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.

Abstract. 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.

Schema matching and value mapping across two heterogenous 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.Index Terms-Schema matching, opaque conditions, embedded schema matching with value mapping.

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. ACM Reference Format:Jaiswal, A., Miller, D. J., and Mitra, P. 2013. Schema matching and embedded value mapping for databases with opaque column names and mixed continuous and discrete-valued data fields.

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 implement 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 a visual analytics environment that uses natural language processing methods 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 documents 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