Data mining

Abstract: As information technology progress, we are able to obtain much information about the advanced research of others. As a result, researchers and research managers need to track the current research trends amid the information flood. In order to support these efforts to gather knowledge of current research, we propose a research trend mining method. The method utilizes an author-topic model for establishing the relationships between authors, topics, and papers by probabilities, and interactively visualizes the relationships using self-organizing maps. We implemented a research area mapping system and validated it with a case study. In addition, we conducted experiments to show the performance of our system. The experimental results indicate that this system can induce the appropriate relationships for finding research trends.

 Data mining;  INFORMATION technology;  citation analysis;  SOM;  author-topic model;  probablistic approach;  self-organizing maps;  self-organising feature maps;  research area mapping system;  research trend mining method;  

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 Data mining;  knowledge discovery;  Association rules;  

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 Data mining;  Visualization;  data discovery;  geo-computation;  stnexus;  

Abstract: Recent years have witnessed the dramatic popularity of online social networking services, in which millions of members publicly articulate mutual friendship relations. Guided by ethnographic research of these online communities, we have designed and implemented a visualization system for playful end-user exploration and navigation of large scale online social networks. Our design builds upon familiar node link network layouts to contribute customized techniques for exploring connectivity in large graph structures, supporting visual search and analysis, and automatically identifying and visualizing community structures. Both public installation and controlled studies of the system provide evidence of the systems usability, capacity for facilitating discovery, and potential for fun and engaged social activity.

 Data mining;  Visualization;  social networks;  exploration;  graphs;  community;  play;  

Abstract: The prevalence of metadata and technologies supporting metadata have failed to achieve their anticipated objectives (shared data, loose coupling, productivity, intelligence) because they represent an unrealistic, or at least incomplete, picture of the concept "metadata". Three emergent assumptions typically adopted by designers using metadata are identified, assumptions that contain the seeds of the inevitable failure of such designs. We also suggest a number of methodologies to extricate the architect (and more critically, partner architects) from such dogma.

 Data mining;  knowledge discovery;  metadata;  meta data;  ontologies (artificial intelligence);  

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 Data mining;  time representation;  moving point objects;  geographic knowledge discovery;  pattern matching;  temporal granularity;  

Abstract: Visualizations leverage innate human capabilities for recognizing interesting aspects of data. Even if users might agree on what is interesting about a visualization, the steps that they use in the knowledge discovery process may be significantly different. This results in an inability to effectively recreate the exact conditions of the discovery process, which we call the knowledge rediscovery problem. Because we cannot expect a user to fully document each of their interactions, there is a need for visualization systems to maintain user trace data in a way that enhances a user's ability to communicate what they found to be interesting, as well as how they found it. We present a model for representing user interactions that articulates with a corresponding set of annotations, or observations that are made during the exploration. Such ability is critical to addressing the knowledge rediscovery problem, and is a fundamental component for systems that must provide information provenance.

 Data mining;  user interfaces;  data visualisation;  information provenance;  knowledge rediscovery problem;  user interactions;  user trace data;  visualization systems;  

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 Data mining;  time representation;  time analysis;  Hagerstrand;  

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 Data mining;  

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 Data mining;  Visualization;  

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 Data mining;  Visualization;  data cube;  multi indexing;  data warehouse;  

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 Data mining;  Visualization;  time representation;  

Abstract: Bernardi, Ratsch, Kania. Saric and Rojas discuss interdisciplinary work as the key to functional genomics. Park discusses network biology (data mining of biological networks). Schatz discusses the construction of analysis environments beyond the genome and the Web. Blaschke and Valencia discuss how molecular biology nomenclature is thwarting information extraction progress. Finally, Ne/spl acute/dellec discusses bibliographic information extraction in genomics

 Data mining;  biology computing;  genetics;  scientific information systems;  World Wide Web;  analysis environment construction;  bibliographic information extraction;  biological networks;  functional genomics;  genomes;  information extraction progress;  information mining;  interdisciplinary work;  molecular biology nomenclature;  network biology;  

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 Data mining;  

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 Data mining;  Visualization;  

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 Data mining;  

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 Data mining;  

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 Data mining;  Visualization;  

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 semiotics;  Data mining;  Visualization;  cognition;  animation;  geographic visualization;  dynamic cartography;  interactivity;  virtual environments;  

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 Data mining;  

Abstract: The advent of remote sensing and survey technologies over the last decade has dramatically enhanced our capabilities to collect terabytes of geographic data on a daily basis. However, the wealth of geographic data cannot be fully realized when information implicit in data is difficult to discern. This confronts GIScientists with an urgent need for new methods and tools that can intelligently and automatically transform geographic data into information and, furthermore, synthesize geographic knowledge. It calls for new approaches in geographic representation, query processing, spatial analysis, and data visualization (Yuan 1998, Miller and Han 2000; Gahegan, 2000). Information scientists face the same challenge as a result of the digital revolution that expedites the production of terabytes of data from credit card transactions, medical examinations, telephone calls, stock values, and other numerous human activities. Collaborative efforts in artificial intelligence, statistics, and databases communities have been the underpinning technologies of knowledge discovery in databases to extract useful information from massive amounts of data in support of decision-making (Gardner 1996, Bhandari et al. 1997, Hedberg 1996).

 Data mining;  knowledge discovery;  geog 597 I;  

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Abstract: The advent of remote sensing and survey technologies over the last decade has dramatically enhanced our capabilities to collect terabytes of geographic data on a daily basis. However, the wealth of geographic data cannot be fully realized when information implicit in data is difficult to discern. This confronts GIScientists with an urgent need for new methods and tools that can intelligently and automatically transform geographic data into information and, furthermore, synthesize geographic knowledge. It calls for new approaches in geographic representation, query processing, spatial analysis, and data visualization (Yuan 1998, Miller and Han 2000; Gahegan, 2000). Information scientists face the same challenge as a result of the digital revolution that expedites the production of terabytes of data from credit card transactions, medical examinations, telephone calls, stock values, and other numerous human activities. Collaborative efforts in artificial intelligence, statistics, and databases communities have been the underpinning technologies of knowledge discovery in databases to extract useful information from massive amounts of data in support of decision-making (Gardner 1996, Bhandari et al. 1997, Hedberg 1996).

 Data mining;  knowledge discovery;  geog 597 I;  

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Abstract: Scientific literature on the Web makes up a massive, noisy, disorganized database. Unlike large, single-source databases such as a corporate customer database, the Web database draws from many sources, each with its own organization. Also, owing to its diversity, most records in this database are irrelevant to an individual researcher. Furthermore, the database is constantly growing in content and changing in organization. All these characteristics make the Web a difficult domain for knowledge discovery. To quickly and easily gather useful knowledge from such a database, users need the help of an information filtering system that automatically extracts only relevant records as they appear in a stream of incoming records. To this end, we have developed the CiteSeer. CiteSeer is an automatic generator of digital libraries of scientific literature. It uses sophisticated acquisition, parsing, and presentation methods to eliminate most of the manual effort of finding useful publications on the Web

 Data mining;  knowledge discovery;  Information retrieval;  World Wide Web;  digital libraries;  natural sciences computing;  information resources;  CiteSeer;  Web database;  automatic extraction;  automatic generator;  disorganized database;  incoming records;  information filtering system;  presentation methods;  publications;  relevant scientific literature discovery;  

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 Data mining;  

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 Data mining;  

Abstract: Technology for collection, transmission, and display of georeferenced data, at scales from architectural to global, has advanced rapidly in the past decade -- resulting in an orders of magnitude increase in availability of georeferenced data. While computational methods allow us to extract information from these masses of data, domain expertise coupled with the power of human vision provides the necessary complement to these computational methods allowing us to extract meaning from the information. Recent and anticipated developments in geographic visualization (GVis) provide the mechanism for linking human experts to computational tools, facilitating the critical step from information extraction to knowledge construction. It is these developments and their potential that I focus on here. GVis synthesizes perspectives on visual representation and analysis, thus far primarily from visualization in scientific computing (ViSC), cartography, and exploratory data analysis (EDA). For GVis to be an effective method in knowledge construction, a closer coupling is needed between the visual methods at the core of GVis and the analytical methods of geocomputation. Beyond this integration, for geo-knowledge construction environments to reach their potential, a more complete understanding must be achieved concerning how people (particularly domain specialists) conceptualize problems and interact with computer systems. Within the International Cartographic association, a four component research agenda for GVis is under development (focusing on issues in representation, interface design, GVis integration with database, geocomputation, and related GIScience research, and cognitive aspects of visualization method development and use). Elements within each component of this research agenda have direct implications for design of knowledge construction environments that involve integration of visualization with computational methods. Here, I will begin with an overview of the ICA research agenda, then focus specifically on selected issues that underlie design of knowledge construction environments linking analytical with visual methods. Particular attention will be directed to four topics: design of exploratory spatiotemporal data analysis methods (particularly the adaptation of EDA to space-time data), integration of GVis with knowledge discovery in database methods, facilitating collaboration in knowledge construction, and the cognitive issues that must be addressed if we are to achieve the next generation of geo-knowledge construction environments that create a more effective conjunction of human and machine capabilities.

 Data mining;  Visualization;  apoala;  

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 Data mining;  knowledge discovery;  apoala;  

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 Data mining;  

Abstract: The ESP2Net project is developing technologies that enable effective collaborative scientific data sharing to support collaboration among scientists, accelerate production of scientific data products, and improve understanding of the science. We have defined a Scientific Experiment Markup Language (SEML) to capture scientific experiments in hypermedia documents as a basic unit of information sharing. A collection of SEML documents can be viewed as an online electronic experiment logbook that captures the entire experiment experience by including the process and interrelationships between experiments to allow an experiment to be re-created. Complementary means of sharing the experiences from scientific experiments (browsing, searching, dissemination, and mining) are provided by integrating OASIS transparent distributed scientific object access, Conquest dynamic distributed query processing services, and active information dissemination services introduced in semantic multicast

 Data mining;  internet;  distributed databases;  scientific information systems;  query processing;  hypermedia;  information dissemination;  page description languages;  Conquest dynamic distributed query processing services;  ESP2Net project;  OASIS transparent distributed scientific object access;  SEML documents;  Scientific Experiment Markup Language;  active information dissemination services;  collaborative scientific data sharing;  hypermedia documents;  information sharing;  online electronic experiment logbook;  scientific data products;  scientific experiments;  semantic multicast;  

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 Data mining;  

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 Data mining;  

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 Data mining;  

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 Data mining;  

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 Data mining;  knowledge discovery;  Visualization;  data visualisation;  client-server systems;  decision support systems;  deductive databases;  knowledge acquisition;  relational databases;  MineSet;  computationally intensive tasks;  database visualization;  decision making;  relational data;  

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 Data mining;  

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 Data mining;  

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 Data mining;  

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 Data mining;  

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 Data mining;  

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 Data mining;  

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 Data mining;  

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 Data mining;