Graph Summarization

The continuous and rapid growth of highly interconnected datasets, which are both voluminous and complex, calls for the development of adequate processing and analytical techniques. One method for condensing and simplifying such datasets is graph summarization. It denotes a series of application-specific algorithms designed to transform graphs into more compact representations while preserving structural patterns, query answers, or specific property distributions. As this problem is common to several areas studying graph topologies, different approaches, such as clustering, compression, sampling, or influence detection, have been proposed, primarily based on statistical and optimization methods. The focus of our chapter is to pinpoint the main graph summarization methods, but especially to focus on the most recent approaches and novel research trends on this topic, not yet covered by previous surveys.Comment: To appear in the Encyclopedia of Big Data Technologie

mARC: Memory by Association and Reinforcement of Contexts

This paper introduces the memory by Association and Reinforcement of Contexts (mARC). mARC is a novel data modeling technology rooted in the second quantization formulation of quantum mechanics. It is an all-purpose incremental and unsupervised data storage and retrieval system which can be applied to all types of signal or data, structured or unstructured, textual or not. mARC can be applied to a wide range of information clas-sification and retrieval problems like e-Discovery or contextual navigation. It can also for-mulated in the artificial life framework a.k.a Conway "Game Of Life" Theory. In contrast to Conway approach, the objects evolve in a massively multidimensional space. In order to start evaluating the potential of mARC we have built a mARC-based Internet search en-gine demonstrator with contextual functionality. We compare the behavior of the mARC demonstrator with Google search both in terms of performance and relevance. In the study we find that the mARC search engine demonstrator outperforms Google search by an order of magnitude in response time while providing more relevant results for some classes of queries

Survey over Existing Query and Transformation Languages

A widely acknowledged obstacle for realizing the vision of the Semantic Web is the inability of many current Semantic Web approaches to cope with data available in such diverging representation formalisms as XML, RDF, or Topic Maps. A common query language is the first step to allow transparent access to data in any of these formats. To further the understanding of the requirements and approaches proposed for query languages in the conventional as well as the Semantic Web, this report surveys a large number of query languages for accessing XML, RDF, or Topic Maps. This is the first systematic survey to consider query languages from all these areas. From the detailed survey of these query languages, a common classification scheme is derived that is useful for understanding and differentiating languages within and among all three areas

First results in terrain mapping for a roving planetary explorer

To perform planetary exploration without human supervision, a complete autonomous rover must be able to model its environment while exploring its surroundings. Researchers present a new algorithm to construct a geometric terrain representation from a single range image. The form of the representation is an elevation map that includes uncertainty, unknown areas, and local features. By virtue of working in spherical-polar space, the algorithm is independent of the desired map resolution and the orientation of the sensor, unlike other algorithms that work in Cartesian space. They also describe new methods to evaluate regions of the constructed elevation maps to support legged locomotion over rough terrain

Simplifying syntactic and semantic parsing of NL-based queries in advanced application domains

The paper presents a high level query language (MDDQL) for databases, which relies on an ontology driven automaton. This is simulated by the human-computer interaction mode for the query construction process, which is driven by an inference engine operating upon a frames based ontology description. Therefore, given that the query construction process implicitly leads to the contemporary construction of high level query trees prior to submission of the query for transformation and execution to a semantic middle-ware, syntactic and semantic parsing of a query with conventional techniques, i.e., after completion of its formulation, becomes obsolete. To this extent, only, as meaningful as possible, queries can be constructed at a low typing, learning, syntactic and semantic parsing effort and regardless the preferred natural (sub)language. From a linguistics point o view, it turns out that the query construction mechanism can easily be adapted and work with families of natural languages, which underlie another type order such as Subject-Object-Verb as opposed to the typical Subject-Verb-Object type order, which underlie most European languages. The query construction mechanism has been proved as practical in advanced application domains, such as those provided by medical applications, with an advanced and hardly understood terminology for naive users and the public

SPIE: A Framework For Advanced Database Topics

In the ever expanding universe of database skills and knowledge we propose a framework that can be used to classify advanced database topics. We use the framework to present five advanced database modules that can be successfully incorporated in an advanced database course. These modules were built to illustrate advanced topics and were tested and refined in advanced database courses over several semesters. The skills demonstrated in the modules go beyond what is typically taught in an the introductory level database course but are important in today’s highly demanding business environment