Business intelligence (BI) software is the code and tools that allow you to view different components of a business using a single visual platform, making comprehending mountains of data easier. Applications that include reports, analytics, statistics, and historical and predictive modeling are all examples of BI applications. Currently, we are in the second generation of BI software, called BI 2.0. This generation is focused on writing BI software that is predictive, adaptive, simple, and interactive. As computers and software have evolved, more data can be presented to end users with increasingly visually rich techniques. Rich Internet application (RIA) technologies such as Microsoft Silverlight can be used to transform traditional user interfaces filled with boring data into fully interactive analytical applications to deliver insight from large data sets quickly. Furthermore, RIAs include 3D spatial design capabilities that allow for interesting layouts of aggregated data beyond a simple list or grid. BI 2.0 implemented via RIA technology can truly bring out the power of BI and deliver it to an average user via the Web. Next-Generation Business Intelligence Software with Rich Internet Applications provides developers, designers, and architects a solid foundation of BI design and architecture concepts with Microsoft Silverlight. This book covers key BI design concepts and how they can be applied without requiring an existing BI infrastructure. The author, Bart Czernicki, will show you how to build small BI applications by example that are interactive, highly visual, statistical, predictive, and most importantly, intuitive to the user. BI isn't just for the executive branch of a Fortune 500 company; it is for the masses. Let Next-Generation Business Intelligence Software with Rich Internet Applications show you how to unlock the rich intelligence you already have.

Business intelligence (BI) software is the code and tools that allow you to view different components of a business using a single visual platform, making comprehending mountains of data easier. Applications that include reports, analytics, statistics, and historical and predictive modeling are all examples of BI applications. Currently, we are in the second generation of BI software, called BI 2.0. This generation is focused on writing BI software that is predictive, adaptive, simple, and interactive. As computers and software have evolved, more data can be presented to end users with increasingly visually rich techniques. Rich Internet application (RIA) technologies such as Microsoft Silverlight can be used to transform traditional user interfaces filled with boring data into fully interactive analytical applications to deliver insight from large data sets quickly. Furthermore, RIAs include 3D spatial design capabilities that allow for interesting layouts of aggregated data beyond a simple list or grid. BI 2.0 implemented via RIA technology can truly bring out the power of BI and deliver it to an average user via the Web. Next-Generation Business Intelligence Software with Rich Internet Applications provides developers, designers, and architects a solid foundation of BI design and architecture concepts with Microsoft Silverlight. This book covers key BI design concepts and how they can be applied without requiring an existing BI infrastructure. The author, Bart Czernicki, will show you how to build small BI applications by example that are interactive, highly visual, statistical, predictive, and most importantly, intuitive to the user. BI isn't just for the executive branch of a Fortune 500 company; it is for the masses. Let Next-Generation Business Intelligence Software with Rich Internet Applications show you how to unlock the rich intelligence you already have.

Business intelligence (BI) software allows you to view different components of a business using a single visual platform, which makes comprehending mountains of data easier. BI is everywhere. Applications that include reports, analytics, statistics, and historical and predictive modeling are all examples of business intelligence. Currently, we are in the second generation of business intelligence software—called BI 2.0—which is focused on writing business intelligence software that is predictive, adaptive, simple, and interactive. As computers and software have evolved, more data can be presented to end users with increasingly visually rich techniques. Rich Internet application (RIA) technologies such as Microsoft Silverlight can be used to transform traditional user-interfaces filled with boring data into fully interactive analytical applications that quickly deliver insight from large data sets. Furthermore, RIAs now include 3D spatial-design capabilities that move beyond a simple list or grid and allow for interesting layouts of aggregated data. BI 2.0 implemented via an RIA technology can truly bring out the power of business intelligence and deliver it to an average user on the Web. Silverlight 4 Business Intelligence Software provides developers, designers, and architects with a solid foundation in business intelligence design and architecture concepts for Microsoft Silverlight. This book covers key business intelligence design concepts and how they can be applied without an existing BI infrastructure. Author Bart Czernicki provides you with examples of how to build small BI applications that are interactive, highly visual, statistical, predictive—and most importantly—intuitive to the end-user. Business intelligence isn’t just for the executive branch of a Fortune 500 company—it is for the masses. Let Silverlight 4 Business Intelligence Software show you how to unlock the rich intelligence you already have.

Business intelligence (BI) software allows you to view different components of a business using a single visual platform, which makes comprehending mountains of data easier. BI is everywhere. Applications that include reports, analytics, statistics, and historical and predictive modeling are all examples of business intelligence. Currently, we are in the second generation of business intelligence software—called BI 2.0—which is focused on writing business intelligence software that is predictive, adaptive, simple, and interactive. As computers and software have evolved, more data can be presented to end users with increasingly visually rich techniques. Rich Internet application (RIA) technologies such as Microsoft Silverlight can be used to transform traditional user-interfaces filled with boring data into fully interactive analytical applications that quickly deliver insight from large data sets. Furthermore, RIAs now include 3D spatial-design capabilities that move beyond a simple list or grid and allow for interesting layouts of aggregated data. BI 2.0 implemented via an RIA technology can truly bring out the power of business intelligence and deliver it to an average user on the Web. Silverlight 4 Business Intelligence Software provides developers, designers, and architects with a solid foundation in business intelligence design and architecture concepts for Microsoft Silverlight. This book covers key business intelligence design concepts and how they can be applied without an existing BI infrastructure. Author Bart Czernicki provides you with examples of how to build small BI applications that are interactive, highly visual, statistical, predictive—and most importantly—intuitive to the end-user. Business intelligence isn't just for the executive branch of a Fortune 500 company—it is for the masses. Let Silverlight 4 Business Intelligence Software show you how to unlock the rich intelligence you already have.

This book contains extended and revised papers from the 16th International Conference on Enterprise Information Systems, ICEIS 2014, held in Lisbon, Portugal, in April 2014. The 24 papers presented in this volume were carefully reviewed and selected from a total of 313 submissions. The book also contains two full-paper invited talks. The selected papers reflect state-of-the-art research that is oriented toward real-world applications and highlight the benefits of information systems and technology for industry and services. They are organized in topical sections on databases and information systems integration, artificial intelligence and decision support systems, information systems analysis and specification, software agents and Internet computing, human–computer interaction, and enterprise architecture.

Algorithms and Applications for Academic Search, Recommendation and Quantitative Association Rule Mining presents novel algorithms for academic search, recommendation and association rule mining that have been developed and optimized for different commercial as well as academic purpose systems. Along with the design and implementation of algorithms, a major part of the work presented in the book involves the development of new systems both for commercial as well as for academic use. In the first part of the book the author introduces a novel hierarchical heuristic scheme for re-ranking academic publications retrieved from standard digital libraries. The scheme is based on the hierarchical combination of a custom implementation of the term frequency heuristic, a time-depreciated citation score and a graph-theoretic computed score that relates the paper's index terms with each other. In order to evaluate the performance of the introduced algorithms, a meta-search engine has been designed and developed that submits user queries to standard digital repositories of academic publications and re-ranks the top-n results using the introduced hierarchical heuristic scheme. In the second part of the book the design of novel recommendation algorithms with application in different types of e-commerce systems are described. The newly introduced algorithms are a part of a developed Movie Recommendation system, the first such system to be commercially deployed in Greece by a major Triple Play services provider. The initial version of the system uses a novel hybrid recommender (user, item and content based) and provides daily recommendations to all active subscribers of the provider (currently more than 30,000). The recommenders that we are presenting are hybrid by nature, using an ensemble configuration of different content, user as well as item-based recommenders in order to provide more accurate recommendation results. The final part of the book presents the design of a quantitative association rule mining algorithm. Quantitative association rules refer to a special type of association rules of the form that antecedent implies consequent consisting of a set of numerical or quantitative attributes. The introduced mining algorithm processes a specific number of user histories in order to generate a set of association rules with a minimally required support and confidence value. The generated rules show strong relationships that exist between the consequent and the antecedent of each rule, representing different items that have been consumed at specific price levels. This research book will be of appeal to researchers, graduate students, professionals, engineers and computer programmers.

In today's world, as the volume of business and consumer data continues to grow at an unprecedented pace, there is increasing desire to utilize that data in new and innovative and ways to provide insight and improve decision making. For businesses, data is being generated from transactions, machine logs, digital media and feeds from sensors and wireless devices at a volume and velocity not seen before. When combined with data from external sources such as partners, or from the Internet from blogs, social networking sites, YouTube, Facebook and Twitter, it has the capability to provide organizations with new insight, a more holistic picture of customer and stakeholder behavior and new ways of gaining competitive advantage. Consumers are being presented with applications of increasing analytical sophistication, leading to growing comfort in making fact-based decisions. New devices will help monitor energy usage within the home and provide insight on the optimal times to schedule devices and run household appliances. As data volumes continue to grow, systems will need to automate the uncovering of patterns and trends in data if they are to scale. Business Intelligence (BI) software, which has traditionally been used to gain insight from data, will need to evolve and new capabilities developed to support these significant changes. Areas of growth and opportunities for new innovation within the BI software industry will be explored that will enable stakeholders to take full advantage of this new and exciting opportunity.