Anuj R. Jaiswal

SensePlace2: GeoTwitter analytics support for situational awareness

Geographically-grounded situational awareness (SA) is critical to crisis management and is essential in many other decision making domains that range from infectious disease monitoring, through regional planning, to political campaigning. Social media are becoming an important information input to support situational assessment (to produce awareness) in all domains. Here, we present a geovisual analytics approach to supporting SA for crisis events using one source of social media, Twitter. Specifically, we focus on leveraging explicit and implicit geographic information for tweets, on developing place-time-theme indexing schemes that support overview+detail methods and that scale analytical capabilities to relatively large tweet volumes, and on providing visual interface methods to enable understanding of place, time, and theme components of evolving situations. Our approach is user-centered, using scenario-based design methods that include formal scenarios to guide design and validate implementation as well as a systematic claims analysis to justify design choices and provide a framework for future testing. The work is informed by a structured survey of practitioners and the end product of Phase-I development is demonstrated / validated through implementation in SensePlace2, a map-based, web application initially focused on tweets but extensible to other media.

OMEN: a probabilistic ontology mapping tool

Most existing ontology mapping tools are inexact. Inexact ontology mapping rules, if not rectified, result in imprecision in the applications that use them. We describe a framework to probabilistically improve existing ontology mappings using a Bayesian Network. Omen, an Ontology Mapping ENhancer, is based on a set of meta-rules that captures the influence of the ontology structure and the existing matches to match nodes that are neighbours to matched nodes in the two ontologies. We have implemented a protype ontology matcher that can either map concepts across two input ontologies or enhance existing matches between ontology concepts. Preliminary experiments demonstrate that Omen enhances existing ontology mappings in our test cases.

Uninterpreted Schema Matching with Embedded Value Mapping under Opaque Column Names and Data Values

Schema matching and value mapping across two heterogeneous information sources are critical tasks in applications involving data integration, data warehousing, and federation of databases. Before data can be integrated from multiple tables, the columns and the values appearing in the tables must be matched. The complexity of the problem grows quickly with the number of data attributes/columns to be matched and due to multiple semantics of data values. Traditional research has tackled schema matching and value mapping independently. We propose a novel method that optimizes embedded value mappings to enhance schema matching in the presence of opaque data values and column names. In this approach, the fitness objective for matching a pair of attributes from two schemas depends on the value mapping function for each of the two attributes. Suitable fitness objectives include the euclidean distance measure, which we use in our experimental study, as well as relative (cross) entropy. We propose a heuristic local descent optimization strategy that uses sorting and two-opt switching to jointly optimize value mappings and attribute matches. Our experiments show that our proposed technique outperforms earlier uninterpreted schema matching methods, and thus, should form a useful addition to a suite of (semi) automated tools for resolving structural heterogeneity.

An architecture for creating collaborative semantically capable scientific data sharing infrastructures

Increasingly, scientists are seeking to collaborate and share data among themselves. Such sharing is can be readily done by publishing data on the World-Wide Web. Meaningful querying and searching on such data depends upon the availability of accurate and adequate metadata that describes the data and the sources of the data. In this paper, we outline the architecture of an implemented cyber-infrastructure for chemistry that provides tools for users to upload datasets and their metadata to a database. Our proposal combines a two level metadata system with a centralized database repository and analysis tools to create an effective and capable data sharing infrastructure. Our infrastructure is extensible in that it can handle data in different formats and allows different analytic tools to be plugged in.

A Platform for Visualizing and Experimenting with Measures of Semantic Similarity in Ontologies and Concept Maps

This article describes research in the ongoing search for better semantic similarity tools: such methods are important when attempting to reconcile or integrate knowledge, or knowledge-related resources such as ontologies and database schemas. We describe an extensible, open platform for experimenting with different measures of similarity for ontologies and concept maps. The platform is based around three different types of similarity, that we ground in cognitive principles and provide a taxonomy and structure by which new similarity methods can be integrated and used. The platform supports a variety of specific similarity methods, to which researchers can add others of their own. It also provides flexible ways to combine the results from multiple methods, and some graphic tools for visualizing and communicating multi-part similarity scores. Details of the system, which forms part of the ConceptVista open codebase, are described, along with associated details of the interfaces by which users can add new methods, choose which methods are used and select how multiple similarity scores are aggregated. We offer this as a community resource, since many similarity methods have been proposed but there is still much confusion about which one(s) might work well for different geographical problems; hence a test environment that all can access and extend would seem to be of practical use. We also provide some examples of the platform in use.

Schema matching and embedded value mapping for databases with opaque column names and mixed continuous and discrete-valued data fields

Schema matching and value mapping across two information sources, such as databases, are critical information aggregation tasks. Before data can be integrated from multiple tables, the columns and values within the tables must be matched. The complexities of both these problems grow quickly with the number of attributes to be matched and due to multiple semantics of data values. Traditional research has mostly tackled schema matching and value mapping independently, and for categorical (discrete-valued) attributes. We propose novel methods that leverage value mappings to enhance schema matching in the presence of opaque column names for schemas consisting of both continuous and discrete-valued attributes. An additional source of complexity is that a discrete-valued attribute in one schema could in fact be a quantized, encoded version of a continuous-valued attribute in the other schema. In our approach, which can tackle both “onto” and bijective schema matching, the fitness objective for matching a pair of attributes from two schemas exploits the statistical distribution over values within the two attributes. Suitable fitness objectives are based on Euclidean-distance and the data log-likelihood, both of which are applied in our experimental study. A heuristic local descent optimization strategy that uses two-opt switching to optimize attribute matches, while simultaneously embedding value mappings, is applied for our matching methods. Our experiments show that the proposed techniques matched mixed continuous and discrete-valued attribute schemas with high accuracy and, thus, should be a useful addition to a framework of (semi) automated tools for data alignment.

GeoCAM: A geovisual analytics workspace to contextualize and interpret statements about movement

This article focuses on integrating computational and visual methods in a system that supports analysts to identify, extract, map, and relate linguistic accounts of movement. We address two objectives: (1) build the conceptual, theoretical, and empirical framework needed to represent and interpret human-generated directions; and (2) design and imple- ment a geovisual analytics workspace for direction document analysis. We have built a set of geo-enabled, computational methods to identify documents containing movement statements, and avisual analytics environment that uses naturallanguageprocessing meth- ods iteratively with geographic database support to extract, interpret, and map geographic movement references in context. Additionally, analysts can provide feedback to improve computational results. To demonstrate the value of this integrative approach, we have realized a proof-of-concept implementation focusing on identifying and processing docu- ments that contain human-generated route directions. Using our visual analytic interface, an analyst can explore the results, provide feedback to improve those results, pose queries against a database of route directions, and interactively represent the route on a map.

VAST 2007 Contest TexPlorer

TexPlorer is an integrated system for exploring and analyzing vast amount of text documents. The data processing modules of TexPlorer consist of named entity extraction, entity relation extraction, hierarchical clustering, and text summarization tools. Using time line tool, tree-view, table-view, and concept maps, TexPlorer provides visualizations from different aspects and allows analysts to explore vast amount of text documents efficiently.

TextPlorer: An application supporting text analysis

TexPlorer is an integrated system for exploring and analyzing large amounts of text documents. The data processing modules of TexPlorer consist of named entity extraction, entity relation extraction, hierarchical clustering, and text summarization tools. Using a timeline tool, tree-view, table-view, and concept maps, TexPlorer provides an analytical interface for exploring a set of text documents from different perspectives and allows users to explore vast amount of text documents efficiently.