Relevant bibliographies by topics / Online Analytical Processing (OLAP)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Online Analytical Processing (OLAP)'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 June 2025

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Journal articles on the topic "Online Analytical Processing (OLAP)"

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Jing, Changhong, Wenjie Liu, Jintao Gao, and Ouya Pei. "Research and implementation of HTAP for distributed database." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 39, no. 2 (2021): 430–38. http://dx.doi.org/10.1051/jnwpu/20213920430.

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Data processing can be roughly divided into two categories, online transaction processing OLTP(on-line transaction processing) and online analytical processing OLAP(on-line analytical processing). OLTP is the main application of traditional relational databases, and it is some basic daily transaction processing, such as bank pipeline transactions and so on. OLAP is the main application of the data warehouse system, it supports some more complex data analysis operations, focuses on decision support, and provides popular and intuitive analysis results. As the amount of data processed by enterprises continues to increase, distributed databases have gradually replaced stand-alone databases and become the mainstream of applications. However, the current business supported by distributed databases is mainly based on OLTP applications, lacking OLAP implementation. This paper proposes an implementation method of HTAP for distributed database CBase, which provides an implementation method of OLAP analysis for CBase, and can easily deal with data analysis of large amounts of data.
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Honda, Masayuki, and Takehiro Matsumoto. "System Replacement to a New HIS and Data Warehouse." Journal of Advanced Computational Intelligence and Intelligent Informatics 16, no. 1 (2012): 38–41. http://dx.doi.org/10.20965/jaciii.2012.p0038.

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Large-scale hospital information systems (HIS) generally consist of (i) online transaction processing (OLTP) and (ii) online analytical processing (OLAP) systems. Electronic medical records (EMR) are a major OLTP element. The data warehouse (DWH) assumes many important OLAP roles and maintains an institution’s medical care at a high level by providing EMR with the best practice cases available. This article focuses mainly on why OLTP and OLAP are needed and what roles the DWH plays, which means that the DWH has its own utilities and supplementary merits. The background of this discussion is closely related to the HIS at Nagasaki University Hospital introduced before the DWH is discussed.
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Chou, Chung-Hsien, Masahiro Hayakawa, Atsushi Kitazawa, and Phillip Sheu. "GOLAP: Graph-Based Online Analytical Processing." International Journal of Semantic Computing 12, no. 04 (2018): 595–608. http://dx.doi.org/10.1142/s1793351x18500071.

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Graph-based Online Analytical Processing (GOLAP) extends Online Analytical Processing (OLAP) to address graph-based problems that involve object attributes. Based on graph data, GOLAP can answer user queries related to combinatorial optimization, structural analytics, and influence analytics. Besides, since a GOLAP system is an online interactive system that requires fast response time, the execution time for graph-problem queries is essentially critical. Thus, how to speed up the execution time of specific graph problems becomes a challenge in GOLAP. In this paper, we show several methods to speed up the running time, including graph data reduction and approximation. In this paper, we survey classes of graph-based queries, challenges for GOLAP, and solutions that GOLAP provides.
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Sun, Lei, Hong Mei Xing, and Wei Wang. "Basic Technology of OLAP." Advanced Materials Research 1030-1032 (September 2014): 1892–95. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.1892.

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s.The technology and principle involved in the on-line analytical processing is presented in theory, such as an overview of the on-line analytical technology, the operation of the on-line analytical technology, etc, which sets a technological foundation to build a system to realize online analysis.
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Yoon, Seung-Chul, Tae Sung Shin, Kurt Lawrence, and Deana R. Jones. "Development of Online Egg Grading Information Management System with Data Warehouse Technique." Applied Engineering in Agriculture 36, no. 4 (2020): 589–604. http://dx.doi.org/10.13031/aea.13675.

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Highlights Digital data collection and management system is developed for the USDA-AMS’s shell-egg grading program. Database system consisting of OLTP, data warehouse and OLAP databases enables online data entry and trend reporting. Data and information management is done through web application servers. Users access the databases via web browsers. Abstract . This paper is concerned with development of web-based online data entry and reporting system, capable of centralized data storage and analytics of egg grading records produced by USDA egg graders. The USDA egg grading records are currently managed in paper form. While there is useful information for data-driven knowledge discovery and decision making, the paper-based egg grading record system has fundamental limitations in effective and timely management of such information. Thus, there has been a demand to electronically and digitally store and manage the egg grading records in a database for data analytics and mining, such that the quality trends of eggs observed at various levels (e.g., nation or state) are readily available to decision makers. In this study, we report the design and implementation of a web-based online data entry and reporting information system (called USDA Egg Grading Information Management System, EGIMS), based on a data warehouse framework. The developed information system consisted of web applications for data entry and reporting, and internal databases for data storage, aggregation, and query processing. The internal databases consisted of online transaction processing (OLTP) database for data entry and retrieval, data warehouse (DW) for centralized data storage and online analytical processing (OLAP) database for multidimensional analytical queries. Thus, the key design goal of the system was to build a system platform that could provide the web-based data entry and reporting capabilities while rapidly updating the OLTP, DW and OLAP databases. The developed system was evaluated by a simulation study with statistically-modeled egg grading records of one hypothetical year. The study found that the EGIMS could handle approximately up to 600 concurrent users, 32 data entries per second and 164 report requests per second, on average. The study demonstrated the feasibility of an enterprise-level data warehouse system for the USDA and a potential to provide data analytics and data mining capabilities such that the queries about historical and current trends can be reported. Once fully implemented and tested in the field, the EGIMS is expected to provide a solution to modernize the egg grading practice of the USDA and produce the useful information for timely decisions and new knowledge discovery. Keywords: Data warehouse, Database, OLTP, OLAP, Egg grading, Information management, Web application, Information system, Data.
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Sulianta, Feri, and Philothra Clarissa Raina. "Kelola Kubikal Data Transaksional Sistem Informasi Rumah Sakit Dengan Teknik Online Analytical Processing." MIND Journal 1, no. 1 (2018): 1. http://dx.doi.org/10.26760/mindjournal.v1i1.1.

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Data transaksional rumah sakit dapat diberdayakan lebih lanjut untuk ragam keperluan dan bukan hanya sebagai arsip riwayat pasien perseorangan saja. Berbagai informasi berharga dapat diungkapkan dari data transkasional rumah sakit yang dihasilkan dari sistem rekam medis.Dalam kasus ini untuk mendapatkan kejelasan yang melibatkan informasi menyeluruh yang juga melibatkan ragam sudut pandang dapat disolusikan dengan teknik Online Analytical Processing (OLAP). Teknik ini mampu mengakomodasi kelengkapan data yang nantinya menjadi framework untuk dianalisa secara seksama Mengacu pada data rekam medis dimana setiap pasien memiliki banyak keluhan dan latar belakang yang berbeda yang terelasi dengan sang pasien. Teknik OLAP mampu menyajikan data dalam bentuk multidimensi. Selanjutnya, OLAP akan melakukan eksekusi data yakni slicing(irisan) dan dicing(rotasi) yakni meringkas dan mengumpulkan sejumlah besar data, melakukan filtering, pengurutan, dan memberikan peringkat (rangking) yang akan memperkaya temuan berharga dari data kubikal.
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Jin, Jennifer, and Masahiro Hayakawa. "Network analysis and GOLAP." Encyclopedia with Semantic Computing and Robotic Intelligence 02, no. 02 (2018): 1850016. http://dx.doi.org/10.1142/s2529737618500168.

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Online Analytical Processing (OLAP) is an effective approach to analyzing various complex business problems, and graph is considered as a common scheme to represent the business datasets. Network analysis is a broad analytics scheme for exploring the connectivity and deriving useful analytics results. However, network analysis for graph-based OLAP presents a set of more specific analytics methods by utilizing graph model, network property, and OLAP principles. In this paper, we present a comprehensive survey on network analysis conducted on graph model for the purpose of OLAP, and we summarize the current research focus, paradigms, and the future needs on the target technology.
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Tri Susilo, Andri Anto. "PENERAPAN METODE OLAP PADA PROSES PENILAIAN KINERJA GURU (STUDI KASUS : SMA BINASATRIA KOTA LUBUKLINGGAU)." JUTIM (Jurnal Teknik Informatika Musirawas) 3, no. 2 (2018): 65–73. http://dx.doi.org/10.32767/jutim.v3i2.357.

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AbstrakPenilaian atas kinerja guru sangat dibutuhkan sebagai bahan evaluasi untuk meningkatkan mutu dan kualitas dalam hal belajar dan mengajar. Adapun kriteria penilaian tersebut adalah Pedagogik, Kepribadian, Sosial, Profesial. Penilaian kinerja guru di SMA Bina Satria masih menggunakan cara manual yaitu pegawai tata usaha harus memeriksa data guru dengan mencari berkas terlebih dahulu. Yang menjadi kendala adalah proses penilaian ini sangat rumit, misalnya mencari data atau berkas guru dan proses penghitungan nilai yang panjang serta rumit jika dilakukan secara manual memerlukan waktu yang lama dan rentan terjadi kesalahan pada laporan atau rekap penilaian beserta laporan data lainnya. dibutuhkan suatu metode yang dapat digunakan dalam melakukan proses perhitungan penilaian kinerja guru tersebut.Metode yang sering digunakan dalam proses perhitungan keninerja adalah metode OLAP (Online Analytical Processing). Metode OLAP (Online Analytical Processing) adalah suatu metode khusus untuk melakukan analisis data yang terdapat di dalam media penyimpanan data (database) dan kemudian membuat laporannya sesuai dengan permintaan user. Luaran dari proses perhitungan dengan metode OLAP adalah hasil peringkat dari guru atau pengajar yang dapat digunakan oleh kepala sekolah dalam mengevaluasi kinerja guru. Kata Kunci : Penilaian, Kinerja Guru, OLAP Abstract Assessment of teacher performance is needed as an evaluation material to improve quality and quality in terms of learning and teaching. The assessment criteria are Pedagogic, Personality, Social, Professional. Teacher performance appraisal at Bina Satria High School still uses manual methods, namely administrative staff must examine teacher data by looking for files first. The problem is that the assessment process is very complicated, for example searching for data or teacher files and the process of calculating values that are long and complicated if done manually requires a long time and is prone to errors in reports or recapitulation along with other data reports. a method is needed that can be used in carrying out the teacher's performance appraisal calculation process. The method often used in the performance calculation process is the OLAP (Online Analytical Processing) method. OLAP (Online Analytical Processing) method is a special method for analyzing data contained in data storage media (databases) and then making reports according to user requests. The output of the calculation process with the OLAP method is the result of a rating from the teacher or teacher that can be used by the principal in evaluating teacher performance.Keywords: Assessment, Teacher Performance, OLA
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Alkharouf, Nadim W., D. Curtis Jamison, and Benjamin F. Matthews. "Online Analytical Processing (OLAP): A Fast and Effective Data Mining Tool for Gene Expression Databases." Journal of Biomedicine and Biotechnology 2005, no. 2 (2005): 181–88. http://dx.doi.org/10.1155/jbb.2005.181.

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Gene expression databases contain a wealth of information, but current data mining tools are limited in their speed and effectiveness in extracting meaningful biological knowledge from them. Online analytical processing (OLAP) can be used as a supplement to cluster analysis for fast and effective data mining of gene expression databases. We used Analysis Services 2000, a product that ships with SQLServer2000, to construct an OLAP cube that was used to mine a time series experiment designed to identify genes associated with resistance of soybean to the soybean cyst nematode, a devastating pest of soybean. The data for these experiments is stored in the soybean genomics and microarray database (SGMD). A number of candidate resistance genes and pathways were found. Compared to traditional cluster analysis of gene expression data, OLAP was more effective and faster in finding biologically meaningful information. OLAP is available from a number of vendors and can work with any relational database management system through OLE DB.
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Liliana, Lydia, Henny Hartono, and Devi Yurisca Bernanda. "INTEGRASI DATA MINING DAN ONLINE ANALYTICAL PROCESSING (OLAP) PADA DATA PERFORMA SISWA." Jurnal Sisfokom (Sistem Informasi dan Komputer) 9, no. 3 (2020): 400–406. http://dx.doi.org/10.32736/sisfokom.v9i3.1022.

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Pertumbuhan teknologi membawa dampak terhadap peningkatan data untuk digunakan bagi setiap orang. Akumulasi data tersebut telah menciptakan pola data yang semakin banyak, namun perolehan informasi dari data tersebut masih minim. Oleh karena itu, saat ini diperlukan suatu teknik analisa data dalam mencari pola dari kumpulan data tersebut, salah satunya adalah data mining. Data mining merupakan proses pencarian informasi baru dari kumpulan data yang besar untuk menemukan informasi baru sebagai bahan pertimbangan dalam pengambilan keputusan di berbagai bidang, seperti bidang pendidikan. Dalam bidang pendidikan, banyak menghasilkan berbagai macam data, seperti data performa siswa dalam persiapan mengikuti ujian. Data tersebut dapat dianalisis dengan menggunakan metode On-Line Analytical Processing (OLAP) untuk menemukan pola dari data performa siswa tersebut. Penelitian ini berfokus pada proses integrasi data mining yang terdiri dari association, clustering, classification dan forecasting dengan kombinasi metode On-Line Analytical Processing (OLAP) pada data performa siswa. Penulis juga menggunakan bantuan tools Power OLAP untuk membantu analisa metode data mining. Hasil dari penelitian ini adalah penemuan pola baru dalam proses identifikasi kelompok data tersebut, seperti informasi mengenai rata-rata hasil ujian siswa berdasarkan persiapan ujian yang dilakukan dalam bentuk grafik sebagai alat pemodelan dari data, sehingga pengetahuan baru tersebut dapat membantu pihak universitas/sekolah untuk melalukan klasifikasi mengenai tingkat kelulusan dan dapat menetukan strategi dalam meningkatkan kelulusan siswa pada tahun - tahun berikutnya.
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Dissertations / Theses on the topic "Online Analytical Processing (OLAP)"

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Kraft, Tobias. "Rewrite-Strategien für generierte Anfragesequenzen im Online Analytical Processing." [S.l. : s.n.], 2002. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB9967003.

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Zhao, Hongyan. "A visualization tool to support Online Analytical Processing." [Gainesville, Fla.] : University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE0000622.

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Sapia, Carsten. "PROMISE modeling and predicting user behavior for online analytical processing applications /." [S.l.] : [s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=962681628.

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Marquardt, Justus. "Metadatendesign zur Integration von online analytical processing in das Wissensmanagement." Hamburg Kovač, 2007. http://www.verlagdrkovac.de/978-3-8300-3598-5.htm.

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Marquardt, Justus. "Metadatendesign zur Integration von Online Analytical Processing in das Wissensmanagement /." Hamburg : Kovač, 2008. http://www.verlagdrkovac.de/978-3-8300-3598-5.htm.

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Kamath, Akash S. "An efficient algorithm for caching online analytical processing objects in a distributed environment." Ohio : Ohio University, 2002. http://www.ohiolink.edu/etd/view.cgi?ohiou1174678903.

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Chomo, Michal. "Optimalizace zpracování dat o síti Tor pomocí OLAP." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-403126.

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Tor is a distributed network providing privacy and anonymity on the Internet. Information about Tor is publicly available in a form of consensus documents. Existing tools that are able to display this information do not provide both historical and detailed view of it. A tool named Consensus Parser that provides a detailed, historical view of this information and extends it with geolocation and DNS information, was created as a part of TARZAN research project at Brno University of Technology. It stores the information in regular files on disk and makes it accessible via REST API. This thesis extends Consensus Parser with MariaDB ColumnStore database with a schema designed to conform to OLAP needs. The searching capabilities of Consensus Parser were enhanced by adding 109 new endpoints to 12 existing ones and adding the ability to limit the retrieved information to certain fields only. Disk space needed for storing the information was reduced by a factor of five.
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Tocci, Gabriel. "A Comparison of Leading Database Storage Engines in Support of Online Analytical Processing in an Open Source Environment." Digital Commons @ East Tennessee State University, 2013. https://dc.etsu.edu/etd/1111.

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Online Analytical Processing (OLAP) has become the de facto data analysis technology used in modern decision support systems. It has experienced tremendous growth, and is among the top priorities for enterprises. Open source systems have become an effective alternative to proprietary systems in terms of cost and function. The purpose of the study was to investigate the performance of two leading database storage engines in an open source OLAP environment. Despite recent upgrades in performance features for the InnoDB database engine, the MyISAM database engine is shown to outperform the InnoDB database engine under a standard benchmark. This result was demonstrated in tests that included concurrent user sessions as well as asynchronous user sessions using data sets ranging from 6GB to 12GB. Although MyISAM outperformed InnoDB in all test performed, InnoDB provides ACID compliant transaction technologies are beneficial in a hybrid OLAP/OLTP system.
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Jakawat, Wararat. "Graphs enriched by Cubes (GreC) : a new approach for OLAP on information networks." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSE2087/document.

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L'analyse en ligne OLAP (Online Analytical Processing) est une des technologies les plus importantes dans les entrepôts de données, elle permet l'analyse multidimensionnelle de données. Cela correspond à un outil d'analyse puissant, tout en étant flexible en terme d'utilisation pour naviguer dans les données, plus ou moins en profondeur. OLAP a été le sujet de différentes améliorations et extensions, avec sans cesse de nouveaux problèmes en lien avec le domaine et les données, par exemple le multimedia, les données spatiales, les données séquentielles, etc. A l'origine, OLAP a été introduit pour analyser des données structurées que l'on peut qualifier de classiques. Cependant, l'émergence des réseaux d'information induit alors un nouveau domaine intéressant qu'il convient d'explorer. Extraire des connaissances à partir de larges réseaux constitue une tâche complexe et non évidente. Ainsi, l'analyse OLAP peut être une bonne alternative pour observer les données avec certains points de vue. Différents types de réseaux d'information peuvent aider les utilisateurs dans différentes activités, en fonction de différents domaines. Ici, nous focalisons notre attention sur les réseaux d'informations bibliographiques construits à partir des bases de données bibliographiques. Ces données permettent d'analyser non seulement la production scientifique, mais également les collaborations entre auteurs. Il existe différents travaux qui proposent d'avoir recours aux technologies OLAP pour les réseaux d'information, nommé ``graph OLAP". Beaucoup de techniques se basent sur ce qu'on peut appeler cube de graphes. Dans cette thèse, nous proposons une nouvelle approche de “graph OLAP” que nous appelons “Graphes enrichis par des Cubes” (GreC). Notre proposition consiste à enrichir les graphes avec des cubes plutôt que de construire des cubes de graphes. En effet, les noeuds et/ou les arêtes du réseau considéré sont décrits par des cubes de données. Cela permet des analyses intéressantes pour l'utilisateur qui peut naviguer au sein d'un graphe enrichi de cubes selon différents niveaux d'analyse, avec des opérateurs dédiés. En outre, notons quatre principaux aspects dans GreC. Premièrement, GreC considère la structure du réseau afin de permettre des opérations OLAP topologiques, et pas seulement des opérations OLAP classiques et informationnelles. Deuxièmement, GreC propose une vision globale du graphe avec des informations multidimensionnelles. Troisièmement, le problème de dimension à évolution lente est pris en charge dans le cadre de l'exploration du réseau. Quatrièmement, et dernièrement, GreC permet l'analyse de données avec une évolution du réseau parce que notre approche permet d'observer la dynamique à travers la dimension temporelle qui peut être présente dans les cubes pour la description des noeuds et/ou arêtes. Pour évaluer GreC, nous avons implémenté notre approche et mené une étude expérimentale sur des jeux de données réelles pour montrer l'intérêt de notre approche. L'approche GreC comprend différents algorithmes. Nous avons validé de manière expérimentale la pertinence de nos algorithmes et montrons leurs performances<br>Online Analytical Processing (OLAP) is one of the most important technologies in data warehouse systems, which enables multidimensional analysis of data. It represents a very powerful and flexible analysis tool to manage within the data deeply by operating computation. OLAP has been the subject of improvements and extensions across the board with every new problem concerning domain and data; for instance, multimedia, spatial data, sequence data and etc. Basically, OLAP was introduced to analyze classical structured data. However, information networks are yet another interesting domain. Extracting knowledge inside large networks is a complex task and too big to be comprehensive. Therefore, OLAP analysis could be a good idea to look at a more compressed view. Many kinds of information networks can help users with various activities according to different domains. In this scenario, we further consider bibliographic networks formed on the bibliographic databases. This data allows analyzing not only the productions but also the collaborations between authors. There are research works and proposals that try to use OLAP technologies for information networks and it is called Graph OLAP. Many Graph OLAP techniques are based on a cube of graphs.In this thesis, we propose a new approach for Graph OLAP that is graphs enriched by cubes (GreC). In a different and complementary way, our proposal consists in enriching graphs with cubes. Indeed, the nodes or/and edges of the considered network are described by a cube. It allows interesting analyzes for the user who can navigate within a graph enriched by cubes according to different granularity levels, with dedicated operators. In addition, there are four main aspects in GreC. First, GreC takes into account the structure of network in order to do topological OLAP operations and not only classical or informational OLAP operations. Second, GreC has a global view of a network considered with multidimensional information. Third, the slowly changing dimension problem is taken into account in order to explore a network. Lastly, GreC allows data analysis for the evolution of a network because our approach allows observing the evolution through the time dimensions in the cubes.To evaluate GreC, we implemented our approach and performed an experimental study on a real bibliographic dataset to show the interest of our proposal. GreC approach includes different algorithms. Therefore, we also validated the relevance and the performances of our algorithms experimentally
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Belcin, Andrei. "Smart Cube Predictions for Online Analytic Query Processing in Data Warehouses." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/41956.

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A data warehouse (DW) is a transformation of many sources of transactional data integrated into a single collection that is non-volatile and time-variant that can provide decision support to managerial roles within an organization. For this application, the database server needs to process multiple users’ queries by joining various datasets and loading the result in main memory to begin calculations. In current systems, this process is reactionary to users’ input and can be undesirably slow. In previous studies, it was shown that a personalization scheme of a single user’s query patterns and loading the smaller subset into main memory the query response time significantly shortened the query response time. The LPCDA framework developed in this research handles multiple users’ query demands, and the query patterns are subject to change (so-called concept drift) and noise. To this end, the LPCDA framework detects changes in user behaviour and dynamically adapts the personalized smart cube definition for the group of users. Numerous data mart (DM)s, as components of the DW, are subject to intense aggregations to assist analytics at the request of automated systems and human users’ queries. Subsequently, there is a growing need to properly manage the supply of data into main memory that is in closest proximity to the CPU that computes the query in order to reduce the response time from the moment a query arrives at the DW server. As a result, this thesis proposes an end-to-end adaptive learning ensemble for resource allocation of cuboids within a a DM to achieve a relevant and timely constructed smart cube before the time in need, as a way of adopting the just-in-time inventory management strategy applied in other real-world scenarios. The algorithms comprising the ensemble involve predictive methodologies from Bayesian statistics, data mining, and machine learning, that reflect the changes in the data-generating process using a number of change detection algorithms. Therefore, given different operational constraints and data-specific considerations, the ensemble can, to an effective degree, determine the cuboids in the lattice of a DM to pre-construct into a smart cube ahead of users submitting their queries, thereby benefiting from a quicker response than static schema views or no action at all.
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Books on the topic "Online Analytical Processing (OLAP)"

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Codd, E. F. Providing OLAP (on-line analytical processing) to user-analysts: An IT mandate. Codd & Date, Inc., 1993.

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On-line analytical processing systems for business. Quorum, 1997.

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ACM, International Workshop on Data Warehousing and OLAP (1st 1998 Washington D. C. ). DOLAP '98: ACM First International Workshop on Data Warehousing and OLAP in conjunction with the Seventh International Conference on Information and Knowledge Management (CIKM '98), Nov. 7, 1998, Washington, D.C., USA. ACM Press, 1999.

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Parker, Philip M. The 2007-2012 World Outlook for Online Analytical Processing (OLAP). ICON Group International, Inc., 2006.

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Parker, Philip M. The 2007-2012 Outlook for Online Analytical Processing (OLAP) in India. ICON Group International, Inc., 2006.

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Parker, Philip M. The 2007-2012 Outlook for Online Analytical Processing (OLAP) in Japan. ICON Group International, Inc., 2006.

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Parker, Philip M. The 2007-2012 Outlook for Online Analytical Processing (OLAP) in Greater China. ICON Group International, Inc., 2006.

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Parker, Philip M. The 2007-2012 Outlook for Online Analytical Processing (OLAP) in the United States. ICON Group International, Inc., 2006.

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Celko, Joe. Joe Celko's Analytics and OLAP in SQL (The Morgan Kaufmann Series in Data Management Systems). Morgan Kaufmann, 2006.

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Celko, Joe. Joe Celko's Analytics and OLAP in SQL (The Morgan Kaufmann Series in Data Management Systems). Morgan Kaufmann, 2006.

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Book chapters on the topic "Online Analytical Processing (OLAP)"

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Scholl, Marc H., Svetlana Mansmann, Matteo Golfarelli, and Stefano Rizzi. "Visual Online Analytical Processing (OLAP)." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_447.

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Scholl, Marc H., Svetlana Mansmann, Matteo Golfarelli, and Stefano Rizzi. "Visual Online Analytical Processing (OLAP)." In Encyclopedia of Database Systems. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_447-3.

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Jukic, Nenad, Boris Jukic, and Mary Malliaris. "Online Analytical Processing (OLAP) for Decision Support." In Handbook on Decision Support Systems 1. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-48713-5_13.

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Hughes, Robert C. "Online Analytical Processing and the OLAP Cube Multidimensional Database." In Human Capital Systems, Analytics, and Data Mining. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315153650-6.

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Codd, E. F. "OLAP On-Line Analytical Processing." In Controlling-Praxis erfolgreicher Unternehmen. Gabler Verlag, 1998. http://dx.doi.org/10.1007/978-3-322-82721-0_6.

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Determann, Lorenz. "On-Line Analytical Processing (OLAP)." In Modellierung Analytischer Informationssysteme. Deutscher Universitätsverlag, 2002. http://dx.doi.org/10.1007/978-3-663-07724-4_3.

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Chamoni, Peter, and Peter Gluchowski. "On-Line Analytical Processing (OLAP)." In Das Data Warehouse-Konzept. Gabler Verlag, 1998. http://dx.doi.org/10.1007/978-3-322-99372-4_13.

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Chamoni, Peter, and Peter Gluchowski. "On-Line Analytical Processing (OLAP)." In Das Data Warehouse-Konzept. Gabler Verlag, 2000. http://dx.doi.org/10.1007/978-3-322-89533-2_9.

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Dawson, Catherine. "Online analytical processing." In A–Z of Digital Research Methods. Routledge, 2019. http://dx.doi.org/10.4324/9781351044677-36.

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Abelló, Alberto, and Oscar Romero. "Online Analytical Processing." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_252.

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Conference papers on the topic "Online Analytical Processing (OLAP)"

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Chen, Chen, Xifeng Yan, Feida Zhu, Jiawei Han, and Philip S. Yu. "Graph OLAP: Towards Online Analytical Processing on Graphs." In 2008 Eighth IEEE International Conference on Data Mining (ICDM). IEEE, 2008. http://dx.doi.org/10.1109/icdm.2008.30.

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Freitas, Charles, Roberta Gouveia, and Adenilton Silva. "Online Analytical Processing em Ambientes Virtuais de Aprendizagem da Educação a Distância." In IV Workshop de Desafios da Computação aplicada à Educação. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/desafie.2015.10035.

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Este artigo apresenta o processo Knowledge Discovery in Databases (KDD) aplicado no contexto da Educação a Distância (EaD) e dos ambientes virtuais de aprendizagem. O objetivo consiste em aplicar o processo KDD para traçar o perfil da EaD no Brasil e efetuar uma análise acurada dos cursos realizados em ambientes e-learning, bem como revelar os principais desafios da área. A metodologia consiste em coletar dados abertos governamentais de alta granularidade dos sistemas Universidade Aberta do Brasil (UAB) e e-MEC, em seguida aplicar o processo ETL, organizar e integrar os dados em um Data Warehouse e aplicar as tecnologias OLAP, visando obter uma visão contextualizada e realista da modalidade a distância no âmbito nacional.
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Nascimento, Lorenna Christ'na, Lucas Knust, Ramon Santos, et al. "Análise de Dados Pluviométricos Multi-fonte Baseada em Técnicas OLAP e de Visualização: uma Abordagem Prática." In Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/wcama.2021.15731.

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Estudos sobre o clima têm ganhado relevância devido ao aumento do número de eventos climáticos com impactos severos observados na última década, em especial em áreas urbanas. Por exemplo, ocorrências de grandes valores acumulados de chuva podem causar inundações e deslizamentos de terra, impactando o trânsito da cidade e até mesmo custando a vida de cidadãos. Para possibilitar o monitoramento de volumes de chuva, estações pluviométricas se encontram instaladas pelo país. Entretanto, tais estações são controladas por múltiplas organizações e oferecem dados em formatos distintos. Neste artigo propomos a ferramenta TEMPO (sisTema dE Monitoramento PluviométricO) que utiliza técnicas OLAP (Online Analytical Processing) para propor mecanismos de armazenamento, consulta e análise eficientes de dados pluviométricos. Para avaliar a ferramenta, apresentamos um estudo de caso de integração e análise dos dados do CEMADEN e do Alerta Rio.
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Santos, Rafael, Fábio Nunes, Manoela Oliveira, and Methanias Júnior. "Um Survey sobre a utilização de técnicas de Data Mining e Data Analytics por agências de investigação criminal do Brasil." In XIII Simpósio Brasileiro de Sistemas de Informação. Sociedade Brasileira de Computação, 2017. http://dx.doi.org/10.5753/sbsi.2017.6092.

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Em investigações criminais complexas, os envolvidos lidam com uma quantidade enorme e complexa de dados que necessitam de recursos computacionais especializados na extração de informações e correlações relevantes para o processo investigativo. Neste cenário, é necessário que haja apoio computacional, desde a etapa de armazenamento e integração entre diferentes bases de dados, até a etapa de análise estatística e descoberta de padrões. Este artigo discute os resultados de um Survey aplicado aos principais órgãos de combate ao crime organizado, tais como as agências de Inteligência de Segurança Pública – ISP, os Laboratórios de Tecnologia de Combate à Lavagem de Dinheiro – LABLDs e os Grupos de Atuação Especial de Repressão ao Crime Organizado – GAECO. O objetivo principal foi o de conhecer o cenário atual da utilização de ferramentas de análise de dados nessas agências, projetando as necessidades de pesquisa e investimentos nesta área. Entre os resultados encontrados, observou-se que 40% dos pesquisados não conhecem e 15% não utilizam soluções de ETL (Extract, Transform and Load), apesar de todos (100%) declararem possuir pelo menos uma ferramenta de Data Mining no seu local de trabalho, bem como também declararem (100%) possuir pelo menos uma ferramenta de OLAP/BI (Online Analytical Processing/Business Intelligence). Por fim e com proeminente destaque, apenas 2,77% dos pesquisados utilizam diretamente algum algoritmo de Mineração de Dados para extração de conhecimento. Este cenário evidencia, inicialmente, que a maior parte dos órgãos especializados em investigação do Brasil ainda não aplica efetivamente as técnicas de Data Mining e de Data Analytics em suas atividades.
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Zou, Benyuan, Jinguo You, Jiaman Ding, and Hao Sun. "TAVO: A Tree-like Analytical View for OLAP." In 2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM). IEEE, 2019. http://dx.doi.org/10.1109/pacrim47961.2019.8985106.

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Tinnefeld, Christian, Donald Kossmann, Martin Grund, et al. "Elastic online analytical processing on RAMCloud." In the 16th International Conference. ACM Press, 2013. http://dx.doi.org/10.1145/2452376.2452429.

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Xie, Zhongle, Hongbin Ying, Cong Yue, Meihui Zhang, Gang Chen, and Beng Chin Ooi. "Cool, a COhort OnLine analytical processing system." In 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 2020. http://dx.doi.org/10.1109/icde48307.2020.00056.

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Correia, Jose, Maribel Yasmina Santos, Carlos Costa, and Carina Andrade. "Fast Online Analytical Processing for Big Data Warehousing." In 2018 International Conference on Intelligent Systems (IS). IEEE, 2018. http://dx.doi.org/10.1109/is.2018.8710583.

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Gonçalves, Rui C., José Pereira, and Ricardo Jimenez-Peris. "Design of an RDMA Communication Middleware for Asynchronous Shuffling in Analytical Processing." In Workshop on Towards Convergence of Big Data, SQL, NoSQL, NewSQL, Data streaming/CEP, OLTP and OLAP. SCITEPRESS - Science and and Technology Publications, 2016. http://dx.doi.org/10.5220/0005923703480351.

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Easmin, Nilufar, K. M. Azharul Hasan, and Nilufa Parvin. "An efficient implementation scheme for multidimensional online analytical processing." In 2008 11th International Conference on Computer and Information Technology (ICCIT). IEEE, 2008. http://dx.doi.org/10.1109/iccitechn.2008.4802985.

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