Relevant bibliographies by topics / Data warehouse

Academic literature on the topic 'Data warehouse'

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Published: 4 June 2021
Last updated: 6 September 2023

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Journal articles on the topic "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 “STUDENTS DW” 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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Dissertations / Theses on the topic "Data warehouse"

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Sarkis, Laura Costa. "Data warehouse." Florianópolis, SC, 2001. http://repositorio.ufsc.br/xmlui/handle/123456789/80047.

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Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Ciência da Computação.
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Este trabalho descreve os conceitos básicos do ambiente do Data Warehouse, abordando em especial o processo de migração de dados. São expostas algumas técnicas e tecnologias mais recentes existentes no mercado com esta finalidade. A partir de um estudo inicial sobre os conceitos de Data Warehouse, delimitou-se o trabalho em função do processo de migração dos dados. Com este propósito, foram estudadas quatro abordagens e elaborada uma análise comparativa na tentativa de determinar qual delas é a mais adequada ao processo. Em um processo de migração de dados é importante garantir também a qualidade dos dados, em decorrência disto, o trabalho contém a descrição de uma abordagem que trata de como é realizado o processo para a qualidade de dados em Data Warehouse. São citadas também algumas ferramentas existentes no mercado que possam possivelmente atender aos processos de migração de dados para o Data Warehouse e qualidade de dados.
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Mathew, Avin D. "Asset management data warehouse data modelling." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/19310/1/Avin_Mathew_Thesis.pdf.

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Data are the lifeblood of an organisation, being employed by virtually all business functions within a firm. Data management, therefore, is a critical process in prolonging the life of a company and determining the success of each of an organisation’s business functions. The last decade and a half has seen data warehousing rising in priority within corporate data management as it provides an effective supporting platform for decision support tools. A cross-sectional survey conducted by this research showed that data warehousing is starting to be used within organisations for their engineering asset management, however the industry uptake is slow and has much room for development and improvement. This conclusion is also evidenced by the lack of systematic scholarly research within asset management data warehousing as compared to data warehousing for other business areas. This research is motivated by the lack of dedicated research into asset management data warehousing and attempts to provide original contributions to the area, focussing on data modelling. Integration is a fundamental characteristic of a data warehouse and facilitates the analysis of data from multiple sources. While several integration models exist for asset management, these only cover select areas of asset management. This research presents a novel conceptual data warehousing data model that integrates the numerous asset management data areas. The comprehensive ethnographic modelling methodology involved a diverse set of inputs (including data model patterns, standards, information system data models, and business process models) that described asset management data. Used as an integrated data source, the conceptual data model was verified by more than 20 experts in asset management and validated against four case studies. A large section of asset management data are stored in a relational format due to the maturity and pervasiveness of relational database management systems. Data warehousing offers the alternative approach of structuring data in a dimensional format, which suggests increased data retrieval speeds in addition to reducing analysis complexity for end users. To investigate the benefits of moving asset management data from a relational to multidimensional format, this research presents an innovative relational vs. multidimensional model evaluation procedure. To undertake an equitable comparison, the compared multidimensional are derived from an asset management relational model and as such, this research presents an original multidimensional modelling derivation methodology for asset management relational models. Multidimensional models were derived from the relational models in the asset management data exchange standard, MIMOSA OSA-EAI. The multidimensional and relational models were compared through a series of queries. It was discovered that multidimensional schemas reduced the data size and subsequently data insertion time, decreased the complexity of query conceptualisation, and improved the query execution performance across a range of query types. To facilitate the quicker uptake of these data warehouse multidimensional models within organisations, an alternate modelling methodology was investigated. This research presents an innovative approach of using a case-based reasoning methodology for data warehouse schema design. Using unique case representation and indexing techniques, the system also uses a business vocabulary repository to augment case searching and adaptation. The system was validated through a case-study where multidimensional schema design speed and accuracy was measured. It was found that the case-based reasoning system provided a marginal benefit, with a greater benefits gained when confronted with more difficult scenarios.
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Mathew, Avin D. "Asset management data warehouse data modelling." Queensland University of Technology, 2008. http://eprints.qut.edu.au/19310/.

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Data are the lifeblood of an organisation, being employed by virtually all business functions within a firm. Data management, therefore, is a critical process in prolonging the life of a company and determining the success of each of an organisation’s business functions. The last decade and a half has seen data warehousing rising in priority within corporate data management as it provides an effective supporting platform for decision support tools. A cross-sectional survey conducted by this research showed that data warehousing is starting to be used within organisations for their engineering asset management, however the industry uptake is slow and has much room for development and improvement. This conclusion is also evidenced by the lack of systematic scholarly research within asset management data warehousing as compared to data warehousing for other business areas. This research is motivated by the lack of dedicated research into asset management data warehousing and attempts to provide original contributions to the area, focussing on data modelling. Integration is a fundamental characteristic of a data warehouse and facilitates the analysis of data from multiple sources. While several integration models exist for asset management, these only cover select areas of asset management. This research presents a novel conceptual data warehousing data model that integrates the numerous asset management data areas. The comprehensive ethnographic modelling methodology involved a diverse set of inputs (including data model patterns, standards, information system data models, and business process models) that described asset management data. Used as an integrated data source, the conceptual data model was verified by more than 20 experts in asset management and validated against four case studies. A large section of asset management data are stored in a relational format due to the maturity and pervasiveness of relational database management systems. Data warehousing offers the alternative approach of structuring data in a dimensional format, which suggests increased data retrieval speeds in addition to reducing analysis complexity for end users. To investigate the benefits of moving asset management data from a relational to multidimensional format, this research presents an innovative relational vs. multidimensional model evaluation procedure. To undertake an equitable comparison, the compared multidimensional are derived from an asset management relational model and as such, this research presents an original multidimensional modelling derivation methodology for asset management relational models. Multidimensional models were derived from the relational models in the asset management data exchange standard, MIMOSA OSA-EAI. The multidimensional and relational models were compared through a series of queries. It was discovered that multidimensional schemas reduced the data size and subsequently data insertion time, decreased the complexity of query conceptualisation, and improved the query execution performance across a range of query types. To facilitate the quicker uptake of these data warehouse multidimensional models within organisations, an alternate modelling methodology was investigated. This research presents an innovative approach of using a case-based reasoning methodology for data warehouse schema design. Using unique case representation and indexing techniques, the system also uses a business vocabulary repository to augment case searching and adaptation. The system was validated through a case-study where multidimensional schema design speed and accuracy was measured. It was found that the case-based reasoning system provided a marginal benefit, with a greater benefits gained when confronted with more difficult scenarios.
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Sharathkumar, Sudhindra. "An Automated Data Warehouse." ScholarWorks@UNO, 2003. http://scholarworks.uno.edu/td/36.

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An increasing number of organizations are implementing data warehouses to strengthen their decision support systems. This comes with the challenges of the population and the periodic update of data warehouses. In this thesis, we present a tool that provides users with features to create a warehouse database and transform structures of the source database into structures for the warehouse database. It is highly interactive, easy to use, and hides the underlying complexity of manual SQL code generation from its users. Attributes from source tables can be mapped into new attributes in the warehouse database tables using aggregate functions. Then, relevant data is automatically transported from the source database to the newly created warehouse. The tool thus integrates warehouse creation, schema mapping and data population into a single generalpurpose tool. This tool has been designed as a component of the framework for an automated data warehouse being developed at theComputer Science Department, University of New Orleans. Users of this framework are the database administrators, who will also be able to synchronize updates of multiple copies of the data warehouse. Warehouse images that need to be updated are taken offline and applications that need to access the data warehouse can now access any of the other image warehouses. The Switching Application built into this framework switches between databases in a way that is totally transparent to applications so that they do not realize existence of multiple copies of the data warehouse. In effect, even non-technical users can create, populate and update data warehouses with minimal time and effort.
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Qian, Yi. "Financial aid data warehouse /." Connect to title online, 2008. http://minds.wisconsin.edu/handle/1793/34216.

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Hinrichs, Holger. "Datenqualitätsmanagement in Data-warehouse-Systemen." [S.l. : s.n.], 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=964461552.

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Mazzola, Irany Salgado. "Projeto de data warehouse dimensional." Florianópolis, SC, 2002. http://repositorio.ufsc.br/xmlui/handle/123456789/83465.

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Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Ciência da Computação.
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O objetivo do trabalho é propor um projeto de modelagem de um Data Warehouse Dimensional para uma empresa do ramo varejista, com diversas áreas cujas informações alimentam o sistema de informações apoiado no DW. Um Data Warehouse é uma tecnologia de articulação inacabada, que estrutura e formaliza as informações e os dados de uma organização a partir de uma modelagem que pode vir a ser: a) relacional, ou seja, baseada em um modelo de banco de dados relacional, que atualmente é mais usado em transações on-line; b) dimensional, que pode ser baseado em bancos de dados cujas dimensões dão uma visão operacional e administrativa mais completa e muito menos complexa do todo da organização como sistema informativo. O projeto da modelagem proposto no trabalho tem como objetivo apresentar as várias etapas que compõem a construção de um Data Warehouse, assim como mostrar seus componentes mais utilizados na visualização de informações históricas e não detalhadas para análise on-line. A utilização desta tecnologia permite que dados de valor organizacional de períodos superiores a cinco anos possam ser arquivados, visando a apreciação crítica do comportamento da organização, enquanto se projetam estratégias operacionais e táticas mais eficazes em seu gerenciamento.
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Kanna, Rajesh. "Managing XML data in a relational warehouse on query translation, warehouse maintenance, and data staleness /." [Gainesville, Fla.] : University of Florida, 2001. http://etd.fcla.edu/etd/uf/2001/anp4011/Thesis.PDF.

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Thesis (M.S.)--University of Florida, 2001.
Title from first page of PDF file. Document formatted into pages; contains x, 75 p.; also contains graphics. Vita. Includes bibliographical references (p. 71-74).
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Redgert, Rebecca. "Evaluating Data Quality in a Data Warehouse Environment." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208766.

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The amount of data accumulated by organizations have grown significantly during the last couple of years, increasing the importance of data quality. Ensuring data quality for large amounts of data is a complicated task, but crucial to subsequent analysis. This study investigates how to maintain and improve data quality in a data warehouse. A case study of the errors in a data warehouse was conducted at the Swedish company Kaplan, and resulted in guiding principles on how to improve the data quality. The investigation was done by manually comparing data from the source systems to the data integrated in the data warehouse and applying a quality framework based on semiotic theory to identify errors. The three main guiding principles given are (1) to implement a standardized format for the source data, (2) to implement a check prior to integration where the source data are reviewed and corrected if necessary, and (3) to create and implement specific database integrity rules. Further work is encouraged on establishing a guide for the framework on how to best perform a manual approach for comparing data, and quality assurance of source data.
Mängden data som ackumulerats av organisationer har ökat betydligt under de senaste åren, vilket har ökat betydelsen för datakvalitet. Att säkerställa datakvalitet för stora mängder data är en komplicerad uppgift, men avgörande för efterföljande analys. Denna studie undersöker hur man underhåller och förbättrar datakvaliteten i ett datalager. En fallstudie av fel i ett datalager på det svenska företaget Kaplan genomfördes och resulterade i riktlinjer för hur datakvaliteten kan förbättras. Undersökningen gjordes genom att manuellt jämföra data från källsystemen med datat integrerat i datalagret och genom att tillämpa ett kvalitetsramverk grundat på semiotisk teori för att kunna identifiera fel. De tre huvudsakliga riktlinjerna som gavs är att (1) implementera ett standardiserat format för källdatat, (2) genomföra en kontroll före integration där källdatat granskas och korrigeras vid behov, och (3) att skapa och implementera specifika databasintegritetsregler. Vidare forskning uppmuntras för att skapa en guide till ramverket om hur man bäst jämför data genom en manuell undersökning, och kvalitetssäkring av källdata.
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Öhman, Mikael. "a Data-Warehouse Solution for OMS Data Management." Thesis, Umeå universitet, Institutionen för datavetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-80688.

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A database system for storing and querying data of a dynamic schema has been developed based on the kdb+ database management system and the q programming language for use in a financial setting of order and execution services. Some basic assumptions of mandatory fields of the data to be stored are made including that the data are time-series based.A dynamic schema enables an Order-Management System (OMS) to store information not suitable or usable when stored in log files or traditional databases. Log files are linear, cannot be queried effectively and are not suitable for the volumes produced by modern OMSs. Traditional databases are typically row-oriented which does not suit time-series based data and rely on the relational model which uses statically typed sets to store relations.The created system includes software that is capable of mining the actual schema stored in the database and visualize it. This enables ease of exploratory querying and production of applications which use the database. A feedhandler has been created optimized for handling high volumes of data. Volumes in finance are steadily growing as the industry continues to adopt computer automation of tasks. Feedhandler performance is important to reduce latency and for cost savings as a result of not having to scale horizontally. A study of the area of algorithmic trading has been performed with focus on transaction-cost analysis. Fundamental algorithms have been reviewed.A proof of concept application has been created that simulates an OMS storing logs on the execution of a Volume Weighted Average Price (VWAP) trading algorithm. The stored logs are then used in order to improve the performance of the trading algorithm through basic data mining and machine learning techniques. The actual learning algorithm focuses on predicting intraday volume patterns.
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Books on the topic "Data warehouse"

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The data warehouse method: Integrated data warehouse support environments. Upper Saddle River, N.J: Prentice Hall PTR, 1999.

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Enterprise data warehouse. Upper Saddle River, N.J: Prentice Hall PTR, 1999.

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Vaisman, Alejandro, and Esteban Zimányi. Data Warehouse Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-662-65167-4.

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Schütte, Reinhard, Thomas Rotthowe, and Roland Holten, eds. Data Warehouse Managementhandbuch. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56847-3.

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von Maur, Eitel, and Robert Winter, eds. Data Warehouse Management. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55477-3.

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Vaisman, Alejandro, and Esteban Zimányi. Data Warehouse Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54655-6.

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H, Inmon William, ed. Data warehouse performance. New York: John Wiley, 1998.

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H, Inmon William, ed. Data warehouse performance. New York: Wiley, 1999.

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Kimball, Ralph. The data warehouse toolkit: Practical techniques for building dimensional data warehouses. New York: John Wiley & Sons, 1996.

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Laberge, Robert. The data warehouse mentor: Practical data warehouse and business intelligence insights. San Francisco, Calif: McGraw-Hill, 2011.

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Book chapters on the topic "Data warehouse"

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Kaiser, Bernd-Ulrich. "Data Warehouse." In Unternehmensinformation mit SAP®-EIS, 23–46. Wiesbaden: Vieweg+Teubner Verlag, 1998. http://dx.doi.org/10.1007/978-3-322-94272-2_3.

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Kaiser, Bernd-Ulrich, and Stephen Fedtke. "Data Warehouse." In Unternehmensinformation mit SAP®-EIS, 23–46. Wiesbaden: Vieweg+Teubner Verlag, 1999. http://dx.doi.org/10.1007/978-3-663-07821-0_3.

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Song, Il-Yeol. "Data Warehouse." In Encyclopedia of Database Systems, 1–2. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_882-3.

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Hopfmann, Lienhard. "Data Warehouse." In Power Tools, 203–12. Wiesbaden: Gabler Verlag, 2001. http://dx.doi.org/10.1007/978-3-322-84461-3_17.

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Colhoun, O. "Data Warehouse." In Springer Reference Medizin, 656. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-48986-4_826.

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Colhoun, O. "Data Warehouse." In Lexikon der Medizinischen Laboratoriumsdiagnostik, 1. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-49054-9_826-1.

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Song, Il-Yeol. "Data Warehouse." In Encyclopedia of Database Systems, 657–58. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_882.

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Herden, Olaf. "Data Warehouse." In Taschenbuch Datenbanken, 430–60. München: Carl Hanser Verlag GmbH & Co. KG, 2015. http://dx.doi.org/10.3139/9783446440265.014.

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Cordts, Sönke, Gerold Blakowski, and Gerhard Brosius. "Data Warehouse." In Datenbanken für Wirtschaftsinformatiker, 405–27. Wiesbaden: Vieweg+Teubner, 2011. http://dx.doi.org/10.1007/978-3-8348-8192-2_19.

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Song, Il-Yeol. "Data Warehouse." In Encyclopedia of Database Systems, 876–78. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_882.

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Conference papers on the topic "Data warehouse"

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Caniupan, M., and A. Placencia. "Data Warehouse Fixer: Fixing Inconsistencies in Data Warehouses." In 2011 30th International Conference of the Chilean Computer Science Society (SCCC 2011). IEEE, 2011. http://dx.doi.org/10.1109/sccc.2011.5.

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Moulai, Hadjer, and Habiba Drias. "From Data Warehouse to Information Warehouse." In the International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3230905.3230914.

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ElGamal, Neveen, Ali ElBastawissy, and Galal Galal-Edeen. "Data warehouse testing." In the Joint EDBT/ICDT 2013 Workshops. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2457317.2457319.

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Yi, Xun, Russell Paulet, Elisa Bertino, and Guandong Xu. "Private data warehouse queries." In the 18th ACM symposium. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2462410.2462418.

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Arfaoui, Nouha, and Jalel Akaichi. "Vehicle trajectory data warehouse." In ICC '17: Second International Conference on Internet of Things, Data and Cloud Computing. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3018896.3056790.

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Blazic, G., P. Poscic, and D. Jaksic. "Data warehouse architecture classification." In 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). IEEE, 2017. http://dx.doi.org/10.23919/mipro.2017.7973657.

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Turki, Ines Zouari, Faiza Ghozzi Jedidi, and Rafik Bouaziz. "Multiversion data warehouse constraints." In the ACM 13th international workshop. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1871940.1871945.

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Ravat, Franck, Olivier Teste, and Giles Zurfluh. "Towards data warehouse design." In the eighth international conference. New York, New York, USA: ACM Press, 1999. http://dx.doi.org/10.1145/319950.320028.

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Shuigeng Zhou, Aoying Zhou, Xiaopeng Tao, and Yunfa Hu. "Hierarchically distributed data warehouse." In Proceedings Fourth International Conference/Exhibition on High Performance Computing in the Asia-Pacific Region. IEEE, 2000. http://dx.doi.org/10.1109/hpc.2000.843558.

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Karima, Tekaya, Abdellaziz Abdelatif, and Ounalli Habib. "Data Warehouse Decentralization Strategy." In 2010 IEEE 7th International Conference on e-Business Engineering (ICEBE). IEEE, 2010. http://dx.doi.org/10.1109/icebe.2010.109.

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Reports on the topic "Data warehouse"

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Kramer, Mitchell. Data Warehouse Databases. Boston, MA: Patricia Seybold Group, March 2003. http://dx.doi.org/10.1571/fw3-6-03cc.

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Kramer, Mitchell. DB2 Data Warehouse Enterprise Edition. Boston, MA: Patricia Seybold Group, September 2003. http://dx.doi.org/10.1571/pr9-11-03cc.

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Lee, Sangkeun, Supriya Chinthavali, Mallikarjun Shankar, Claire Zeng, and Stephen Hendrickson. Energy Finance Data Warehouse Manual. Office of Scientific and Technical Information (OSTI), November 2016. http://dx.doi.org/10.2172/1342691.

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Kramer, Mitchell. PSGroup Bull's-Eye: Data Warehouse Databases. Boston, MA: Patricia Seybold Group, November 2003. http://dx.doi.org/10.1571/psgb11-20-03cc.

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Kramer, Mitchell. PSGroup Bull's-Eye: Data Warehouse Databases. Boston, MA: Patricia Seybold Group, November 2003. http://dx.doi.org/10.1571/psgb11-26-03cc.

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Kramer, Mitchell. PSGroup Bull's-Eye: Data Warehouse Databases. Boston, MA: Patricia Seybold Group, December 2003. http://dx.doi.org/10.1571/psgb12-4-03cc.

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Reddy, Prameela V., and Charles G. Schroeder. Data Warehouse Architecture for Army Installations. Fort Belvoir, VA: Defense Technical Information Center, November 1999. http://dx.doi.org/10.21236/ada371882.

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Kramer, Mitchell. Data Warehouse Database Feature Comparison Matrix. Boston, MA: Patricia Seybold Group, November 2003. http://dx.doi.org/10.1571/cm11-13-03cc.

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Ulmer, Craig, Nathan Fabian, Todd Kordenbrock, Shyamali Mukherjee, and Ron Oldfield. ATDM Data Management FY2015: Data Warehouse Progress Report. Office of Scientific and Technical Information (OSTI), February 2021. http://dx.doi.org/10.2172/1770714.

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Zhang, Xin, and Elke A. Rundensteiner. Data Warehouse Maintenance Under Concurrent Schema and Data Updates. Fort Belvoir, VA: Defense Technical Information Center, August 1998. http://dx.doi.org/10.21236/ada386236.

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