Business Intelligence Applications and the Web: Models, Systems and Technologies summarizes current research advances in BI and the Web, emphasizing research solutions, techniques, and methodologies which combine both areas in the interest of building better BI solutions. This comprehensive collection aims to emphasize the interconnections that exist among the two research areas and to highlight the benefits of combined use of BI and Web practices, which so far have acted rather independently, often in cases where their joint application would have been sensible.

Business Intelligence Applications and the Web: Models, Systems and Technologies

With the rapid development of web technology, an increasing number of enterprises having been seeking for a method to facilitate business decision making process, power the bottom line, and achieve a fully coordinated organization, called business intelligence (BI). Unfortunately, traditional BI tends to be unmanageable, risky and prohibitively expensive, especially for Small and Medium Enterprises (SMEs). The emergence of cloud computing and Software as a Service (SaaS) provides a cost effective solution. Recently, business intelligence applications delivered via SaaS, termed as Business Intelligence as a Service (SaaS BI), has proved to be the next generation in BI market. However, since SaaS BI just comes in its infant stage, a general framework maybe poorly considered. Therefore, in this paper we proposed a general conceptual framework for SaaS BI, and presented several possible future directions of SaaS BI.

A Conceptual Framework for Business Intelligence as a Service (SaaS BI)

Business analysts frequently use Cockpits or Dashboards as front ends to data warehouses for inspecting and comparing multidimensional data at various levels of detail. These tools, however, perform badly in supporting a business analyst in his or her business intelligence task of understanding and evaluating a business within its environmental context through comparative data analysis. With important business knowledge either unrepresented or represented in a form not processable by automatic reasoning, the analyst is limited in the analyses that can be formulated and she or he heavily suffers from information overload with the need to re-judge similar situations again and again, and to re-discriminate between already explained and novel relationships between data. In an ongoing research project we try to overcome these limitations by applying and extending semantic technologies, such as ontologies and business rules, for comparative data analysis. The resulting Semantic Cockpit assists and guides the business analyst due to reasoning about various kinds of knowledge, explicitly represented by machine-processable ontologies, such as organisation-internal knowledge, organisation external domain knowledge, the semantics of measures and scores, knowledge about insights gained from previous analysis, and knowledge about how to act upon unusually low or high comparison scores. This paper outlines the architecture of the Semantic Cockpit and introduces its core ideas by a sample use case.

Semantic cockpit: an ontology-driven, interactive business intelligence tool for comparative data analysis

This chapter describes the convergence of two of the most influential technologies in the last decade, namely business intelligence (BI) and the Semantic Web (SW). Business intelligence is used by almost any enterprise to derive important business-critical knowledge from both internal and (increasingly) external data. When using external data, most often found on the Web, the most important issue is knowing the precise semantics of the data. Without this, the results cannot be trusted. Here, Semantic Web technologies come to the rescue, as they allow semantics ranging from very simple to very complex to be specified for any web-available resource. SW technologies do not only support capturing the “passive” semantics, but also support active inference and reasoning on the data. The chapter first presents a motivating running example, followed by an introduction to the relevant SW foundation concepts. The chapter then goes on to survey the use of SW technologies for data integration, including semantic data annotation and semantics-aware extract, transform, and load processes (ETL). Next, the chapter describes the relationship of multidimensional (MD) models and SW technologies, including the relationship between MD models and SW formalisms, and the use of advanced SW reasoning functionality on MD models. Finally, the chapter describes in detail a number of directions for future research, including SW support for intelligent BI querying, using SW technologies for providing context to data warehouses, and scalability issues. The overall conclusion is that SW technologies are very relevant for the future of BI, but that several new developments are needed to reach the full potential.

Semantic Web Technologies for Business Intelligence

Business Intelligence aims at providing decision support based on available information and tailored to the recipient‟s context due to the level of aggregation and form of delivery. The future internet will dramatically broaden both the spectrum of available information and the user‟s possible contexts. Further, we do not only expect a quantitative dart but a qualitative switch which will require the application of new methods and paradigms. This paper‟s aim is to systematically analyse implications of the future internet on Business Intelligence in order to identify possible perspectives, chances and risks for BI and vice versa by summarizing and categorizing current research.

A Business Intelligence Perspective on the Future Internet

Despite the importance of analytical tools to organizations, they still lack the inference power needed to solve the requests of decision makers in a flexible and smarter way. We present a framework called SBI -- Semantic Business Intelligence - in which we apply ontologies on the description of business rules and concepts in order to support semantic-analytical functionalities that extend traditional OLAP operations. Such approach enables developers to customize BI solutions according to organizations' specific analytical requirements and allows developers align BI solutions to the latest business analytic requirements. We present how semantic inference is supported using batch and on-the-fly based strategies. In addition, we show how such semantic infrastructure makes the access to heterogeneous data sources transparent. Finally, we illustrate the benefits of our approach through Extracta, an analytical tool that relies on SBI ontologies and modules. Extracta enables an easy access to information and provides novel exploratory funcionalities based on semantics towards faster and smarter decisions.

SBI: a semantic framework to support business intelligence

Fierce competition in the digital economy and increasing volume of available data are forcing organizations to find efficient ways to gain valuable information and knowledge to improve the efficiency of their business processes. Business Intelligence (BI) solutions offer the means to transform data to information and derive knowledge through analytical tools in order to support decision making. Analytical tools should support decision makers to find information quickly and enable them to make well-informed decisions.

/pdf/adding-semantics-to-business-intelligence-towards-a-smarter-2edhwu0xfn.pdf

Marcio Napoli

Papers

SBI: a semantic framework to support business intelligence

Adding Semantics to Business Intelligence: Towards a Smarter Generation of Analytical Tools