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Querying Graphs
Graph data modeling and querying arises in many practical application domains such as social and biological networks where the primary focus is on concepts and their relationships and the rich patterns in these complex webs of interconnectivity. In this book, we present a concise unified view on the basic challenges which arise over the complete life cycle of formulating and processing queries on graph databases. To that purpose, we present all major concepts relevant to this life cycle, formulated in terms of a common and unifying ground: the property graph data model—the pre-dominant data model adopted by modern graph database systems. We aim especially to give a coherent and in-depth perspective on current graph querying and an outlook for future developments. Our presentation is self-contained, covering the relevant topics from: graph data models, graph query languages and graph query specification, graph constraints, and graph query processing. We conclude by indicating major open research challenges towards the next generation of graph data management systems.
Ontology Alignment Using Biologically-inspired Optimisation Algorithms
It is investigated how biologically-inspired optimisation methods can be used to compute alignments between ontologies. Independent of particular similarity metrics, the developed techniques demonstrate anytime behaviour and high scalability. Due to the inherent parallelisability of these population-based algorithms it is possible to exploit dynamically scalable cloud infrastructures - a step towards the provisioning of Alignment-as-a-Service solutions for future semantic applications.
Querying Graphs
Graph data modeling and querying arises in many practical application domains such as social and biological networks where the primary focus is on concepts and their relationships and the rich patterns in these complex webs of interconnectivity. In this book, we present a concise unified view on the basic challenges which arise over the complete life cycle of formulating and processing queries on graph databases. To that purpose, we present all major concepts relevant to this life cycle, formulated in terms of a common and unifying ground: the property graph data model—the pre-dominant data model adopted by modern graph database systems. We aim especially to give a coherent and in-depth perspective on current graph querying and an outlook for future developments. Our presentation is self-contained, covering the relevant topics from: graph data models, graph query languages and graph query specification, graph constraints, and graph query processing. We conclude by indicating major open research challenges towards the next generation of graph data management systems.
Conceptual Modeling for E-Business and the Web
The objective of the workshops associated with the ER2000 19th International Conference on Conceptual Modeling was to give participants the opportunity to present and discuss emerging, hot topics, thus adding new perspectives to conceptual modeling. This attracts communities which have begun to or which have already recognized the importance of conceptual modeling for solving their problems. To meet this objective, we selected the following two topics: { Conceptual Modeling Approaches for E-Business (eCOMO2000) aimed at studying the application of conceptual modeling techniques speci cally to e-business. { The World Wide Web and Conceptual Modeling (WCM2000) which analyzes how conceptual mod...
Engineering and Deployment of Cooperative Information Systems
Today, technologies for engineering and deployment of cooperative information systems have become increasingly critical in the construction of practically all types of large-scale distributed systems. Stimulating forums with different focuses are thus still in need of researchers and professionals from academia and industry to exchange ideas and experience and to establish working relationships. The idea to organize in China an academic event focusing on current topics in the field was born during the IFIP World Computer Congress 2000 that was held in Beijing, China. And here are the proceedings of EDCIS 2002! This volume comprises the technical research papers accepted for presentation at E...
Exploring New Frontiers of Theoretical Informatics
In recent years, IT application scenarios have evolved in very innovative ways. Highly distributed networks have now become a common platform for large-scale distributed programming, high bandwidth communications are inexpensive and widespread, and most of our work tools are equipped with processors enabling us to perform a multitude of tasks. In addition, mobile computing (referring specifically to wireless devices and, more broadly, to dynamically configured systems) has made it possible to exploit interaction in novel ways. To harness the flexibility and power of these rapidly evolving, interactive systems, there is need of radically new foundational ideas and principles; there is need to...
Technologies for E-Services
This book constitutes the thoroughly refereed post-proceedings of the 6th International Workshop on Technologies for E-Services held in September 2005. The nine revised full papers presented together with one keynote article were carefully reviewed and selected from forty submissions for inclusion in the book. Their common purpose is to identify the technical issues, models and infrastructures that enable enterprises to provide e-services to other businesses and individual customers.
The Semantic Web
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Database and XML Technologies
This book constitutes the refereed proceedings of the 5th International XML Database Symposium, XSym 2007, held in Vienna, Austria, in September 2007 in conjunction with the International Conference on Very Large Data Bases, VLDB 2007. The papers cover all current aspects of core database technology for XML data management, XML and data integration, and development and deployment of XML applications.
Data Profiling
Data profiling refers to the activity of collecting data about data, {i.e.}, metadata. Most IT professionals and researchers who work with data have engaged in data profiling, at least informally, to understand and explore an unfamiliar dataset or to determine whether a new dataset is appropriate for a particular task at hand. Data profiling results are also important in a variety of other situations, including query optimization, data integration, and data cleaning. Simple metadata are statistics, such as the number of rows and columns, schema and datatype information, the number of distinct values, statistical value distributions, and the number of null or empty values in each column. More...
