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Materialized view selection (MVS) is the problem of selecting an appropriate set of views to be materialized to speed up analytical query processing of data warehouses. Online analytical processing (OLAP) of queries is an essential application of the MVS problem, in which, the response times of the queries are reduced by storing the selected views. Views are intermediate results of query processing and are selected in the MVS problem to be stored and will then be exploited in answering process of several queries. Views are usually organized as a view representation structure in the MVS problem. Multiple Views Processing Plan (MVPP) is a standard structure used for view representation in the MVS problem. Due to the tremendous amount of data, constructing the MVPP is a challenge in the big data applications. The MR-MVPP (Map-Reduce-based construction of the MVPP) is the proposed method of this paper to address this problem. The MR-MVPP performs a set similarity join (similarity-based join) on the base relations and views using the map-reduce model and the hashing technique. The MVPP construction time in the proposed method is reduced by avoiding redundant calculations in the process of creating the MVPP. The performance of the proposed method is empirically evaluated. According to the results of the experiments, the execution time of the MR-MVPP method is better than the other methods. The average time improvement is about 26.5 units. This improvement is better than the other similar researches in this area and is significant due to the high volume of data in real applications. Moreover, the proposed method works well in terms of the effectiveness of the created MVPP and has about a 50% coverage rate for view selection methods. Deterministic methods are more accurate than hashing methods and can be utilized for set similarity join as future work to probably improve the effectiveness of the constructed MVPP.

Organizational management, as well as hospital management, could not work precisely without defining the performance indicators to control all business process. This situation encourages the need for information and data analysis availability. BI includes applications, infrastructure, tools and practices that enable organizations to access and analyze data and information to improve and optimize the decisions and organization performance. BI has the potential to improve the quality, efficiency and effectiveness of hospital health services as well. The objective of this study was to design business intelligence prototype for the hospital. BI design was carried out with a Business Intelligence Roadmap approach which has 6 main stages, namely: (1) Justification, (2) Planning, (3) Business Analysis, (4) Design, (5) Construction and (6) Deployment. Data were collected from hospital activities includes registration, Electronic Medical Record (EMR) in the Imaging, Laboratory, Pharmacy, Operating Theater and Medical Check-Up departments activities. Designing BI was preceded by identifying technical and non-technical needs, then continued by designing BI itself. BI roadmap approach was used for this propose. Technical requirements for designing BI include hardware and software infrastructure readiness, while non-technical requirements include Business Analysis which consists of Project Requirements Definition, Data Analysis, Application Prototyping and Metadata repository Analysis. Designing BI itself includes: Designing a multidimensional database and designing ETL. The user interfaces for BI was shown in the Performance Dashboard, which allows organizations to track all aspects of their daily business activities and performance.

The increasing data scales in today’s business sectors coupled with the necessity of risk management raise the importance of business intelligence tools as an integrated solution for the insurance industry. These tools have mostly been used to achieve effective risk management. Although methods of risk management in the insurance industry have been proposed many years ago, the research effort has primarily been focused on predictive analyses. This study aimed to investigate the role of business intelligence as a solution to illustrate its potential in risk management particularly for decision-makers in agricultural insurance. We hypothesized that this would make a preferable decision in uncertain conditions. Sample data from the online transaction process system of Iran agricultural insurance fund were preprocessed in SQL server. Multidimensional online analytical processing architecture was analyzed using Targit business intelligence tool. Our results identified financial risks that lead to a framework of controlling risk based on business intelligence in the agricultural insurance fund.

Transaction data in superstore sales data are very useful for company development, can be used to describe and forecast or predict future sales transaction data and to study the past about business opportunities and challenges. The use of Business Intelligence (BI) technology can help analyze large amounts of data, in addition, BI is a powerful tool for quality analysis and company analysis. This study designed an information system using the BI approach to analyze transaction data on superstore sales data. The research focus is on report data, namely superstore sales data regarding sales transactions. This study uses the OLAP method to describe data visualization so that it provides benefits and competitive advantages. This system can improve the quality of decisions taken in solving the problem of abundant data accumulation, monitoring operational activities, fulfilling information needs and effective data management. Business intelligence is expected for company leaders to be able to understand the data that will have been processed in understanding visual forms and can easily absorb the information needed to make decisions for the company. In addition, with the design of website-based business intelligence that is effective and efficient to produce opportunities in making decisions to predict the increase or decrease that will occur in the coming years using the histories in the superstore sales data of the previous years

Food Station is a city-owned enterprise owned by the Jakarta Provincial Government whose tasks include monitoring and publication of supply data and rice price through Cipinang Rice Market so that it is useful as a guide for traders and policy makers in relevant government institutions. This study aims to design a dashboard to visualize the price and supply of rice through Cipinang Rice Market. The research stages consist of identification of information requirement, data warehouse design and On-line Analytical Processing (OLAP) database, transfer of operational data from excel file and web scrapping into OLAP database through Extract, Transform, Load (ETL) process and create dashboard using PowerBI application. The result of this research is a data warehouse that can facilitate the analysis with some fact table that is price, quantity of rice enter, quantity of rice out and stock and some dimension table that is place dimension (city of origin / destination), dimension time, dimension of rice type and dimension market and dashboard of various price and supply charts that can be selected based on available dimensions. Keywords: dashboard, data warehouse, rice price, rice supply, visualization

Дранко Олег Иванович, д-р техн. наук, ведущий научный сотрудник, Институт проблем управления им. В.А. Трапезникова Российской академии наук, г. Москва; olegdranko@gmail.com. Логиновский Олег Витальевич, д-р техн. наук, профессор, заведующий кафедрой информационно-аналитического обеспечения управления в социальных и экономических системах, Южно-Уральский государственный университет, г. Челябинск; loginovskiiov@susu.ru. Приказчиков Сергей Александрович, младший научный сотрудник, Институт проблем управления им. В.А. Трапезникова Российской академии наук, г. Москва; prikazchikovs@yandex.ru. O.I. Dranko1, olegdranko@gmail.com, O.V. Loginovskiy2, loginovskiiov@susu.ru, S.A. Prikazchikov1, prikazchikovs@yandex.ru 1 V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, Russian Federation 2 South Ural State University, Chelyabinsk, Russian Federation Экономический кризис, вызванный пандемией COVID-19, приводит к значительному падению базы начисления налогов и доходов бюджета. Рассматривается экспресс-моделирование доходов регионального бюджета в зависимости от глубины кризиса. Делается оценка по одному из регионов – Челябинской области. Цель исследования. Построение механизма экспресс-оценки доходной части регионального бюджета на основании доступных статистических данных. Разработка комплексной модели прогнозирования показателей финансовой отчетности предприятий региона с последующим прогнозом доходной части регионального бюджета. Материалы и методы. Используются имитационные модели макроэкономических показателей доходов бюджета региона в увязке с моделью финансового прогнозирования показателей предприятий. Для обработки финансовой информации предприятий региона используются методы обработки больших массивов данных. Результаты. Построена модель, увязывающая изменение показателей финансовой отчетности предприятий с динамикой доходных статей регионального бюджета. На основании сценарных допущений проведены расчеты изменения показателей финансовой отчетности, налогооблагаемой базы предприятий региона, а также их вклада в региональный бюджет. Разработанная модель позволила сформировать прогноз доходной части бюджета в условиях коронакризиса. Заключение. Данный подход позволяет объединить мезо- и микроэкономические подходы и проводить моделирование экономического развития региона как многоуровневой системы. Предложенная модель является типовой и может применяться для анализа развития других регионов страны. The economic crisis caused by the COVID-19 pandemic leads to a significant drop in incomes and tax payments. We consider express modeling of regional budget revenues depending on the depth of the crisis. The assessment is made for one of the Russian regions – the Chelyabinsk region. Purpose of the study. To build a mechanism based on available statistics for express estimation of the regional budget revenues. The comprehensive model for forecasting the region's enterprises' financial results, followed by a forecast of the regional budget revenues, was worked out. Materials and methods. Simulation models of macroeconomic indicators of the region's budget revenues are used in conjunction with enterprises' financial forecasting models. Big Data Analytics was applied to process financial statement data of the regional enterprises. Results. The model links the change in enterprises' financial results with the regional budget revenue dynamics. Based on scenario assumptions, the calculation of the change in financial results and tax bases of regional enterprises was made and the estimation of their contribution to the regional budget. The model allowed to form a forecast of revenues of the budget in the COVID-19 pandemic conditions. Conclusion. This approach combines meso- and microeconomics approaches and simulates the region's economic development as a multilevel system. The proposed model is universal and can be applied to consider the development of any Russian region.

PurposeBusiness intelligence (BI) is a strategic approach that can use analytical tools to collect and integrate information, apply business rules and ensure the appropriate visible output of organizational information. This study aims to present the design and implementation of BI in areas of business process improvement for production, distribution and customer services.Design/methodology/approachThis study highlights the process of BI in the production, distribution and customer services based on the National Food Products Company (NFPC) in the United Arab Emirates (UAE). This study discusses the step-by-step development process of BI and refers to graphical illustrations of the business needs and the organization's target key performance indicators (KPI).FindingsBased on the business needs and chosen KPIs to maximize production and improve distribution and customer services the BI tool shows that the “star scheme” is the most appropriate one. Relational Online Analytical Processing (ROLAP) based on Mondrian system is employed as Online Analytical Processing (OLAP) architecture since the NFPC's technological infrastructure was better adapted to this vision. The analysis starts with data retrieval from two databases' customer' and production and distribution databases. Finally, visualization and reporting processes that respect the end-users improve the NFPC's decisions.Practical implicationsThe study will help other organizations, BI developers, data warehouses (DW) developers and administrators, project managers as well as academic researchers understand how to develop a successful BI framework and implement BI based on business needs.Originality/valueThis is a unique and original study on the BI experience from a UAE-based organization and will encourage other organizations to apply BI in their business process.

This paper presents an architecture for the data warehouse of outpatient healthcare (DWOP) as a data repository collects data from two different sources (Databases of outpatient healthcare and Excel files from hospitals) and provides storage, functionality and responsiveness to queries to meet decision makers requirements.Successfully supporting managerial decision-making is critically dependent upon the availability of integrated, high quality information organized and presented in a timely and easily understood manner. “On-Line Analytical Processing (OLAP) is utilized for decision support to get interesting information” from the data warehouse with a rapid execution time. OLAP is considered one of Business Intelligence tools.

The provision of information to the marketing information system is investigated in this paper. The collection of information on the server side and on network nodes is considered. It is stated that the addition of any information collection software to the server is not possible or can slow down the server. It is proposed to place sensors in network nodes on the approach to the server, which unloads the server from unnecessary software. An example of such Web Traffic Warehouse system is considered. Here the work is carried out at the level of protocols and data collection takes place at the level of TCP/IP packets. Three different approaches to the analysis of transactions are analyzed: identification of transactions taking into account the visits duration, identification of transactions by the method of maximum reference depth and division of transactions according to temporal principles. The scheme of the system of operative processing of Internet data on the basis of OLAP-technologies which is based on the principle of collecting operative data from various sources which then are cleared, integrated and put in relational storage is constructed. Also, the definition of OLAP as a set of tools for multidimensional analysis of data stored in the repository is investigated. It provides the company with the most convenient and fast means of accessing, viewing and analyzing business information, and provides the user with natural, intuitive data model, organizing them into multidimensional cubes, the axes of the multidimensional coordinate system are the main attributes of the analyzed business process. The problem of integration of data warehouses (accounting system) with formation systems (data showcases) is analyzed. Based on the analysis, the model of the distribution-order-sale data show window is built, which is based on two types of tables – fact tables and measurement tables is constructed. Thus, the table of sales facts in the developed system contains such fields as Cusomerfact ID, Salesperson, Product ID, Quantity Sold, Discount, Total Amount, etc. Due to the model it is highlighted that while creating show windows there is the organization of three key stages – obtaining data from the source systems, converting them into the desired form and then loading them into the target system.

As cloud service becomes more and more capable, available and powerful, wiseCIO has emerged from an innovative roadmap toward archival Content Management Service (aCMS) and massive Content Delivery Service (mCDS) in support of Anything-as-a-Service (XaaS) via Digital Archiving and Transformed Analytics (DATA); DATA aims to automate UBC with FAST solutions throughout a feasible, analytical, scalable and testable approach. This paper, based on the novel wiseCIO (web-based intelligent service engaging Cloud Intelligence Outlet), presents digital archiving and transformed analytics via machine learning automata for intelligent UBC processes to liaise with Universal interface for human-computer interaction, enable Brewing aggregation (differing from traditional web browsing), and engage Centered user experience. As one of the most practical aspects of artificial intelligence, machine learning is applied to analytical model building and massive and/or multidimensional Online Analytical Processing (mOLAP) for more intelligent cloud service with little explicit coding required. DATA is central to useful information via archival transformation and analytics, and utilizable intelligence for Business, Education and Entertainment (iBEE) in support of decision-making. As a result, DATA orchestrates wiseCIO to promote ACTiVE XaaS that enables accessibility, contextuality and traceability of information for vast engagement with various cloud services, such as aCMS (archival Content Management Service), COSA (Context-Oriented Screening Aggregation), DASH (Deliveries Assembled for fast Search and Hits), OLAS (Online Learning via Analytical Synthesis), REAP (Rapid Extension and Active Presentation), and SPOT (Special Points On Top) with great ease.

International audience; A decision support system is used by decision makers for a long time. But, in some cases, the originally designed multidimensional schema does not cover the entire needs of decision makers, which can change over time. One such unfulfilled needs, is using facts to describe dimension members. In this article, we propose a methodology to transform the constellation schema of a data warehouse by integrating factual data into a dimension. The proposed methodology and algorithms enrich a constellation multidimensional schema with new analytical possibilities for decision makers. This enrichment has repercussions for the entire multidimensional schema that are managed by multidimensional modeling, hierarchy calculation and the hierarchy version. In this article, we present a theoretical view of the proposed methodology supported by a case study, an implemented prototype and a complete evaluation based on a standard benchmark.

Online analytical processing (OLAP) approach is widely used in business intelligence to cater the multidimensional queries for decades. In this era of cutting-edge technology and the internet, data generation rates have been rising exponentially. Internet of things sensors and social media platforms are some of the major contributors, leading toward the absolute data boom. Storage and speed are the crucial parameters and undoubtedly the burning issues in efficient data handling. The key idea here is to address these two challenges of big data computing in OLAP. In this article, the authors have proposed and implemented OLAP on Hadoop by Indexing (OOHI). OOHI offers a simplified multidimensional model that stores dimensions in the schema server and measures on the Hadoop cluster. Overall setup is divided into various modules, namely: data storage module (DSM), dimension encoding module (DEM), cube segmentation module, segment selection module (SSM), and block selection and process (BSAP) module. Serialization and deserialization concept applied by DSM for storage and retrieval of the data for efficient space utilization. Integer encoding adopted by DEM in dimension hierarchy is selected to escape sparsity problem in multidimensional big data. To reduce search space by chunks of the cube from the queried chunks, SSM plays an important role. Map reduce-based indexing approach and series of seek operations of BSAP module were integrated to achieve parallelism and fault tolerance. Real-time oceanography data and supermarket data sets are applied to demonstrate that OOHI model is data independent. Various test cases are designed to cover the scope of each dimension and volume of data set. Comparative results and performance analytics portray that OOHI outperforms in data storage, dice, slice, and roll-up operations compared with Hadoop based OLAP.

This special issue was open for submissions in the field of big data in business. Accordingly, this special issue includes five contributions to the fields of business process innovation in the big data era, unstructured big data analytical methods in firms, online analytical processing approach for business intelligence in big data, geospatial insights for retail recommendation using similarity measures, and big data and operational changes through interactive data visualization. A bibliometric approach is used to visualize and highlight the exciting literature on big data followed by highlighting the contribution of this special issue.

Biodiversity is one of emerging issue over decades; many have performed research to map and to document the data over the world. This issue is very important due to the event of extinction have been accelerating happening because of human extinction. Bengkulu, as one of the province lied in one of 19 hotspots in the world, Sundanese, has experienced the degradation of flora and fauna over the case of forest degradation and habitat loss. Although many application and software has been developed to solve the case, the existences of data standardization still become an issue over this problem. In this research, study of data integration had been developed to make the process of biodiversity data acquisition can be more effective and efficient. The system proposed the integration based on OLAP and OLTP that will be connected to IUCN, as one of the biggest center for monitoring the loss of biodiversity all around the world. This application had been built with web based using UML design and followed SDLC to provide the best fit of the need. This research had also succeeded to build the automated integration to show the record of dynamic number over biodiversity existences in Bengkulu. The application had been tested using black box and has the perfect performance (100%) over the testing that can help the monitoring process over biodiversity data.

The Religious Research, Development and Training Agency of the Ministry of Religious Affairs as a supervisory unit for Widyaiswara functional positions, has the task of evaluating the performance of Widyaiswara of the Ministry of Religious Affairs. That demands the availability of a need for reports or data that presented quickly and accurately when the Widyaiswara performance evaluation process is conducted every year. The problem that occurs these days is that the data on the result of credit score of Widyaiswara assessment are stored in an unstructured excel file. This study utilizes the data warehouse and business intelligence in the process of Widyaiswara performance evaluation. The OLTP (Online Transaction Process) Data that presented for data warehouse is the result of credit score of Widyaiswara assessment. The planning of data warehouse conducted through nine-steps methodology that created by Kimball and Ross, then those data were analyzed using OLAP (Online Analytical Processing) in the application of qliksense in the form of dashboard business intelligence to present the data in a faster visual form. The result, giving the information to the leader to evaluate the performance of Widyaiswara, especially in making decision such as circular letter to improve the quality of Widyaiswara performance, the minimum score limit in the performance agreement, reward in the form of certificate of appreciation for the highest score and punishment in the form of warning letter for the low total score. &nbsp, Badan Litbang dan Diklat Kementerian Agama sebagai unit pembina instansi jabatan fungsional Widyaiswara, memiliki tugas untuk melakukan evaluasi kinerja Widyaiswara Kementerian Agama. Hal tersebut menuntut tersedianya kebutuhan terhadap laporan atau data yang disajikan secara cepat dan akurat ketika proses evaluasi kinerja Widyaiswara dilakukan setiap tahun. Permasalahan yang muncul saat ini adalah, data hasil penilaian angka kredit Widyaiswara tersimpan dalam bentuk file excel dan tidak terstruktur. Penelitian ini memanfaatkan data warehouse dan business intelligence dalam proses evaluasi kinerja Widyaiswara. Data OLTP (Online Transaction Process) yang ditampilkan untuk data warehouse adalah hasil penilaian angka kredit Widyaiswara. Perancangan data warehouse dilakukan melalui nine-steps methodology yang dirancang oleh Kimball dan Ross, selanjutnya data tersebut dianalisis menggunakan OLAP (Online Analytical Processing) pada aplikasi qliksense dalam bentuk dashboard business intelligence untuk menyajikan data dalam bentuk visual. Hasilnya, memberikan informasi kepada pimpinan untuk mengevaluasi kinerja Widyaiswara, terutama dalam membuat keputusan seperti surat edaran peningkatan kualitas kinerja Widyaiswara, batas nilai minimal dalam perjanjian kinerja, reward berupa piagam penghargaan dengan total nilai tertinggi serta punishment berupa surat peringatan jika mendapatkan total nilai rendah.

Introduction: Management is essential for meeting the requirements of a project and the computational tools of Business Intelligence (BI) have great potential to contribution, providing management information about the business. Tools of this type are used in several sectors of the industry, but in civil construction, the focus of this work, the scenario is different, with much to aadvance. Therefore, a survey of BI tools applicable to the construction sector and its uses ar presented. Method: It conducts a systematic review of the literature, which analyzed 595 articles from six databases (ACM, Engineering Village, IEEE, Material Science Engineering, Science Direct, Scopus and Web of Science). Results: It identifies12 different applications, mainly in cost management, budget and job security. In the applications, uses of Data Warehouse and OLAP technologies were evidenced. In addition, it was found that most BI tools were developed for each company to the detriment of commercial software. Conclusion: There are several BI tools for civil construction, with different purposes. Most softwares were developed for each case studied due to the unique characteristics of the construction sector. It is believed that the large-scale adoption of the tools involves cooperation between companies, class entities and universities. Limitations were found in the research regarding the characterization of companies, due to the absence of this data in the analyzed articles. Finally, it is suggested that the challenges of implementing the technologies and the verified limitations can be addressed in future studies. Introdução: A gestão é essencial para que se cumpram os requisitos de um projeto, e as ferramentas computacionais de Business Intelligence (BI) têm grande potencial de contribuição, fornecendo informações gerenciais sobre o negócio. Ferramentas desse tipo são utilizadas em diversos setores da indústria, porém na construção civil, foco deste trabalho, o cenário é diferente, com muito a avançar. Diante disso, apresenta-se um levantamento das ferramentas de BI aplicáveis ao setor da construção e suas utilizações. Método: Conduz uma revisão sistemática da literatura, que analisou 595 artigos de seis bases de dados (ACM, Engineering Village, IEEE, Material Science Engineering, Science Direct, Scopus e Web of Science). Resultados: Identifica 12 diferentes aplicações, principalmente na área de gestão de custos, orçamento da obra e segurança do trabalho. Nas aplicações, foram evidenciadas utilizações das tecnologias de Data Warehouse e OLAP. Verifica que a maioria das ferramentas de BI foram desenvolvidas para cada empresa em detrimento dos softwares comerciais. Conclusão: Existem diversas ferramentas de BI para a construção civil, com diferentes aplicações. A maioria dos softwares foram desenvolvidos para cada caso estudado devido às características únicas do setor da construção. A adoção em larga escala das ferramentas passe pela cooperação entre empresas, entidades de classe e universidades. Verifica limitações na pesquisa quanto à caracterização das empresas, devido à ausência desses dados nos artigos analisados. Sugere que os desafios de implementação das tecnologias e as limitações verificadas sejam abordados em estudos futuros.

In the field of Business Intelligence we require quick and efficient analysis of multidimensional data. Data warehouse (DW) and Online analytical processing (OLAP) approaches have been developed fo...

En un entorno de producción los procesos de toma de decisiones son importantes debido a los impactos que estos generan sobre otros procesos. Para tal fin resulta conveniente acceder a la información almacenada en los grandes almacenes de datos a través de un modelo menos complejo, los Datamarts. Un Datamart permite optimizar el proceso de aprovechamiento de la información, a través del agrupamiento de los factores de interés que inciden sobre un hecho o hechos en particular. Así pues, se realizó una investigación del tipo proyectiva y estableciendo como objetivo general el desarrollo de un Datamart basado en el modelo estrella, orientado hacia los modelos de sistemas productivos agropecuarios. La optimización del proceso de extracción y visualización de los datos almacenados en el Datamart, fue llevada a cabo a través de la implementación de un cubo OLAP. Utilizando herramientas de software como SQL Server Management para el diseño de la base de datos, el entorno de desarrollo integrado Visual Studio para la ejecución y diseño de los procesos de extracción, transformación y carga de los datos, y de Power BI como herramienta de Inteligencia de Negocio para la generación de informes y visualizaciones dinámicas de los indicadores establecidos. Palabras Clave: datamart, data warehouse, base de datos, inteligencia de negocio. Referencias [1].B. Mazon, A. Pan and R. Tinoco. Análisis de Datos Agropecuarios. 1ra. Ed. Ecuador: UTMACH, 2018. [2].A. De Mauro, M. Greco y M. Grimaldi. “Una definición formal de Big Data basada en sus características esenciales. Revisión de la biblioteca”, Library Review, vol. 65 no. 3, pp. 122-135, Abril 2016. [3].P. Muñoz. “Desarrollo de una arquitectura de Big Data para registros mercantiles”. Trabajo de grado, UCV, Caracas, 2016. [4]. Z. Jourdan, R.K. Rainer y T.E. Marshall. 2Business Intelligence: An Analysis of Literature. Information System Management”, IEEE Engineering Management Review, vol. 25, no 2, p. 121-131, Marzo 2008. [5].W.H. Inmon. Building the Data Warehouse. 3ra. ed. New York: Wiley, 2002. [6].C. J. Date. Introducción a los sistemas de bases de datos. 7ma. ed. México: Pearson Educación, 2001. [7]Cubo OLAP. (2020, mayo 10). Wikipedia. [En línea] Disponible en: https://es.wikipedia.org/wiki/Cubo_OLAP. [Último acceso: 13 de marzo de 2020]. [8]B.R. Dario. Data Warehousing: Investigación y sistematización de conceptos. Hefesto: Metodología propia para la construcción de un Datawarehouse. 1ra. ed. Cordoba: Argentina, 2010.

This paper proposes the use of a new technique, the Stochastic Multicriteria Acceptability Analysis (SMAA), to evaluate education quality at school level out of the PISA multidimensional database. SMAA produces rankings with Monte Carlo Generation of weights to estimate the probability that each school is in a certain position of the aggregate ranking, thus avoiding any arbitrary intervention of researchers. We use the rankings in 4 waves of PISA assessment to compare SMAA outcomes with Benefit of Doubt (BoD), showing that differentiation of weights matters. Considering the whole set of feasible weights by means of SMAA, we then estimate multidimensional inequality in education, and we disentangle inequality into a ‘within’ and a ‘between’ country component, in addition to a component due to overlapping, using the multidimensional ANOGI. We find that, over time, inequality within countries has increased substantially. Overlapping among countries, particularly in the upper part of the distribution has also increased quite substantially suggesting excellence is spreading among countries.

This article sets in context Data Warehouses (DWs) and Online Analytical Processing (OLAP) against the backdrop of databases and Big Data and shows how data warehouses and OLAP were incorporated in...

The effectiveness of a Business Intelligence System is hugely dependent on these three fundamental components, 1) Data Acquisition (ETL), 2) Data Storage (Data Warehouse), and 3) Data Analytics (OLAP). The predominant challenges with these fundamental components are Data Volume, Data Variety, Data Integration, Complex Analytics, Constant Business changes, Lack of skill sets, Compliance, Security, Data Quality, and Computing requirements. There is no comprehensive documentation that talks about guidelines for ETL, Data Warehouse and OLAP to include the recent trends such as Data Latency (to provide real-time data), BI flexibility (to accommodate changes with the explosion of data) and SelfService BI. This research paper attempts to fill this gap by analyzing existing scholarly articles in the last three to five years to compile guidelines for effective design, implementation, and assessment of DW, ETL, and OLAP in BI.

Recent years have witnessed the booming of big data analytical applications (BDAAs). This trend provides unrivaled opportunities to reveal the latent patterns and correlations embedded in the data, and thus productive decisions may be made. This was previously a grand challenge due to the notoriously high dimensionality and scale of big data, whereas the quality of service offered by providers is the first priority. As BDAAs are routinely deployed on Clouds with great complexities and uncertainties, it is a critical task to manage the service level agreements (SLAs) so that a high quality of service can then be guaranteed. This study performs a systematic literature review of the state of the art of SLA-specific management for Cloud-hosted BDAAs. The review surveys the challenges and contemporary approaches along this direction centering on SLA. A research taxonomy is proposed to formulate the results of the systematic literature review. A new conceptual SLA model is defined and a multi-dimensional categorization scheme is proposed on its basis to apply the SLA metrics for an in-depth understanding of managing SLAs and the motivation of trends for future research. Refereed/Peer-reviewed

The recent debates on personalizing analyses in a Big Data context are one of the most solicited challenges for business intelligence (BI) administrators. The high-volume, the high-variety, and the high-velocity of Big Data have produced difficulty in storing, processing, and analyzing data in traditional systems. These 3Vs (volume, velocity, and variety) created many new challenges and make them difficult to extract the specific needs of the users. In addition, the user may be faced with the problem of disorientation; he does not know what information really corresponds to his needs. The information personalization systems aim to overcome these problems of disorientation by using a user profile. The effectiveness of the personalization system in a Big Data context is to demonstrate by the relevance and accuracy of the content of the results obtained, according to the needs of the user and the context of the research. Nevertheless, most of the recent research focused on the relational data warehouse personalizing and ignored the integration of the user context into the analysis of OLAP cubes, which is the first concerned to execute the user's multidimensional queries. To deal with this, the authors propose in this article a dynamic personalizing approach in Big Data context using OLAP cubes, based on the Content-Based Filtering, and the Query Expansion techniques. The first step in the proposal consists of processing the user queries by an enrichment technique in order to integrate the user profile and his searching context to reduce the searching space in the OLAP cube, and use the expansion technique to extend the scope of the analysis in the OLAP cube. The retrieved results are: “as relevant as possible” compared to the user's initial request. Afterward, they use information filtering techniques such as content-based filtering to personalize the analysis in the reduced data cube according to the term frequency and cosine similarity. Finally, they present a case study and experiences results to evaluate and validate their approach. Vol.15 Issue 2 77 101

The Intentional Analytics Model (IAM) has been recently envisioned as a new paradigm to couple OLAP and analytics. It relies on two basic ideas: (i) letting the user explore data by expressing her analysis intentions rather than the data she needs, and (ii) returning enhanced cubes, i.e., multidimensional data annotated with knowledge insights in the form of interesting model components (e.g., clusters). In this paper we contribute to give a proof-of-concept for the IAM vision by delivering an end-to-end implementation of , one of the five intention operators introduced by IAM. Among the research challenges left open in IAM, those we address are (i) automatically tuning the size of models (e.g., the number of clusters), (ii) devising a measure to estimate the interestingness of model components, (iii) selecting the most effective chart or graph for visualizing each enhanced cube depending on its features, and (iv) devising a visual metaphor to display enhanced cubes and interact with them. We assess the validity of our approach in terms of user effort for formulating intentions, effectiveness, efficiency, and scalability.

This business development in the globalized era provoked fierce competition especially with the application of it to support his daily process especially in data management, therefore, the BAW Tour & Travel needs a system for data processing so that data can be stored and integrated with each other or what's called data warehouse and followed by other regulatory ETL, OLAP methods in analysis and quick decision-making. Using the data warehouse can figure out a spike in visitors' visits so it can predict people to be able to predict the number of guides during a month in case there's a spike that reduces anyone's denial of acceptance on the grounds of lack of a guide and to booked a motel because it's already full.

Recent developments in business intelligence have not only led to the availability of more and more heterogeneous data but also to more and more heterogeneous user groups accessing them. Consequent...

Commercial OLAP systems are economically unavailable for organizations with limited financial capabilities. Analytical processing of large amounts of data in these organizations can be accomplished using open-source software systems on a cost-effective cluster platform. Previously created Clusterix-like DBMS using a regular query processing plan is not efficient enough. Therefore, research on such systems was developed with a focus on a full load of processor cores and using the GPU acceleration (systems Clusterix-N, N—from new) up to the development of a system comparable in efficiency to the open-source system Spark, which is currently considered the most promising. The development methodology was based on the constructive system modeling methodology.

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.

This paper presents CirroData, a high-performance SQL-on-Hadoop system designed for Big Data analytics workloads. As a home-grown enterprise-level online analytical processing (OLAP) system with more than seven-year research and development (R&D) experiences, we share our design details to the community about how to achieve high performance in CirroData. Multiple optimization techniques have been discussed in the paper. The effectiveness and the efficiency of all these techniques have been proved by our customers’ daily usage. Benchmark-level studies, as well as several real application case studies of CirroData, have been presented in this paper. Our evaluations show that CirroData can outperform various types of counterpart database systems in the community, such as “Spark+Hive”, “Spark+HBase”, Impala, DB-X/Y, Greenplum, HAWQ, and others. CirroData can achieve up to 4.99x speedup compared with Greenplum, HAWQ, and Spark in the standard TPC-H queries. Application-level evaluations demonstrate that CirroData outperforms “Spark+Hive” and “Spark+HBase” by up to 8.4x and 38.8x, respectively. In the meantime, CirroData achieves the performance speedups for some application workloads by up to 20x, 100x, 182.5x, 92.6x, and 55.5x as compared with Greenplum, DB-X, Impala, DB-Y, and HAWQ, respectively.

Today, the relational database is not suitable for data management due to the large variety and volume of data which are mostly untrusted. Therefore, NoSQL has attracted the attention of companies. Despite it being a proper choice for managing a variety of large volume data, there is a big challenge and difficulty in performing online analytical processing (OLAP) on NoSQL since it is schema-less. This article aims to introduce a model to overcome null value in converting document-oriented NoSQL databases into relational databases using parallel similarity techniques. The proposed model includes four phases, shingling, chunck, minhashing, and locality-sensitive hashing MapReduce (LSHMR). Each phase performs a proper process on input NoSQL databases. The main idea of LSHMR is based on the nature of both locality-sensitive hashing (LSH) and MapReduce (MR). In this article, the LSH similarity search technique is used on the MR framework to extract OLAP cubes. LSH is used to decrease the number of comparisons. Furthermore, MR enables efficient distributed and parallel computing. The proposed model is an efficient and suitable approach for extracting OLAP cubes from an NoSQL database.

The article deals with the issue of conceptual modelling of the MOLAP database structures used in multidimensional data analysis for BI. Management information for decision-making purposes was collected in the MOLAP database and delivered to the user via the management cockpit. Building OLAP databases is discussed from the database designer point of view The meta model of the OLAP database was described in conceptual and logical terms. The work includes a critical analysis of the meta-model assumptions for the design of systems for multidimensional data analysis. A postulate to revise the classic meta-model was presented and a new type of relationship was proposed. The assumptions of the meta-model for modelling and simulating business decisions were presented, as well as the application of meta modeling to define business processes. Based on Gozinto graphs, the meta model of structural and technological developments was defined

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

Many real systems produce network data or highly interconnected data, which can be called information networks. These information networks form a critical component in modern information infrastructure, constituting a large graph data volume. The analysis of information network data covers several technological areas, among them OLAP technologies. OLAP is a technology that enables multi-dimensional and multi-level analysis on a large volume of data, providing aggregated data visualizations with different perspectives. This article presents a literature review on the main applications of OLAP technology in the analysis of information network data. To achieve such goal, it shows a systematic review to list the works that apply OLAP technologies in graph data. It defines seven comparison criteria (Materialization, Network, Selection, Aggregation, Model, OLAP Operations, Analytics) to qualify the works found based on their functionalities. The works are analyzed according to each criterion and discussed to identify trends and challenges in the application of OLAP in the information network.

The analysis of the semantic changes of Korean verbs is an important part of Korean culture research. It is helpful for Korean researchers to analyze the historical evolution of Korean language and to deduce the teaching, learning, and dissemination of Korean language by strengthening the analysis of the semantic changes of Korean verbs. On the difficulty of extracting and analyzing the semantic variation characteristics of Korean verbs, firstly, this paper is based on database technology, big data mining, big data analysis, online analytical processing technology, and verb feature analysis technology. Secondly, taking the semantic change characteristics of Korean verbs as the object of study. What’s more, the system frame design of Korean verb semantic change feature analysis system based on massive culture corpus data is established. Finally, it is shown that the designed feature analysis system has a good application effect through the analysis of the relevant use cases and user feedback information of the system in this year.

With data explosion in scale and variety, OLAP databases play an increasingly important role in serving real-time analysis with low latency (e.g., hundreds of milliseconds), especially when incoming queries are complex and ad hoc in nature. Moreover, these systems are expected to provide high query concurrency and write throughput, and support queries over structured and complex data types (e.g., JSON, vector and texts). In this paper, we introduce AnalyticDB, a real-time OLAP database system developed at Alibaba. AnalyticDB maintains all-column indexes in an asynchronous manner with acceptable overhead, which provides low latency for complex ad-hoc queries. Its storage engine extends hybrid row-column layout for fast retrieval of both structured data and data of complex types. To handle large-scale data with high query concurrency and write throughput, AnalyticDB decouples read and write access paths. To further reduce query latency, novel storage-aware SQL optimizer and execution engine are developed to fully utilize the advantages of the underlying storage and indexes. AnalyticDB has been successfully deployed on Alibaba Cloud to serve numerous customers (both large and small). It is capable of holding 100 trillion rows of records, i.e., 10PB+ in size. At the same time, it is able to serve 10m+ writes and 100k+ queries per second, while completing complex queries within hundreds of milliseconds.

Cloud-native databases become increasingly important for the era of cloud computing, due to the needs for elasticity and on-demand usage by various applications. These challenges from cloud applications present new opportunities for cloud-native databases that cannot be fully addressed by traditional on-premise enterprise database systems. A cloud-native database leverages software-hardware co-design to explore accelerations offered by new hardware such as RDMA, NVM, kernel bypassing protocols such as DPDK. Meanwhile, new design architectures, such as shared storage, enable a cloud-native database to decouple computation from storage and provide excellent elasticity. For highly concurrent workloads that require horizontal scalability, a cloud-native database can leverage a shared-nothing layer to provide distributed query and transaction processing. Applications also require cloud-native databases to offer high availability through distributed consensus protocols. At Alibaba, we have explored a suite of technologies to design cloud-native database systems. Our storage engine, X-Engine and PolarFS, improves both write and read throughputs by using a LSM-tree design and self-adapted separation of hot and cold data records. Based on these efforts, we have designed and implemented POLARDB and its distributed version POLARDB-X, which has successfully supported the extreme transaction workloads during the 2018 Global Shopping Festival on November 11, 2018, and achieved commercial success on Alibaba Cloud. We have also designed an OLAP system called AnalyticDB (ADB in short) for enabling real-time interactive data analytics for big data. We have explored a self-driving database platform to achieve autoscaling and intelligent database management. We will report key technologies and lessons learned to highlight the technical challenges and opportunities for cloud-native database systems at Alibaba.

In big data analytics, advanced analytic techniques operate on big datasets aimed at complementing the role of traditional OLAP for decision making. To enable companies to take benefit of these techniques despite the lack of in-house technical skills, the H2020 TOREADOR Project adopts a model-driven architecture for streamlining analysis processes, from data preparation to their visualization. In this article, we propose a new approach named SkyViz focused on the visualization area, in particular on (1) how to specify the user’s objectives and describe the dataset to be visualized, (2) how to translate this specification into a platform-independent visualization type, and (3) how to concretely implement this visualization type on the target execution platform. To support step (1), we define a visualization context based on seven prioritizable coordinates for assessing the user’s objectives and conceptually describing the data to be visualized. To automate step (2), we propose a skyline-based technique that translates a visualization context into a set of most suitable visualization types. Finally, to automate step (3), we propose a skyline-based technique that, with reference to a specific platform, finds the best bindings between the columns of the dataset and the graphical coordinates used by the visualization type chosen by the user. SkyViz can be transparently extended to include more visualization types on one hand, more visualization coordinates on the other. The article is completed by an evaluation of SkyViz based on a case study excerpted from the pilot applications of the TOREADOR Project.

PT. X is one of the best airlines in Indonesia. The business intelligence system can participate as a tool to provide accurate and useful information for decision makers within the time limit that is determined to support decision making in the company's E-service services. The purpose of this study is to identify the factors that are attributes of E-Service services at PT. X. and proposed the adoption of the Business Intelligence system model for PT.X airline E-service services as a proposed service improvement. The data used are customer comment data on the Jakarta route to Singapore and Singapore to Jakarta for the period January to December 2015. The research method used is a combination to develop business intelligence research is the Pareto Diagram, Unified Modeling Language (UML), Naïve Bayes Data Mining algorithm, On Line Analytical Processing (OLAP), Extract, Transform, Loading (ETL), and Data Warehousing. From the results of data processing that has been done, it can be seen the factors attributes of E-service services are case origin, comment type, flight number, root case, and unit to charge. From the results of the calculation of the second stage of Naïve Bayes data mining, it is obtained that the greatest probability of the highest probability on the Jakarta route to Singapore is the prior probability between the customer care classification class and the suggestion form with a prior probability value of 0.92, between the inflight service classification class and the customer care priority value. equal to 1, class classification comment type compliment and customer care with prior values probability of 0.76. The prior probability of the greatest probability on the Singapore route to Jakarta is the prior probability between the customer care classification class and the suggestion form with the prior probability value of 0.92, between the inflight service classification class and the customer care with a probability prior value of 1, and between class classifications comment type compliment and customer care with a prior value of probability of 0.78. Based on the results of the largest posterior calculations, the proposed improvements were prioritized more on divisions or units to charge.

The accessibility of devices that track the positions of moving objects has attracted many researchers in Mobility Online Analytical Processing (Mobility OLAP). Mobility OLAP makes use of trajectory data warehousing techniques, which typically include a path of moving objects at a particular point in time. The Semantic Web (SW) users have published a large number of moving object datasets that include spatial and non-spatial data. These data are available as open data and require advanced analysis to aid in decision making. However, current SW technologies support advanced analysis only for multidimensional data warehouses and Online Analytical Processing (OLAP) over static spatial and non-spatial SW data. The existing technology does not support the modeling of moving object facts, the creation of basic mobility analytical queries, or the definition of fundamental operators and functions for moving object types. This article introduces the QB4MobOLAP vocabulary, which enables the analysis of mobility data stored in RDF cubes. This article defines Mobility OLAP operators and SPARQL user-defined functions. As a result, QB4MobOLAP vocabulary and the Mobility OLAP operators are evaluated by applying them to a practical use case of transportation analysis involving 8826 triples consisting of approximately 7000 fact triples. Each triple contains nearly 1000 temporal data points (equivalent to 7 million records in conventional databases). The execution of six pertinent spatiotemporal analytics query samples results in a practical, simple model with expressive performance for the enabling of executive decisions on transportation analysis.

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

Hybrid storage systems are widely used in big data fields to balance system performance and cost. However, due to a poor understanding of the characteristics of database block requests, past studies in this area cannot fully utilize the performance gain from emerging storage devices. This study presents a hybrid storage database system, called HeuristicDB, which uses an emerging non-volatile memory (NVM) block device as an extension of the database buffer pool. To consider the unique performance behaviors of NVM block devices and the block-level characteristics of database requests, a set of heuristic rules that associate database (block) requests with the appropriate quality of service for the purpose of caching priority are proposed. Using online analytical processing (OLAP) and online transactional processing (OLTP) benchmarks, both trace-based examination and system implementation on MySQL are carried out to evaluate the effectiveness of the proposed design. The experimental results indicate that HeuristicDB provides up to 75% higher performance and migrates 18X fewer data between storage and the NVM block device than existing systems.

Modern database systems for online analytical processing (OLAP) typically rely on in-memory processing. Keeping all active data in DRAM severely limits the data capacity and makes larger deployments much more expensive than disk-based alternatives. Byte-addressable persistent memory (PMEM) is an emerging storage technology that bridges the gap between slow-but-cheap SSDs and fast-but-expensive DRAM. Thus, research and industry have identified it as a promising alternative to pure in-memory data warehouses. However, recent work shows that PMEM's performance is strongly dependent on access patterns and does not always yield good results when simply treated like DRAM. To characterize PMEM's behavior in OLAP workloads, we systematically evaluate PMEM on a large, multi-socket server commonly used for OLAP workloads. Our evaluation shows that PMEM can be treated like DRAM for most read access but must be used differently when writing. To support our findings, we run the Star Schema Benchmark on PMEM and DRAM. We show that PMEM is suitable for large, read-heavy OLAP workloads with an average query runtime slowdown of 1.66x compared to DRAM. Following our evaluation, we present 7 best practices on how to maximize PMEM's bandwidth utilization in future system designs.

Developers often prefer flexibility over upfront schema design, making semi-structured data formats such as JSON increasingly popular. Large amounts of JSON data are therefore stored and analyzed by relational database systems. In existing systems, however, JSON's lack of a fixed schema results in slow analytics. In this paper, we present JSON tiles, which, without losing the flexibility of JSON, enables relational systems to perform analytics on JSON data at native speed. JSON tiles automatically detects the most important keys and extracts them transparently - often achieving scan performance similar to columnar storage. At the same time, JSON tiles is capable of handling heterogeneous and changing data. Furthermore, we automatically collect statistics that enable the query optimizer to find good execution plans. Our experimental evaluation compares against state-of-the-art systems and research proposals and shows that our approach is both robust and efficient.

Geospatial information plays an important role in environmental modelling, resource management, business operations, and government policy. However, very little or no commonality between formats of various geospatial data has led to difficulties in utilizing the available geospatial information. These disparate data sources must be aggregated before further extraction and analysis may be performed. The objective of this paper is to develop a framework called PlaniSphere, which aggregates various geospatial datasets, synthesizes raw data, and allows for third party customizations of the software. PlaniSphere uses NASA World Wind to access remote data and map servers using Web Map Service (WMS) as the underlying protocol that supports service-oriented architecture (SOA). The results show that PlaniSphere can aggregate and parses files that reside in local storage and conforms to the following formats: GeoTIFF, ESRI shape files, and KML. Spatial data retrieved using WMS from the Internet can create geospatial data sets (map data) from multiple sources, regardless of who the data providers are. The plug-in function of this framework can be expanded for wider uses, such as aggregating and fusing geospatial data from different data sources, by providing customizations to serve future uses, which the capacity of the commercial ESRI ArcGIS software is limited to add libraries and tools due to its closed-source architectures and proprietary data structures. Analysis and increasing availability of geo-referenced data may provide an effective way to manage spatial information by using large-scale storage, multidimensional data management, and Online Analytical Processing (OLAP) capabilities in one system.

A database management system is a constant application of science that provides a platform for the creation, movement, and use of voluminous data. The area has witnessed a series of developments and technological advancements from its conventional structured database to the recent buzzword, bigdata. This paper aims to provide a complete model of a relational database that is still being widely used because of its well known ACID properties namely, atomicity, consistency, integrity and durability. Specifically, the objective of this paper is to highlight the adoption of relational model approaches by bigdata techniques. Towards addressing the reason for this in corporation, this paper qualitatively studied the advancements done over a while on the relational data model. First, the variations in the data storage layout are illustrated based on the needs of the application. Second, quick data retrieval techniques like indexing, query processing and concurrency control methods are revealed. The paper provides vital insights to appraise the efficiency of the structured database in the unstructured environment, particularly when both consistency and scalability become an issue in the working of the hybrid transactional and analytical database management system.

Research projects will often use the latest hardware to achieve orders-of-magnitude performance improvements while ignoring the (usually hefty) associated price tag. Real-world deployments typically follow suit, requiring expensive computing infrastructures that cost even more to power and cool.In this paper, we challenge the conventional wisdom that high-end hardware is absolutely necessary for state-of-the-art performance and instead advocate for a radically different approach based on cheap single-board computers (SBCs). While others have previously explored similar ideas for computationally simple and easily partitionable use cases (e.g., key-value stores), so-called "wimpy" nodes have traditionally been rejected as unsuitable for more complex workloads. We believe, however, that recent hardware advancements driven by the mobile computing market call this orthodoxy into question. For example, our microbenchmarks show that one popular SBC, the Raspberry Pi 3B+, offers single-core compute performance that is surprisingly competitive with many server-grade Intel Xeon and ARM-based CPUs at a fraction of the cost and energy consumption.To make our case, we conducted an extensive experimental study, beginning with a series of microbenchmarks to identify the strengths and weaknesses of SBCs relative to server-grade CPUs. Then, to evaluate the ability of SBCs to handle more complex use cases, we analyzed the performance of an in-memory OLAP workload in both single-node and distributed settings. Overall, our results demonstrate up to several orders of magnitude in cost reductions coupled with substantial energy savings when compared to traditional on-premises and cloud deployments, all without a significant increase in absolute runtimes.