Online Analytical Processing Applications Research Articles

The Project-Based Learning Study of Insurance Information Courses to Simulate the Application of Online Analytical Processing

The use of data warehouses combined with online analytical processing (OLAP) platforms has become popular in Taiwan’s insurance market. However, most schools do not have an insurance data warehouse and OLAP platform for student learning in Taiwan. The researched courses are insurance information system courses for two university classes. Based on the teacher’s experience and innovativeness, those courses are integrated using the guided project-based learning approach. Students need to build a customer micro-database, analyze customer figures through pivot analysis charts, and plan marketing campaigns. The study finds a project-based learning approach is helpful to enhance students’ OLAP analysis abilities. Secondly, the research finds that the flexibility for students to choose the topic of their project is one of the key success factors. Thirdly, the evaluation share of the student’s learning scores is important for the completion of the project. Fourthly, the courses are accompanied with satisfaction questionnaires to monitor the learning results and analyze the learning satisfaction for students among course A, course B, and the college average. Those students in the two classes both have higher satisfaction scores than the college average, but there are still differences between the classes after the t-test.

User-Centric Interference-Aware Load Balancing for Cloud-Deployed Applications

VMs deployed in cloud environments are prone to performance interference due to dynamic and unpredictable contention for shared physical resources among colocated tenants. Current provider-centric solutions, such as careful co-scheduling of VMs and/or VM migration, require a priori profiling of customer VMs, which is infeasible in public clouds. Further, such solutions are not always aware of the user's SLO requirements or application bottlenecks. This paper presents DIAL, an interference-aware load balancing framework that can directly be employed by cloud users without requiring any assistance from the provider. The key idea behind DIAL is to infer the demand for contended resources on the physical hosts, which is otherwise hidden from users. Estimates of the colocated load are then used to dynamically shift load away from compromised VMs without violating the application's tail latency SLOs. We implement DIAL for web and online analytical processing applications, and show, via experimental results on OpenStack and AWS clouds, that DIAL can reduce tail latencies by as much as 70% compared to existing solutions.

Multidimensional Hierarchical Browser, Keyword Search, and Automatic Management of Photos within Smartphones

Recently new mobile devices such as cellular phones, smartphones, and digital cameras are popularly used to take photos. By virtue of these convenient instruments, we can take many photos easily, but we suffer from the difficulty of managing an d searching photos due to their large volume. This paper develops a mobile application software, called Photo Cube, which automatically extracts various metadata for photos (e.g., date/time, address, pla ce name, weather, personal event, etc.) by taking advantage of sensors and programming functions embedded in mobile smartphones like Android phones or iPhones. To avoid heavy network traffic and high processing overhead, it clusters photos into a set of cluste rs hierarchically by GPSs and it extracts the metadata for each centroid photo of clusters automatically. Then it constructs and sto res the hierarchies of clusters based on the date/time, and a ddress within the extracted metadata as well as the other metadata into photo database tables in the flash memory of smartphones. Furthe rmore, the system builds a multidimensional cube view for the photo database, which is popularly used in OLAP(On-Line Analytical Processing) applications and it facilitates the top-down browsing of photos over several dimensions such as date/time, address, etc. In addition to the hierarchical browsing, it provides users with keyword search function in order to find photos over every metadata of the p hoto database in a user-friendly manner. With these convenient features of the Photo Cube, therefore, users will be able to manage and search a large number of photos easily, without inputting any additional information but with clicking simply the shutter in a camera.

OLAP parallel query processing in clouds with C‐ParGRES

SummaryThe advent of big data technologies has changed the way many companies manage their data. Several companies moved their data to the cloud using the concept of database‐as‐a‐service (DBaaS). Moving databases to the cloud presents several challenges related to flexible and scalable management of data. Although some of these companies migrated to NoSQL databases, most still rely on relational databases in the cloud to manage data, especially data that is critical to the decision making process. Online analytical processing (OLAP) queries take a long time to be processed, thus demanding high‐performance capabilities from their associated database systems to get results in a feasible time. In this article, we propose a middleware solution that can be deployed in any cloud provider, named C‐ParGRES, which explores database replication and interquery and intraquery parallelism to efficiently support OLAP queries in the cloud. C‐ParGRES is an extension of ParGRES, an open‐source database cluster middleware for high‐performance OLAP query processing in clusters. C‐ParGRES exploits cloud capabilities such as on‐demand resource provisioning and elasticity. In addition, C‐ParGRES can create multiple and independent virtual clusters for different database and users. We evaluate C‐ParGRES with two real‐world OLAP applications, both from the Brazilian Institute of Geography and Statistics. Results show that C‐ParGRES is a cost‐effective solution for OLAP query processing in the cloud.

Implementation of online analytical processing for Indonesia agricultural commodities using JavaScript

Ministry of Agriculture, Republic of Indonesia provide Indonesia agricultural commodities data which presented in the simple tabular format. It is a challenging task to analyze this growing data and generate data summarization. This study was conducted to implement online analytical processing (OLAP) application to provide an analytical tool for agricultural commodities data. The OLAP application is built using RESTful website architecture with Node.Js and Angular.Js. The back-end side uses Node.Js as a server and PostgreSQL as data storage. In front-end side, we use HTML5, Wijmo, and Angular.Js for visualizing data summary in the form of crosstab table and chart. OLAP operations are used in this application to perform analysis of some feature of agricultural commodities data. The features include crop production, productivity, and harvested area.

Integrating and analyzing medical and environmental data using ETL and Business Intelligence tools.

Processing data that originates from different sources (such as environmental and medical data) can prove to be a difficult task, due to the heterogeneity of variables, storage systems, and file formats that can be used. Moreover, once the amount of data reaches a certain threshold, conventional mining methods (based on spreadsheets or statistical software) become cumbersome or even impossible to apply. Data Extract, Transform, and Load (ETL) solutions provide a framework to normalize and integrate heterogeneous data into a local data store. Additionally, the application of Online Analytical Processing (OLAP), a set of Business Intelligence (BI) methodologies and practices for multidimensional data analysis, can be an invaluable tool for its examination and mining. In this article, we describe a solution based on an ETL + OLAP tandem used for the on-the-fly analysis of tens of millions of individual medical, meteorological, and air quality observations from 16 provinces in Spain provided by 20 different national and regional entities in a diverse array for file types and formats, with the intention of evaluating the effect of several environmental variables on human health in future studies. Our work shows how a sizable amount of data, spread across a wide range of file formats and structures, and originating from a number of different sources belonging to various business domains, can be integrated in a single system that researchers can use for global data analysis and mining.

Data Warehouse Schemas using Multidimensional Data Model for Retail

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

Data Warehouse Analisa Prestasi Akademik Siswa di SMP Roudlotul Jadid Lumajang

The absence of a system that can provide fast analysis system is a hallmark of the assessment process is based on the transactional database, such as that which is now used in SMP Roudlotul Lumajang Jadid. This is due to the presence of the transactional data is not intended to be used in Online Analytical Processing (OLAP) that provides functions to analyze the data in order to improve the responsiveness and flexibility of the current system used in the school institution. Then the final project was creation of applications for analyzing OLAP junior high student academic achievement. The final task was done in order to obtain the system requirements, design, implement and test the performance of OLAP applications for analysis so as to provide benefits and correct the problem at school. Problem analysis is done by the system needs analysis, design and implementation, including design datawarehouse and OLAP design. In junior high student achievement analysis Roudlotul Jadid Lumajang can know that the level of mastery students has increased in the 2nd half, for the average value of the class and teacher attendance increase is most dominant in the 2nd half than 1st half. Keyword: OLAP, Data Warehouse, Akademik, Data Mining

Data Warehouse Analisa Prestasi Akademik Siswa di SMP Roudlotul Jadid Lumajang

The absence of a system that can provide fast analysis system is a hallmark of the assessment process is based on the transactional database, such as that which is now used in SMP Roudlotul Lumajang Jadid. This is due to the presence of the transactional data is not intended to be used in Online Analytical Processing (OLAP) that provides functions to analyze the data in order to improve the responsiveness and flexibility of the current system used in the school institution. Then the final project was creation of applications for analyzing OLAP junior high student academic achievement. The final task was done in order to obtain the system requirements, design, implement and test the performance of OLAP applications for analysis so as to provide benefits and correct the problem at school. Problem analysis is done by the system needs analysis, design and implementation, including design datawarehouse and OLAP design. In junior high student achievement analysis Roudlotul Jadid Lumajang can know that the level of mastery students has increased in the 2nd half, for the average value of the class and teacher attendance increase is most dominant in the 2nd half than 1st half. Keyword: OLAP, Data Warehouse, Akademik, Data Mining

An MDA approach for developing secure OLAP applications: Metamodels and transformations

Decision makers query enterprise information stored in Data Warehouses (DW) by using tools (such as On-Line Analytical Processing (OLAP) tools) which employ specific views or cubes from the corporate DW or Data Marts, based on multidimensional modelling. Since the information managed is critical, security constraints have to be correctly established in order to avoid unauthorized access. In previous work we defined a Model-Driven based approach for developing a secure DW repository by following a relational approach. Nevertheless, it is also important to define security constraints in the metadata layer that connects the DW repository with the OLAP tools; that is, over the same multidimensional structures that end users manage. This paper incorporates a proposal for developing secure OLAP applications within our previous approach: it improves a UML profile for conceptual modelling; it defines a logical metamodel for OLAP applications; and it defines and implements transformations from conceptual to logical models, as well as from logical models to secure implementation in a specific OLAP tool (SQL Server Analysis Services).

An architecture for automatically developing secure OLAP applications from models

ContextDecision makers query enterprise information stored in Data Warehouses (DW) by using tools (such as On-Line Analytical Processing (OLAP) tools) which use specific views or cubes from the corporate DW or Data Marts, based on the multidimensional modeling. Since the information managed is critical, security constraints have to be correctly established in order to avoid unauthorized accesses. ObjectiveIn previous work we have defined a Model-Driven based approach for developing a secure DWs repository by following a relational approach. Nevertheless, is also important to define security constraints in the metadata layer that connects the DWs repository with the OLAP tools, that is, over the same multidimensional structures that final users manage. This paper defines a proposal to develop secure OLAP applications and incorporates it into our previous approach. MethodOur proposal is composed of models and transformations. Our models have been defined using the extension capabilities from UML (conceptual model) and extending the OLAP package of CWM with security (logical model). Transformations have been defined by using a graphical notation and implemented into QVT and MOFScript. Finally, this proposal has been evaluated through case studies. ResultsA complete MDA architecture for developing secure OLAP applications. The main contributions of this paper are: improvement of a UML profile for conceptual modeling; definition of a logical metamodel for OLAP applications; and definition and implementation of transformations from conceptual to logical models, and from logical models to the secure implementation into a specific OLAP tool (SSAS). ConclusionOur proposal allows us to develop secure OLAP applications, providing a complete MDA architecture composed of several security models and automatic transformations towards the final secure implementation. Security aspects are early identified and fitted into a most robust solution that provides us a better information assurance and a saving of time in maintenance.

The Application of OLAP in Logistics System of Steel-Production

The cost control of Enterprises, logistics cost has become one of the important components. In this paper, based on SQL Server2000 database steel production logistics information multi-dimensional data set is established based on the on-line analytical processing (OLAP) base. The production logistics information data of steelmaking workshop in the data mining process come true, which will help enterpriser to make decisions.

Cloud BI: Future of business intelligence in the Cloud

In self-hosted environments it was feared that business intelligence (BI) will eventually face a resource crunch situation due to the never ending expansion of data warehouses and the online analytical processing (OLAP) demands on the underlying networking. Cloud computing has instigated a new hope for future prospects of BI. However, how will BI be implemented on Cloud and how will the traffic and demand profile look like? This research attempts to answer these key questions in regards to taking BI to the Cloud. The Cloud hosting of BI has been demonstrated with the help of a simulation on OPNET comprising a Cloud model with multiple OLAP application servers applying parallel query loads on an array of servers hosting relational databases. The simulation results reflected that extensible parallel processing of database servers on the Cloud can efficiently process OLAP application demands on Cloud computing.

PENGEMBANGAN DATA WAREHOUSE DAN ON-LINE ANALYTICAL PROCESSING (OLAP) UNTUK PENEMUAN INFORMASI DAN ANALISIS DATA (Studi Kasus : Sistem Informasi Penerimaan Mahasiswa Baru STMIK AMIKOM PURWOKERTO)

Background preparation of this thesis report, Silva (2007) mentions that the competition for students between universities was inevitable as it grows, universities should equip themselves to survive in the competition that ultimately colleges that are not sensitive to compete. The demand for a high performance organization that has also faced by STMIK AMIKOM Purwokerto. Existing data on the colleges that are so big and much needed storage space allocation will be helped and be efficient with the data warehouse (Wikramanayake, 2006). Based on the data management / decision makers in education institutions is always a constant attempt to find an information / decision analysis that are useful in the search for new students. For the needs of the analysis method was used OLAP (OnLine Analytical Processing) which presents the data in the form of multidimensional query that is easier to be presented and analyzed. Data collection methods used in the preparation of this report include the method of observation of interviews and field studies, literature, internet search, as well as observing systems that are already running. This research was conducted with the following steps: 1) The analysis system that includes the current SIPMB analysis, identification of the cause of the problem, analysis of system requirements. 2) System design, including design of data warehouses and OLAP applications PMB. This design produces data warehouse architecture, data modeling dimensional, the establishment of a data warehouse, OLAP stakeholders PMB, PMB OLAP viewpoint, scenario, model systems, and user interface design. 3) Implementation, covers the application of OLAP applications PMB and operations performed included, 4) testing system, test the product in accordance with ISO 9126 software includes six quality characteristics of Functionality, Reliability, Usability, efficiency, Maintainability, Portability. In order not to interfere the performance of the operational databases (OLTP) SIPMB process of creating a data warehouse is done on different machines by performing replicate of the database used SIPMB. For the needs of the application is made data analysis OLAP PMB. These applications could present multidimensional data in grid view. Analysis of these data may include analysis of specialization in new student enrollment based on the period of new admissions, enrollment surge, home school, home province and district, Pengembangan Data Warehouse dan On-line Analytical Processing (OLAP) Untuk Penemuan Informasi Dan Analisis Data Jurnal Telematika Vol. 4 No.2 Agustus 2011 14 registration information, day, month, or year. So that the results of data analysis can be the management in determining the appropriate marketing strategies to increase the number of freshmen applicants either currently running or for admission in the coming year.

Improving performance by creating a native join-index for OLAP

The performance of online analytical processing (OLAP) is critical for meeting the increasing requirements of massive volume analytical applications. Typical techniques, such as in-memory processing, column-storage, and join indexes focus on high performance storage media, efficient storage models, and reduced query processing. While they effectively perform OLAP applications, there is a vital limitation: mainmemory database based OLAP (MMOLAP) cannot provide high performance for a large size data set. In this paper, we propose a novel memory dimension table model, in which the primary keys of the dimension table can be directly mapped to dimensional tuple addresses. To achieve higher performance of dimensional tuple access, we optimize our storage model for dimension tables based on OLAP query workload features. We present directly dimensional tuple accessing (DDTA) based join (DDTAJOIN), a technique to optimize query processing on the memory dimension table by direct dimensional tuple access. We also contribute by proposing an optimization of the predicate tree to shorten predicate operation length by pruning useless predicate processing. Our experimental results show that the DDTA-JOIN algorithm is superior to both simulated row-store main memory query processing and the open-source column-store main memory database MonetDB, thanks to the reduced join cost and simple yet efficient query processing.

Application of on-line analytical processing in evaluating factors influencing effect of posterior lumbar internal fixation in treatment of lumbar spinal nerve root damage

Application of on-line analytical processing in evaluating factors influencing effect of posterior lumbar internal fixation in treatment of lumbar spinal nerve root damage

OLAP-based collaborative educational decision

In the construction of education information system, lots of business data have been accumulated. How to use historical data for collaborative decision-making is very important. Several collaborative decision-making themes were introduced and multi-dimensional cubes were designed. Then On-Line Analytical Processing (OLAP) was performed and the corresponding results were given. These accelerate the application of data warehouse and OLAP in educational decision and improve the ability of the analysis and the level of decision for educational data.

Easier surveillance of climate-related health vulnerabilities through a Web-based spatial OLAP application

BackgroundClimate change has a significant impact on population health. Population vulnerabilities depend on several determinants of different types, including biological, psychological, environmental, social and economic ones. Surveillance of climate-related health vulnerabilities must take into account these different factors, their interdependence, as well as their inherent spatial and temporal aspects on several scales, for informed analyses. Currently used technology includes commercial off-the-shelf Geographic Information Systems (GIS) and Database Management Systems with spatial extensions. It has been widely recognized that such OLTP (On-Line Transaction Processing) systems were not designed to support complex, multi-temporal and multi-scale analysis as required above. On-Line Analytical Processing (OLAP) is central to the field known as BI (Business Intelligence), a key field for such decision-support systems. In the last few years, we have seen a few projects that combine OLAP and GIS to improve spatio-temporal analysis and geographic knowledge discovery. This has given rise to SOLAP (Spatial OLAP) and a new research area. This paper presents how SOLAP and climate-related health vulnerability data were investigated and combined to facilitate surveillance.ResultsBased on recent spatial decision-support technologies, this paper presents a spatio-temporal web-based application that goes beyond GIS applications with regard to speed, ease of use, and interactive analysis capabilities. It supports the multi-scale exploration and analysis of integrated socio-economic, health and environmental geospatial data over several periods. This project was meant to validate the potential of recent technologies to contribute to a better understanding of the interactions between public health and climate change, and to facilitate future decision-making by public health agencies and municipalities in Canada and elsewhere. The project also aimed at integrating an initial collection of geo-referenced multi-scale indicators that were identified by Canadian specialists and end-users as relevant for the surveillance of the public health impacts of climate change. This system was developed in a multidisciplinary context involving researchers, policy makers and practitioners, using BI and web-mapping concepts (more particularly SOLAP technologies), while exploring new solutions for frequent automatic updating of data and for providing contextual warnings for users (to minimize the risk of data misinterpretation). According to the project participants, the final system succeeds in facilitating surveillance activities in a way not achievable with today's GIS. Regarding the experiments on frequent automatic updating and contextual user warnings, the results obtained indicate that these are meaningful and achievable goals but they still require research and development for their successful implementation in the context of surveillance and multiple organizations.ConclusionSurveillance of climate-related health vulnerabilities may be more efficiently supported using a combination of BI and GIS concepts, and more specifically, SOLAP technologies (in that it facilitates and accelerates multi-scale spatial and temporal analysis to a point where a user can maintain an uninterrupted train of thought by focussing on "what" she/he wants (not on "how" to get it) and always obtain instant answers, including to the most complex queries that take minutes or hours with OLTP systems (e.g., aggregated, temporal, comparative)). The developed system respects Newell's cognitive band of 10 seconds when performing knowledge discovery (exploring data, looking for hypotheses, validating models). The developed system provides new operators for easily and rapidly exploring multidimensional data at different levels of granularity, for different regions and epochs, and for visualizing the results in synchronized maps, tables and charts. It is naturally adapted to deal with multiscale indicators such as those used in the surveillance community, as confirmed by this project's end-users.

Multiversion join index for multiversion data warehouse

The data warehouse (DW) technology is developed in order to support the integration of external data sources (EDSs) for the purpose of advanced data analysis by On-Line Analytical Processing (OLAP) applications. Since contents and structures of integrated EDSs may evolve in time, the content and schema of a DW must evolve too in order to correctly reflect the evolution of EDSs. In order to manage a DW evolution, we developed the multiversion data warehouse (MVDW) approach. In this approach, different states of a DW are represented by the sequence of persistent DW versions that correspond either to the real world state or to a simulation scenario. Typically, OLAP applications execute star queries that join multiple fact and dimension tables. An important optimization technique for this kind of queries is based on join indexes. Since in the MVDW fact and dimension data are physically distributed among multiple DW versions, standard join indexes need extensions. In this paper we present the concept of a multiversion join index (MVJI) applicable to indexing dimension and fact tables in the MVDW. The MVJI has a two-level structure, where an upper level is used for indexing attributes and a lower level is used for indexing DW versions. The paper also presents the theoretical upper bound (pessimistic) analysis of the MVJI performance characteristic with respect to I/O operations. The analysis is followed by experimental evaluation. It shows that the MVJI increases a system performance for queries addressing multiple DW versions with exact match and range predicates.

Integration of OLAP and data mining for analysis of results from dependability evaluation experiments

This paper proposes the application of On-Line Analytical Processing (OLAP) and data mining approaches to analyse the large amount of raw data collected in fault injection campaigns and dependability benchmarking experiments. We use data warehousing technologies to store raw results from different experiments in a multidimensional structure where raw data can be analysed by means of OLAP tools. Moreover, we present an approach for identifying the key infrastructural factors determining the behaviour of computer systems in the presence of faults by the application of data mining methods on the data sets. Results obtained with the proposed techniques identified important factors impacting performance and dependability that could not have been revealed solely by the benchmark measures.