Decision Support Systems

In present business competition, organizations are emphasizing on optimal decision support systems (DSS) to enhance its growth-oriented decision support system process. To meet these requirements, the enterprise solutions facilitate certain robust and productive business intelligence (BI) applications. On the other hand, to retain market, organizations are emphasizing on service innovation discovery and its redesign (SIDRD) paradigm using BI applications. To accomplish SIDRD, DSS-oriented BI applications require huge feedback datasets to process where the data could be retrieved from certain feedback channel or data warehouses (DWs). The data warehouses encompass datasets from varied resources, and to ensure optimal data extraction and retrieval, these DWs need robust data mining schemes compatible with multidimensional data model (MDDM). On the other hand, to provide most precise and accurate DSS applications, the data security and its privacy preservation are inevitable. Considering all these requirements, in this paper, a robust privacy-preserved mining model called Commutative RSA (CRSA) has been developed and implemented with C5.0 decision tree algorithm to achieve SIDRD objectives with BI utilities. The developed paradigm has exhibited optimal performance for optimized accuracy, overheads, and secure rule set generation for BI. The performance for the developed SIDRD has been analyzed in terms of coverage accuracy and F1 scores, and the overall system has performed optimal.

Decision support systems able to capitalize on publicly available high resolution datasets have become increasingly valuable to agroecosystem, hydrologic and urban system stakeholders. In this paper we address the common agroecosystem modeling problem of weather-based risk forecasting. We compare storage system designs for an expandable crop disease forecasting system that relies on multiple gridded weather forecast inputs to artificial neural network disease risk models. A traditional relational database management system (PostgreSQL), a NoSQL database system (MongoDB) and a scientific file format version (netCDF) of a single crop disease risk modeling system in one region of the country, for potato late blight in the US Great Lakes region, were designed and compared for speed. To test expandability, another crop disease risk modeling system, for modeling risk of economically significant deoxynivalenol (eDON) accumulation due to Fusarium head blight of barley in the northern US Great Plains, was also created in the three formats. Speeds for the three types of systems were fairly similar. Expandability, which is becoming highly desirable in agroecosystem model design, differed based on designer priorities.

In the recent era of data science, AI, and machine learning the amount of data has increased exponentially, and future growth is predicted to be even greater. For the last 4 decades RDBMS is way for data storage, but with the rise of cloud computing online applications have taken place of the stand-alone applications, while distributed servers have taken the place of traditional dedicated servers. Cloud computing has become very efficient because of the lower cost, ability to scale up and down, flexibility, and pay-as-you-go model. This is the major change after the birth of the Internet. Cloud computing has revolutionized the way data is stored and accessed. Cloud databases are an integral part of the cloud computing paradigm, offering scalability, flexibility, and cost-efficiency for managing large volumes of data. Cloud databases such as Amazon RDS, IBM DB2 and MongoDB are becoming popular. Cloud databases find the restrictions of older relational databases. Data storage, retrieval, modification, and analysis are essentially the functions of cloud databases. Cloud databases are mostly used by data-intensive applications including data warehousing, data mining, and business intelligence. Applications for supply chain management, online e-commerce, and transactional data management, including banking and airline reservations, also benefit from cloud storage. This research paper reviews the concept of cloud databases, exploring their features, it examines the architecture of cloud databases, the comparison between traditional Relational Database Management Systems (RDBMS) and NoSQL databases, and popular implementations in the cloud environment. Additionally, the challenges faced in their development and deployment.

The growing importance of big data has headed enterprises to advance their big data analytics capability to strengthen their firm performance. This study tests how big data capability impact on business intelligence infrastructure to achieve firm performance measures such as operational performance and marketing performance. This study is based on the recent literature on the knowledge-based view, big data capability, IT capability, and business intelligence. The primary survey of 272 responses from Chinese firms’ IT managers and big data analysts are used to uncover the relationship in the proposed model. The finding shows that the big data analytics capability significantly impacts on business intelligence infrastructure that in turn positively impact on operational performance and marketing performance. Further, the business intelligence infrastructure partially mediates between big data analytics capability and operational performance, and fully mediates between big data analytics capability and marketing performance. This research contributes to the information systems literature such as big data analytic capability, business intelligence, and firm performance measures, and thus offers grounds to extend more widespread studies in this field. This study adds to the literature on the theory and practical bases for big data capability and business intelligence infrastructure.

This paper discusses the concepts of data warehouse technology and its importance for decision support system (DSS). The DSS as one kind smart software system of the important application value, provides, each kind of decision information as well as the commercial question solution for the enterprise. Thus Data warehouse can meet the requirements of the database management subsystems of DSS, and is fitted to form its technology frame. Then we put forth the structure of data warehouse and its main functional components. Along with widespread application of the data warehouse, DSS based on data warehouse arises at the historic moment. Begin with actual demand of the DSS, characteristic and the structure of the data warehouse have been analyzed. DSS based on the data warehouse technology is elaborated, and finally give the application example.

In the era of Big Data, more and more stream data is available. In the same way, Decision Support Systems (DSS) tools, such as data warehouses and alert systems, become more and more sophisticated, and conceptual modeling tools are consequently mandatory for successfully DSS projects. Formalisms such as UML and ER have been widely used in the context of classical information and data warehouse systems, but they have not been investigated yet for stream data warehouses to deal with alert systems. Therefore, in this article, the authors introduce the notion of Active Stream Data Warehouse (ASDW) and this article proposes a UML profile for designing Active Stream Data Warehouses. Indeed, this article extends the ICSOLAP profile to take into account continuous and window OLAP queries. Moreover, this article studies the duality of the stream and OLAP decision-making process and the authors propose a set of ECA rules to automatically trigger OLAP operators. The UML profile is implemented in a new OLAP architecture, and it is validated using an environmental case study concerning the wind monitoring.

- In today’s data-intensive business environment, organizations increasingly depend on Business Intelligence (BI) tools to support evidence-based decision-making and sustain competitive advantage. This study examines how managers at Mind Box LLC perceive and utilize BI tools as Decision Support Systems (DSS) in their routine and strategic decision processes. Using a descriptive research design, primary data were collected from 100 managers across departments through a structured questionnaire, complemented by secondary data from journals and company documents. The analysis employed percentage statistics, chi-square tests, correlation, and one-way ANOVA to evaluate BI usage patterns, perceived benefits, challenges, and satisfaction levels. Results show high adoption of BI tools, with 75% of managers using them at least weekly and a majority reporting moderate to advanced BI knowledge. Managers report that BI tools improve decision quality, reduce uncertainty and risk, enable faster decisions, and contribute to competitive advantage. Key barriers include insufficient training, data quality issues, and system complexity, while no significant differences in satisfaction were found across managerial levels. The findings underscore that BI tools are effective DSS enablers when supported by robust data governance, continuous training, user-friendly design, and a data-driven culture. The study offers practical recommendations for strengthening BI adoption and maximizing its decision support value at Mind Box and similar technology-driven organizations. This study contributes to the growing literature on BI-enabled decision-making in technology-driven organizations. Keywords: Business Intelligence, Decision Support Systems, managerial perception, data-driven decision-making, Mind Box.

Data processing is crucial in the insurance industry, due to the important information that is contained in the data. Business Intelligence (BI) allows to better manage the various activities as for companies working in the insurance sector. Business Intelligence based on the Decision Support System (DSS), makes it possible to improve the efficiency of decisions and processes, by improving them to the individual characteristics of the agents. In this direction, Key Performance Indicators (KPIs) are valid tools that help insurance companies to understand the current market and to anticipate future trends. The purpose of the present paper is to discuss a case study, which was developed within the research project "DSS / BI HUMAN RESOURCES", related to the implementation of an intelligent platform for the automated management of agents' activities. The platform includes BI, DSS, and KPIs. Specifically, the platform integrates Data Mining (DM) algorithms for agent scoring, K-means algorithms for customer clustering, and a Long Short-Term Memory (LSTM) artificial neural network for the prediction of agents KPIs. The LSTM model is validated by the Artificial Records (AR) approach, which allows to feed the training dataset in data-poor situations as in many practical cases using Artificial Intelligence (AI) algorithms. Using the LSTM-AR method, an analysis of the performance of the artificial neural network is carried out by changing the number of records in the dataset. More precisely, as the number of records increases, the accuracy increases up to a value equal to 0.9987.

SummaryData Warehouses are a fundamental component of today’s Business Intelligence infrastructure. They allow to consolidate heterogeneous data from distributed data stores and transform it into strategic indicators for decision making. In this tutorial we give an overview of current state of the art and point out to next challenges in the area. In particular, this includes to cope with more complex data, both in structure and semantics, and keeping up with the demands of new application domains such as Web, financial, manufacturing, genomic, biological, life science, multimedia, spatial, and spatiotemporal applications. We review consolidated resaerch in spatio-temporal databases, and open research fields, like real-time Business Intelligence and Semantic Web Data Warehousing and OLAP.

As cloud computing developing rapidly, and Internet of Things and social network applying widely, data type and quantity are growing at an amazing speed. The era of big data has come. However, these create various challenges concerning: Capture, transport, storage, sharing, management, analysis, security, etc. How to trustfully and securely capture and transport data, is a very crucial issue for big data. In the paper, this work aims to construct a trusted big data capture and transport architecture. Firstly, the concepts of big data, trust and reputation, and wireless sensor network are discussed. Then a trusted big data capture and transport architecture using wireless sensor network is designed in detail. Moreover, some implementations of this architecture are described. Records and analysis show this trusted architecture is efficient, and can resist internal compromise attack and external attack.

The Business intelligence (BI) literature has grown rapidly during the last three decades. Yet the knowledge about its theoretical underpinnings and its links to strategy research remain elusive. This state of affairs draws from two different scientific communities (informatics and business) that have generated multiple research streams, which duplicate research, neglect each other’s contributions, and overlook the relationship between BI and strategy. In response, we structure the BI scientific landscape and map its evolution to offer scholars and managers a clear view of where we stand and the way forward. For this endeavor, we conduct a systematic review of articles published in top-tier journals and identify 90 articles covering more than 30 years of scientific research on BI. We then run a co-citation analysis of selected articles and their reference lists. This yielded the structuring of BI scholarly community around six research clusters: Environmental Scanning (ES), Competitive Intelligence (CI), Market Intelligence (MI), Decision Support Systems (DSS), Intelligence Technologies (IT), and Knowing as Practice (KAP). The co-citation network exposed overlapping and divergent theoretical roots across the six clusters and permitted to map the evolution of BI following two pendulum swings. In light of the structure and evolution of the BI research, we offer a future research agenda that links BI to strategy work. Our article, thus, contributes by 1) structuring the theoretical landscape of BI research, 2) deciphering the theoretical roots of BI literature, 3) mapping the evolution of BI scholarly community, and 4) suggesting an agenda for research conducive to strategy.

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.

The paper proposes a new approach of data warehouse minimization by fuzzy-based ETL filter for ETL processes in business intelligence (BI) systems and discusses possibilities to tune up the process. First part introduces common company systems and possible data sources in the company. Second part states the problem with interpreting information in BI systems and explains a data representation in the BI systems. Third part of the paper defines a rule base and input and output values of the expert system. Last part of the paper proposes a two ways to minimize a data - modification border of the fuzzy set and omitting useless combinations of the linguistic variables and modifiers.

The rapid development of enterprises rises an important impetus to improve business productivity and core competence. Insurance industry business intelligence systems can be used to build enterprise data warehouses, master data view and establish analysis and decision support systems which can remarkably improve the efficience of enterprise. This paper analyzes the business intelligence applications in the insurance industry. We systematically analyzes the structure of business intelligence, including the development of Business Intelligence, the requirement of Insurance industry about BI and the process of ETL and model-establishment. We use an example to illustrate in detail how to build enterprise business intelligence applications.

We are in the midst of a healthcare paradigm shift driven by the wide adoption of ubiquitous computing and various modes of information communications technologies. As a result, cities worldwide are undergoing a major process of urbanization with ever increasing wealth of sensing capabilities – hence the Internet of Things (IoT). These trends impose great pressure on how healthcare is done. This paper describes the design and implementation of a situated clinical decision support (SCDSS) system, most appropriate for smart cities. The SCDSS was prototyped and enhanced in a clinic. The SCDSS was then used in a clinic as well as in a university hospital centre. In this article, the system’s architecture, subcomponents and integrated workflow are described. The systems’ design was the result of a knowledge acquisition process involving interviews with five specialists and testing with 50 patients. The reports (specialist consultation report) generated by the SCDSS were shown to general practitioners who were not able to distinguish them from human specialist reports. We propose a context-aware CDSS and assess its effectiveness in managing a wide medical range of patients. Five different patient cases were identified for analysis. The SCDSS was used to produce draft electronic specialist consultations, which were then compared to the original specialists’ consultations. It was found that the SCDSS-generated consults were of better quality for a number of reasons discussed herein. SCDSSs have great promise for their use in the clinical environment of smart cities. Valuable insights into the integration and use of situated clinical decision support systems are highlighted and suggestions for future research are given.