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Designing visual analytics methods for massive collections of movement data
- Cartographica
"... Exploration and analysis of large data sets cannot be carried out using purely visual means but require the involvement of database technologies, computerized data processing, and computational analysis methods. An appropriate combination of these technologies and methods with visualization may faci ..."
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Cited by 21 (5 self)
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Exploration and analysis of large data sets cannot be carried out using purely visual means but require the involvement of database technologies, computerized data processing, and computational analysis methods. An appropriate combination of these technologies and methods with visualization may facilitate synergetic work of computer and human whereby the unique capabilities of each ‘‘partner’ ’ can be utilized. We suggest a systematic approach to defining what methods and techniques, and what ways of linking them, can appropriately support such a work. The main idea is that software tools prepare and visualize the data so that the human analyst can detect various types of patterns by looking at the visual displays. To facilitate the detection of patterns, we must understand what types of patterns may exist in the data (or, more exactly, in the underlying phenomenon). This study focuses on data describing movements of multiple discrete entities that change their positions in space while preserving their integrity and identity. We define the possible types of patterns in such movement data on the basis of an abstract model of the data as a mathematical function that maps entities and times onto spatial positions. Then, we look for data transformations, computations, and visualization techniques that can facilitate the detection of these types of patterns and are suitable for very large data sets – possibly too large for a computer’s memory. Under such constraints, visualization is applied to data that have previously been aggregated and generalized by means of database operations and/or computational techniques.
Distributed mining of spatio-temporal event patterns in sensor networks
, 2007
"... Abstract Many sensor network applications are concerned with discovering interesting patterns among observed real-world events. Often, only limited apriori knowledge exists about the patterns to be found eventually. Here, raw streams of sensor readings are collected at the sink for later offline ana ..."
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Cited by 16 (1 self)
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Abstract Many sensor network applications are concerned with discovering interesting patterns among observed real-world events. Often, only limited apriori knowledge exists about the patterns to be found eventually. Here, raw streams of sensor readings are collected at the sink for later offline analysis – resulting in a large communication overhead. In this position paper, we explore the use of in-network data mining techniques to discover frequent event patterns and their spatial and temporal properties. With that approach, compact event patterns rather than raw data streams are sent to the sink. We also discuss various issues with the implementation of our proposal and report our experience with preliminary experiments.
Spatial Clustering of Structured Objects
- International Conference on Inductive Logic Programming, ILP 2005, volume LNAI 3625
, 2005
"... Abstract. Clustering is a fundamental task in Spatial Data Mining where data consists of observations for a site (e.g. areal units) descriptive of one or more (spatial) primary units, possibly of different type, collected within the same site boundary. The goal is to group structured objects, i.e. d ..."
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Cited by 6 (4 self)
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Abstract. Clustering is a fundamental task in Spatial Data Mining where data consists of observations for a site (e.g. areal units) descriptive of one or more (spatial) primary units, possibly of different type, collected within the same site boundary. The goal is to group structured objects, i.e. data collected at different sites, such that data inside each cluster models the continuity of socio-economic or geographic environment, while separate clusters model variation over the space. Continuity is evaluated according to the spatial organization arising in data, namely discrete spatial structure, expressing the (spatial) relations between separate sites implicitly defined by their geometrical representation and positioning. Data collected within sites that are (transitively) connected in the discrete spatial structure are clustered together according to the similarity on multi-relational descriptions representing their internal structure. CORSO is a novel spatial data mining method that resorts to a multi-relational approach to learn relational spatial data and exploits the concept of neighborhood to capture relational constraints embedded in the discrete spatial structure. Relational data are expressed in a firstorder formalism and similarity among structured objects is computed as degree of matching with respect to a common generalization. The application to real-world spatial data is reported. 1
Spatial data visualization in healthcare: supporting a facility location decision via GIS-based market analysis
- In: Proceedings of the 34th Annual Hawaii International Conference on Systems Sciences. IEEE Computer Society
, 2001
"... As the business of healthcare continues to evolve, it is increasingly important for hospital systems to optimize the quality of capital investment decisions. Since proximity to services plays a major role in an individual’s choice of healthcare provider, decisions concerning facility location and fu ..."
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Cited by 3 (0 self)
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As the business of healthcare continues to evolve, it is increasingly important for hospital systems to optimize the quality of capital investment decisions. Since proximity to services plays a major role in an individual’s choice of healthcare provider, decisions concerning facility location and function are crucial. This paper describes an application of spatial data visualization to support the decisions of locating and sizing a proposed Neonatal Intensive Care Unit (NICU) within a system’s network of rural hospitals. A Geographic Information System (GIS) was used to analyze publicly available and system-specific data. The discovered patterns of healthcare system market share and customer travel were key drivers in the decisionmaking process. The application provides a good example of using a spatial data mining tool as a powerful step in the Knowledge Discovery in Databases (KDD) process.
Quality Assessment in Spatial Clustering of Data Mining
"... Because of the use of computers and its advances in scientific data handling and advancement of various geo and space borne sensors, we are now faced with a large amount of data. Therefore, the development of new techniques and tools that support the transforming the data into useful knowledge has b ..."
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Cited by 1 (0 self)
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Because of the use of computers and its advances in scientific data handling and advancement of various geo and space borne sensors, we are now faced with a large amount of data. Therefore, the development of new techniques and tools that support the transforming the data into useful knowledge has been the focus of the relatively new and interdisciplinary research area named “knowledge discovery in spatial databases or spatial data mining”. Spatial data mining is a demanding field since huge amounts of spatial data have been collected in various applications such as real-estate marketing, traffic accident analysis, environmental assessment, disaster management and crime analysis. Thus, new and efficient methods are needed to discover knowledge from large databases such as crime databases. Because of the lack of primary knowledge about the data, clustering is one of the most valuable methods in spatial data mining. As there exist a number of methods for clustering, a comparative study to select the best one according to their usage has been done in this research. In this paper we use Self Organization Map (SOM) artificial neural network and K-means methods to evaluate the patterns and clusters resulted from each one. Furthermore, the lack of pattern quality assessment in spatial clustering can lead to meaningless or unknown information. Using compactness and separation criteria, validity of SOM and K-means methods has been examined. Data used in this paper has been divided in two sections. First part contains simulated data contain 2D x,y coordinate and second part of data is real data corresponding to crime investigation. The result of this paper can be used to classify study area, based on property crimes. In this work our study area classified into several classes representing high to low crime locations. Thus, accuracy of region partitioning directly depends on clustering quality.
Interview by author
, 1994
"... DNA Polymerase zeta is a major determinant of resistance to platinum-based chemotherapeutic agents ..."
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Cited by 1 (0 self)
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DNA Polymerase zeta is a major determinant of resistance to platinum-based chemotherapeutic agents
LIST OF TABLES.......................................................................................................................................vi
"... May 2003This thesis is dedicated to the proposition that ..."
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Applying Association Rules and Co-location Techniques on Geospatial Web Services
"... Most contemporary GIS have only very basic spatial analysis and data mining functionality and many are confined to analysis that involves comparing maps and descriptive statistical displays like histograms or pie charts. Emerging Web standards promise a network of heterogeneous yet interoperable Web ..."
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Most contemporary GIS have only very basic spatial analysis and data mining functionality and many are confined to analysis that involves comparing maps and descriptive statistical displays like histograms or pie charts. Emerging Web standards promise a network of heterogeneous yet interoperable Web Services. Web Services would greatly simplify the development of many kinds of data integration and knowledge management applications. Geospatial data mining describes the combination of two key market intelligence software tools: Geographical Information Systems and Data Mining Systems. This research aims to develop a Spatial Data Mining web service it uses rule association techniques and correlation methods to explore results of huge amounts of data generated from crises management integrated applications developed. It integrates between traffic systems, medical services systems, civil defense and state of the art Geographic Information Systems and Data Mining Systems functionality in an open, highly extensible, internet-enabled plug-in architecture. The Interoperability of geospatial data previously focus just on data formats and standards. The recent popularity and adoption of the Internet and Web Services has provided a new means of interoperability for geospatial information not just for exchanging data but for analyzing these data during exchange. An integrated, user friendly Spatial Data Mining System available on the internet via a web service offers exciting new possibilities for spatial
A Relational Approach to Sensor Network Data Mining
"... Abstract. In this chapter a relational framework able to model and analyse the data observed by nodes involved in a sensor network is presented. In particular, we propose a powerful and expressive description language able to represent the spatio-temporal relations appearing in sensor network data a ..."
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Abstract. In this chapter a relational framework able to model and analyse the data observed by nodes involved in a sensor network is presented. In particular, we propose a powerful and expressive description language able to represent the spatio-temporal relations appearing in sensor network data along with the environmental information. Furthermore, a general purpose system able to elicit hidden frequent temporal correlations between sensor nodes is presented. The framework has been extended in order to take into account interval-based temporal data by introducing some operators based on a temporal interval logic. A preliminary abstraction step with the aim of segmenting and labelling the real-valued time series into similar subsequences is performed exploiting a kernel density estimation approach. The prposed framework has been evaluated on real world data collected from a wireless sensor network. 1
Urbanization prediction with an ART-MMAP neural network based spatiotemporal data
"... mining method ..."