Online Analytical Processing Technique in Personalizing Student Academic Pattern Behavior for UniSZA Students’ Results

In the context of scientometrics and bibliometrics, several research fields are dealing with bibliographic data. In this paper, we will explore how the combination of online analytical processing (OLAP) analysis and information networks could be an interesting issue. In Business Intelligence, OLAP is a technology supported by data warehousing systems. It provides tools for analyzing data according to multiple dimensions and multiple hierarchical levels. At the same time, several information networks (co-authors network, citations network, institutions network, etc.) can be built based on bibliographic databases. Originally, OLAP was introduced to analyze structured data. However, in this paper, we wonder if, by combining OLAP and information networks, we can provide a new way of analyzing bibliographic data. OLAP should be able to handle information networks and be also useful for monitoring, browsing and analyzing the content and the structure of bibliographic networks. The goal of this survey paper is to review previous work on OLAP and information networks dealing with bibliographic data. We also propose a comparison between traditional OLAP and OLAP on information networks and discuss the challenges OLAP faces regarding bibliographic networks.

SummaryThe Model‐Driven Architecture (MDA) is an approach that aligns modeling and automation for software development. By applying such an approach to data warehouse (DW) projects, we can minimize a great deal of time and cost. Furthermore, most of OnLine Analytical Processing (OLAP) platforms seem to be like black boxes that provide wizards only to business intelligence developers to create and manipulate OLAP objects without allowing their sustainability and migration from a platform to another. That is why many works in the literature have proposed using the MDA approach in DW projects. However, most of them have mainly focused on the generation of the DW relational model from the conceptual one, and they overlooked the OLAP model and the cube implementation.To deal with this problem, we propose in this paper an MDA solution to automate the process of getting OLAP cube and its implementation through a set of metamodels and automatic transformations among them. In fact, the proposal generates the OLAP and DW relational models (PSMs) from the conceptual one, using also a PDM model that describes the target business intelligence platform. After that, the source code to create the cube is got from both PSM models. For this aim, we define a set of transformation rules implemented using the Atlas transformation language. Finally, a case study will be provided to validate our approach.

The industrial use of open source Business Intelligence (BI) tools is becoming more common, but is still not as widespread as for other types of software. It is therefore of interest to explore which possibilities are available for open source BI and compare the tools. In this survey article, we consider the capabilities of a number of open source tools for BI. In the article, we consider a number of Extract-Transform-Load (ETL) tools, database management systems (DBMSs), On-Line Analytical Processing (OLAP) servers, and OLAP clients. We find that, unlike the situation a few years ago, there now exist mature and powerful tools in all these categories. However, the functionality still falls somewhat short of that found in commercial tools.

Business intelligence encompasses all activities dealing with collecting, storing/managing, and analyzing business-relevant data with the objective of generating knowledge as input to decision support. Business intelligence is often used as an umbrella term for data warehousing, reporting and OLAP (online analytical processing), MIS/DSS, and data mining, respectively.If we count all topics listed above, it is obvious that business intelligence has quite a long history also in the tourism domain. As early examples in tourism, we can identify the DINAMO system introduced by American Airlines already in 1988 or TourMIS in 1998.The widespread use of ICT, especially the uptake of the Internet and social media, led to an increase of available data on customers, competitors, and the whole market in all major business domains, including tourism. More powerful hardware and sophisticated methods to store and analyze such data turned business intelligence into one of the fastest-growing technologies and most challenging areas in the last decade.This chapter gives an overview on the topic of business intelligence and all technical components of a BI architecture (i.e., information extraction and transformation, data warehousing, and different mechanisms and tools to access and analyze data, like reporting or OLAP tools, dashboards, or data mining toolsets). Moreover, the chapter looks at the history of BI in tourism and presents and discusses typical application scenarios in tourism. Finally, we look at current trends and latest developments in the area of business intelligence and their expected implications for the tourism domain.KeywordsBusiness intelligenceData warehousingData analysisOnline analytical processingManagement information systemsDecision support systems

Online Analytical Processing (OLAP) comprises tools and algorithms that allow querying multidimensional databases. It is based on the multidimensional model, where data can be seen as a cube such that each cell contains one or more measures that can be aggregated along dimensions. In a Big Data scenario, traditional data warehousing and OLAP operations are clearly not sufficient to address current data analysis requirements, for example, social network analysis. Furthermore, OLAP operations and models can expand the possibilities of graph analysis beyond the traditional graph-based computation. Nevertheless, there is not much work on the problem of taking OLAP analysis to the graph data model. This paper proposes a formal multidimensional model for graph analysis, that considers the basic graph data, and also background information in the form of dimension hierarchies. The graphs in this model are node- and edge-labelled directed multi-hypergraphs, called graphoids, which can be defined at several different levels of granularity using the dimensions associated with them. Operations analogous to the ones used in typical OLAP over cubes are defined over graphoids. The paper presents a formal definition of the graphoid model for OLAP, proves that the typical OLAP operations on cubes can be expressed over the graphoid model, and shows that the classic data cube model is a particular case of the graphoid data model. Finally, a case study supports the claim that, for many kinds of OLAP-like analysis on graphs, the graphoid model works better than the typical relational OLAP alternative, and for the classic OLAP queries, it remains competitive.

In this world, each and every activity is enriched with lot of information. Business and other organization needs information for better decision making. Business Intelligence is a set of methods, process and technologies that transform raw data into meaningful and useful information. Some of the functions of business intelligence technologies are reporting, Online Analytical Processing, Online Transaction processing, data mining, process mining, complex event processing, business performance management, benchmarking and text mining. The applications of business intelligence includes E-commerce recommender system, approval of bank loan, credit/debit card fraud detection etc., In order to obtain business intelligence from large dataset there many techniques are available in data mining such as characterization, discrimination, frequent itemset mining, outlier analysis, cluster analysis and so on. In this proposed algorithm frequent itemset mining and clustering algorithm is used to extract the information from the dataset in order to make the decision making process more efficient and to improve the business intelligence.

Kebutuhan pokok pangan mencakup berbagai jenis makanan yang esensial bagi kesehatan dan keberlangsungan hidup individu dan populasi. Pangan pokok menjadi dasar dari pola makan yang sehat dan memadai bagi masyarakat, dan akses yang cukup terhadap beragam jenis pangan ini menjadi kunci untuk mendukung ketahanan pangan dan kesejahteraan masyarakat secara keseluruhan. Dengan populasi yang besar, Indonesia membutuhkan kemampuan untuk mandiri dalam sektor industri pangan. Bagaimana penggunaan Business Intelligence untuk memantau perubahan harga pangan di provinsi DKI Jakarta sebagai langkah pencegahan terhadap potensi kenaikan harga menjadi permasalah pada penelitian ini. Tujuan penelitian yaitu menerapkan Business Intelligence yang mampu menganalisis data dan menyajikan informasi harga komoditas pangan. Metode penelitian melibatkan analisis kebutuhan, perancangan data warehouse, implementasi Online Analytical Processing (OLAP), serta penggambaran data secara visual. Hasil penelitian menunjukkan bahwa Business Intelligence dimanfaatkan untuk mengawasi fluktuasi harga pangan sebagai langkah pencegahan terhadap potensi kenaikan harga. Business Intelligence memiliki kapabilitas untuk menganalisis dan menyajikan informasi tentang harga rata-rata, harga terendah, serta harga tertinggi berdasarkan berbagai dimensi seperti jenis komoditas, lokasi kota, dan periode waktu.

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.

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Huge amount of data produced in business can be processed to get useful information using B usiness intelligence (BI). Business intelligence comprises the strategies and technologies used by enterprises for the data analysis of business information. BI technologies provide historical, current and predictive views of business operations. Mobile business intelligence (MBI) is the ability to access BI-related data such as Key Performance Indicators (KPIs), business metrics, and dashboards on mobile devices. The MBI provides users with necessary information and content, regardless of their location and time, in order to gain business insights through information analysis using applications optimized for mobile devices. Although in recent years, the business intelligence and analytics has gained an increasing amount of interest among researchers and practitioners, there is a little academic literature available on MBI. This survey is intended to study the adoption of Business Intelligence (BI) with special emphasis on mobile business intelligence in various fields during past 10 years and highlights the challenges faced by users in its implementation. The study also emphasized on relationship between MBI and big data analytics, application of MBI, and difficulties in using the MBI tools. Keywords : Mobile business intelligence, business intelligence, big data, analytic

A company, majorly company that active in commercial (profit orientation) need to analyze their sales performance. By analyzing sales performance, company can increase their sales performance. One of method to analyze sales performance is by collecting historical data that relates to sales and then process that data so that produce information that show company sales performance. A data warehouse is a set of data that has characteristic subject oriented, time variant, integrated, and nonvolatile that help company management in processing of decision making. Design of data warehouse is started from collecting data that relate to sales such as product, customer, sales area, sales transaction, etc. After collecting the data, next is data extraction and transformation. Data extraction is a process f or selecting data that will be loaded into data warehouse. Data transformation is making some change to the data afte r extracted to be more consistent. After transformation processing, data are loaded into data warehouse. Data in data warehouse is processed by OLAP (On Line Analytical Processing) to produce information. Information that are produced from data processing by OLAP are chart and query reporting. Chart reporting are sales chart based on cement type, sales chart based on sales area, sales chart based on plant, monthly and year ly sales chart, and chart based on customer feedback. Query reporting are sales based on cement type, sales area, plant and customer. Keywords: Data warehouse; OLAP; Sales performance analysis; Ready mix market

The Business Intelligence technology, which represents a strong tool not only for decision making support, but also has a big potential in other fields of application, is discussed in this paper. Necessary fundamental definitions are offered and explained to better understand the basic principles and the role of this technology for company management. Article is logically divided into five main parts. In the first part, there is the definition of the technology and the list of main advantages. In the second part, an overview of the system architecture with the brief description of separate building blocks is presented. Also, the hierarchical nature of the system architecture is shown. The technology life cycle consisting of four steps, which are mutually interconnected into a ring, is described in the third part. In the fourth part, analytical methods incorporated in the online analytical processing and data mining used within the business intelligence as well as the related data mining methodologies are summarised. Also, some typical applications of the above-mentioned particular methods are introduced. In the final part, a proposal of the knowledge discovery system for hierarchical process control is outlined. The focus of this paper is to provide a comprehensive view and to familiarize the reader with the Business Intelligence technology and its utilisation.

The economic growth and competitiveness of countries is strongly influenced by their national research and development (R&D) systems. Hence, national R&D managers must make appropriate decisions to develop their R&D systems. For this purpose, an integrated view on the dimensions of national R&D systems is much needed. Due to the distribution of relevant data sources for national, R&D indicators and the complexity of national R&D systems, establishing an integrated view is challenging. In this article, we propose a business intelligence architecture to monitor, analyze, and forecast national, R&D indicators. Data for these indicators are integrated from heterogeneous data sources. In a case study focusing on Iranian R&D indicators, a prototype system is designed and implemented to evaluate its effectiveness in practice. After monitoring the indicators using online analytical processing, trends for the indicators across 11 years are forecasted using time series analysis models. The prototype system provides an integrated view of the Iranian R&D system, so that national R&D managers can use the developed system for monitoring and trend analysis of Iranian R&D indicators. As a result of the analysis of these trends, implications and recommendations for the future of the Iranian R&D system are presented.

Business intelligence (BI) is a compilation of business decision-support technologies that help skilled professionals such as supervisors, managers, and experts make real-time decisions. The number of products and services that the industry has supplied and the use of these technologies have witnessed a significant decreasing the acquisition and storage of vast quantities of data from various sources, which has driven this increase. This paper proposes big data querying and correlative analytical techniques (BDQCA) to evaluate complex and interactive queries where typical query processing systems cannot handle big data queries. The MapReduce paradigm is built to analyze and search web documents from query logs in business analysis. The online Analytic processing(OLAP) framework is introduced to promote operations for multiple database perspectives, such as filtering, aggregation, roll-up, and drill-down. Therefore, this research identifies different big data strategies for a business and provides several corporate business continuity factors that influence strategic decisions. The simulation evaluations are performed based on the robustness of the proposed framework, the performance ratio of 97.1%, and the efficiency ratio of 95.5%.

With the ever more rapid development of information technology, computer and network infrastructures have become an indispensable part of modern life and work. In this evolving context, the importance of computer-assisted information analysis is increasingly evident, highlighting the need for a solid framework to support its further development. This paper attempts to establish a comprehensive, multi-dimensional technological framework to improve capacity in this crucial area. The proposed framework covers core technology areas such as databases, data warehouses, online transaction processing (OLTP), business intelligence (BI), online analytical processing (OLAP) and decision support systems (ss). By combining these fundamentals with cutting-edge technologies, including data extraction, social network analysis, and massive data analysis and advanced online analytical processing, the framework aims to create a holistic system for computer-aided information analysis. The system aims not only to optimize the efficiency and effectiveness of data processing and analysis, but also to promote innovation in the way information is used for decision-making and strategic planning, contributing to the continued development and impact of all industries.

Design of an information volatility measure for health care decision making