Michael P. McGuire

Semantic integration of government data for water quality management

Normative models of e-government typically assert that horizontal (i.e., inter-agency) and vertical (i.e., inter-governmental) integration of data flows and business processes represent the most sophisticated form of e-government, delivering the greatest payoff for both governments and users. This paper concentrates on the integration of data supporting water quality management as an example of how such integration can enable higher levels of e-government. It describes a prototype system that allows users to integrate water monitoring data across many federal, state, and local government organizations and provides novel techniques for information discovery, thus improving information quality and availability for decision making. Specifically, this paper outlines techniques to integrate numerous water quality monitoring data sources, to resolve data disparities, and to retrieve data using semantic relationships among data sources taking advantage of customized user profiles. Preliminary user feedback indicates that these techniques enhance quantity and quality of information available for water quality management.

A user-centered design for a spatial data warehouse for data exploration in environmental research

Abstract The integration of data from diverse fields of ecological research is paramount in the discovery of new ecological patterns and processes. The spatial exploration of an integrated dataset that spans multiple studies and disciplines can allow researchers to gain unforeseen insight into their data, spawn new research questions and hypotheses and identify data gaps. A user-centered approach was taken to design a spatial data warehouse and online analytical processing (OLAP) tools for data exploration in ecological research. The users in this study had diverse needs and current methods of data management do not easily allow for integration and exploration of data in multidimensional space. A generalizable data warehouse design methodology was created based on the results of a user study. This methodology was then demonstrated in the design of a data warehouse for data exploration in stream ecology resulting in a multidimensional data model with a fact table representing biological stream survey measurements and dimension tables representing spatial and categorical site and landscape variables. A generalizable extraction transformation and loading (ETL) workflow was created to integrate data across spatial dimensions before it was loaded into the data warehouse. A prototype data warehouse was implemented using biological stream survey, hydrologic, and vegetation data to observe spatial patterns in biological community distributions. Based on the exploration requirements identified in the user study, prototype OLAP queries were designed to facilitate spatial data cube exploration. Finally, a web-based interface was implemented to allow for multidimensional spatial visualization of biological stream survey data. The data warehouse and interface will allow researchers to explore biological assessment data at multiple spatial scales across many dimensions.

SEMANTIC INTEGRATION AND KNOWLEDGE DISCOVERY FOR ENVIRONMENTAL RESEARCH

Environmental research and knowledge discovery both require extensive use of data stored in various sources and created in different ways for diverse purposes. We describe a new metadata approach to elicit semantic information from environmental data and implement semantic-based techniques to assist users in integrating, navigating, and mining multiple environmental data sources. Our system contains specifications of various environmental data sources and the relationships that are formed among them. User requests are augmented with semantically related data sources and automatically presented as a visual semantic network. In addition, we present a methodology for data navigation and pattern discovery using multi-resolution browsing and data mining. The data semantics are captured and utilized in terms of their patterns and trends at multiple levels of resolution. We present the efficacy of our methodology through experimental results.

Spatiotemporal neighborhood discovery for sensor data

The focus of this paper is the discovery of spatiotemporal neighborhoods in sensor datasets where a time series of data is collected at many spatial locations. The purpose of the spatiotemporal neighborhoods is to provide regions in the data where knowledge discovery tasks such as outlier detection, can be focused. As building blocks for the spatiotemporal neighborhoods, we have developed a method to generate spatial neighborhoods and a method to discretize temporal intervals. These methods were tested on real life datasets including (a) sea surface temperature data from the Tropical Atmospheric Ocean Project (TAO) array in the Equatorial Pacific Ocean and (b)highway sensor network data archive. We have found encouraging results which are validated by real life phenomenon.

Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets

When mining large spatio-temporal datasets, interesting patterns typically emerge where the dataset is most dynamic. These dynamic regions can be characterized by a location or set of locations that exhibit different behaviors from their neighbors and the time periods where these differences are most pronounced. Examples include locally intense areas of precipitation, anomalous sea surface temperature (SST) readings, and locally high levels of water pollution, to name a few. The focus of this paper is to find and analyze the pattern of moving dynamic spatio-temporal regions in large sensor datasets. The approach presented in this paper uses a measure of local spatial autocorrelation over time to determine how pronounced the difference in measurements taken at a spatial location is with those taken at neighboring locations. Dynamic regions are analyzed both globally, in the form of spatial locations and time periods that have the largest difference in local spatial autocorrelation, and locally, in the form of dynamic spatial locations for a particular time period or dynamic time periods for a particular spatial node. Then, moving dynamic regions are identified by determining the spatio-temporal connectivity, extent, and trajectory for groups of locally dynamic spatial locations whose position has shifted from one time period to the next. The efficacy of the approach is demonstrated on two real-world spatio-temporal datasets (a) NEXRAD precipitation and (b) SST. Promising results were found in discovering highly dynamic regions in these datasets depicting several real environmental phenomenon which are validated as actual events of interest.

Characterizing sensor datasets with multi-granular spatio-temporal intervals

Data from sensors and sensor networks are being collected at astronomical rates. This results in a massive dataset that is increasingly difficult to navigate to find interesting time periods where the spatial pattern of a process changes. The ability to navigate to such areas can lead to new knowledge about the factors that contribute to a spatio-temporal process. This paper proposes a method to automatically characterize sensor datasets based on a measure of spatial change over time resulting in a set of multi-granular spatio-temporal intervals. The resulting intervals can be used to focus knowledge discovery tasks at multiple temporal granularities within the dataset. Furthermore, the intervals enable a drill-down-style analysis where events of varying magnitudes can be identified within each granularity. Experiments were performed on a real-world dataset measuring NEXRAD precipitation accumulation. The results show that the multi-granular spatio-temporal intervals identify interesting time periods in the dataset as evidenced by naturally occurring events.

TNeT: Tensor-Based Neighborhood Discovery in Traffic Networks

Traffic networks comprise of sensors monitoring large numbers of roadways and highways with multiple lanes. Data from such sensors can be used for various monitoring tasks such as identifying high usage roads, traffic congestion and HOV lane demarcations. In this paper we propose a method to identify spatial and temporal neighborhoods in such traffic sensor networks. This approach can be used to demarcate HOV lane restrictions at certain time periods and at certain key locations on heavy usage highways. In many cases HOV lane restrictions are dynamic and our approach can provide automatic input to which time periods and locations should be designated as HOV lanes. We propose a spatio-temporal representation model for traffic networks, which models the spatio-temporal data using high-order tensor instead of the traditional vector model. We use tensor operations and tools, such as the High-order Singular Value Decomposition(HOSVD) for dimension reduction. Subsequently, a traditional clustering algorithm such as k-means is applied in the tensor subspace. For temporal neighborhood discovery we apply K-means to the subspace of time. Similarly, for spatial neighborhoods we apply K-means to the subspace of space. In real world traffic data we found that tensor based representations produce much more accurate results than traditional models. In this paper our focus is on traffic datasets which are typically spatio-temporal in nature as they measure a phenomenon at a particular location over a period of time, however this approach is generalizable to other spatiotemporal datasets as well.

Channeling the water data deluge: a system for flexible integration and analysis of hydrologic data

The hydrologic cycle and understanding the relationship between rainfall and runoff is an important component of earth system science, sustainable development, and natural disasters caused by floods. With this in mind, the integration of digital earth data for hydrologic sciences is an important area of research. Currently, it takes a tremendous amount of effort to perform hydrologic analysis at a large scale because the data to support such analyses are not available on a single system in an integrated format that can be easily manipulated. Furthermore, the state-of-the-art in hydrologic data integration typically uses a rigid relational database making it difficult to redesign the data model to incorporate new data types. The HydroCloud system incorporates a flexible document data model to integrate precipitation and stream flow data across spatial and temporal dimensions for large-scale hydrologic analyses. In this paper, a document database schema is presented to store the integrated data-set along with analysis tools such as web services for data access and a web interface for exploratory data analysis. The utility of the system is demonstrated based on a scientific workflow that uses the system for both exploratory data analysis and statistical hypothesis testing.

Mining Semantic Time Period Similarity in Spatio-Temporal Climate Data

Over the last decade, advances in high performance computing and remote sensing have produced a vast amount of spatio-temporal data. One area that this data explosion is most prevalent is climate science. With this in mind, there is an increasing need to characterize large spatio-temporal datasets. One such characterization is to find periods of time that exhibit the same spatio-temporal pattern. The focus of this research is to find similar spatio-temporal patterns for semantic time periods. A semantic time period could be any arbitrary division in time such as year, month, or week. The proposed approach first characterizes the data spatially by using one of three approaches including local entropy, local spatial autocorrelation, and local distance-based outliers, to identify interesting spatial features in the dataset. Then, a location/time period matrix which is analogous to a term/document matrix in natural language processing is created to capture the spatial features for a given semantic time period. This matrix contains a count of for each spatial location, the number of times that it is a feature of interest during a semantic time period. Then using latent semantic analysis, the cosine similarity for each semantic time period is calculated. The results are then clustered using affinity propagation. The results show that the similarity matrix produced by distance-based outliers creates the best clustering. The approach is demonstrated on a modeled global climate dataset where we clustered years from 1948 to 2012.

Mining sensor datasets with spatiotemporal neighborhoods

Many spatiotemporal data mining methods are dependent on how relationships between a spatiotemporal unit and its neighbors are defined. These relationships are often termed the neighborhood of a spatiotemporal object. The focus of this paper is the discovery of spatiotemporal neighborhoods to find automatically spatiotemporal sub-regions in a sensor dataset. This research is motivated by the need to characterize large sensor datasets like those found in oceanographic and meteorological research. The approach presented in this paper finds spatiotemporal neighborhoods in sensor datasets by combining an agglomerative method to create temporal intervals and a graph-based method to find spatial neighborhoods within each temporal interval. These methods were tested on realworld datasets including (a) sea surface temperature data from the Tropical Atmospheric Ocean Project (TAO) array in the Equatorial Pacific Ocean and (b) NEXRAD precipitation data from the Hydro-NEXRAD system. The results were evaluated based on known patterns of the phenomenon being measured. Furthermore, the results were quantified by performing hypothesis testing to establish the statistical significance using Monte Carlo simulations. The approach was also compared with existing approaches using validation metrics namely spatial autocorrelation and temporal interval dissimilarity. The results of these experiments show that our approach indeed identifies highly refined spatiotemporal neighborhoods.