PhotoStand enables the use of a map query interface to retrieve news photos associated with news articles that are in turn associated with the principal locations that they mention collected as a result of monitoring the output of over 10,000 RSS news feeds, made available within minutes of publication, and stored in a PostgreSQL database. The news photos are ranked according to their relevance to the clusters of news articles associated with locations at which they are displayed. This work differs from traditional work in this field as the associated locations and topics (by virtue of the cluster with which the articles containing the news photos are associated) are generated automatically without any human intervention such as tagging, and that photos are retrieved by location instead of just by keyword as is the case for many existing systems. In addition, the clusters provide a filtering step for detecting near-duplicate news photos.

The database architecture and system design of NewsStand, a database system that analyzes and displays streaming news using a map user interface, is described. Special emphasis is given to NewsStand's pipe server, which coordinates individual, independent analysis modules in a processing pipeline, and NewsStand's relational database schema, designed to accommodate responsive spatial querying and retrieval via NewsStand's user interface. Examples of these spatial queries, which are variants of top-k window queries, are also presented. Experiments on the live NewsStand database system demonstrate its capability for rapidly processing large amounts of streaming news as well as the interactivity of its map user interface as measured by database querying.

News sources around the world generate constant streams of information, but effective streaming news retrieval requires an intimate understanding of the geographic content of news. This process of understanding, known as geotagging, consists of first finding words in article text that correspond to location names (toponyms), and second, assigning each toponym its correct lat/long values. The latter step, called toponym resolution, can also be considered a classification problem, where each of the possible interpretations for each toponym is classified as correct or incorrect. Hence, techniques from supervised machine learning can be applied to improve accuracy. New classification features to improve toponym resolution, termed adaptive context features, are introduced that consider a window of context around each toponym, and use geographic attributes of toponyms in the window to aid in their correct resolution. Adaptive parameters controlling the window's breadth and depth afford flexibility in managing a tradeoff between feature computation speed and resolution accuracy, allowing the features to potentially apply to a variety of textual domains. Extensive experiments with three large datasets of streaming news demonstrate the new features' effectiveness over two widely-used competing methods.

NewsStand is a Web-based mapping application that we have developed to enable searching for spatially-referenced information by using a map query interface. Previously, we adapted the Web version to execute on mobile devices such as smartphones where the main issue that was confronted was how to deal with the considerably smaller display screen while retaining access to the application through the browser. In the current work we discuss the issues that we encountered in converting the Web-based application to a native App primarily on the iPhone and iPod Touch. These issues involve how to compensate for the absence of a hovering action as well as how to integrate an interaction restriction to one hand coupled with use of the thumb as the pointing mechanism. In addition, a significant effort is devoted to the implementation of an intuitive mechanism for undoing the most recent actions. Other issues include the formulation of navigation shortcuts to avoid excessive traffic with the supporting database.

News sources on the Web generate constant streams of information, describing many aspects of the events that shape our world. In particular, geography plays a key role in the news, and enabling geographic retrieval of news articles involves recognizing the textual references to geographic locations (called toponyms) present in the articles, which can be difficult due to ambiguity in natural language. Toponym recognition in news is often accomplished with algorithms designed and tested around small corpora of news articles, but these static collections do not reflect the streaming nature of online news, as evidenced by poor performance in tests. In contrast, a method for toponym recognition is presented that is tuned for streaming news by leveraging a wide variety of recognition components, both rule-based and statistical. An evaluation of this method shows that it outperforms two prominent toponym recognition systems when tested on large datasets of streaming news, indicating its suitability for this domain.

Though biomedical research often draws on knowledge from a wide variety of fields, few visualization methods for biomedical data incorporate meaningful cross-database exploration. A new approach is offered for visualizing and exploring a query-based subset of multiple heterogeneous biomedical databases. Databases are modeled as an entity-relation graph containing nodes (database records) and links (relationships between records). Users specify a keyword search string to retrieve an initial set of nodes, and then explore intra- and interdatabase links. Results are visualized with user-defined semantic substrates to take advantage of the rich set of attributes usually present in biomedical data. Comments from domain experts indicate that this visualization method is potentially advantageous for biomedical knowledge exploration.

Ontuition, a system for mapping ontologies, is presented. Transforming data to a usable format for Ontuition involves recognizing and resolving data values corresponding to concepts in multiple ontological domains. In particular, for datasets with a geographic component an attempt is made to identify and extract enough spatio-textual data that specific lat/long values to dataset entries can be assigned. Next, a gazetteer is used to transform the textually-specified locations into lat/long values that can be displayed on a map. Non-spatial ontological concepts are also discovered. This methodology is applied to the National Library of Medicine's very popular clinical trials website (http://clinicaltrials.gov/) whose users are generally interested in locating trials near where they live. The trials are specified using XML files. The location data is extracted and coupled with a disease ontology to enable general queries on the data with the result being of use to a very large group of people. The goal is to do this automatically for such ontology datasets with a locational component.

Information sources on the Internet (e.g., Web versions of newspapers) usually have an implicit spatial reader scope, which is the geographical location for which the content has been primarily produced. Knowledge of the spatial reader scope facilitates the construction of a news search engine that provides readers a set of news sources relevant to the location in which they are interested. In particular, it plays an important role in disambiguating toponyms (e.g., textual specifications of geographical locations) in news articles, as the process of selecting an interpretation for the toponym often reduces to one of selecting an interpretation that seems natural in the context of the spatial reader scope. The key to determining the spatial reader scope of news sources is the notion of local lexicon, which for a location s is a set of concepts such as, but not limited to, names of people, landmarks, and historical events, that are spatially related to s. Techniques to automatically generate the local lexicon of a location by using the link structure of Wikipedia are described and evaluated. A key contribution is the improvement of existing methods used in the semantic relatedness domain to extract concepts spatially related to a given location from the Wikipedia. Results of experiments are presented that indicate that the knowledge of the spatial reader scope significantly improves the disambiguation of textually specified locations in news articles and that using local lexicons is an effective method to determine the spatial reader scopes of news sources.

The successful execution of location-based and feature-based queries on spatial databases requires the construction of spatial indexes on the spatial attributes. This is not simple when the data is unstructured as is the case when the data is a collection of documents such as news articles, which is the domain of discourse, where the spatial attribute consists of text that can be (but is not required to be) interpreted as the names of locations. In other words, spatial data is specified using text (known as a toponym) instead of geometry, which means that there is some ambiguity involved. The process of identifying and disambiguating references to geographic locations is known as geotagging and involves using a combination of internal document structure and external knowledge, including a document-independent model of the audience's vocabulary of geographic locations, termed its spatial lexicon. In contrast to previous work, a new spatial lexicon model is presented that distinguishes between a global lexicon of locations known to all audiences, and an audience-specific local lexicon. Generic methods for inferring audiences' local lexicons are described. Evaluations of this inference method and the overall geotagging procedure indicate that establishing local lexicons cannot be overlooked, especially given the increasing prevalence of highly local data sources on the Internet, and will enable the construction of more accurate spatial indexes.

The spatio-textual spreadsheet is a conventional spreadsheet where spatial attribute values are specified textually. Techniques are presented to automatically find the textually-specified spatial attributes that are present in spreadsheets. Once the spatial attributes have been identified, an accurate translation of the values of the spatial attributes to their actual geographic locations is needed (known as geotagging). The key observation is that spreadsheets with spatial data exhibit spatial coherence --- that is, cells with spatial data that are nearby in the spreadsheet contain data that share spatial characteristics in the real world. These techniques also allow richer search engine results by returning actual tuples from spreadsheets instead of simply links to the spreadsheets. Moreover, when the search key is a particular location, results in proximity to the query can be provided rather than just exact matches.

Twitter is an electronic medium that allows a large user populace to communicate with each other simultaneously. Inherent to Twitter is an asymmetrical relationship between friends and followers that provides an interesting social network like structure among the users of Twitter. Twitter messages, called tweets, are restricted to 140 characters and thus are usually very focused. We investigate the use of Twitter to build a news processing system, called TwitterStand, from Twitter tweets. The idea is to capture tweets that correspond to late breaking news. The result is analogous to a distributed news wire service. The difference is that the identities of the contributors/reporters are not known in advance and there may be many of them. Furthermore, tweets are not sent according to a schedule: they occur as news is happening, and tend to be noisy while usually arriving at a high throughput rate. Some of the issues addressed include removing the noise, determining tweet clusters of interest bearing in mind that the methods must be online, and determining the relevant locations associated with the tweets.

News articles contain a wealth of implicit geographic content that if exposed to readers improves understanding of today's news. However, most articles are not explicitly geotagged with their geographic content, and few news aggregation systems expose this content to users. A new system named NewsStand is presented that collects, analyzes, and displays news stories in a map interface, thus leveraging on their implicit geographic content. NewsStand monitors RSS feeds from thousands of online news sources and retrieves articles within minutes of publication. It then extracts geographic content from articles using a custom-built geotagger, and groups articles into story clusters using a fast online clustering algorithm. By panning and zooming in NewsStand's map interface, users can retrieve stories based on both topical significance and geographic region, and see substantially different stories depending on position and zoom level.

A similarity join operation A BOWTIEepsivB takes two sets of points A, B and a value epsiv isin Ropf, and outputs pairs of points p isin A,q isin B, such that the distance D(p, q) les epsiv. Similarity joins find use in a variety of fields, such as clustering, text mining, and multimedia databases. A novel similarity join algorithm called LSS is presented that executes on a graphics processing unit (GPU), exploiting its parallelism and high data throughput. As GPUs only allow simple data operations such as the sorting and searching of arrays, LSS uses these two operations to cast a similarity join operation as a GPU sort-and-search problem. It first creates, on the fly, a set of space-filling curves on one of its input datasets, using a parallel GPU sort routine. Next, LSS processes each point p of the other dataset in parallel. For each p, it searches an interval of one of the space-filling curves guaranteed to contain all the pairs in which p participates. Using extensive theoretical and experimental analysis, LSS is shown to offer a good balance between time and work efficiency. Experimental results demonstrate that LSS is suitable for similarity joins in large high-dimensional datasets, and that it performs well when compared against two existing prominent similarity join methods.

STEWARD ("Spatio-Textual Extraction on the Web Aiding Retrieval of Documents"), a system for extracting, querying, and visualizing textual references to geographic locations in unstructured text documents, is presented. Methods for retrieving and processing web documents, extracting and disambiguating georeferences, and identifying geographic focus are described. A brief overview of STEWARD's querying capabilities, as well as the design of an intuitive user interface, are provided. Finally, several application scenarios and future extensions to STEWARD are discussed.