This open access book covers the use of data science technologies (like machine learning, NLP, or Time Series Analysis) in economics and finance.

In recent years, great advances have been made in the speed, accuracy, and coverage of automatic word sense disambiguators - systems that given a word appearing in a certain context, can identify the sense of that word. Word Sense Disambiguation (WSD) is traditionally considered an AI-hard problem, that is, a problem which can be solved only by first resolving all the difficult problems in artificial intelligence (AI), such as the representation of common sense and encyclopedic knowledge. Moreover, it has been found that people are inconsistent when asked to disambiguate words and this causes problems when testing the output of an automatic disambiguator. A breakthrough in this field would have a significant impact on many relevant web-base applications, such as information retrieval and information extraction. In the review of disambiguation research, many varied techniques for performing automatic disambiguation are introduced. In this book, three different language independent strategies for word sense disambiguation are proposed and evaluated. The performances of the resulting system have been compared with respect to other well know methods for word sense disambiguation.

There has been increasing interest in building search/index structures to perform similarity search over high-dimensional data, e.g., image databases, document collections, time-series databases, and genome databases. A similarity search problem involves a collection of objects (e.g., documents, images) which are characterized by a collection of relevant features and represented as points in a high-dimensional attribute space. The first part of this book will present a new hierarchical clustering algorithm called Antipole Clustering. The algorithm partitions the set of data objects in clusters such that each one has diameter approximately less than a given value. The algorithm returns a tree structure called Antipole Tree in which the leaves are the final clusters. The second part of this book will present, GraphGrepVF, an application-independent method for querying a database of graphs in order to find all the occurrences of a given subgraph. Many applications in industry, science and engineering share this problem, and increasing the size of the database requires efficient structure searching algorithms.

Since 3/11/2008 Diego Reforgiato Recupero has been a Post Doctoral Researcher at the Department of Computer Science and Telecommunications Engineering (DIIT) working on peer-to-peer video. The support for this fellowship has been given by PROVIDEO, a Marie Curie International Grant (IRG) that Dr. Reforgiato won in summer 2008 together with the University of Catania. From 4/1/2005 to 5/1/2008 Dr. Reforgiato was a Post Doctoral Researcher at the Institute for Advanced Computer Studies, University of Maryland College Park, working with Prof. V.S. Subrahmanian. Dr. Reforgiato received the PhD in December 2004, from the University of Naples Federico II in Computer Sciences. In 2006, he won the Computer World Award with a software he developed, Oasys, an Opinion Analysis System. Dr. Reforgiato received two laurea degrees in Computer Science in 2001 and 2009 with the highest marks. In 2010 he passed the exams in order to join the Italian Engineering Corporation. Several Italian and American newspaper and television channels reported the work about OASYS of Dr. Reforgiato.