241 research outputs found
Multifaceted Geotagging for Streaming News
News sources on the Web generate constant streams of information, describing the events that shape our world. In particular, geography plays a key role in the news, and understanding the geographic information present in news allows for its useful spatial browsing and retrieval. This process of understanding is called geotagging, and involves first finding in the document all textual references to geographic locations, known as toponyms, and second, assigning the correct lat/long values to each toponym, steps which are termed toponym recognition and toponym resolution, respectively. These steps are difficult due to ambiguities in natural language: some toponyms share names with non-location entities, and further, a given toponym can have many location interpretations. Removing these ambiguities is crucial for successful geotagging.
To this end, geotagging methods are described which were developed for streaming news. First, a spatio-textual search engine named STEWARD, and an interactive map-based news browsing system named NewsStand are described, which feature geotaggers as central components, and served as motivating systems and experimental testbeds for developing geotagging methods. Next, a geotagging methodology is presented that follows a multifaceted approach involving a variety of techniques. First, a multifaceted toponym recognition process is described that uses both rule-based and machine learning–based methods to ensure high toponym recall. Next, various forms of toponym resolution evidence are explored. One such type of evidence is lists of toponyms, termed comma groups, whose toponyms share a common thread in their geographic properties that enables correct resolution. In addition to explicit evidence, authors take advantage of the implicit geographic knowledge of their audiences. Understanding the local places known by an audience, termed its local lexicon, affords great performance gains when geotagging articles from local newspapers, which account for the vast majority of news on the Web. Finally, considering windows of text of varying size around each toponym, termed adaptive context, allows for a tradeoff between geotagging execution speed and toponym resolution accuracy. Extensive experimental evaluations of all the above methods, using existing and two newly-created, large corpora of streaming news, show great performance gains over several competing prominent geotagging methods
Geopackage as future ubiquitous GIS data format: a review
The emerging geospatial technologies in earth and space science informatics have led to the advancement in developing international standards for geospatial interoperability. In the last few years, two main trends are making disruptions in geospatial applications; mobile and context sharing. Geospatial data format used in mobile GIS to support advance mobile application is challenged. This is due to the lack of interoperability, open-standard, cross platform and standard APIs for access and management. For instance, most mobile GIS developments are application-dependent, contains redundant geospatial data, consume large storage capacity, and require custom applications for data translation. Based on these issues, new OGC file format named GeoPackage will enable greater geospatial data sharing on mobile and web platform. This data format is an open standard, non-proprietary, platform-independent, container for distribution, and direct use of all kinds of geospatial data will increase cross-platform interoperability, geospatial applications and web services. This presents a comprehensive review of mobile GIS hence, the concept of GeoPackage as a modern geospatial tool was discussed, while its relevance in contemporary geospatial technology are highlighted
Democratic Transitions and the Future of Asylum Law
The United States\u27s commitment to protecting refugees is dying a slow death. Two developments have contributed to its demise. The first, widely heralded, is the United States Congress\u27s evisceration of procedural safeguards such as judicial review. The second development is more insidious: expansion of the asylum law doctrine, which holds that changed country conditions can defeat an otherwise valid asylum claim. In an age in which democracy seems triumphant throughout the world, the combination of severely curtailed judicial review and mechanical application of the changed conditions doctrine relegates refugees, as well as asylum law itself, to an uncertain future.\u27 This article argues that the rise of the changed country conditions doctrine stems from judicial and administrative confusion about both the role of both subjective and objective factors in asylum law and the nature of democratic transitions
Digital Image Access & Retrieval
The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio
A scalable analysis framework for large-scale RDF data
With the growth of the Semantic Web, the availability of RDF datasets from multiple domains
as Linked Data has taken the corpora of this web to a terabyte-scale, and challenges
modern knowledge storage and discovery techniques. Research and engineering on RDF
data management systems is a very active area with many standalone systems being introduced.
However, as the size of RDF data increases, such single-machine approaches meet
performance bottlenecks, in terms of both data loading and querying, due to the limited
parallelism inherent to symmetric multi-threaded systems and the limited available system
I/O and system memory. Although several approaches for distributed RDF data processing
have been proposed, along with clustered versions of more traditional approaches, their
techniques are limited by the trade-off they exploit between loading complexity and query
efficiency in the presence of big RDF data. This thesis then, introduces a scalable analysis
framework for processing large-scale RDF data, which focuses on various techniques to
reduce inter-machine communication, computation and load-imbalancing so as to achieve
fast data loading and querying on distributed infrastructures.
The first part of this thesis focuses on the study of RDF store implementation and parallel
hashing on big data processing. (1) A system-level investigation of RDF store implementation
has been conducted on the basis of a comparative analysis of runtime characteristics
of a representative set of RDF stores. The detailed time cost and system consumption is
measured for data loading and querying so as to provide insight into different triple store
implementation as well as an understanding of performance differences between different
platforms. (2) A high-level structured parallel hashing approach over distributed memory is
proposed and theoretically analyzed. The detailed performance of hashing implementations
using different lock-free strategies has been characterized through extensive experiments,
thereby allowing system developers to make a more informed choice for the implementation
of their high-performance analytical data processing systems.
The second part of this thesis proposes three main techniques for fast processing of large
RDF data within the proposed framework. (1) A very efficient parallel dictionary encoding
algorithm, to avoid unnecessary disk-space consumption and reduce computational complexity of query execution. The presented implementation has achieved notable speedups
compared to the state-of-art method and also has achieved excellent scalability. (2) Several
novel parallel join algorithms, to efficiently handle skew over large data during query processing.
The approaches have achieved good load balancing and have been demonstrated
to be faster than the state-of-art techniques in both theoretical and experimental comparisons.
(3) A two-tier dynamic indexing approach for processing SPARQL queries has been
devised which keeps loading times low and decreases or in some instances removes intermachine
data movement for subsequent queries that contain the same graph patterns. The
results demonstrate that this design can load data at least an order of magnitude faster than
a clustered store operating in RAM while remaining within an interactive range for query
processing and even outperforms current systems for various queries
Engineering Agile Big-Data Systems
To be effective, data-intensive systems require extensive ongoing customisation to reflect changing user requirements, organisational policies, and the structure and interpretation of the data they hold. Manual customisation is expensive, time-consuming, and error-prone. In large complex systems, the value of the data can be such that exhaustive testing is necessary before any new feature can be added to the existing design. In most cases, the precise details of requirements, policies and data will change during the lifetime of the system, forcing a choice between expensive modification and continued operation with an inefficient design.Engineering Agile Big-Data Systems outlines an approach to dealing with these problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals, and, in a number of case studies, shows how the tools and methodology have been used to improve a variety of academic and business systems
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