A Semantic Approach for News Recommendation

News items play an increasingly important role in the current business decision processes. Due to the large amount of news published every day it is difficult to find the new items of one’s interest. One solution to this problem is based on employing recommender systems. Traditionally, these recommenders use term extraction methods like TF-IDF combined with the cosine similarity measure. In this chapter, we explore semantic approaches for recommending news items by employing several semantic similarity measures. We have used existing semantic similarities as well as proposed new solutions for computing semantic similarities. Both traditional and semantic recommender approaches, some new, have been implemented in Athena, an extension of the Hermes news personalization framework. Based on the performed evaluation, we conclude that semantic recommender systems in general outperform traditional recommenders systems with respect to accuracy, precision, and recall, and that the new semantic recommenders have a better F-measure than existing semantic recommenders.

A Data Mining Service to Assist Instructors Involved in Virtual Education

In this chapter we present a BI application delivered as a service on-demand. In particular, it is a data mining service that aims to help instructors involved in distance education to discover their students’ behavior profiles and models about how they navigate and work in their virtual courses offered in Learning Content Management Systems such as Blackboard or Moodle. The main characteristic is that the users do not require data mining knowledge to use the service; they only have to send a data file according to one of the templates provided by the system and request the results. The service carries out the KDD process itself. Furthermore, the service provides an interface based on Web services, which can be called by external software. In short, the chapter talks about the necessity of a service with these characteristics and includes the description of its architecture and its method of operation as well as a discussion about some of the patterns it offers and how these provide instructors valuable knowledge to make decisions.

An Extraction, Transformation, and Loading Tool Applied to a Fuzzy Data Mining System

In this chapter, we address the problem of integrating semantically heterogeneous data (including data expressed in natural language), which are collected from various questionnaires published in different websites, into a Data Warehouse. We present an extension of the sentences and architecture of data mining Fuzzy Structured Query Language as an extraction, transformation, and loading tool to integrate semantically heterogeneous data from these websites. Moreover, we show a case study using the questionnaires (carried out during several years) about the courses on Information and Communication Technologies which are taught in the Business Studies implanted at the University of Granada (Spain). With this integrated information, the Data Warehouse user can make several analyses with the benefit of an easy linguistic interpretability. The solution proposed here can be used to similar integration problems.

OpAL

The past years have marked the birth and development of the Social Web, where people freely express and search for opinions on all possible topics. This phenomenon has been proven to have a great impact on many business sectors globally. Given the proven importance of the subjective data on the Web, but bearing in mind the difficulties inherent to their textual peculiarities and large volume, efficient techniques must be employed to process this data, so that it can be fully exploited to the benefit of potential users and companies. We present the OpAL system, which implements an efficient approach to mine, classify and statistically summarize opinions, grounded on the feature-based Opinion Mining paradigm. In this approach, all components are studied, implemented and optimized using different NLP techniques. Results of different in-house and competition evaluations show that the system components have a good performance and that the techniques considered are efficient. We finally complete the proposed approach by presenting a method for opinion retrieval, which is robust and multilingual. Thus, we offer an integrated solution to build a system that is able to fully respond to user needs, from the querying to the summarized output stage. Implemented at a large scale, such systems can benefit the business environment and its customers everywhere.

Towards Ad-Hoc and Collaborative Business Intelligence

The success of organizations and business networks depends on fast and well-founded decisions taken by the relevant people in their specific area of responsibility. To enable timely and well-founded decisions, it is often necessary to perform ad-hoc analyses in a collaborative manner involving domain experts, line-of-business managers, key suppliers, or customers. Current Business Intelligence (BI) solutions fail to meet the challenges of ad-hoc and collaborative decision support, thus slowing down and hurting organizations. To move towards ad-hoc and collaborative BI, we envision a highly scalable and flexible BI platform. The main building blocks of this platform are a flexible and efficient concept for the management of business context information, an intuitive and powerful methodology for the configuration of a BI system, a concept of an information self-service for business users over data sources within and across organizations, a collaborative decision making environment, and an architecture for the whole system that complements current BI systems.

A Unified Approach for Taxonomy-Based Technology Forecasting

For decision makers and researchers working in a technical domain, understanding the state of their area of interest is of the highest importance. For this reason, we consider in this chapter, a novel framework for Web-based technology forecasting using bibliometrics (i.e. the analysis of information from trends and patterns of scientific publications). The proposed framework consists of a few conceptual stages based on a data acquisition process from bibliographic online repositories: extraction of domain-relevant keywords, the generation of taxonomy of the research field of interests and the development of early growth indicators which helps to find interesting technologies in their first phase of development. To provide a concrete application domain for developing and testing our tools, we conducted a case study in the field of renewable energy and in particular one of its subfields: Waste-to-Energy (W2E). The results on this particular research domain confirm the benefit of our approach.

BIN

Cooperation is seen by companies as one of the major means for increasing flexibility and innovating. Business intelligence (BI) platforms are aimed at serving individual companies, and they cannot operate over networks of companies characterized by an organizational, lexical, and semantic heterogeneity. In this chapter we propose a framework, called Business Intelligence Network (BIN), for sharing BI functionalities over complex networks of companies that are chasing mutual advantages through the sharing of strategic information. A BIN is based on a network of peers, one for each company participating in the consortium. Peers are equipped with independent BI platforms that expose some querying functionalities aimed at sharing business information for the decision-making process. After proposing an architecture for a BIN, we outline the main research issues involved in its building and operating, and we focus on the definition of an ad hoc language for expressing semantic mappings between the multidimensional schemata owned by the different peers, aimed at enabling query reformulation over the network.

Business Intelligence-as-a-Service

In recent years, the data warehousing infrastructures have undergone many changes in various aspects. This is usually due to many factors: the emergence of Software-as-a-Service (SaaS) architecture model; the success of agile and iterative Data Warehouse (DW) development approaches; the introduction of new approaches based on the Model Driven Architecture (MDA); the changing needs of organizations and the extension of the DW into new application areas; and the evolving of standards and open-source technologies. This chapter explores several aspects that may influence the next-generation of data warehousing platforms: the architectural aspects for business intelligence-as-a-service deployment, the promising open industry standards and technologies recommended for use, and the emerging methodological aspects for DW components engineering.

Interaction Mining

In this chapter we present the major challenges of a new trend in business analytics, namely Interaction Mining. With the proliferation of unstructured data as the result of people interacting with each other using digital networked devices, classical methods in text business analytics are no longer effective. We identified the causes of their failure as being related to the inadequacy of dealing with conversational data. We propose then to move from Text Mining towards Interaction Mining, and we make several cases for this transition in areas such as marketing research, social media analytics, and customer relationship management. We also propose a roadmap for the future development of Interaction Mining by challenging the current practices in business intelligence and information visualization.

Incorporating Text OLAP in Business Intelligence

As the amount of data grows very fast inside and outside of an enterprise, it is getting important to seamlessly analyze both data types for total business intelligence. The data can be classified into two categories: structured and unstructured. For getting total business intelligence, it is important to seamlessly analyze both of them. Especially, as most of business data are unstructured text documents, including the Web pages in Internet, we need a Text OLAP solution to perform multidimensional analysis of text documents in the same way as structured relational data. We first survey the representative works selected for demonstrating how the technologies of text mining and information retrieval can be applied for multidimensional analysis of text documents, because they are major technologies handling text data. And then, we survey the representative works selected for demonstrating how we can associate and consolidate both unstructured text documents and structured relation data for obtaining total business intelligence. Finally, we present a future business intelligence platform architecture as well as related research topics. We expect the proposed total heterogeneous business intelligence architecture, which integrates information retrieval, text mining, and information extraction technologies all together, including relational OLAP technologies, would make a better platform toward total business intelligence.