20,530 research outputs found
Summarizing information from Web sites on distributed power generation and alternative energy development
The World Wide Web (WWW) has become a huge repository of information and knowledge, and an essential channel for information exchange. Many sites and thousands of pages of information on distributed power generation and alternate energy development are being added or modified constantly and the task of finding the most appropriate information is getting difficult. While search engines are capable to return a collection of links according to key terms and some forms of ranking mechanism, it is still necessary to access the Web page and navigate through the site in order to find the information. This paper proposes an interactive summarization framework called iWISE to facilitate the process by providing a summary of the information on the Web site. The proposed approach makes use of graphical visualization, tag clouds and text summarization. A number of cases are presented and compared in this paper with a discussion on future work
Txt2vz: a new tool for generating graph clouds
We present txt2vz (txt2vz.appspot.com), a new tool for automatically generating a visual summary of unstructured text data found in documents or web sites. The main purpose of the tool is to give the user information about the text so that they can quickly get a good idea about the topics covered. Txt2vz is able to identify important concepts from unstructured text data and to reveal relationships between those concepts. We discuss other approaches to generating diagrams from text and highlight the differences between tag clouds, word clouds, tree clouds and graph clouds
Tag-Cloud Drawing: Algorithms for Cloud Visualization
Tag clouds provide an aggregate of tag-usage statistics. They are typically
sent as in-line HTML to browsers. However, display mechanisms suited for
ordinary text are not ideal for tags, because font sizes may vary widely on a
line. As well, the typical layout does not account for relationships that may
be known between tags. This paper presents models and algorithms to improve the
display of tag clouds that consist of in-line HTML, as well as algorithms that
use nested tables to achieve a more general 2-dimensional layout in which tag
relationships are considered. The first algorithms leverage prior work in
typesetting and rectangle packing, whereas the second group of algorithms
leverage prior work in Electronic Design Automation. Experiments show our
algorithms can be efficiently implemented and perform well.Comment: To appear in proceedings of Tagging and Metadata for Social
Information Organization (WWW 2007
Collaborative OLAP with Tag Clouds: Web 2.0 OLAP Formalism and Experimental Evaluation
Increasingly, business projects are ephemeral. New Business Intelligence
tools must support ad-lib data sources and quick perusal. Meanwhile, tag clouds
are a popular community-driven visualization technique. Hence, we investigate
tag-cloud views with support for OLAP operations such as roll-ups, slices,
dices, clustering, and drill-downs. As a case study, we implemented an
application where users can upload data and immediately navigate through its ad
hoc dimensions. To support social networking, views can be easily shared and
embedded in other Web sites. Algorithmically, our tag-cloud views are
approximate range top-k queries over spontaneous data cubes. We present
experimental evidence that iceberg cuboids provide adequate online
approximations. We benchmark several browser-oblivious tag-cloud layout
optimizations.Comment: Software at https://github.com/lemire/OLAPTagClou
Key Phrase Extraction of Lightly Filtered Broadcast News
This paper explores the impact of light filtering on automatic key phrase
extraction (AKE) applied to Broadcast News (BN). Key phrases are words and
expressions that best characterize the content of a document. Key phrases are
often used to index the document or as features in further processing. This
makes improvements in AKE accuracy particularly important. We hypothesized that
filtering out marginally relevant sentences from a document would improve AKE
accuracy. Our experiments confirmed this hypothesis. Elimination of as little
as 10% of the document sentences lead to a 2% improvement in AKE precision and
recall. AKE is built over MAUI toolkit that follows a supervised learning
approach. We trained and tested our AKE method on a gold standard made of 8 BN
programs containing 110 manually annotated news stories. The experiments were
conducted within a Multimedia Monitoring Solution (MMS) system for TV and radio
news/programs, running daily, and monitoring 12 TV and 4 radio channels.Comment: In 15th International Conference on Text, Speech and Dialogue (TSD
2012
A Tutorial on Clique Problems in Communications and Signal Processing
Since its first use by Euler on the problem of the seven bridges of
K\"onigsberg, graph theory has shown excellent abilities in solving and
unveiling the properties of multiple discrete optimization problems. The study
of the structure of some integer programs reveals equivalence with graph theory
problems making a large body of the literature readily available for solving
and characterizing the complexity of these problems. This tutorial presents a
framework for utilizing a particular graph theory problem, known as the clique
problem, for solving communications and signal processing problems. In
particular, the paper aims to illustrate the structural properties of integer
programs that can be formulated as clique problems through multiple examples in
communications and signal processing. To that end, the first part of the
tutorial provides various optimal and heuristic solutions for the maximum
clique, maximum weight clique, and -clique problems. The tutorial, further,
illustrates the use of the clique formulation through numerous contemporary
examples in communications and signal processing, mainly in maximum access for
non-orthogonal multiple access networks, throughput maximization using index
and instantly decodable network coding, collision-free radio frequency
identification networks, and resource allocation in cloud-radio access
networks. Finally, the tutorial sheds light on the recent advances of such
applications, and provides technical insights on ways of dealing with mixed
discrete-continuous optimization problems
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