse." Informatik-Spektrum 20, no. 1 (February 20, 1997): 48–49. http://dx.doi.org/10.1007/s002870050052.

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Rostek, Katarzyna. "Data Analytical Processing in Data Warehouses." Foundations of Management 2, no. 1 (January 1, 2010): 99–116. http://dx.doi.org/10.2478/v10238-012-0023-x.

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Data Analytical Processing in Data Warehouses The article presents issues connected with processing information from data warehouses (the analytical enterprise databases) and two basic types of analytical data processing in data warehouse. The genesis, main definitions, scope of application and real examples from business implementations will be described for each type of analysis. There will be presented copyrighted method of knowledge discovering in databases, together with practical guidelines for its proper and effective use in the enterprise.

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Dehdouh, Khaled, Omar Boussaid, and Fadila Bentayeb. "Big Data Warehouse." International Journal of Decision Support System Technology 12, no. 1 (January 2020): 1–24. http://dx.doi.org/10.4018/ijdsst.2020010101.

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In the Big Data warehouse context, a column-oriented NoSQL database system is considered as the storage model which is highly adapted to data warehouses and online analysis. Indeed, the use of NoSQL models allows data scalability easily and the columnar store is suitable for storing and managing massive data, especially for decisional queries. However, the column-oriented NoSQL DBMS do not offer online analysis operators (OLAP). To build OLAP cubes corresponding to the analysis contexts, the most common way is to integrate other software such as HIVE or Kylin which has a CUBE operator to build data cubes. By using that, the cube is built according to the row-oriented approach and does not allow to fully obtain the benefits of a column-oriented approach. In this article, the focus is to define a cube operator called MC-CUBE (MapReduce Columnar CUBE), which allows building columnar NoSQL cubes according to the columnar approach by taking into account the non-relational and distributed aspects when data warehouses are stored.

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M Kirmani, Mudasir. "Dimensional Modeling Using Star Schema for Data Warehouse Creation." Oriental journal of computer science and technology 10, no. 04 (October 13, 2017): 745–54. http://dx.doi.org/10.13005/ojcst/10.04.07.

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Data Warehouse design requires a radical rebuilding of tremendous measures of information, frequently of questionable or conflicting quality, drawn from various heterogeneous sources. Data Warehouse configuration assimilates business learning and innovation know-how. The outline of theData Warehouse requires a profound comprehension of the business forms in detail. The principle point of this exploration paper is to contemplate and investigate the transformation model to change over the E-R outlines to Star Schema for developing Data Warehouses. The Dimensional modelling is a logical design technique used for data warehouses. This research paper addresses various potential differences between the two techniques and highlights the advantages of using dimensional modelling along with disadvantages as well. Dimensional Modelling is one of the popular techniques for databases that are designed keeping in mind the queries from end-user in a data warehouse. In this paper the focus has been on Star Schema, which basically comprises of Fact table and Dimension tables. Each fact table further comprises of foreign keys of various dimensions and measures and degenerate dimensions if any. We also discuss the possibilities of deployment and acceptance of Conversion Model (CM) to provide the details of fact table and dimension tables according to the local needs. It will also highlight to why dimensional modelling is preferred over E-R modelling when creating data warehouse.

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Tiwari, Rajdev, Anubhav Tiwari, and Manu Pratap Singh. "Fuzzy-Rule Based Adaptive Data Warehouse." International Journal of Applied Evolutionary Computation 3, no. 1 (January 2012): 47–65. http://dx.doi.org/10.4018/jaec.2012010103.

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Data Warehouses (DWs) are aimed to empower the knowledge workers with information and knowledge which helps them in decision making. Technically, the DW is a large reservoir of integrated data that does not provide the intelligence or the knowledge demanded by users. The burden of data analysis and extraction of information and knowledge from integrated data still lies upon the analyst’s shoulder. The overhead of analysts can be taken off by architecting a new generation data warehouses systems those shall be capable of capturing, organizing and representing knowledge along with the data and information in it. This new generation DW may be called as Knowledge Warehouse (KW) shall exhibit decision making capabilities themselves and can also supplement the Decision Support Systems (DSS) in making decisions quickly and effortlessly. This paper proposes and simulates a fuzzy-rule based adaptive knowledge warehouse with capabilities to learn and represent implicit knowledge by means of adaptive neuro fuzzy inference system (ANFIS).

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Islam, Md Rakibul, Syed Mithun Ali, Amir Mohammad Fathollahi-Fard, and Golam Kabir. "A novel particle swarm optimization-based grey model for the prediction of warehouse performance." Journal of Computational Design and Engineering 8, no. 2 (February 24, 2021): 705–27. http://dx.doi.org/10.1093/jcde/qwab009.

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Abstract Warehouses constitute a key component of supply chain networks. An improvement to the operational efficiency and the productivity of warehouses is crucial for supply chain practitioners and industrial managers. Overall warehouse efficiency largely depends on synergic performance. The managers preemptively estimate the overall warehouse performance (OWP), which requires an accurate prediction of a warehouse’s key performance indicators (KPIs). This research aims to predict the KPIs of a ready-made garment (RMG) warehouse in Bangladesh with a low forecasting error in order to precisely measure OWP. Incorporating advice from experts, conducting a literature review, and accepting the limitations of data availability, this study identifies 13 KPIs. The traditional grey method (GM)—the GM (1, 1) model—is established to estimate the grey data with limited historical information but not absolute. To reduce the limitations of GM (1, 1), this paper introduces a novel particle swarm optimization (PSO)-based grey model—PSOGM (1, 1)—to predict the warehouse’s KPIs with less forecasting error. This study also uses the genetic algorithm (GA)-based grey model—GAGM (1, 1)—the discrete grey model—DGM (1, 1)—to assess the performance of the proposed model in terms of the mean absolute percentage error and other assessment metrics. The proposed model outperforms the existing grey models in projecting OWP through the forecasting of KPIs over a 5-month period. To find out the optimal parameters of the PSO and GA algorithms before combining them with the grey model, this study adopts the Taguchi design method. Finally, this study aims to help warehouse professionals make quick OWP estimations in advance to take control measures regarding warehouse productivity and efficiency.

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Phyllis, Chepkonga. "Determination of Performance Indicators for Warehouse Evaluation: A Case of Medium Sized Warehouses in Nakuru Town." Journal of Procurement & Supply Chain 5, no. 2 (November 11, 2021): 32–38. http://dx.doi.org/10.53819/81018102t5034.

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Evaluation of warehouse processes is required for decision making purposes and the improvement of warehouse operations. To evaluate warehouse processes, it is essential to identify key indicators in the warehouse operations. This research was intended to identify the significant indicators of warehouse performance that would support management decision making on the improvement of warehouse operations. In this research 20 indicators were identified in four warehouse activities based on Frazelle model. The most important indicators in each warehouse were then determined. The study was conducted through a descriptive design using a survey of 3 medium warehouses in Nakuru. The study employed purposive sampling to select the sample and sample elements. The sample size of 10 warehouse management officers were used in the study. Structured questionnaire was adopted as data collection instrument. The research findings indicated that most important performance indicators for receiving is productivity, for storage is space utilization, for order picking is cycle time and for shipping is productivity. Process improvement steps were proposed based on benchmarking among warehouses handling similar goods. It was noted that different types of warehouses require different indicators. The study concluded that measurement of warehouse processes is important and enables operations improvements and cost reduction. Keywords: Warehouse procedures, warehouse performance, warehouse improvement, medium sized warehouses, Nakuru town, Kenya

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Atay, Canan Eren, and Georgia Garani. "Maintaining Dimension's History in Data Warehouses Effectively." International Journal of Data Warehousing and Mining 15, no. 3 (July 2019): 46–62. http://dx.doi.org/10.4018/ijdwm.2019070103.

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A data warehouse is considered a key aspect of success for any decision support system. Research on temporal databases have produced important results in this field, and data warehouses, which store historical data, can clearly benefit from such studies. A slowly changing dimension is a dimension in which any of its attributes in a data warehouse can change infrequently over time. Although different solutions have been proposed, each has its own particular disadvantages. The authors propose the Object-Relational Temporal Data Warehouse (O-RTDW) model for the slowly changing dimensions in this research work. Using this approach, it is possible to keep track of the whole history of an object in a data warehouse efficiently. The proposed model has been implemented on a real data set and tested successfully. Several limitations implied in other solutions, such as redundancy, surrogate keys, incomplete historical data, and creation of additional tables are not present in our solution.

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Mujiono, Mujiono, and Aina Musdholifah. "Pengembangan Data Warehouse Menggunakan Pendekatan Data-Driven untuk Membantu Pengelolaan SDM." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 10, no. 1 (January 31, 2016): 1. http://dx.doi.org/10.22146/ijccs.11184.

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The basis of bureaucratic reform is the reform of human resources management. One supporting factor is the development of an employee database. To support the management of human resources required including data warehouse and business intelligent tools. The data warehouse is an integrated concept of reliable data storage to provide support to all the needs of the data analysis. In this study developed a data warehouse using the data-driven approach to the source data comes from SIMPEG, SAPK and electronic presence. Data warehouses are designed using the nine steps methodology and unified modeling language (UML) notation. Extract transform load (ETL) is done by using Pentaho Data Integration by applying transformation maps. Furthermore, to help human resource management, the system is built to perform online analytical processing (OLAP) to facilitate web-based information. In this study generated BI application development framework with Model-View-Controller (MVC) architecture and OLAP operations are built using the dynamic query generation, PivotTable, and HighChart to present information about PNS, CPNS, Retirement, Kenpa and Presence

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YAO, CAN-ZHONG, JI-NAN LIN, and XU-ZHOU ZHENG. "MULTIFRACTAL DETRENDED CROSS-CORRELATION ANALYSIS FOR LARGE-SCALE WAREHOUSE-OUT BEHAVIORS." Fractals 23, no. 04 (December 2015): 1550044. http://dx.doi.org/10.1142/s0218348x15500449.

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Based on cross-correlation algorithm, we analyze the correlation property of warehouse-out quantity of different warehouses, respectively, and different products of each warehouse. Our study identifies that significant cross-correlation relationship for warehouse-out quantity exists among different warehouses and different products of a warehouse. Further, we take multifractal detrended cross-correlation analysis for warehouse-out quantity among different warehouses and different products of a warehouse. The results show that for the warehouse-out behaviors of total amount, different warehouses and different products of a warehouse significantly follow multifractal property. Specifically for each warehouse, the coupling relationships of rebar and wire rod reveal long-term memory characteristics, no matter for large fluctuation or small one. The cross-correlation effect on long-range memory property among warehouses probably has less to do with product types,and the long-term memory of YZ warehouse is greater than others especially in total amount and wire rod product. Finally, we shuffle and surrogate data to explore the source of multifractal cross-correlation property in logistics system. Taking the total amount of warehouse-out quantity as example, we confirm that the fat-tail distribution of warehouse-out quantity sequences is the main factor for multifractal cross-correlation. Through comparing the performance of the multifractal detrended cross-correlation analysis (MF-DCCA), centered multifractal detrending moving average cross-correlation analysis (MF-X-DMA) algorithms, the forward and backward MF-X-DMA algorithms, we find that the forward and backward MF-X-DMA algorithms exhibit a better performance than the other ones.

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Shobirin, Kheri Arionadi, Adi Panca Saputra Iskandar, and Ida Bagus Alit Swamardika. "Data Warehouse Schemas using Multidimensional Data Model for Retail." International Journal of Engineering and Emerging Technology 2, no. 1 (September 23, 2017): 84. http://dx.doi.org/10.24843/ijeet.2017.v02.i01.p17.

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A data warehouse are central repositories of integrated data from one or more disparate sources from operational data in On-Line Transaction Processing (OLTP) system to use in decision making strategy and business intelligent using On-Line Analytical Processing (OLAP) techniques. Data warehouses support OLAP applications by storing and maintaining data in multidimensional format. Multidimensional data models as an integral part of OLAP designed to solve complex query analysis in real time.

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Wen, Wei Jun. "Research on the Incremental Updating Mechanism of Marine Environmental Data Warehouse." Applied Mechanics and Materials 668-669 (October 2014): 1378–81. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.1378.

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Marine environment data warehouse can store massive data. After the full amount of historical data has been initially loaded, the incremental update mode must be applied to ensure timely updates of data. In this paper, in view of the marine environment data warehouse’s characteristics, such as massive data amount, large number of historical data and low update frequency, a complete set of mechanisms for incremental update of marine environment data warehouse was proposed to greatly improve the operating efficiency of marine environment data warehouse.

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Wiwik, Budiawan, Singgih Saptadi, and Ary Arvianto. "The Development of Data Warehouse to Support Data Mining Technique for Traffic Accident Prediction." E3S Web of Conferences 73 (2018): 12007. http://dx.doi.org/10.1051/e3sconf/20187312007.

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Traffic accidents are one of the major health problems that cause serious death in the world and ranks 9th in the world. Traffic accidents in Indonesia ranks 5th in the world. One effort to improve traffic safety is to design traffic accident prediction models. Prediction models will utilize accident-related data in traffic through data mining processing. The data warehouse offers benefits as a basis for data mining. Building an effective data warehouse requires knowledge and attention to key issues in database design, data acquisition and processing, as well as data access and security. This study is the first step in the development of data mining accidents based prediction system. The output of this initial stage is the design of data warehouses that can provide periodic and incidental data to the data mining process, especially in the prediction of accidents. The method used to design data warehouse is Entity Relationship Diagram (ERD).

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Aljuwaiber, Abobakr. "Data Warehousing as Knowledge Pool : A Vital Component of Business Intelligence." International Journal of Computer Science, Engineering and Information Technology 12, no. 4 (August 31, 2022): 21–26. http://dx.doi.org/10.5121/ijcseit.2022.12402.

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Increasing amounts of information and diverse formats have forced organizations to create large data repositories in response to the information explosion in the 21st century. As a result, the model of a data warehouse has been introduced to define a large data repository. The purpose of this article is to describe the principles of data warehousing in business and how it can enhance the generation of new knowledge throughout the organization. Definitions of data warehousing are considered and include methods of its use, namely query and data simulation. The steps required prior to transforming the raw data into a data warehouse and project goals for data warehouses and metadata are also discussed. The objective of this document is to provide a clear and simple description of data warehousing terms and concepts, especially for busy managers and laymen. They may only need basic and direct information about data warehouses to gain a complete understanding of the principles of the data warehouse.

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Sari, Devi Julisca, Siska Kurnia Gusti, Elin Haerani, and Fadhilah Syafria. "DESAIN ARSITEKTUR DATA WAREHOUSE PADA DATA TRANSAKSI PENJUALAN ROTTE BAKERY." Jurnal Teknik Informasi dan Komputer (Tekinkom) 5, no. 2 (December 23, 2022): 253. http://dx.doi.org/10.37600/tekinkom.v5i2.605.

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The increasingly fierce competition between competitors requires companies to be able to compete and maintain their existence in order to continue to grow, for that utilizing information technology such as data warehouses will play a large enough role, because optimal data processing will produce quality information in supporting companies to take appropriate policies. as well as increasing the productivity and effectiveness of the company's performance. The application of the data warehouse can be started by making an architectural design that will be made, for that the researcher aims to provide recommendations for the design of the data warehouse architecture on the sales transaction data of Rotte Bakery by applying the Nine Steps Kimball method. The final result of this research is the application of the Nine Steps Kimball method and the integration of transaction data through the ETL process (extract, transform, load) successfully produces data stored in the data warehouse only the data that is needed and has been uninformed, so that data processing only takes a long time. shorter time in supporting appropriate policy making and achieving business strategies in order to be able to keep pace with the business competition

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Aufaure, Marie-Aude, Alfredo Cuzzocrea, Cécile Favre, Patrick Marcel, and Rokia Missaoui. "An Envisioned Approach for Modeling and Supporting User-Centric Query Activities on Data Warehouses." International Journal of Data Warehousing and Mining 9, no. 2 (April 2013): 89–109. http://dx.doi.org/10.4018/jdwm.2013040105.

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In this vision paper, the authors discuss models and techniques for integrating, processing and querying data, information and knowledge within data warehouses in a user-centric manner. The user-centric emphasis allows us to achieve a number of clear advantages with respect to classical data warehouse architectures, whose most relevant ones are the following: (i) a unified and meaningful representation of multidimensional data and knowledge patterns throughout the data warehouse layers (i.e., loading, storage, metadata, etc); (ii) advanced query mechanisms and guidance that are capable of extracting targeted information and knowledge by means of innovative information retrieval and data mining techniques. Following this main framework, the authors first outline the importance of knowledge representation and management in data warehouses, where knowledge is expressed by existing ontology or patterns discovered from data. Then, the authors propose a user-centric architecture for OLAP query processing, which is the typical applicative interface to data warehouse systems. Finally, the authors propose insights towards cooperative query answering that make use of knowledge management principles and exploit the peculiarities of data warehouses (e.g., multidimensionality, multi-resolution, and so forth).

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Viti, Francesco, Serge P. Hoogendoorn, Lambertus H. (Ben) Immers, Chris M. J. Tampère, and Sascha Hoogendoorn Lanser. "National Data Warehouse." Transportation Research Record: Journal of the Transportation Research Board 2049, no. 1 (January 2008): 176–85. http://dx.doi.org/10.3141/2049-21.

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Golfarelli, Matteo, and Stefano Rizzi. "Data Warehouse Testing." International Journal of Data Warehousing and Mining 7, no. 2 (April 2011): 26–43. http://dx.doi.org/10.4018/jdwm.2011040102.

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Testing is an essential part of the design life-cycle of a software product. Although most phases of data warehouse design have received considerable attention in the literature, not much research has been conducted concerning data warehouse testing. In this paper, the authors introduce a number of data mart-specific testing activities, classify them in terms of what is tested and how it is tested, and show how they can be framed within a reference design method to devise a comprehensive and scalable approach. Finally, the authors discuss some practical evidences emerging from a real case study.

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Sakka, Amir, Sandro Bimonte, Francois Pinet, and Lucile Sautot. "Volunteer Data Warehouse." International Journal of Data Warehousing and Mining 17, no. 3 (July 2021): 1–21. http://dx.doi.org/10.4018/ijdwm.2021070101.

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With the maturity of crowdsourcing systems, new analysis possibilities appear where volunteers play a crucial role by bringing the implicit knowledge issued from practical and daily experience. At the same time, data warehouse and OLAP systems represent the first citizen of decision-support systems. They allow analyzing a huge volume of data according to the multidimensional model. The more the multidimensional model reflects the decision-makers' analysis needs, the more the DW project is successful. However, when volunteers are involved in the design of DWs, existing DW design methodologies present some limitations. In this work, the authors present the main features of volunteer data warehouse (VDW) design, and they study the main existing DW design methodology to find out how they can contribute to fulfil the features needed by this particular DW approach. To provide a formal framework to classify existing work, they provide a study of differences between classical DW users and volunteers. The paper also presents a set of open issues for VDW.

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Karami, Mahtab, Azin Rahimi, and Ali Hosseini Shahmirzadi. "Clinical Data Warehouse." Health Care Manager 36, no. 4 (2017): 380–84. http://dx.doi.org/10.1097/hcm.0000000000000113.

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Tolxdorff, Thomas, and Frank Puppe. "Klinisches Data Warehouse." Informatik-Spektrum 39, no. 3 (April 15, 2016): 233–37. http://dx.doi.org/10.1007/s00287-016-0968-3.

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Rabuzin, Kornelije. "Deductive Data Warehouses." International Journal of Data Warehousing and Mining 10, no. 1 (January 2014): 16–31. http://dx.doi.org/10.4018/ijdwm.2014010102.

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This paper presents the idea of deductive data warehouses. Deductive data warehouses rely on deductive databases but instead of a database in the background a data warehouse is used. The authors show how Datalog (as a logic programming language) can be used to perform OLAP analysis on data. Since data warehouses don't use all the technologies that databases do (locking, transactions, integrity constraints, etc., which are not relevant in this context), some things are different and simpler then when working with deductive databases. The authors demonstrate the idea on an example and the authors show that Datalog rules can be used to perform OLAP analysis on data.

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Atigui, Faten, Franck Ravat, Jiefu Song, Olivier Teste, and Gilles Zurfluh. "Facilitate Effective Decision-Making by Warehousing Reduced Data." International Journal of Decision Support System Technology 7, no. 3 (July 2015): 36–64. http://dx.doi.org/10.4018/ijdsst.2015070103.

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The authors' aim is to provide a solution for multidimensional data warehouse's reduction based on analysts' needs which will specify aggregated schema applicable over a period of time as well as retain only useful data for decision support. Firstly, they describe a conceptual modeling for multidimensional data warehouse. A multidimensional data warehouse's schema is composed of a set of states. Each state is defined as a star schema composed of one fact and its related dimensions. The derivation between states is carried out through combination of reduction operators. Secondly, they present a meta-model which allows managing different states of multidimensional data warehouse. The definition of reduced and unreduced multidimensional data warehouse schema can be carried out by instantiating the meta-model. Finally, they describe their experimental assessments and discuss their results. Evaluating their solution implies executing different queries in various contexts: unreduced single fact table, unreduced relational star schema, reduced star schema and reduced snowflake schema. The authors show that queries are more efficiently calculated within a reduced star schema.

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Pinet, François. "Brief Report on the Advanced Use of Prolog for Data Warehouses." Applied Sciences 12, no. 21 (November 5, 2022): 11223. http://dx.doi.org/10.3390/app122111223.

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Data warehouses have demonstrated their applicability in numerous application fields such as agriculture, the environment and health. This paper proposes a general framework for defining a data warehouse and its aggregations using logic programming. The objective is to show that data managers can easily express, in Prolog, traditional data warehouse queries and combine data aggregation operations with other advanced Prolog features. It is shown that this language provides advanced features to aggregate information in an in-memory database. This paper targets data managers; it shows them the direct writing of data warehouse queries in Prolog using an easily understandable syntax. The queries are not necessarily in an optimal form from a processing point of view, but a data manager can easily use or write them.

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Mathur, Sonali, Shankar Lal Gupta, and Payal Pahwa. "Enhancing Security in Banking Environment Using Business Intelligence." International Journal of Information Retrieval Research 10, no. 4 (October 2020): 21–34. http://dx.doi.org/10.4018/ijirr.2020100102.

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Data warehouses are the most valuable assets of an organization and are basically used for critical business and decision-making purposes. Data from different sources is integrated into the data warehouse. Thus, security issues arise as data is moved from one place to another. Data warehouse security addresses the methodologies that can be used to secure the data warehouse by protecting information from being accessed by unauthorized users for maintaining the reliability of the data warehouse. A data warehouse invariably contains information which needs to be considered extremely sensitive and confidential. Protecting this information is invariably very important as data in the data warehouse is accessed by users at various levels in the organization. The authors propose a method to protect information based on an encryption scheme which secures the data in the data warehouse. This article presents the most feasible security algorithm that can be used for securing the data stored in the operational database so as to prevent unauthorized access.

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Alasta, Amro F., and Muftah A. Enaba. "The Impact of Using Data Warehouse on Manpower Employment Decision Support System." Advanced Materials Research 383-390 (November 2011): 4653–59. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.4653.

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Since the use of computers in business world, data collection has become one of the most important issues due to the available knowledge in the data; such data has been stored in database. Database system was developed which led to the evolvement of hierarchical and relational database followed by Standard Query Language (SQL). As data size increases, the need for more control and information retrieval increase. These increases lead to the development of data mining systems and data warehouses. This paper focuses on the use of data warehouse as a supporting tool in decision making. We to study the effectiveness of data warehouse techniques in the sense of time and flexibility in our case study (Manpower Employment). The study will conclude with a comparison of traditional relational database and the use of data warehouse. The fundamental role of data warehouse is to provide data for supporting decision-making process. Data in data warehouse environment is multidimensional data store. We can simply say that data warehouse is a process not a product, for assembling and managing data from various sources for the purpose of gaining a single detailed view of part or all an establishment. The data warehouse concept has changed the nature of decision support system, by adding new benefits for improving and expanding the scope, accuracy, and accessibility of data. The warehouse is the link between the application and raw data, which is scattered in separate database but now is unified. The objectives of this work are to study the impact of using data warehouse on Manpower Employment Decision Support System, in the sense as far as the data quality concern. We will focus on the benefits gained from using data warehouse, and why it is more powerful than the use of traditional databases in decision making. The case study will be the Libyan national manpower employment agency. The data warehouse will collect database scattered from different sources in Libya in order to compare the performance and time.

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Rahman, Nayem, Peter W. Burkhardt, and Kevin W. Hibray. "Object Migration Tool for Data Warehouses." International Journal of Strategic Information Technology and Applications 1, no. 4 (October 2010): 55–73. http://dx.doi.org/10.4018/jsita.2010100104.

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Data warehouses contain numerous software applications and thousands of objects that make those applications work. Many companies maintain multiple data warehouses depending on business requirements for example development, testing and production. Installing objects and keeping them synchronized across all environments can be a challenging task due to the sheer number of objects and complexity. Software objects stored in a Source Control system must be installed on target warehouse environments. Manual copy procedures are possible but very inefficient. Developers spend much time preparing installation and migration scripts that are prone to syntax errors. This paper proposes an Object Migration and Apply Tool (OMAT) that automates software installation across all warehouses for anyone using manual procedures. An automated tool can help eliminate error prone manual procedures, increase flawless object installation and reduce installation time. The OMAT tool is easy to use through a web browser and includes many useful features that support the development life cycle, the Sarbanes-Oxley (SOX) Act requirements and enforce numerous business requirements. OMAT is designed to support the construction and maintenance of an enterprise-wide, strategic data warehouse faster and better.

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Primasari, Dewi, Khidir Zahid Muchtadiabillah, and Freza Riana. "Application of Fuzzy C Means and TOPSIS in Warehouse Selection at PT Warung Islami Bogor." Jurnal Riset Informatika 5, no. 3 (June 9, 2023): 311–20. http://dx.doi.org/10.34288/jri.v5i3.517.

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PT Warung Islami Bogor needs a warehouse to store goods that come from suppliers. Currently, the selection of warehouses is still done manually and is subjective. It is feared that this will lead to inaccuracies in renting the warehouse. So an application is needed to assist companies in choosing a warehouse. The fuzzy C-Means method can be used to classify warehouse data based on the characteristics of each group. After obtaining the next group is to make a rating of each group. One method that can be used is the TOPSIS method. The TOPSIS method can be applied to this application to rank the data warehouses that have been grouped. In the selection of this warehouse, there are several criteria. The criteria used are price, building area, distance from the head office (HO), parking area, and number of floors. The calculation process is done by dividing the warehouse data into several groups and ranking them to obtain the best recommendations. This application uses the PHP programming language with the Laravel framework—testing using a black box. Fuzzy C-Means and TOPSIS calculations show that Warehouse CCC is the best warehouse in Cluster 1 with a value of 0.797, and the Warehouse in Front of Gas Station Villa Bogor Indah is the best in Cluster 2 with a value of 0.613.

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Rahman, Nayem. "Saving DBMS Resources While Running Batch Cycles in Data Warehouses." International Journal of Technology Diffusion 1, no. 2 (April 2010): 42–55. http://dx.doi.org/10.4018/jtd.2010040102.

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In a large data warehouse, thousands of jobs run during each cycle in dozens of subject areas. Many of the data warehouse tables are quite large and they need to be refreshed at the right time, several times a day, to support strategic business decisions. To enable cycles to run more frequently and keep the data warehouse environment stable the database system’s resource utilization must be optimal. This paper discusses refreshing data warehouses using a metadata model to make sure jobs under batch cycles run on an as-needed basis. The metadata model limits execution of the stored procedures in different analytical subject areas to source data changes in the source staging subject area tables, and then implements refreshes of analytical tables for which new data has arrived from the operational databases. The load is skipped if source data has not changed. Skipping unnecessary loads via this metadata driven approach enables significant database resources savings. The resource savings statistics based on an actual production data warehouse demonstrate an excellent reduction of computing resources consumption achieved by the proposed techniques.

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J. AlMeghari, Mayada. "Data Warehouse Signature: A Framework for Implementing Security Issues in Data Warehouses." Journal of Computer Sciences and Applications 5, no. 1 (August 26, 2017): 17–24. http://dx.doi.org/10.12691/jcsa-5-1-3.

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Bimonte, Sandro, Omar Boussaid, Michel Schneider, and Fabien Ruelle. "Design and Implementation of Active Stream Data Warehouses." International Journal of Data Warehousing and Mining 15, no. 2 (April 2019): 1–21. http://dx.doi.org/10.4018/ijdwm.2019040101.

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In the era of Big Data, more and more stream data is available. In the same way, Decision Support Systems (DSS) tools, such as data warehouses and alert systems, become more and more sophisticated, and conceptual modeling tools are consequently mandatory for successfully DSS projects. Formalisms such as UML and ER have been widely used in the context of classical information and data warehouse systems, but they have not been investigated yet for stream data warehouses to deal with alert systems. Therefore, in this article, the authors introduce the notion of Active Stream Data Warehouse (ASDW) and this article proposes a UML profile for designing Active Stream Data Warehouses. Indeed, this article extends the ICSOLAP profile to take into account continuous and window OLAP queries. Moreover, this article studies the duality of the stream and OLAP decision-making process and the authors propose a set of ECA rules to automatically trigger OLAP operators. The UML profile is implemented in a new OLAP architecture, and it is validated using an environmental case study concerning the wind monitoring.

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Nugroho, Kresno Budi. "Pengembangan Data Warehouse Penerimaan Mahasiswa Baru Untuk Informasi Strategik Pada Universitas BSI." Jurnal Kajian Ilmiah 18, no. 2 (May 3, 2018): 168. http://dx.doi.org/10.31599/jki.v18i2.293.

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Institusi pendidikan perlu memiliki pengetahuan yang lebih, dalam mengevaluasi, merancang dan mengambil keputusan. Dimana dari pengetahun tersebut bisa didapat dari data kegiatan operasional yang tersimpan didalam database institusi pendidikan ke dalam data warehose, sehingga dapat digunakan sebagai pendukung dalam proses pengambilan keputusan. Data Warehouse memiliki peran besar dalam penyediaan strategik informasi yang bisa digunakan untuk memenuhi kebutuhan pihak manajemen dalam konteks bisnis. Penelitian ini mengkaji pengembangan data warehouse untuk data penerimaan mahasiswa baru pada universitas BSI Bandung, yang mana dapat menggali informasi-informasi penting yang dapat membantu mengambilan keputusan strategik dalam menunjang kegiatan promosi di Universitas BSI Bandung. Hasil dari penelitian ini adalah sebuah data warehouse lengkap dengan aplikasi presentasi data berbasis web. Tujuan akhir dari penelitian ini adalah untuk menghasilkan rancangan data warehouse yang dapat mendukung kebutuhan pihak manajemen dalam mengambil keputusan dengan cara memberikan informasi yang strategik tentang penerimaan mahasiswa baru, yang menghasilkan ringkasan informasi yang akurat dan berguna sebagai masukan untuk menentukan strategi dalam melakukan promosi.

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Pecoraro, Fabrizio, Daniela Luzi, and Fabrizio L. Ricci. "Developing HL7 CDA-Based Data Warehouse for the Use of Electronic Health Record Data for Secondary Purposes." ACI Open 03, no. 01 (January 2019): e44-e62. http://dx.doi.org/10.1055/s-0039-1688936.

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Background The growing availability of clinical and administrative data collected in electronic health records (EHRs) have led researchers and policy makers to implement data warehouses to improve the reuse of EHR data for secondary purposes. This approach can take advantages from a unique source of information that collects data from providers across multiple organizations. Moreover, the development of a data warehouse benefits from the standards adopted to exchange data provided by heterogeneous systems. Objective This article aims to design and implement a conceptual framework that semiautomatically extracts information collected in Health Level 7 Clinical Document Architecture (CDA) documents stored in an EHR and transforms them to be loaded in a target data warehouse. Results The solution adopted in this article supports the integration of the EHR as an operational data store in a data warehouse infrastructure. Moreover, data structure of EHR clinical documents and the data warehouse modeling schemas are analyzed to define a semiautomatic framework that maps the primitives of the CDA with the concepts of the dimensional model. The case study successfully tests this approach. Conclusion The proposed solution guarantees data quality using structured documents already integrated in a large-scale infrastructure, with a timely updated information flow. It ensures data integrity and consistency and has the advantage to be based on a sample size that covers a broad target population. Moreover, the use of CDAs simplifies the definition of extract, transform, and load tools through the adoption of a conceptual framework that load the information stored in the CDA in the data warehouse.

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CALVANESE, DIEGO, GIUSEPPE DE GIACOMO, MAURIZIO LENZERINI, DANIELE NARDI, and RICCARDO ROSATI. "DATA INTEGRATION IN DATA WAREHOUSING." International Journal of Cooperative Information Systems 10, no. 03 (September 2001): 237–71. http://dx.doi.org/10.1142/s0218843001000345.

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Information integration is one of the most important aspects of a Data Warehouse. When data passes from the sources of the application-oriented operational environment to the Data Warehouse, possible inconsistencies and redundancies should be resolved, so that the warehouse is able to provide an integrated and reconciled view of data of the organization. We describe a novel approach to data integration in Data Warehousing. Our approach is based on a conceptual representation of the Data Warehouse application domain, and follows the so-called local-as-view paradigm: both source and Data Warehouse relations are defined as views over the conceptual model. We propose a technique for declaratively specifying suitable reconciliation correspondences to be used in order to solve conflicts among data in different sources. The main goal of the method is to support the design of mediators that materialize the data in the Data Warehouse relations. Starting from the specification of one such relation as a query over the conceptual model, a rewriting algorithm reformulates the query in terms of both the source relations and the reconciliation correspondences, thus obtaining a correct specification of how to load the data in the materialized view.

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Di Tria, Francesco, Ezio Lefons, and Filippo Tangorra. "A Framework for Evaluating Design Methodologies for Big Data Warehouses." International Journal of Data Warehousing and Mining 14, no. 1 (January 2018): 15–39. http://dx.doi.org/10.4018/ijdwm.2018010102.

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This article describes how the evaluation of modern data warehouses considers new solutions adopted for facing the radical changes caused by the necessity of reducing the storage volume, while increasing the velocity in multidimensional design and data elaboration, even in presence of unstructured data that are useful for providing qualitative information. The aim is to set up a framework for the evaluation of the physical and methodological characteristics of a data warehouse, realized by considering the factors that affect the data warehouse's lifecycle when taking into account the Big Data issues (Volume, Velocity, Variety, Value, and Veracity). The contribution is the definition of a set of criteria for classifying Big Data Warehouses on the basis of their methodological characteristics. Based on these criteria, the authors defined a set of metrics for measuring the quality of Big Data Warehouses in reference to the design specifications. They show through a case study how the proposed metrics are able to check the eligibility of methodologies falling in different classes in the Big Data context.

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Syaputra, Muhammad Dwiky, Alwis Nazir, Siska Kurnia Gusti, Suwanto Sanjaya, and Fadhilah Syafria. "Data Warehouse Design For Sales Transactions on CV. Sumber Tirta Anugerah." Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi 8, no. 2 (December 12, 2022): 88. http://dx.doi.org/10.24014/coreit.v8i2.19800.

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Many data warehouses are implemented in companies engaged in retail, CV. Sumber Tirta Anugerah is one of the paint product retail companies that has not implemented it yet. As time goes by, the sales transaction data is getting more and more difficult to process because it is still stored in Microsoft Excel. This is a serious problem in utilizing historical data to assist in making a decision. It is difficult to store sales data because the data is quite large and a lot. Based on the above problems, a data warehouse design is needed for sales transaction data. This data warehouse design uses Kimball's nine-steps method and star schema. To perform the ETL process (extract, transform, and load) using Pentaho software. In this data warehouse design, Tableau software is used to visualize the processed data into a graph and dashboard report. The result of this research is a data warehouse design using nine steps and a star schema which gets a transformation response time of 4048 MS.

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Xu, Zhichen, Ying Gao, and Andrew Davidson. "Keep Your Distributed Data Warehouse Consistent at a Minimal Cost." Proceedings of the ACM on Management of Data 1, no. 2 (June 13, 2023): 1–25. http://dx.doi.org/10.1145/3589770.

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Large data warehouses store interdependent tables that are updated independently in response to business logic changes or late arrival of critical data. To keep the warehouse consistent, changes to upstream tables need to be propagated to downstream tables in a timely fashion. However, a naive change propagation algorithm can cause many unnecessary updates or recalculations of downstream tables, which drives up the cost of data warehouse management. In this paper, we describe our solution that can ensure the eventual consistency of the data warehouse while avoiding unnecessary table updates. We also show that the optimal trade-off between computational cost reduction and meeting data freshness constraints can be found by solving a dynamic programming problem. The proposed solution is currently in production to manage the YouTube Data Warehouse and has reduced update requests by 25% by eliminating non-trivial duplicates. These requests would have been carried out by large batch jobs over big data. Eliminating them has led to a proportionate reduction in computing resources. One key advantage of our approach is that it can be used in a heterogeneous, distributed data warehouse environment where the operator software may not have complete control over the query processors. This is because our approach only relies on having dependency information for tables and can operate on the post-state of data sources.

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Xu, Ren Feng, and Ding Ju Zhu. "An Approach for Data Warehouse Based on Grid." Applied Mechanics and Materials 263-266 (December 2012): 1925–28. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.1925.

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Grid based data warehouse (GDW) distinguishes itself from other data warehouses based on single computer, parallel computer, cluster, or distributed network due to the characteristics of the grid environment. This article proposes an approach for GDW. GDW approach is good at adapting different hardware platforms and bottom databases by making full exploitation of the heterogeneity and transparency of the grid .

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Kovačević, Vlado, Ljiljana Rajnović, and Predrag Vuković. "Warehouse receipt finance in the agricultural sectors: Lessons learned in Serbia." Ekonomika 69, no. 1 (2023): 59–68. http://dx.doi.org/10.5937/ekonomika2301059k.

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Aim of this paper is to analyse the role of warehouse receipts in the agribusiness sector, as well as prerequisites for successful public warehouses system. Warehouse receipt emerge as a promising financial instrument for farmers and other owners of agricultural products to provide loan against warehouse receipts. Warehouse receipts also offer alternative investment opportunities for banks by creating a new asset class. The paper analyses the experiences of the Serbia, which introduced the system of public warehouses in 2009. Applied research methodology includes literature review, comparation analyses and Indemnity fund' data analyses. According to the results of this paper, Serbian legal framework can be good example for other countries aiming to establish public warehouses system as a system consisting on all necessary prerequisite to establidh successful public warehouse system. After positive experiences in the initial years, the development of the public warehouse system in Serbia came to a standstill in 2015., when two frauds in the public warehouse system occurred, which led to a loss of confidence in public. An important lesson learned in Serbia is that in addition to complete legislation based on the experience of successful public warehouse systems, it is necessary to implement legal provisions in practice. According to the results of the paper, the main cause of the crash of public warehouses in Serbia is despite the legal provision on the establishment of a special inspection for public warehouses, this most important link has never been established in practice. Lessons learned in Serbia can be a useful experience for other countries, j the process of establishing this important system for agribusiness sector. This paper provides first comprehensive analyses of the public warehouse system in the Serbia.

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Irawan, Ragil Yoga, Budi Susanto, and Yuan Lukito. "Building Data Warehouse and Dashboard of Church Congregation Data." Jurnal Terapan Teknologi Informasi 3, no. 2 (July 13, 2021): 85–94. http://dx.doi.org/10.21460/jutei.2019.32.183.

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A data warehouse is essential for an organization to process and analyze data coming from the organization. Hence, a data warehouse together with a dashboard to visualize the processed data are built to accommodate the need of the church administrator to analyze a large set of church congregation data. The data warehouse is built using the Kimball principle. This Kimball principle emphasizes the implementation of a dimensional model in the data warehouse, not a relational model used in a regular transactional database. An ETL process that contains extract, transform and load processes is used to retrieve all data from the regular transactional database and transform the data so the data can be loaded into the data warehouse. A dashboard is then built to visualize the data from the data warehouse so the users can view the processed data easily. Users can also export the processed data into an excel file that can be downloaded from the dashboard. A web service is built to get data from the data warehouse and return it to the dashboard.

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Verma, Swati. "Implementation of Data Warehouse in ERP System." Indian Journal of Applied Research 3, no. 9 (October 1, 2011): 46–48. http://dx.doi.org/10.15373/2249555x/sept2013/41.

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Abdalaziz Ahmedl, Reyan, and Tarig Mohamed Ahmed. "Generating Data Warehouse Schema." International Journal in Foundations of Computer Science & Technology 4, no. 1 (January 31, 2014): 1–16. http://dx.doi.org/10.5121/ijfcst.2014.4101.

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Oates, J. "Evaluating data warehouse toolkits." IEEE Software 15, no. 1 (1998): 52–54. http://dx.doi.org/10.1109/52.646882.

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Forbes, S. R., R. G. Burke, C. E. Day, and H. P. Varma. "CHS Bathymetric Data Warehouse." Canadian Journal of Remote Sensing 24, no. 3 (September 1998): 246–54. http://dx.doi.org/10.1080/07038992.1998.10855246.

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Journal articles on the topic 'Data warehouse'

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