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Опубліковано: 6 вересня 2023

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Статті в журналах з теми "OLAP Systems"

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Camilleri, Carl, Joseph G. Vella, and Vitezslav Nezval. "HTAP With Reactive Streaming ETL." Journal of Cases on Information Technology 23, no. 4 (October 2021): 1–19. http://dx.doi.org/10.4018/jcit.20211001.oa10.

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In database management systems (DBMSs), query workloads can be classified as online transactional processing (OLTP) or online analytical processing (OLAP). These often run within separate DBMSs. In hybrid transactional and analytical processing (HTAP), both workloads may execute within the same DBMS. This article shows that it is possible to run separate OLTP and OLAP DBMSs, and still support timely business decisions from analytical queries running off fresh transactional data. Several setups to manage OLTP and OLAP workloads are analysed. Then, benchmarks on two industry standard DBMSs empirically show that, under an OLTP workload, a row-store DBMS sustains a 1000 times higher throughput than a columnar DBMS, whilst OLAP queries are more than 4 times faster on a columnar DBMS. Finally, a reactive streaming ETL pipeline is implemented which connects these two DBMSs. Separate benchmarks show that OLTP events can be streamed to an OLAP database within a few seconds.
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2

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 (January 20, 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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3

Torres, Manuel, José Samos, and Eladio Garví. "Closing Ontologies to Define OLAP Systems." International Journal of Information Retrieval Research 4, no. 4 (October 2014): 1–16. http://dx.doi.org/10.4018/ijirr.2014100101.

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Ontologies can be used in the construction of OLAP (On-Line Analytical Processing) systems. In such a context, ontologies are mainly used either to enrich cube dimensions or to define ontology based-dimensions. On the one hand, if dimensions are enriched using large ontologies, like WordNet, details that are beyond the scope of the dimension may be added to it. Even, dimensions may be obscured because of the massive incorporation of related attributes. On the other hand, if ontologies are used to define a dimension, it is possible that a simplified version of the ontology is needed to define the dimension, especially when the used ontology is too complex for the dimension that is being defined. These problems may be solved using one of the existing mechanisms to define ontology views. Therefore, concepts that are not needed for the domain ontology are kept out of the view. However, this view must be closed so that, no ontology component has references to components that are not included in the view. In this work, two basic approaches are proposed: enlargement and reduction closure.
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4

Colliat, George. "OLAP, relational, and multidimensional database systems." ACM SIGMOD Record 25, no. 3 (September 1996): 64–69. http://dx.doi.org/10.1145/234889.234901.

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5

Baltzer, Oliver, Frank Dehne, and Andrew Rau Chaplin. "OLAP for moving object data." International Journal of Intelligent Information and Database Systems 7, no. 1 (2013): 79. http://dx.doi.org/10.1504/ijiids.2013.051745.

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6

Pedersen, Torben Bach, Junmin Gu, Arie Shoshani, and Christian S. Jensen. "Object-extended OLAP querying." Data & Knowledge Engineering 68, no. 5 (May 2009): 453–80. http://dx.doi.org/10.1016/j.datak.2008.10.008.

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7

Dehne, Frank, Todd Eavis, and Boyong Liang. "Compressing Data Cube in Parallel OLAP Systems." Data Science Journal 6 (2007): S184—S197. http://dx.doi.org/10.2481/dsj.6.s184.

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8

SanthoshBaboo, S., and P. Renjith Kumar. "Next Generation Data Warehouse Design with OLTP and OLAP Systems Sharing same Database." International Journal of Computer Applications 72, no. 13 (June 26, 2013): 45–50. http://dx.doi.org/10.5120/12557-9282.

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9

Kalnis, Panos, and Dimitris Papadias. "Proxy-server architectures for OLAP." ACM SIGMOD Record 30, no. 2 (June 2001): 367–78. http://dx.doi.org/10.1145/376284.375712.

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10

Sharma, Pitambar, and Piyush Girdhar. "Online Analytical Processing (OLAP)." Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552) 1, no. 3 (March 31, 2014): 01–04. http://dx.doi.org/10.53555/nncse.v1i3.520.

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Анотація:
This paper is basically accustomed define On-Line Analytical method (OLAP), WHO uses it and why, and to review the key choices required for OLAP code. On-Line Analytical method (OLAP) could also be a category of code technology that allows analysts, managers and executives to appreciate insight into info through fast, consistent, interactive access to an honest reasonably gettable views of {data of information} that has been transformed from data to mirror spatiality of the enterprise as understood by the user. whereas OLAP systems have the ability to answer "who?" and "what?" queries, it's their ability to answer "what if?" and "why?" that sets them except info Warehouses. OLAP applications span a variety of structure functions. Finance departments use OLAP for applications like budgeting, activity-based accountancy (allocations), cash performance analysis, and cash modelling. Sales analysis and prognostication square measure a pair of the OLAP applications found in sales departments. Among totally different applications, promoting departments use OLAP for analysis, sales prognostication, promotions analysis, consumer analysis, and market/customer segmentation. Typical manufacturing OLAP applications embody production coming up with and defect analysis. Although OLAP applications square measure found in wide divergent sensible areas, all of them want the following key choices like 3-D views of information, Calculation –intensive capabilities and Time Intelligence.
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Дисертації з теми "OLAP Systems"

1

Kotsis, Nikolaos. "Multidimensional aggregation in OLAP systems." Thesis, University of Strathclyde, 2000. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=21149.

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On-line analytical processing (OLAP) provides multidimensional data analysis to support decision making. OLAP queries require extensive computation based on aggregation along many dimensions and hierarchies. The time required to process these queries has traditionally prevented the interactive analysis of large databases and in order to accelerate query-response time, precomputed results are often stored as materialised views for later retrieval. This adds a prohibitive storage overhead when applied to the whole set of aggregates, known as the data cube. Storage space and computation time can be significantly reduced by partial computation. The challenge in implementing the data cube has been to select the minimum number of views for materialisation, while retaining fast query response time. This thesis makes significant contributions to this area by introducing the Low Redundancy (L-R) approach which provides the means for the selection, computation and storage of nonredu ndant aggregates. Firstly, through the introduction of a novel technique, redundant aggregates are identified thus allowing only distinct aggregates to be computed and stored. Secondly, further redundancy is identified and eliminated using a second novel technique which stores these distinct aggregates in a compact differential form. Novel algorithms were introduced to implement these techniques and provide a solution which is both scalable and low in complexity. Both techniques have been evaluated using real and synthetic datasets with experimental results, and have achieved significant savings in computation time and storage space compared to the conventional approach. Savings have been shown to increase as dimensionality increases. Existing techniques for implementing the data cube differ from the L-R approach but they can be integrated with it to achieve faster query-response time. Finally, the implications of this work reach beyond the area of OLAP to the fields of decision support systems, user interfaces and data mining.
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2

Aho, Milja. "Optimisation of Ad-hoc analysis of an OLAP cube using SparkSQL." Thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-329938.

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An Online Analytical Processing (OLAP) cube is a way to represent a multidimensional database. The multidimensional database often uses a star schema and populates it with the data from a relational database. The purpose of using an OLAP cube is usually to find valuable insights in the data like trends or unexpected data and is therefore often used within Business intelligence (BI). Mondrian is a tool that handles OLAP cubes that uses the query language MultiDimensional eXpressions (MDX) and translates it to SQL queries. Apache Kylin is an engine that can be used with Apache Hadoop to create and query OLAP cubes with an SQL interface. This thesis investigates whether the engine Apache Spark running on a Hadoop cluster is suitable for analysing OLAP cubes and what performance that can be expected. The Star Schema Benchmark (SSB) has been used to provide Ad-Hoc queries and to create a large database containing over 1.2 billion rows. This database was created in a cluster in the Omicron office consisting of five worker nodes and one master node. Queries were then sent to the database using Mondrian integrated into the BI platform Pentaho. Amazon Web Services (AWS) has also been used to create clusters with 3, 6 and 15 slaves to see how the performance scales. Creating a cube in Apache Kylin on the Omicron cluster was also tried, but was not possible due to the cluster running out of memory. The results show that it took between 8.2 to 11.9 minutes to run the MDX queries on the Omicron cluster. On both the Omicron cluster and the AWS cluster, the SQL queries ran faster than the MDX queries. The AWS cluster ran the queries faster than the Omicron cluster, even though fewer nodes were used. It was also noted that the AWS cluster did not scale linearly, neither for the MDX nor the SQL queries.
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3

Maknytė, Lina. "Intelektualių veslo sistemų modeliavimas." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2012. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2012~D_20120620_111437-00453.

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Pirmoje darbo dalyje yra nagrinėjamos organizacijų ekonominės problemos ir ieškoma kaip tos problemos gali būti pašalintos. Išanalizavus kokios gali būti problemos pradėta analizuoti intelektualios verslo sistemos, kaip vienas geriausių sprendimo būdų. Darbe taip pat nagrinėjama intelektualių sistemų apibrėţimas, paaiškinama architektūra ir išskiriama kuo BIS skiriasi nuo kitų informacinių valdymo sistemų. Antroje dalyje yra nagrinėjamos pagrindinės problemos susijusios su intelektualiomis verslo sistemomis ir jų diegimu organizacijose. Analizuojama šiuo metu labiausiai paplitusios technologijos OLAP ir QlikView, kurios naudoja atmintį duomenų krovimui. Analizuojami jų privalumai ir trūkumai taip pat skirtumai. Trečioje dalyje analizuojama intelektualių verslo sistemų projektavimo metodika. Keliami klausimai ką reikia daryti, kad gautume sistemą atitinkančią organizacijos lūkesčius. Analizuojama projektavimo struktūra naudojant UML diagramas, taip pat elementų svarba pačioje sistemoje. Ketvirtoje dalyje yra projektuojami intelektualių verslo sistemų modeliai, kurie yra tik pavyzdiniai, kurie parodo kokie privalumai ir kaip veikia intelektualios verslo sistemos vykdant pirkimo funkcijas panaudojant marketinginius principus.
In the first part of master thesis analyzing the main problems, which are in organization management. The main purpose of this part is to find a solution how can resolve this problems. Business intelligence systems are the best solution. In this part analyzing business intelligence systems definition, architecture and also analyze what is different in business intelligence systems comparing with other informatics management systems. The second part of work analyzes business intelligence problems related with creation, developing and using BIS. Analyze the most popular tools like OALP and QlikView. Compare the OLAP and QlikView. Analyze QlikView like data loading from memory. Explain the advantages and disadvantages of QlikView and OLAP. The third part analyzes designing and planning of business intelligence systems. Analyze what is the main purpose to create good system which will be useful in organizations. In this part also analyze the UML modems for BIS. In the last part is presenting solutions for problems which was analyzed in the second part. The examples are created using UML diagrams.
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4

Fischer, Ulrike. "Forecasting in Database Systems." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-133281.

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Time series forecasting is a fundamental prerequisite for decision-making processes and crucial in a number of domains such as production planning and energy load balancing. In the past, forecasting was often performed by statistical experts in dedicated software environments outside of current database systems. However, forecasts are increasingly required by non-expert users or have to be computed fully automatically without any human intervention. Furthermore, we can observe an ever increasing data volume and the need for accurate and timely forecasts over large multi-dimensional data sets. As most data subject to analysis is stored in database management systems, a rising trend addresses the integration of forecasting inside a DBMS. Yet, many existing approaches follow a black-box style and try to keep changes to the database system as minimal as possible. While such approaches are more general and easier to realize, they miss significant opportunities for improved performance and usability. In this thesis, we introduce a novel approach that seamlessly integrates time series forecasting into a traditional database management system. In contrast to flash-back queries that allow a view on the data in the past, we have developed a Flash-Forward Database System (F2DB) that provides a view on the data in the future. It supports a new query type - a forecast query - that enables forecasting of time series data and is automatically and transparently processed by the core engine of an existing DBMS. We discuss necessary extensions to the parser, optimizer, and executor of a traditional DBMS. We furthermore introduce various optimization techniques for three different types of forecast queries: ad-hoc queries, recurring queries, and continuous queries. First, we ease the expensive model creation step of ad-hoc forecast queries by reducing the amount of processed data with traditional sampling techniques. Second, we decrease the runtime of recurring forecast queries by materializing models in a specialized index structure. However, a large number of time series as well as high model creation and maintenance costs require a careful selection of such models. Therefore, we propose a model configuration advisor that determines a set of forecast models for a given query workload and multi-dimensional data set. Finally, we extend forecast queries with continuous aspects allowing an application to register a query once at our system. As new time series values arrive, we send notifications to the application based on predefined time and accuracy constraints. All of our optimization approaches intend to increase the efficiency of forecast queries while ensuring high forecast accuracy.
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5

Jernberg, Robert, and Tobias Hultgren. "Flexible Data Extraction for Analysis using Multidimensional Databases and OLAP Cubes." Thesis, KTH, Data- och elektroteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-123393.

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Bright is a company that provides customer and employee satisfaction surveys, and uses this information to provide feedback to their customers. Data from the surveys are stored in a relational database and information is generated both by directly querying the database as well as doing analysis on extracted data. As the amount of data grows, generating this information takes increasingly more time. Extracting the data requires significant manual work and is in practice avoided. As this is not an uncommon issue, there is a substantial theoretical framework around the area. The aim of this degree project is to explore the different methods for achieving flexible and efficient data analysis on large amounts of data. This was implemented using a multidimensional database designed for analysis as well as an OnLine Analytical Processing (OLAP) cube built using Microsoft's SQL Server Analysis Services (SSAS). The cube was designed with the possibility to extract data on an individual level through PivotTables in Excel. The implemented prototype was analyzed, showing that the prototype consistently delivers correct results severalfold as efficient as the current solution as well as making new types of analysis possible and convenient. It is concluded that the use of an OLAP cube was a good choice for the issue at hand, and that the use of SSAS provided the necessary features for a functional prototype. Finally, recommendations on possible further developments were discussed.
Bright är ett företag som tillhandahåller undersökningar för kund- och medarbetarnöjdhet, och använder den informationen för att ge återkoppling till sina kunder. Data från undersökningarna sparas i en relationsdatabas och information genereras både genom att direkt fråga databasen såväl som att göra manuell analys på extraherad data. När mängden data ökar så ökar även tiden som krävs för att generera informationen. För att extrahera data krävs en betydande mängd manuellt arbete och i praktiken undviks det. Då detta inte är ett ovanligt problem finns det ett gediget teoretiskt ramverk kring området. Målet med detta examensarbete är att utforska de olika metoderna för att uppnå flexibel och effektiv dataanalys på stora mängder data. Det implementerades genom att använda en multidimensionell databas designad för analys samt en OnLine Analytical Processing (OLAP)-kub byggd med Microsoft SQL Server Analysis Services (SSAS). Kuben designades med möjligheten att extrahera data på en individuell nivå med PivotTables i Excel. Den implementerade prototypen analyserades vilket visade att prototypen konsekvent levererar korrekta resultat flerfaldigt så effektivt som den nuvarande lösningen såväl som att göra nya typer av analys möjliga och lättanvända. Slutsatsen dras att användandet av en OLAP-kub var ett bra val för det aktuella problemet, samt att valet att använda SSAS tillhandahöll de nödvändiga funktionaliteterna för en funktionell prototyp. Slutligen diskuterades rekommendationer av möjliga framtida utvecklingar.
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6

Bell, Daniel M. "An evaluative case report of the group decision manager : a look at the communication and coordination issues facing online group facilitation /." free to MU campus, to others for purchase, 1998. http://wwwlib.umi.com/cr/mo/fullcit?p9901215.

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7

Jäcksch, Bernhard [Verfasser]. "A Plan For OLAP: Optimization Of Financial Planning Queries In Data Warehouse Systems / Bernhard Jäcksch." München : Verlag Dr. Hut, 2011. http://d-nb.info/1017353700/34.

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8

Funke, Florian Andreas [Verfasser], Alfons [Akademischer Betreuer] Kemper, Thomas [Akademischer Betreuer] Neumann, and Stefan [Akademischer Betreuer] Manegold. "Adaptive Physical Optimization in Hybrid OLTP & OLAP Main-Memory Database Systems / Florian Andreas Funke. Gutachter: Thomas Neumann ; Alfons Kemper ; Stefan Manegold. Betreuer: Alfons Kemper." München : Universitätsbibliothek der TU München, 2015. http://d-nb.info/1076124976/34.

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9

Feng, Haitang. "Data management in forecasting systems : optimization and maintenance." Phd thesis, Université Claude Bernard - Lyon I, 2012. http://tel.archives-ouvertes.fr/tel-00997235.

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Анотація:
Forecasting systems are usually based on data warehouses for data strorage, and OLAP tools for historical and predictive data visualization. Aggregated predictive data could be modified. Hence, the research issue can be described as the propagation of an aggregate-based modification in hirarchies and dimensions in a data warehouse enironment. Ther exists a great number of research works on related view maintenance problems. However, to our knowledge, the impact of interactive aggregate modifications on raw data was not investigated. This CIFRE thesis is supported by ANRT and the company Anticipeo. The application of Anticipeo is a sales forecasting system that predicts future sales in order to draw appropriate business strategy in advance. By the beginning of the thesis, the customers of Anticipeo were satisfied the precision of the prediction results, but not with the response time. The work of this thesis can be generalized into two parts. The first part consists in au audit on the existing application. We proposed a methodology relying on different technical solutions. It concerns the propagation of an aggregate-based modification in a data warehouse. the second part of our work consists in the proposition of a newx allgorithms (PAM - Propagation of Aggregated-baseed Modification) with an extended version (PAM II) to efficiently propagate in aggregate-based modification. The algorithms identify and update the exact sets of source data anf other aggregated impacted by the aggregated modification. The optimized PAM II version archieves better performance compared to PAM when the use of additional semantics (e.g. dependencies) is possible. The experiments on real data of Anticipeo proved that the PAM algorithm and its extension bring better perfiormance when a backward propagation.
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10

Westerlund, Elisabeth, and Hanna Persson. "Implementation of Business Intelligence Systems : A study of possibilities and difficulties in small IT-enterprises." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-255915.

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This thesis is written at the department of Business Studies at Uppsala University. The study addresses the differences in possibilities and difficulties of implementing business intelligence (BI)-systems among small IT-enterprises. BI-systems support enterprises in decision-making. To answer the aim of this thesis, theories regarding organizational factors determining a successful implementation of a BI-system were used. Theories regarding components of BI- systems, data warehouse (DW) and online analytical processing (OLAP) were also used. These components enable the decision-support provided by a BI-system. A qualitative study was performed, at four different IT-enterprises, to gather the empirical material. Interviews were performed with CEOs and additional employees at the enterprises. After the empirical material was gathered an analysis was performed to draw conclusion regarding the research topic. The study has concluded that there are differences in possibilities and difficulties of implementing BI-systems among small IT-enterprises. A difference among the enterprises is the perceived ability to finance an implementation. Another difference is in the managerial- and organizational support of an implementation, but also in the business need of using a BI- system in decision-making. There are also differences in how the enterprises use a DW. Not all enterprises benefits from the ability of a DW to manage complex and large amounts of data, neither from the advanced analysis performed by OLAP. The enterprises thus need to examine further if the use of a BI-system is beneficial and would be used successfully in their company.
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Книги з теми "OLAP Systems"

1

OLAP Solutions. New York: John Wiley & Sons, Ltd., 2002.

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2

OLAP solutions: Building multidimensional information systems. New York: Wiley Computer Pub., 1997.

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3

OLAP solutions: Building multidimensional information systems. 2nd ed. New York: Wiley Computer Pub., 2002.

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4

Institute, SAS, ed. SAS 9.1.3 OLAP Server: User's guide. Cary, NC: SAS Pub., 2005.

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Institute, SAS, ed. SAS 9.1.3 OLAP server administrator's guide. 3rd ed. Cary, NC: SAS Pub., 2005.

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6

Institute, SAS, ed. SAS 9.1.3 OLAP server: Administrator's guide. 5th ed. Cary, NC: SAS Institute, 2006.

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7

IBM Data Management Solutions Education Services. Extracting information for strategic decision making using MITS. United States?]: IBM Data Management Solutions, Education Services, 2001.

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8

Ji yu mo hu tui li xi tong de gong ye guo cheng shu ju wa jue. Beijing Shi: Ji xie gong ye chu ban she, 2009.

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9

Koutsoukis, Nikitas-Spiros. Decision modelling and information systems: The information value chain. Boston, MA: Kluwer Academic Publishers, 2004.

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Gautam, Mitra, ed. Decision modelling and information systems: The information value chain. Boston: Kluwer Academic Publishers, 2003.

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Частини книг з теми "OLAP Systems"

1

Wang, Zhengkui. "Graph OLAP." In Encyclopedia of Database Systems, 1–5. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_80627-1.

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Wang, Zhengkui. "Graph OLAP." In Encyclopedia of Database Systems, 1656–61. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_80627.

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3

Marcel, Patrick. "OLAP Personalization and Recommendation." In Encyclopedia of Database Systems, 1–5. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_3191-3.

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4

Ravat, Franck, and Olivier Teste. "Personalization and OLAP Databases." In Annals of Information Systems, 1–22. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-87431-9_4.

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Marcel, Patrick. "OLAP Personalization and Recommendation." In Encyclopedia of Database Systems, 2550–55. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_3191.

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6

Taniar, David, and Wenny Rahayu. "Online Analytical Processing (OLAP)." In Data-Centric Systems and Applications, 501–39. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81979-8_19.

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Kozmina, Natalija, and Laila Niedrite. "Research Directions of OLAP Personalizaton." In Information Systems Development, 345–56. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9790-6_28.

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8

Scholl, Marc H., Svetlana Mansmann, Matteo Golfarelli, and Stefano Rizzi. "Visual Online Analytical Processing (OLAP)." In Encyclopedia of Database Systems, 1–10. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_447-3.

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9

Scholl, Marc H., Svetlana Mansmann, Matteo Golfarelli, and Stefano Rizzi. "Visual Online Analytical Processing (OLAP)." In Encyclopedia of Database Systems, 4517–27. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_447.

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10

Layouni, Olfa, Fahad Alahmari, and Jalel Akaichi. "Recommending Multidimensional Spatial OLAP Queries." In Smart Innovation, Systems and Technologies, 405–15. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39345-2_35.

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Тези доповідей конференцій з теми "OLAP Systems"

1

Krohn-Grimberghe, Artus, Alexandros Nanopoulos, and Lars Schmidt-Thieme. "Integrating OLAP and recommender systems." In the ACM 13th international workshop. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1871940.1871959.

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2

Bhan, Madhu, Suresh Kumar T V, and Rajanikanth K. "Size Estimation of OLAP Systems." In Third International Conference on Computer Science & Information Technology. Academy & Industry Research Collaboration Center (AIRCC), 2013. http://dx.doi.org/10.5121/csit.2013.3649.

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3

Zhang, Kaiwen, Mohammad Sadoghi, and Hans-Arno Jacobsen. "DL-Store: A Distributed Hybrid OLTP and OLAP Data Processing Engine." In 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2016. http://dx.doi.org/10.1109/icdcs.2016.71.

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4

"COLLABORATIVE OLAP WITH TAG CLOUDS - Web 2.0 OLAP Formalism and Experimental Evaluation." In 4th International Conference on Web Information Systems and Technologies. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001515800050012.

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5

Farias Batista Leite, Daniel, Claudio de Souza Baptista, Maxwell Guimaraes de Oliveira, Jose Amilton Moura Acioli Filho, and Tiago Eduardo da Silva. "ExpOLAP: Towards exploratory OLAP." In 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA). IEEE, 2016. http://dx.doi.org/10.1109/aiccsa.2016.7945731.

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6

Caron, Emiel, and Hennie Daniels. "Sensitivity Analysis in OLAP Databases." In 20th International Conference on Enterprise Information Systems. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006791702210228.

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7

"INTEGRATION OF PROFILE IN OLAP SYSTEMS." In International Conference on Knowledge Discovery and Information Retrieval. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003669103200332.

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8

Nummenmaa, Jyrki. "Logical design of OLAP cubes." In 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2009). IEEE, 2009. http://dx.doi.org/10.1109/icicisys.2009.5357947.

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9

"Adding Recommendations to OLAP Reporting Tool." In 15th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004439801690176.

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10

Caron, Emiel, and Hennie Daniels. "BUSINESS ANALYSIS IN THE OLAP CONTEXT." In 11th International Conference on Enterprise Information Systems. SCITEPRESS - Science and Technology Publications, 2009. http://dx.doi.org/10.5220/0001989103250330.

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Звіти організацій з теми "OLAP Systems"

1

Rigotti, Christophe, Patrick Marcel, and Mohand-Saïd Hacid. A Rule-Based Data Manipulation Language for OLAP Systems. Aachen University of Technology, 1997. http://dx.doi.org/10.25368/2022.76.

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2

Ellis, Abraham, Carl Lenox, Jay Johnson, Jimmy Edward Quiroz, and Benjamin L. Schenkman. Initial operating experience of the 12-MW La Ola photovoltaic system. Office of Scientific and Technical Information (OSTI), October 2011. http://dx.doi.org/10.2172/1031297.

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