Anand Padmanabhan

A parallel computing approach to viewshed analysis of large terrain data using graphics processing units

Viewshed analysis, often supported by geographic information system, is widely used in many application domains. However, as terrain data continue to become increasingly large and available at high resolutions, data-intensive viewshed analysis poses significant computational challenges. General-purpose computation on graphics processing units (GPUs) provides a promising means to address such challenges. This article describes a parallel computing approach to data-intensive viewshed analysis of large terrain data using GPUs. Our approach exploits the high-bandwidth memory of GPUs and the parallelism of massive spatial data to enable memory-intensive and computation-intensive tasks while central processing units are used to achieve efficient input/output (I/O) management. Furthermore, a two-level spatial domain decomposition strategy has been developed to mitigate a performance bottleneck caused by data transfer in the memory hierarchy of GPU-based architecture. Computational experiments were designed to evaluate computational performance of the approach. The experiments demonstrate significant performance improvement over a well-known sequential computing method, and an enhanced ability of analyzing sizable datasets that the sequential computing method cannot handle.

A self-organized grouping (SOG) method for efficient Grid resource discovery

This paper presents a self-organized grouping (SOG) method that achieves efficient Grid resource discovery by forming and maintaining autonomous resource groups. Each group dynamically aggregates a set of resources that are similar to each other in some pre-specified resource characteristic. The SOG method takes advantage of the strengths of both centralized and decentralized approaches that were previously developed for Grid/P2P resource discovery. The design of the SOG method minimizes the overhead incurred in forming and maintaining groups and maximizes resource discovery performance. The way SOG method handles resource discovery queries is metaphorically similar to searching for a word in an English dictionary by identifying its alphabetical groups at the first place. It is shown from a series of computational experiments that SOG method achieves more stable (i.e., independent of the factors such as resource densities, and Grid sizes) and efficient lookup performance than other existing approaches.

A Scalable Framework for Spatiotemporal Analysis of Location-based Social Media Data

In the past several years, social media (e.g., Twitter and Facebook) has been experiencing a spectacular rise and popularity, and becoming a ubiquitous discourse for content sharing and social networking. With the widespread of mobile devices and location-based services, social media typically allows users to share whereabouts of daily activities (e.g., check-ins and taking photos), and thus strengthens the roles of social media as a proxy to understand human behaviors and complex social dynamics in geographic spaces. Unlike conventional spatiotemporal data, this new modality of data is dynamic, massive, and typically represented in stream of unstructured media (e.g., texts and photos), which pose fundamental representation, modeling and computational challenges to conventional spatiotemporal analysis and geographic information science. In this paper, we describe a scalable computational framework to harness massive location-based social media data for efficient and systematic spatiotemporal data analysis. Within this framework, the concept of space-time trajectories (or paths) is applied to represent activity profiles of social media users. A hierarchical spatiotemporal data model, namely a spatiotemporal data cube model, is developed based on collections of space-time trajectories to represent the collective dynamics of social media users across aggregation boundaries at multiple spatiotemporal scales. The framework is implemented based upon a public data stream of Twitter feeds posted on the continent of North America. To demonstrate the advantages and performance of this framework, an interactive flow mapping interface (including both single-source and multiple-source flow mapping) is developed to allow real-time, and interactive visual exploration of movement dynamics in massive location-based social media at multiple scales.

Experimentation with local consensus ontologies with implications for automated service composition

Agent technologies represent a promising approach for the integration of interorganizational capabilities across distributed, networked environments. However, knowledge sharing interoperability problems can arise when agents incorporating differing ontologies try to synchronize their internal information. Moreover, in practice, agents may not have a common or global consensus ontology that will facilitate knowledge sharing and integration of functional capabilities. We propose a method to enable agents to develop a local consensus ontology during operation time as needed. By identifying similarities in the ontologies of their peer agents, a set of agents can discover new concepts/relations and integrate them into a local consensus ontology on demand. We evaluate this method, both syntactically and semantically, when forming local consensus ontologies with and without the use of a lexical database. We also report on the effects when several factors, such as the similarity measure, the relation search level depth, and the merge order, are varied. Finally, experimenting in the domain of agent-supported Web service composition, we demonstrate how our method allows us to successfully autonomously form service-oriented local consensus ontologies.

Local consensus ontologies for B2B-oriented service composition

Agents seeking to discover and compose needed Web services may face knowledge sharing interoperability problems due to differing ontologies. In practice, agents may not have a global consensus ontology that will facilitate knowledge sharing and integration of required services. We investigate a method for agents to develop local consensus ontologies to aid in the communication within a multi-agent system of business-to-business (B2B) agents. We compare variations of syntactic and semantic similarity matching to form local consensus ontologies with and without the use of a lexical database.

CyberGIS Gateway for enabling data‐rich geospatial research and education

This paper describes CyberGIS Gateway as an online problem‐solving environment for multiple science communities to conduct data‐rich geospatial research and education. CyberGIS Gateway is a key modality in the CyberGIS software environment. Scalable gateway application integration has been the focus of CyberGIS Gateway in order to efficiently develop highly interactive online geographic information systems (GIS) user interface components and couple a rich collection of heterogeneous and distributed geospatial data and analytical services for advanced cyberGIS capabilities on advanced cyberinfrastructure. An open mashup and service API approach is developed to address the integration challenges in CyberGIS Gateway application development. This approach is applied and evaluated in developing several representative cyberGIS data and analytical applications. The experience gained from the integration practice is shared. The education and outreach activities in CyberGIS Gateway are presented to illustrate the impact of CyberGIS Gateway in GIScience communities and the effective collaboration within the science gateway community. Copyright

FluMapper: A cyberGIS application for interactive analysis of massive location-based social media

Social media have experienced a spectacular rise in popularity, attracting hundreds of millions of users and generating unprecedented amount of content that increasingly contain location and place information. Collectively, the massive location information in these data provides an excellent opportunity to better understand many geographic phenomena and geospatial dynamics in a timely fashion. Recent studies capitalizing on social networking and media data show significant societal impacts in many areas including prediction of stock market and infectious disease surveillance. However, because location‐based social media data are often massive, generated dynamically, and unstructured, significant computation, data, and visualization challenges need to be resolved. This research aims to demonstrate the use of massive social media data to interactively analyze spatiotemporal events across spatial and temporal scales, by establishing a data‐driven framework using cyberGIS—geographic information systems (GIS) based on advanced cyberinfrastructure—to resolve aforementioned challenges. Specifically, FluMapper—an application on the CyberGIS Gateway—is employed as a case study to demonstrate the data‐driven framework and seamless integration of massive location‐based social media data and spatial analytical services within the online problem solving environment of the Gateway. FluMapper presents integrated results from two complementary spatial analyses: (i) an interactive exploration of spatial distribution of flu risk and (ii) dynamic mapping of movement patterns, across multiple spatial, and temporal scales. The seamless integration of these two analyses through the framework illustrates the potential of cyberGIS to resolve the compute and data challenges of analyzing near real‐time social media data in an efficient and scalable manner and to support interactive visualization. Copyright

Towards provenance-aware geographic information systems

GIS (Geographic Information Systems) play an important role to acquire and communicate geospatial knowledge based on spatial data and the use of spatial analysis, modeling, and visualization. The assurance of the validity and quality of spatial data handling and analysis remains a great challenge, in part, because of sophisticated procedures are often required for collaborative geospatial problem-solving and decision making. These procedures, when specified as knowledge derivation workflows, require carefully configured parameters and spatiotemporal specifications guided by specific contexts and purposes. The information of spatial data lineage and related analysis workflow is defined as spatial provenance in this research. Such information is often not well recorded or managed during spatial data handling and related analysis. This paper presents a provenance-aware GIS architecture that incorporates spatial provenance to address this shortcoming and facilitate the assurance of validity and quality of spatial data handling and analysis. Spatial provenance in this architecture is generated and managed to allow queries on data lineage and workflow information to support geospatial problem-solving. Basic elements of spatial provenance are captured using a spatial provenance model. The illustration of the provenance-aware GIS architecture and its proof-of-concept implementation reveals the similarity and difference in the use of spatial provenance in GIS applications. Overall, the architecture and implementation described in the paper demonstrates the necessity and feasibility of introducing provenance into GIS.

FluMapper: an interactive CyberGIS environment for massive location-based social media data analysis

Social media, such as social network (e.g., Facebook), microblogs (e.g. Twitter) have experienced a spectacular rise in popularity, and attracting hundreds of millions of users generating unprecedented amount of information. Twitter, for example, has rapidly gained approximately 500 million registered users as of 2012, generating 340 million tweets daily. Although each tweet is limited to only 140 characters, the aggregate of millions of tweets may provide a realistic representation of landscapes for a certain topic of interest. Furthermore, with widespread use of location aware mobile devices, users are sharing their whereabouts through social media services. This has resulted in a dramatic increase in volume of spatial data and they are becoming a crucial attribute of social media. These location-based social media thus could provide valuable insights to understanding many geographic phenomena. Recent studies capitalizing on social networking and media data show significant societal impacts, in many areas including infectious disease tracking [1].

Spatiotemporal transformation of social media geostreams: a case study of Twitter for flu risk analysis

Georeferenced social media data streams (social media geostreams) are providing promising opportunities to gain new insights into spatiotemporal aspects of human interactions on cyber space and their relation with real-world activities. In particular, such opportunities are motivating public health researchers to improve the surveillance of disease epidemics by means of spatiotemporal analysis of social media geostreams. One essential requirement in achieving such geostream-based disease surveillance is to establish scalable data infrastructures capable of real-time transformation of massive geostreams into spatiotemporally organized data to which analytical methods are readily applicable. To fulfill this requirement, this study develops a data pipeline solution where multiple computational components are integrated to collect, process, and aggregate social media geostreams in near real time. As a test case, this solution focuses on one well-known social media geostream, the Twitter data stream, and one type of disease epidemics, the flu. The pipeline solution facilitates multiscale spatiotemporal analysis of flu risks by collecting geotagged tweets from the Twitter Streaming API, identifying flu-related tweets through keyword match, aggregating tweets at multiple spatial granularities in near real time, and storing tweets and the aggregate statistics in a distributed NoSQL database. Although developed for the surveillance of flu epidemics, the pipeline would serve as a general framework for building scalable data infrastructures that can support real-time spatiotemporal analysis of social media geostreams in the application domains beyond disease mapping and public health.