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Social pension and security systems worldwide are experiencing difficulties in maintaining the financing required to provide promised benefits. Despite their economic and political difficulties, many Latin American countries have arguably been more proactive and innovative with the implementation of social security reform than other regions of the world. Suggestions for reform of the Social Security system in Venezuela are provided by integrating many of the same concepts found in other restructurings in Latin America. It is the conclusion of this analysis that, given the political and economic instability of the region, a major overhaul of the existing system is not politically feasible. An incremental approach with minimal disruption is more attainable given the circumstances.

This book is an attempt to reflect on what we have learned from financial policies and financial crises in Latin America. The 21 chapters in this volume capture the developments in various ways. They cover theoretical contributions, regional empirical studies, and specific inquiries on Argentina, Brazil, Chile, Colombia, Cuba, Ecuador, Mexico, Peru and Venezuela. The breadth of methodologies implemented suggests that researchers are looking at Latin American financial markets through a variety of lenses. The chapters are divided into 7 parts, including, in Part I, an initial overview. Part II examines the foreign exchange markets in Latin America and their interactions with other markets. Part III discusses dollarization issues in the region. Part IV then takes up the issue of banking in Latin America. Equity and bond markets are considered in Parts V and VI, respectively. Lastly, Part VII considers pension systems in Latin America. Taken as a whole, the 21 chapters seize the excitement of studying Latin America and provide lessons that are applicable around the world.

The purpose of this paper is to provide an integrated semantic information retrieval (IR) solution based on an ontology-improved vector space model for situations where a digital collection is established or curated. It aims to create a retrieval approach which could return the results by meanings rather than by keywords.

In this paper, the authors propose a semantic term frequency algorithm to create a semantic vector space model (SeVSM) based on ontology. To support the calculation, a multi-branches tree model is created to represent the ontology and a set of algorithms is developed to operate it. Then, a semantic ontology-based IR system based on the SeVSM model is designed and developed to verify the effectiveness of the proposed model.

The experimental study using 30 queries from 15 different domains confirms the effectiveness of the SeVSM and the usability of the proposed system. The results demonstrate that the proposed model and system can be a significant exploration to enhance IR in specific domains, such as a digital library and e-commerce.

This research not only creates a semantic retrieval model, but also provides the application approach via designing and developing a semantic retrieval system based on the model. Comparing with most of the current related research, the proposed research studies the whole process of realizing a semantic retrieval.

The purpose of this paper is to present a two‐phase approach for designing an efficient tailored but flexible storage solution for resource description framework (RDF) data based on its query workload characteristics.

The approach consists of two phases. The vertical partitioning phase which aims of reducing the number of join operations in the query evaluation process, while the adjustment phase aims to maintain the efficiency of the performance of the query processing by adapting the underlying schema to cope with the dynamic nature of the query workloads.

The authors perform comprehensive experiments on two real‐world RDF datasets to demonstrate that the approach is superior to the state‐of‐the‐art techniques in this domain.

The main motivation behind the authors' approach is that several benchmarking studies have recently shown that each RDF dataset requires a tailored table schema in order to achieve efficient performance during query processing. None of the previous approaches have considered this limitation.

The purpose of this paper is to formulate a novel model for enhancing the effectiveness of existing digital forensic readiness (DFR) schemes by leveraging the capabilities of cyber threat information sharing.

This paper uses a quantitative methodology to identify the most popular cyber threat intelligence (CTI) elements and introduces a lightweight approach to correlate those with potential forensic value, resulting in the quick and accurate triaging and identification of patterns of malicious activities.

While threat intelligence exchange steadily becomes a common practice for the prevention or detection of security incidents, the proposed approach highlights its usefulness for the digital forensics (DF) domain.

The proposed model can help organizations to improve their DFR posture, and thus minimize the time and cost of cybercrime incidents.

Describes an approach to real‐time expert systems for integrated network management. Presents an overview of expert systems technology, proceeding to develop the requirements for a real‐time system in network management. Describes a system being developed (1989) by Pacific Bell. Describes NETREX, a prototype real‐time system, aimed at automatically maintaining Pacific Bell′s internal data networks. Concentrates on an application for solving problems with travel tickets in real time.

This study aims to develop a Web-based application system called Infomediary of Taiwanese Indigenous Peoples (ITIP) that can help individuals comprehend the society and culture of indigenous people. The ITIP is based on the use of Semantic Web technologies to integrate a number of data sources, particularly including the bibliographic records of a museum. Moreover, an ontology model was developed to help users search cultural collections by topic concepts.

Two issues were identified that needed to be addressed: the integration of heterogeneous data sources and semantic-based information retrieval. Two corresponding methods were proposed: SPARQL federated queries were designed for data integration across the Web and ontology-driven queries were designed to semantically search by knowledge inference. Furthermore, to help users perform searches easily, three searching interfaces, namely, ethnicity, region and topic, were developed to take full advantage of the content available on the Web.

Most open government data provides structured but non-resource description framework data, Semantic Web consumers, therefore, require additional data conversion before the data can be used. On the other hand, although the library, archive and museum (LAM) community has produced some emerging linked data, very few data sets are released to the general public as open data. The Semantic Web’s vision of “web of data” remains challenging.

This study developed data integration from various institutions, including those of the LAM community. The development was conducted based on the mode of non-institution members (i.e. institutional outsiders). The challenges encountered included uncertain data quality and the absence of institutional participation.

The task of a document retrieval system is to match a query, perhaps in natural language, against a large number of natural language documents. Neural networks are known to be good pattern matchers. This article describes investigations in implementing a document retrieval system based on a neural network model. It shows that many of the standard strategies of information retrieval are applicable in a neural network model.