Using Users' Expectations to Adapt Business Intelligence Systems

This paper takes a look at the general characteristics of business or economic intelligence system. The role of the user within this type of system is emphasized. We propose two models which we consider important in order to adapt this system to the user. The first model is based on the definition of decisional problem and the second on the four cognitive phases of human learning. We also describe the application domain we are using to test these models in this type of system

Business intelligence systems and user's parameters: an application to a documents' database

This article presents earlier results of our research works in the area of modeling Business Intelligence Systems. The basic idea of this research area is presented first. We then show the necessity of including certain users' parameters in Information systems that are used in Business Intelligence systems in order to integrate a better response from such systems. We identified two main types of attributes that can be missing from a base and we showed why they needed to be included. A user model that is based on a cognitive user evolution is presented. This model when used together with a good definition of the information needs of the user (decision maker) will accelerate his decision making process

A model for Business Intelligence Systems’ Development

Often, Business Intelligence Systems (BIS) require historical data or data collected from var-ious sources. The solution is found in data warehouses, which are the main technology used to extract, transform, load and store data in the organizational Business Intelligence projects. The development cycle of a data warehouse involves lots of resources, time, high costs and above all, it is built only for some specific tasks. In this paper, we’ll present some of the aspects of the BI systems’ development such as: architecture, lifecycle, modeling techniques and finally, some evaluation criteria for the system’s performance.BIS (Business Intelligence Systems), Data Warehouses, OLAP (On-Line Analytical Processing), Object-Oriented Modeling

THE EFFECTS OF MARKET INTELLIGENCE SYSTEMS ON SALES REVENUE AMONG FRENCH BEAN PRODUCERS: A CASE STUDY OF OL-DONYO SABUK, MACHAKOS COUNTY, KENYA

Businesses operate in a world in which information is more readily and publicly available than ever before. Thanks to the development of the Internet, information on market trends, legislation, customers, suppliers, competitors, distributors, product development and almost every other conceivable topic is available at the click of a mouse. Search engines, online libraries, company websites and other sources provide information in an increasingly plentiful, easy to find, and easy to digest way. Small-scale farmers continue to sell their French beans to middlemen at throw away prices yet there are exporting companies that can buy their beans at high prices for profitability. This has been brought about by the possible missing information about the French beans marketing trends and the profitability of the crop, limited access to the necessary capital to make the switch possible, poor infrastructure necessary to bring the crops to export outlets, high risk of the export markets (for instance, from hold-up problems selling to exporters), limited human capital necessary to adopt successfully a new agricultural technology, for instance the Global Good Agricultural Practices (GlobalGAP) and Maximum Residue Levels (MRLs)requirements, and misperception by researchers and policy makers about the true profit opportunities and risk of crops grown for export markets. This study was conducted to assess the impact of market intelligence systems on sales revenue of French bean farmers in Ol-Donyo Sabuk of Machakos County, Kenya. To achieve this overall objective, three specific objectives were addressed, namely; (1) to establish the existing French beans marketing channels in Ol-Donyo Sabuk, (2) to compare the sales revenues of French bean farmers with and without market intelligence systems, and (3) to compare return on capital for different actors within the French bean value chain. Systemic random sampling was used to select 120 farmers for this study. Data were collected through administering questionnaire for personal interviews. Data analysis was carried out using descriptive statistics such as percentages, and means to answer the stated objectives. In addition, statistical package for social sciences (SPSS) was used to analyse data. The study revealed that 30 percent of the 120 sampled French bean producers had access to French bean market intelligence systems, which is a small proportions of farmers compared to those who did not have access. The results revealed that 30 percent of the 120 sampled French bean producers were selling their produce as a group and had access to market intelligence systems 70 percent of the 120 sampled French bean producers not having access to market intelligence systems thus selling their produce to brokers. The results showed that group farmers selling their product to exporters had a higher return on capital as compared to individual farmers selling their produce to middlemen. xiii Based on the results of this study, it is recommended that the government and other key players in the horticulture industry enhance extension services to French bean producers by training them on market intelligence systems and stringent EU market requirements in order to improve on sales revenues from the crop and subsequent return on capital. Further the government establishes a French beans value addition plant that will cater for all farmers in French beans production and a high return on capital will go to Kenya economy but not to foreigners who own most of the value addition plants. This will too provide employment to many. The brokers should be removed from the production chain because they misuse farmers making profits where they did not invest and exporters would be advised to improve on their mode of produce payment and produce rejection handling.There is need to do away with hawkers and brokers within the value chain by having binding contracts and steady markets. Based on the findings, policy implications were drawn for improvingthe quality of French beans immensely by farmers through complying with GlobalGAP right from land preparation to harvesting and adhering to stipulated MRLs, proper postharvest handling of the produce with thorough grading and subsequent proper storage

Conceptual Model of Business Value of Business Intelligence Systems

With advances in the business intelligence area, there is an increasing interest for the introduction of business intelligence systems into organizations. Although the opinion about business intelligence and its creation of business value is generally accepted, economic justification of investments into business intelligence systems is not always clear. Measuring the business value of business intelligence in practice is often not carried out due to the lack of measurement methods and resources. Even though the perceived benefits from business intelligence systems, in terms of better information quality or achievement of information quality improvement goals, are far from being neglected, these are only indirect business benefits or the business value of such systems. The true business value of business intelligence systems hides in improved business processes and thus in improved business performance. The aim of the paper is to propose a conceptual model to assess business value of business intelligence systems that was developed on extensive literature review, in-depth interviews, and case study analysis for researching business intelligence systems’ absorbability capabilities or key factors facilitating usage of quality information provided by such systems respectively

A unified view of data-intensive flows in business intelligence systems : a survey

Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft

Towards Robust Artificial Intelligence Systems

Adoption of deep neural networks (DNNs) into safety-critical and high-assurance systems has been hindered by the inability of DNNs to handle adversarial and out-of-distribution input. State-of-the-art DNNs misclassify adversarial input and give high confidence output for out-of-distribution input. We attempt to solve this problem by employing two approaches, first, by detecting adversarial input and, second, by developing a confidence metric that can indicate when a DNN system has reached its limits and is not performing to the desired specifications. The effectiveness of our method at detecting adversarial input is demonstrated against the popular DeepFool adversarial image generation method. On a benchmark of 50,000 randomly chosen ImageNet adversarial images generated for CaffeNet and GoogLeNet DNNs, our method can recover the correct label with 95.76% and 97.43% accuracy, respectively. The proposed attribution-based confidence (ABC) metric utilizes attributions used to explain DNN output to characterize whether an output corresponding to an input to the DNN can be trusted. The attribution based approach removes the need to store training or test data or to train an ensemble of models to obtain confidence scores. Hence, the ABC metric can be used when only the trained DNN is available during inference. We test the effectiveness of the ABC metric against both adversarial and out-of-distribution input. We experimental demonstrate that the ABC metric is high for ImageNet input and low for adversarial input generated by FGSM, PGD, DeepFool, CW, and adversarial patch methods. For a DNN trained on MNIST images, ABC metric is high for in-distribution MNIST input and low for out-of-distribution Fashion-MNIST and notMNIST input

Knowledge representation by connection matrices: A method for the on-board implementation of large expert systems

Extremely large knowledge sources and efficient knowledge access characterizing future real-life artificial intelligence applications represent crucial requirements for on-board artificial intelligence systems due to obvious computer time and storage constraints on spacecraft. A type of knowledge representation and corresponding reasoning mechanism is proposed which is particularly suited for the efficient processing of such large knowledge bases in expert systems