Building Data Warehouses Using The Enterprise Modeling Framework

Tlds paper proposes an enterprise modeling framework for the deployment of data warehouses. The framework provides the information roadmap coordinating source data and different data warehouses across the business enterprise. The paper introduces a solution to address data warehousing issues at the enterprise level while avoiding the pitfalls of creating enterprise data warehouses and universal data marts. It further proposes a change of paradigm from point solutions focus to a methodology driven by enterprise requirements to meet the challenges of the new economy. The proposed framework emphasizes the separation of the conceptual construct from the physical and operational constructs of an enterprise. It points out the differences and dependencies of analytic and operational processes and how data warehouses and operational data stores respectively support their information requirements. This paper will demonstrate how the enterprise modeling framework for data warehousing can produce business benefits

Toward a Model Undergraduate Curriculum for the Emerging Business Intelligence and Analytics Discipline

Business intelligence (BI) combined with business analytics (BA) is an increasingly prominent strategic objective for many organizations. As a pedagogical subject, BI/BA is still in its infancy, and, in order for this to mature, we need to develop an undergraduate model BI/BA curriculum. BI/BA as an academic domain is emerging as a hybrid of disciplines, including information systems, statistics, management science, artificial intelligence, computer science, and business practice/theory. Based on IS 2010’s model curriculum constructs (Topi et al., 2010), we explore two curricular options: a BI/BA concentration in a typical IS major and a comprehensive, integrated BI/BA undergraduate major. In support, we present evidence of industry need for BI/BA, review the current state of BI/BA education, and compare anticipated requirements for BI/BA curricula with the IS 2010 model curriculum. For this initial phase of curricular design, we postulate a preliminary set of knowledge areas relevant for BI/BA pedagogy in a multi-disciplinary framework. Then we discuss avenues for integrating these knowledge areas to develop professionally prepared BI/BA specializations at the undergraduate level. We also examine implications for both AACSB and ABET accreditation and describe the next phase of applying the IS 2010 concept structure to BI/BA curriculum development

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Retail Shelf Analytics Through Image Processing and Deep Learning

The present thesis promotes an innovative approach based on modern deep learning and image processing techniques for retail shelf analytics within an actual business context. To achieve this goal, the research focused on recent developments in computer vision while maintaining a business-oriented approach. The project involved the full-stack software development of a product to analyze structured and unstructured data and provide business intelligence services for retail systems

Development of a real-time business intelligence (BI) framework based on hex-elementization of data points for accurate business decision-making

The desire to use business intelligence (BI) to enhance efficiency and effectiveness of business decisions is neither new nor revolutionary. The promise of BI is to provide the ability to capture interrelationship from data and information to guide action towards a business goal. Although BI has been around since the 1960s, businesses still cannot get competitive information in the form they want, when they want and how they want. Business decisions are already full of challenges. The challenges in business decision-making include the use of a vast amount of data, adopting new technologies, and making decisions on a real-time basis. To address these challenges, businesses spend valuable time and resources on data, technologies and business processes. Integration of data in decision-making is crucial for modern businesses. This research aims to propose and validate a framework for organic integration of data into business decision-making. This proposed framework enables efficient business decisions in real-time. The core of this research is to understand and modularise the pre-established set of data points into intelligent and granular “hex-elements” (stated simply, hex-element is a data point with six properties). These intelligent hex-elements build semi-automatic relationships using their six properties between the large volume and high-velocity data points in a dynamic, automated and integrated manner. The proposed business intelligence framework is called “Hex-Elementization” (or “Hex-E” for short). Evolution of technology presents ongoing challenges to BI. These challenges emanate from the challenging nature of the underlying new-age data characterised by large volume, high velocity and wide variety. Efficient and effective analysis of such data depends on the business context and the corresponding technical capabilities of the organisation. Technologies like Big Data, Internet of Things (IoT), Artificial Intelligence (AI) and Machine Learning (ML), play a key role in capitalising on the variety, volume and veracity of data. Extricating the “value” from data in its various forms, depth and scale require synchronizing technologies with analytics and business processes. Transforming data into useful and actionable intelligence is the discipline of data scientists. Data scientists and data analysts use sophisticated tools to crunch data into information which, in turn, are converted into intelligence. The transformation of data into information and its final consumption as actionable business intelligence is an end-to-end journey. This end-to-end transformation of data to intelligence is complex, time-consuming and resource-intensive. This research explores approaches to ease the challenges the of end-to-end transformation of data into intelligence. This research presents Hex-E as a simplified and semi-automated framework to integrate, unify, correlate and coalesce data (from diverse sources and disparate formats) into intelligence. Furthermore, this framework aims to unify data from diverse sources and disparate formats to help businesses make accurate and timely decisions

How Business Intelligence Can Influence the Delivery of Excellence in Botswana Accountancy College (BAC)

In today’s turbulent and ever changing environment, every business small or large is struggling to remain competitive and to manage the growing amount of data being generated from a number of existing (legacy) systems. Organizations have to align their business processes with their available information technology (IT) infrastructure to beat competition. In the tertiary education landscape, Botswana Accountancy College (BAC) could exploit the business-IT synergy through implementing a data warehouse strategy. Data warehousing can consolidate and unlock actionable information from the huge deposits of data lurking in the organization. Strategic decision making would be based on available accurate, subject-oriented, past and current information. With a data warehouse (DW) in place, BAC could have a unified view of its organizational performance; it is able to check on performance measures and become more agile to provide superior services to customers than would happen with any other tertiary institution at the moment. DW can support all decision making information needs for all potential end-users at strategic, tactical and operational levels. We argue that this type of business intelligence will propel BAC to become a center of higher education excellence. Results of study showed a high level of readiness for BAC to benefit from the business intelligence that could be derived from a data warehousing strategy

Transportation data InTegration and ANalytic

State transportation agencies regularly collect and store various types of data for different uses such as planning, traffic operations, design, and construction. These large datasets contain treasure troves of information that could be fused and mined, but the size and complexity of data mining require the use of advanced tools such as big data analytics, machine learning, and cluster computing. TITAN (Transportation data InTegration and ANalytics) is an initial prototype of an interactive web-based platform that demonstrates the possibilities of such big data software. The current study succeeded in showing a user-friendly front end, graphical in nature, and a scalable back end capable of integrating multiple big databases with minimal latencies. This thesis documents how the key components of TITAN were designed. Several applications, including mobility, safety, transit performance, and predictive crash analytics, are used to explore the strengths and limitations of the platform. A comparative analysis of the current TITAN platform with traditional database systems such as Oracle and Tableau is also conducted to explain who needs to use the platform and when to use which platform. As TITAN was shown to be feasible and efficient, the future research direction should aim to add more types of data and deploy TITAN in various data-driven decision-making processes.Includes bibliographical reference