DATA WAREHOUSE AND BUSINESS INTELLIGENCE STRATEGIES AND TRENDS

In recent decades following the evolution of information technology, decision support systems have played an important role by presenting the necessary information resulted from the operational systems processes. By continuing improvement of the methods as well as the contribution of technological advance the applicability of decision support systems is now generalized and has reached the status of complex systems of business intelligence. Business Intelligence is about creating intelligence about a business based on a cyclic flow which consists of capturing, analyzing, planning and implementation resulting in streamlining the organization.Decisions, DSS, Data Driven, Business Intelligence, Data Warehouse

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

The Business Intelligence and the Better Decisions in Management

The paper intends to present more reasons in the favour of promoting the business intelligence (BI) in the managerial practice. Business intelligence is an umbrella term that encompasses the processes, tools, and technologies required to turn data into information, and information into knowledge and plans that drive effective business activity. BI encompasses data warehousing technologies and processes on the back end, and query, reporting, analysis, and information delivery tools (that is, BI tools) and processes on the front end. Organizations need to increase their focus on decision-making. In particular, they need to think again about the relationship between information and decision-making. Business intelligence and its predecessor concepts decision support, executive information systems, have been circulating for several decades in business.business intelligence, decisions, management.

Executive Information Systems' Multidimensional Models

Executive Information Systems are design to improve the quality of strategic level of management in organization through a new type of technology and several techniques for extracting, transforming, processing, integrating and presenting data in such a way that the organizational knowledge filters can easily associate with this data and turn it into information for the organization. These technologies are known as Business Intelligence Tools. But in order to build analytic reports for Executive Information Systems (EIS) in an organization we need to design a multidimensional model based on the business model from the organization. This paper presents some multidimensional models that can be used in EIS development and propose a new model that is suitable for strategic business requests.Executive Information Systems (EIS), Decision Support Systems (DSS), multidimensional models, Business Intelligence tools, On-Line Analytical Processing (OLAP)

The necessities for building a model to evaluate Business Intelligence projects- Literature Review

In recent years Business Intelligence (BI) systems have consistently been rated as one of the highest priorities of Information Systems (IS) and business leaders. BI allows firms to apply information for supporting their processes and decisions by combining its capabilities in both of organizational and technical issues. Many of companies are being spent a significant portion of its IT budgets on business intelligence and related technology. Evaluation of BI readiness is vital because it serves two important goals. First, it shows gaps areas where company is not ready to proceed with its BI efforts. By identifying BI readiness gaps, we can avoid wasting time and resources. Second, the evaluation guides us what we need to close the gaps and implement BI with a high probability of success. This paper proposes to present an overview of BI and necessities for evaluation of readiness. Key words: Business intelligence, Evaluation, Success, ReadinessComment: International Journal of Computer Science & Engineering Survey (IJCSES) Vol.3, No.2, April 201

Artificial Intelligence in the Context of Human Consciousness

Artificial intelligence (AI) can be defined as the ability of a machine to learn and make decisions based on acquired information. AI’s development has incited rampant public speculation regarding the singularity theory: a futuristic phase in which intelligent machines are capable of creating increasingly intelligent systems. Its implications, combined with the close relationship between humanity and their machines, make achieving understanding both natural and artificial intelligence imperative. Researchers are continuing to discover natural processes responsible for essential human skills like decision-making, understanding language, and performing multiple processes simultaneously. Artificial intelligence attempts to simulate these functions through techniques like artificial neural networks, Markov Decision Processes, Human Language Technology, and Multi-Agent Systems, which rely upon a combination of mathematical models and hardware

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

Knowledge Representation and WordNets

Knowledge itself is a representation of “real facts”. Knowledge is a logical model that presents facts from “the real world” witch can be expressed in a formal language. Representation means the construction of a model of some part of reality. Knowledge representation is contingent to both cognitive science and artificial intelligence. In cognitive science it expresses the way people store and process the information. In the AI field the goal is to store knowledge in such way that permits intelligent programs to represent information as nearly as possible to human intelligence. Knowledge Representation is referred to the formal representation of knowledge intended to be processed and stored by computers and to draw conclusions from this knowledge. Examples of applications are expert systems, machine translation systems, computer-aided maintenance systems and information retrieval systems (including database front-ends).knowledge, representation, ai models, databases, cams